Erasmus Mundus Master Course: IMETE
Thesis submitted in partial fulfilment of the requirements for the joint academic degree of:
International Master of Science in Environmental Technology and
Engineering
an Erasmus Mundus Master Course from
Ghent University (Belgium), ICTP (Czech Republic), UNESCO-IHE (the Netherlands)
Ammonium removal by means of algae and nitrifying bacteria
treating swine waste manure
Host Universities and Institutes:
Thanh Tung Nguyen
Supervisors:
Mentors:
Prof. Sarina Ergas
Prof. Piet Lens
Dr. Peter van der Steen
Dr. Wang Meng
Ms. Eun Young Lee
Dr. Qiong Zhang
2011 - 2013
This thesis was elaborated in University of South Florida, US and defended at UNESCO-IHE, the Netherlands within the
framework of the European Erasmus Mundus Programme “Erasmus Mundus International Master of Science in
Environmental Technology and Engineering " (Course N° 2011-0172)
Certification
This is an unpublished MSc. thesis and is not prepared for further
distribution. The author and the promoter give the permission to use this
thesis for consultation and to copy parts of it for personal use. Every other
use is subject to copyright laws, more specifically the source must be
extensively specified when using results from this thesis.
The Supervisors
The Author
Prof. Sarina Ergas
Prof. Piet Lens
Dr. Peter van der Steen
Thanh Tung Nguyen
i
Ammonium Removal by Means of
Algae and Nitrifying Bacteria Treating Swine Waste Manure
Master Thesis
by
Thanh Tung NGUYEN
Supervisors
Prof. Dr. Sarina Ergas
Prof. Dr. Piet Lens
Dr. P. van der Steen
Mentor:
Dr. Wang Meng
Ms. Eun Young Lee
Dr. Qiong Zhang
Delft & Tampa
September 2013
ii
Abstract
This study presents the use of algal specie Chlorella sp. and nitrifying bacteria cultured
in a photobioreactor to evaluate ammonium removal from anaerobically digested swine
centrate. The study was carried out in two types of reactor: continuous culture in airlift
photobioreactors and semi-continuous culture in 1L glass bottles. Experiment 1 with
airlift reactors only revealed the complete ammonium removal mainly via nitrification
in both algae only and algal-nitrifier mixture reactors, but could not clarify the role of
photo-oxygennation by algae contributing to nitrification process due to problems with
reactor configurations and operational conditions. Eperiment 2 with 1L glass bottles
showed a complete NH4+-N removal from swine centrate with NH4+-N loading rate of
23-30 mg/L/d achieved in both algae and mixture reactors. The NH4+-N removal mostly
relied on nitrification process rather than by biomass uptake. Also, algal photooxygenation could support nitrification in both reactors, in which the nitrification
products were mainly NO2--N in algae reactor and were both NO2--N and NO3--N with
significant concentrations in mixture reactor. In general, algae culture performed more
reliably and effectively than mixture reactor did. Throughout experiment, algae reactor
maintained complete NH4+-N removal capacity, even when the inlet NH4+-N loading
rate was doubled. In mixture reactor, the system could not perform complete NH4+-N
removal when the rotifers appeared. Both reactors could show good potential in produce
high-yield and well-settleable biomasses.
Keywords: Chlorella sp., nitrifier, airlift reactor, ammonium removal, nitrification, rotifer
iii
Acknowledgements
There are many people who assisted me throughout my hardy yet interesting research work
and thesis writing. I would like to first thank to Professor Sarina Ergas, from University of
South Florida (USF), for giving me a chance to come and study in USF. Throughout
experimental period, she has been constantly helping and supporting me, patiently providing
technical guidance, encourages, guidelines and answers to my questions. Also, I am deeply
grateful to Dr. Peter van der Steen and Professor Piet Lens, both from UNESCO-IHE, for
being extremely supportive, helpful and insightful throughout the process and providing
constructive feedback, advice and suggestions that inspired me to learn and work. I am
thankful to Dr. Wang Meng, Ms. Lee Eun Young and Dr. Zhang Qiong, for conducting and
mentoring me during the lab work in USF. I acknowledge Dr. Maja Simpraga in particular
and IMETE Coordination Board in general, for supporting me on administrative and visa
issues of my trip to USF. My special thanks to Mr. Fred Kruis and Mr. Peter Herrings, from
UNESCO-IHE for the advice and communication in the IHE laboratory, my extremely
friendly USF friends (Lucie Krayzelova, Veronica Aponte, Maureen Kinyua, Merrill Peyton
Dilbeck, Thomas Lynn, Oscar Peña,…) for sharing unforgettable moment and helping me in
USF laboratory, my beloved IMETE friends (Isabel Del Agua, Freddy Dudy,…) for sharing
knowledge and supporting transportation during my stay in Delft, my Vietnamese friend,
Minh Trung Nguyen, for providing me accommodation in Delft. Last but not least, I am in
debt to my parents and friends for their constant encouragement.
Any opinions, findings, conclusions, or recommendations stated in this thesis study are
merely from the author’s and do not necessarily reflect and represent the views of any other
individuals, programs or organizations.
iv
Table of Contents
Abstract ................................................................................................................................. iii
Acknowledgements ............................................................................................................... iv
List of tables ......................................................................................................................... vii
List of figures ....................................................................................................................... vii
List of symbols ...................................................................................................................... ix
1
INTRODUCTION ................................................................................................................ 10
1.1
1.2
1.3
1.4
2
Background .............................................................................................................. 10
Aim and boundary of the study ................................................................................ 12
Overall objective ...................................................................................................... 12
Specific objectives.................................................................................................... 13
LITERATURE REVIEW ...................................................................................................... 14
2.1
Microalgae ................................................................................................................ 14
2.1.1 Characteristic of microalgae................................................................................. 14
2.1.2 Microalgae cultivation and harvesting ................................................................. 14
2.1.3 Advantages and application of microalgae in wastewater treatment ................... 15
2.1.4 Limiting factors of algal growth........................................................................... 16
2.2
Photobioreactors ....................................................................................................... 18
2.3
Nitrification process ................................................................................................. 19
2.3.1 Principle of nitrification ....................................................................................... 19
2.3.2 Kinetic of nitrification .......................................................................................... 20
2.3.3 Factors influencing nitrification process .............................................................. 20
2.4
Microalgal-bacterial consortium .............................................................................. 23
2.4.1 Interaction between algae and bacteria ................................................................ 23
2.4.2 Algal-bacterial community analysis ..................................................................... 24
2.5
Anaerobically digested swine wastewater for algal cultivation ............................... 25
3
MATERIALS AND METHODS ............................................................................................. 26
3.1
Culture medium ........................................................................................................ 26
3.2
Real swine waste centrate ........................................................................................ 26
3.3
Microalgae and nitrifying bacteria cultivation ......................................................... 27
3.4
Reactor setup ............................................................................................................ 28
3.4.1 Experiment 1 – Continuous culture in airlift reactors .......................................... 28
3.4.2 Experiment 2 – Semi-continuous culture in glass bottles .................................... 30
3.5
Sampling................................................................................................................... 31
3.6
Analytical methods ................................................................................................... 31
3.6.1 pH, DO, alkalinity and light intensity .................................................................. 31
3.6.2 Transmittance ....................................................................................................... 31
3.6.3 Chlorophyll a........................................................................................................ 32
3.6.4 Dry weight (DW) ................................................................................................. 32
3.6.5 Soluble COD ........................................................................................................ 33
3.6.6 Nitrite NO2-, Nitrate NO3- and Ortho-Phosphate PO43- ........................................ 33
3.6.7 Ammonium........................................................................................................... 34
3.6.8 Total Nitrogen (TN) ............................................................................................. 34
3.6.9 Microscopic observation ...................................................................................... 34
v
3.6.10 Settleability of biomass .................................................................................... 34
3.7
Calculation ............................................................................................................... 34
3.7.1 Estimating theoretical oxygen balance - continuous flow airlift reactors ............ 34
3.7.2 Ammonium conversion rate ................................................................................. 36
3.7.3 Nitrogen balance .................................................................................................. 36
3.7.4 Volumentric biomass productivity ....................................................................... 36
3.7.5 Areal biomass productivity .................................................................................. 37
3.8
Data processing ........................................................................................................ 37
4
EXPERIMENT 1 – CONTINUOUS CULTURE IN AIRLIFT REACTOR .................................... 38
4.1
Comparison between algae and mixture reactors ..................................................... 38
4.1.1 Results – Comparison between algae & mixture reactors .................................... 38
4.1.2 Discussion – Comparison between algae and mixture reactors ........................... 39
4.2
Mixture reactor when varying SRT, air flow rate .................................................... 41
4.2.1 Results – Mixture reactor when varying SRT, air flow rate ................................ 42
4.2.2 Discussions – Mixture reactor when varying SRT, air flow rate ......................... 44
4.3
Conclusions and recommendations .......................................................................... 46
5
EXPERIMENT 2 – SEMI-CONTINUOUS CULTURE IN GLASS BOTTLES ............................... 48
5.1
Results – Semi-continuous culture in glass bottles .................................................. 48
5.2
Discussion – Semi-continuous culture in glass bottles ............................................ 54
5.2.1 Nitrifier reactor ..................................................................................................... 54
5.2.2 Algae reactor ........................................................................................................ 54
5.2.3 Mixture reactor ..................................................................................................... 55
5.2.4 Ammonium conversion rate ................................................................................. 58
5.2.5 Dark test ............................................................................................................... 59
5.2.6 Comparison among three reactors ........................................................................ 60
5.3
Conclusion – Semi-continuous culture in glass bottle ............................................. 64
6
RECOMMENDATIONS FOR FUTURE WORKS ..................................................................... 65
7
REFERENCES .................................................................................................................... 66
8
APPENDIXES ..................................................................................................................... 74
8.1
Appendix 1 – Parameters of airlift reactors in Experiment 1 ................................... 75
8.2
Appendix 2 – Parameters of airlift reactors in Experiment 2 ................................... 77
8.3
Appendix 3 – Soluble COD profile in Experiment 2 ............................................... 79
8.4
Appendix 4 – Ammonium conversion rate in Experiment 2 ................................... 79
8.5
Appendix 5 – Dark test in Experiment 2 with glass bottle....................................... 80
8.6
Appendix 6 – Settleability of biomasses in Experiment 2 with glass bottle ............ 80
8.7
Appendix 7 – Transmittance of effluents in Experiment 2 with glass bottle ........... 80
8.8
Appendix 8 – Test for differences between algae and mixture reactors during the
first 18 days in Experiment 2 ............................................................................................... 81
8.9
Appendix 9 – Test for differences of nitrogen species in algae and mixture reactors
during dark test ..................................................................................................................... 86
8.10 Appendix 10 – Test for differences of COD removal among three reactors in
Experiment 2 ........................................................................................................................ 89
vi
List of tables
Table 1.1: Typical characteristics and possible environmental impacts of raw swine waste .. 11
Table 2.1: Characteristics of AD swine waste applied in some algal studies ......................... 25
Table 3.1: Characteristic of ground water obtained at Botanical Garden ............................... 26
Table 3.2: Typical characteristics of AD swine centrate obtained from week 7 to week 12 .. 26
Table 3.3: Operational parameters applied in Experiment 1 with airlift reactors ................... 29
Table 3.4: Operating parameters applied in Experiment 2 with glass bottle ........................... 30
Table 4.1: Theoretical oxygen balance in mixture reactor based on calculation in Section
3.7.1 ....................................................................................................................... 42
Table 4.2: Soluble COD and COD removal in mixture reactor .............................................. 42
Table 5.1: Total nitrogen and nitrogen balance in nitrifier, algae and mixture reactors ......... 52
Table 5.2: Comparison of ammonium removal by algae and bacteria found in some studies 60
Table 5.3: Comparison of TN removal by biomass uptake by algae and bacteria found in
some studies .......................................................................................................... 61
Table 5.4: Comparison of COD removal by algae and bacteria found in some studies .......... 61
Table 5.5: Comparison of biomass productivity by algae and bacteria found in some studies
............................................................................................................................... 62
Table 8.1: Characteristics of feed and effluents of continuous cultures in Experiment 1 ....... 75
Table 8.2: Characteristics of feed and effluents of semi-continuous cultures in Experiment 2
............................................................................................................................... 77
Table 8.3: Soluble COD of feed and effluents of semi-continuous cultures in Experiment 2 79
Table 8.4: Nitrogen species when measuring ammonium conversion rates in algae and
mixture cultures during Experiment 2 ................................................................... 79
Table 8.5: DO profile of algae reactor in Experiment 2 when it was put in the dark .............. 80
Table 8.6: Nitrogen species algae and mixture cultures during dark test ................................ 80
Table 8.7: TSS unsettled and its percentage in reactors .......................................................... 80
Table 8.8: Light transmittance of feed and effluents on day 14,15, 16 in Experiment 2 ........ 81
List of figures
Figure 1.1: Schematic for proposed treatment process of swine waste by Kinyua (2013) ..... 12
Figure 3.1: Anaerobically digested swine wastewater after pre-treatment by centrifuge and
anthracite filtration. Its low light transmittance could negatively affect the algal
growth .................................................................................................................... 27
Figure 3.2: Schematic of airlift reactor and reactor arrangement ............................................ 28
Figure 3.3: Layout of reactor arrangement (CW refers to Cool White Fluorescent Lamp) .... 28
Figure 3.4: Experiment 2 with three 1L-glass bottles growing algae only, nitrifier only and
mixture of algal-nitrifiers under semi-continuous culture ..................................... 30
Figure 4.1: DO(a), pH (b), alkalinity (c), dry weight (d), Chlorophyll a (e) and biomass
productivity (f) profiles of algae and mixture cultures in airlift reactors during the
first 15 day experiment .......................................................................................... 38
Figure 4.2: Ammonium (NH4+-N), nitrate (NO3--N) and nitrite (NO2--N) profiles of algae (a)
and mixture (b) cultures in airlift reactors during the first 15 day experiment. .... 39
vii
Figure 4.3: Performance of mixture reactor when varying SRT, air flow rate over Phases ... 43
Figure 4.4: Microscopic photos (x40) of algae flocs (left) and rotifers (right, in red circle) in
mixture airlift reactor on day 28. Number of algae was reduced markedly and
algae cells aggregated to form bigger flocs. New type of algae (filamentous algae)
was also observed .................................................................................................. 45
Figure 5.1: Developing trends of DO, pH and alkalinity of three reactors nitrifiers, algae and
mixture respectively over experimental period ..................................................... 48
Figure 5.2: Developing trends of Chlorophyll a (a), VSS (b), TSS (c) and biomass
productivity (d) of nitrifiers, algae and mixture reactors respectively over
experimental period ............................................................................................... 49
Figure 5.3: Ammonium conversions of nitrifier (a), algae (b) and mixture (c) reactors over
experimental period ............................................................................................... 50
Figure 5.4: Ammonium conversion rates in algae and mixture reactors on day 21 ................ 51
Figure 5.5: DO variation in algae reactor during dark test on day 28 ..................................... 51
Figure 5.6: Variation of nitrogen species in algae (a) and mixture (b) reactors in dark test on
day 28 .................................................................................................................... 52
Figure 5.7: Soluble COD removal on average of samples collected on day 13, 18, 24 and 27
............................................................................................................................... 53
Figure 5.8: Typical light transmittances of feed and effluents from three reactors ................ 53
Figure 5.9: Settleability characteristics of three reactors measured on day 27, expressed by
the percentage of TSS remained in solution over time ......................................... 53
Figure 5.10: Microscopic observation (x40) illustrates the development of rotifers and the
decrease of algae population in mixture reactors during experiment period......... 56
Figure 5.11: Schematic diagram of co-digestion process combining algae (and bacteria)
biomass cultured in AD swine centrate with fresh swine manure to increase
energy production .................................................................................................. 63
viii
List of symbols
AOB
BOD
C
COD
DO
DON
DW
FA
FNA
H
HRAP, HRAPs
HRT
N
NOB
O
OD
P
PAH
SRT
SS
SVI
TAN
TKN
TN
TP
TSS
VAS
VFA
WWTP
Ammonia Oxidizing Bacteria
Biological Oxygen Demand
Carbon
Chemical Oxygen Demand
Dissolved Oxygen
Dissolved Organic Nitrogen
Dry Weight
Free Ammonia
Free Nitrous Acid
Hydro
High Rate Algal Ponds
Hydraulic Retention Time
Nitrogen
Nitrite Oxidizing Bacteria
Oxygen
Optical Density
Phosphorus
Polycyclic Aromatic Hydrocarbons
Solid Retention Time
Suspended Solid
Sludge Volume Index
Total Ammoniacal Nitrogen
Total Kjeldahl Nitrogen
Total Nitrogen
Total Phosphorus
Total Suspended Solid
Volatile Attached Solids
Volatile Fatty Acids
Wastewater Treatment Plant
ix
1 Introduction
1.1 Background
Nutrient enrichment – increasing concentrations of nitrogen and phosphorus – in streams has
been causing many environmental problems, i.e., eutrophication and aquatic hypoxia.
Therefore, driven by strict regulations, most wastewater treatment facilities are forced to
adopt nutrient removal besides COD, SS removal before discharge. In wastewater treatment
process, nitrogen can be removed by incorporation into discharged waste sludge or ammonia
stripping in anaerobic treatment but with low efficiency. In order to meet more stringent
discharge requirements, another nitrogen removal process is achieved via two steps:
nitrification and denitrification. The former is performed by two specific genera of
autotrophic bacteria: Nitrosomonas, which oxidizes ammonia to nitrite and Nitrobacter,
which oxidizes nitrite to nitrate. Recent modern techniques have discovered that there are not
only two but several genera of nitrifying organisms (Henze, 2008, Choi et al., 2010).
Denitrification is carried out under anoxic conditions, primarily by heterotrophic bacteria, in
which nitrite or nitrate serves as an electron acceptor for the oxidation of organic carbon and
is reduced to nitrogen gas. Also, as an alternative to conventional nitrification-denitrification
process, the newly discovered Anammox process allows a shortcut in the nitrogen removal
process by partial nitrification, producing a mixture of ammonia and nitrite, which both are
then converted into nitrogen gas in the presence of Anammox bacteria. It is clear that
nitrification is the first crucial step in any nitrogen biological removal process. In
conventional activated sludge system, nitrification and COD removal occur simultaneously in
an aeration tank. According to (Tchobanoglous et al., 2003), the mechanical aeration to
supply oxygen demand for aerobic bacteria accounts for more than 50% of the total energy
consumption for a typical secondary wastewater treatment plant using activated sludge
process. Many new technologies have been developed to decrease aeration requirements,
while still meet the treatment standards, such as aerobic granular sludge process (De Kreuk et
al., 2005). Using an algal-bacterial consortium can be a potential solution to reduce aeration
requirements (thereby reducing energy consumption) and more environmentally friendly
alternative to conventional treatment method. Microalgae generate well-dissolved oxygen
photosynthetically, which is required by bacteria to oxidize both organic matter and nitrogen
ammonia, while bacterial respiration converts COD into CO2, which is needed for microalgal
growth via photosynthesis. Nitrogen and phosphorus in wastewater are also effectively
removed via accumulating into algal-bacterial biomass or via pH-enhanced ammonia
stripping phosphate precipitation (Wang et al., 2010b).
In agriculture, the increase in intensive swine production has resulted in an excess of manures.
Land application is a common treatment method for this type of waste. However, the disposal
on land has caused a lot of environmental problems, such as water pollutions due to runoff,
human health risks and GHG emissions. In addition, energy and nutrient values which could
be reclaimed have been wasted during the process. Typical characteristics of raw swine
wastewater are summarized in Table 1.1.
10
MSc Thesis
Table 1.1: Typical characteristics and possible environmental impacts of raw swine waste
Pollutants
Organic
Matters
Solids
COD (g/L)
BOD (g/L)
SS (g/L)
Nutrients
TN (g/L)
TP (g/L)
NH4+-N (g/L)
Pathogens
Campylobacter spp.,
Salmonella, Listeria
monocytogenes, E.coli
O157:H7,
Cryptosporidium
parvum, Giardia
lamblia, Ascaris
Volatile
Organic
Compounds
(VOC)
Trace
elements
Chemicals
of concerns
Typical
concentrations
10-80
1-20
1-8
2-6
0.3-1.1
1.2-3.1
Environmental Impacts
- Carbon based biodegradable compounds degraded
by microorganisms in water bodies leads to low DO
levels that affect aquatic life
- Biodiversity in water bodies is lowered from
depleting DO
- Increase turbidity in water bodies
- Decrease in light penetration affects aquatic plants
that some aquatic animals depend on
Solids encourage the accumulation and transport of
nutrients
- Reduces DO by exerting NOD to water
- High levels lead to eutrophication in water bodies
- Aquatic life is negatively affected
- Fish kills lower biodiversity in water bodies
- TP increases cost for drinking water treatment
plants and produces odors
- NH4+ is toxic to aquatic life
- Waterborne disease that cause illness to humans
and animals
Volatile Fatty Acids
(VFA), phenols,
mercaptans, H2S
N/A
Cu, Zn, B, Mg, Fe and
Pb
N/A
Antibiotics and
pharmaceutical
chemicals
N/A
- Air pollution leading to respiratory health issues to
both the farm workers and the animals
- Odorous compounds can be a nuisance
- May affect human health and the environment if
accumulated in water bodies
- Widespread use of antibiotics to treat illness and
promote growth may lead to antibiotic resistant
pathogens
- Accumulation of pharmaceutical chemicals in
water bodies may affect aquatic life.
[Source: adopted from Master Thesis of Kinyua (2013), Department of Civil and
Environmental Engineering, University of South Florida, Tampa, US]
In an effort to seek a more sustainable treatment method, researchers have proposed anaerobic
digestion (AD) as a robust and suitable solution for this type of high strength wastewater. AD
is preferred over other systems for some benefits. It can generate biogas for energy production,
reduce biomass, odor and GHG production, remove pathogens and provide nutrient-rich
biomass that is safe for fertilizer application. However, the liquid effluent from AD process
still contains high suspended solids, recalcitrant organic matters, phosphate and nitrogen
(mainly as NH4+-N) contents. Many post-treatment technologies have been proposed to
remove and recover nutrient contents, such as full scale AD, membrane microfiltration,
struvite precipitation, UASB post-digestion, PIGMAN(Karakashev et al., 2008). An advanced
process was proposed by Kinyua (2013) to fully remove P and N. It begins with the AD
system for biogas production and COD reduction, followed by struvite precipitation and a
biological nitrogen removal (BNR) system.
Thanh Tung Nguyen
11
Figure 1.1: Schematic for proposed treatment process of swine waste by Kinyua (2013)
[Source: adopted from Master Thesis of Kinyua (2013), Department of Civil and
Environmental Engineering, University of South Florida, Tampa, US]
Traditional BNR systems mainly involve in nitrification-denitrification process. However,
this technique requires a lot of exogenous aeration and energy consumption. Moreover, efforts
of maximizing biogas production during ADS and oxidation of COD during nitrification have
greatly decreased the bioavailable organic carbon required for the metabolism of denitrifying
microorganisms. It is therefore necessary to find another BNR process that requires less
energy, low or no externally added organic carbon. In this context, consortia composed of
bacteria and microalgae are rapidly gaining attention. The consortia have been proven to be
able to support nitrification without exogenous aeration (Karya et al., 2013) to remove
ammonia while also recover carbon, nitrogen and phosphorus via biomass uptake
(Molinuevo-Salces et al., 2010). Additional benefits of employing this consortium are the low
energy consumed in the process, since it is a solar-powered technology, and the biomass
produced may be used as fertilizers or biofuels to offset the bioprocess costs.
1.2 Aim and boundary of the study
The research on combining algae and bacteria to find a low-energy, environmentally friendly
way of treating wastewater has been carried out worldwide with great interest. This study
contributes a piece of knowledge to the overall goal by focusing on the ammonium removal of
the system treating anaerobically digested swine waste, searching for the low cost, low energy
methods of nutrients removal while simultaneously producing biomass for biofuels in
agricultural waste management.
1.3 Overall objective
The overall objective is to evaluate ammonium removal by algae and nitrifiers system treating
anaerobically digested swine waste centrate under controlled conditions.
12
MSc Thesis
1.4 Specific objectives
The specific objective is to evaluate ammonium removal from anaerobically digested swine
wastewater using adapted wild-type Chlorella sp. and nitrifiers cultured in a photobioreactor
by focusing on:




Efficiency and routes of ammonium removal.
Nitrification process in reactors.
Comparing ammonium removal and biomass characteristics between two systems:
one in which biomass consists of algae the other in which biomass consists of a
mixture of algae and nitrifiers.
Ammonium removal when varying conditions of SRT, aeration intensity, and
ammonium loading rate.
Thanh Tung Nguyen
13
2 Literature review
2.1 Microalgae
2.1.1 Characteristic of microalgae
Microalgae are unicellular species that can grow individually, in chains or groups in
freshwater or seawater. Depending on species, their size can vary from several to hundreds of
micrometers. Similar to terrestrial plants, microalgae can grow photoautotrophically: utilize
sunlight as energy source, consume CO2 as carbon source and produce O2 as a byproduct. It
has also been documented to be able to grow heterotrophically or mixotrophically depending
on the availability of light and the types of carbon source (Chojnacka and Noworyta, 2004,
Wang et al., 2010b). With different metabolisms, algae have different elemental compositions
(Chojnacka and Noworyta, 2004). Besides, microalgae also require nutrients (N and P) and
micronutrients for growth. In general, algae prefer NH4+-N to NH4+-N as nitrogen source
(Schumacher et al., 2003, Chan et al., 1979). The following stoichiometrical reactions have
been proposed for algal photosynthesis, with either NH4+-N or NO3--N by Boelee et al. (2012):
100CO2 + 70H2O + 12NH4+ + H2PO42-  C100H178O36N12P + 118O2 + 11H+
100CO2 + 94H2O + 12NO3- + H2PO42-  C100H178O36N12P + 142O2 + 13OH-
(2.1)
(2.2)
In a different way, Oswald (1988a) described the algal photosynthetic production from NH4+N uptake by the following stoichiometry:
106CO2 + 236H2O + 16NH4+ + HPO42-  C106H181O45N16P + 118O2 + 171H2O + 14H+ (2.3)
The chemical composition of microalgae is not constant but varies depending on some factors.
For instance, the Chlorophyll-a and the lipid contents of algal cell are subjected to species,
cell density and growth conditions (Mayer et al., 1997). The typical lipid content in algal cell
is approximately 20% (Benemann, 2008b), and it can be higher when nitrogen becomes the
growth limiting factor (Benemann and Oswald, 1994). The ratio of N:P in algal biomass can
vary from about 4:1 to almost 40:1 depending on algal species and nutrient availability
(Craggs et al., 2011).
2.1.2 Microalgae cultivation and harvesting
Microalgae cultivation has been studied and practiced worldwide with great interest for
commercial benefits, such as biofuels, human food and pharmaceuticals, etc. To achieve a
high algal productivity, a good knowledge of cultivated algae is necessary as each type has its
own optimal growth range. For instance, Spirulina platensis has optimal pH range between 9
and 10 (Boussiba, 1989) while Chlorella sp. prefers lower pH, around 7.5-8. In addition to
operational conditions, light, macro nutrients (C, H, N, O, P) and micronutrients are among
the most important factors that affect algal growth. In commercial production, freshwater and
fertilizers are typically added to supply nutrient contents, but they can be costly (Christenson
and Sims, 2011). To this, many authors has looked into wastewater as an alternative for
nutrient source (Park et al., 2011, Rawat et al., 2011). Many approaches are currently carried
out using wastewater for algal growth in a bench scale (Yuan et al., 2011), pilot scale (Min et
al., 2011) and large scale (Christenson and Sims, 2011, Park et al., 2011) production. A dual
benefit can be achieved when using wastewater as algal culture media: recuperation of
14
MSc Thesis
nutrients by algal production and wastewater treatment. However, not all wastewaters are
economically suitable for algal cultivation. Algae have been reported to favor growing in
nutrient-rich centrate (rejected wastewater from sludge centrifuge, swine wastewater (Kim et
al., 2007)) than in normal municipal wastewaters (raw, after primary and secondary treatment
(Wang et al., 2010b)).
Efficient algal biomass harvest (removal) plays an important role in effective nutrient removal
and cost-effective algal production. Harvesting alga could be a challenge due to small size
(<20µm) of algae, their similar density to water (1.08-1.13 g/ml) and strong negative charge
(Moraine et al., 1979). This process has also increased the overall operational cost
significantly. Some conventional harvesting techniques includes mechanical methods
(filtration, centrifugation, electrostatic autoflocculation), chemical methods (flocculation and
coagulation), and natural methods (gravity sedimentation and aggregation/ bioflocculation).
Microalgal flocculation followed by gravity sedimentation is the most common harvesting
techniques in wastewater treatment using algae. However, this technique is only efficient with
multicellular cyanobacteria such as Spirulina or self-aggregating Phormidium bohneri but not
with small, rapidly growing alga such as Chlorella or Scenedesmus sp (Garcia et al., 2000).
Autoflocculation by electrostatic interactions is still poorly investigated and hard to apply
(Munoz and Guieysse, 2006). The addition of chemical flocculants is efficient and reliable but
expensive and may create secondary contamination (Munoz and Guieysse, 2006, Imase et al.,
2008). Microalgal immobilization in polymeric materials such as carrageenan, chitosan or
alginate has been reported to be effective but difficult to large-scale application due to high
cost (Hoffmann, 2002). Finally, membrane bioreactor and bioflocculation by an algalbaceterial consortium could be promising alternatives and have gained much attention
recently (Munoz and Guieysse, 2006).
2.1.3 Advantages and application of microalgae in wastewater treatment
In general, algae can provide a number of potential benefits: (1) the production of algae is
reported to be higher than those of higher plants (Hernández et al., 2012); (2) algae growth is
independent from arable lands; (3) algae biomass is rich in lipids, proteins and starch, which
could be used as biofuel for energy production, human food and pharmaceuticals, animal feed;
(4) during photosynthetic respiration, algae consume CO2 and produce O2, reduce CO2
emission, which is one of green house gases (5) algae applied in treating wastewater can
reduce WWTP capital and operation costs, has significantly less environmental impact in
terms of water footprint, energy and fertilizer use, and residual nutrient removal to prevent
eutrophication.
Particularly, the advantages of microalgae are fully exploited when it is applied to treat
wastewater. Large-scale production of algae for biofuel production using wastewater was first
carried out in HRAPs by Oswald and Golueke (1960). Algae growing in wastewater
assimilate nutrients and thus subsequent harvest of the algal biomass recovers (or removes)
the nutrients from the wastewater (Garcia et al., 2006, Park and Craggs, 2010). The harvested
algal biomass could be used as animal feeds or low-released fertilizers for land provided that
heavy metals and pathogens in biomass are controlled, or converted through various pathways
to biofuels such as biogas, biodiesel, bioethanol and biocrude (Craggs et al., 2011). It was
estimated that biogas produced from every kg of algae dry weight can provide 1 kWh of
electricity (Benemann and Oswald, 1994). Residual solids rejected from biofuel production
can be again used as fertilizer, animal feed or other valuable co-products (Richmond, 2008).
Furthermore, the algal photosynthesis in wastewater enhances the elimination of many
pathogens and viruses by increasing the temperature, pH and DO concentration of the treated
effluent (Oswald, 1988a, Muñoz et al., 2003). Recently, the application of alga-bacteria has
Thanh Tung Nguyen
15
shown a great opportunity for a low-energy but high efficient treatment of wastewater coupled
with biomass production. Further discussion will be demonstrated in section 2.4.
2.1.4 Limiting factors of algal growth
Algal growth and nutrient uptake are not only affected by critical environmental conditions
(light and temperature), they also depend on operational factors (pH, CO2 and nutrients,
mixing and harvesting) and biological factors (predators and algal pathogens). Consequently,
the growth rate, growth yield and metabolic behavior of alga are substantially influenced by
those conditions as well.
2.1.4.1 Light
The availability and intensity of light, an essential energy source for algal photosynthesis, is
one of the key factors affecting the algal growth (Craggs, 2005). When nutrients are not
inhibitory factors, algal growth increases with rising of light intensity up to light saturation
level where maximum algal growth rate is reached (Torzillo et al., 2003, Chojnacka and
Noworyta, 2004). Light saturation level varies with different algal strains and culture density,
but normally is between 200-400 µmol/m2/s for most algal species (Torzillo et al., 2003,
Ogbonna and Tanaka, 2000). Beyond this range, photoinhibition may happen, decreasing
algal photosynthetic rate and productivity. It is more likely to occur at low microalgal density
because the light intensity to which microalgae are actually exposed is not mitigated by
mutual shading (Richmond, 2000). The higher the light intensity, the higher should be the
biomass concentration (Hu et al., 2000).
2.1.4.2 Temperature
Likewise light intensity, increasing temperature up to an optimum temperature could increase
algal productivity exponentially, but above the optimum point, increasing temperature could
inversely lead to reduction of overall productivity (Torzillo et al., 2003, Richmond et al.,
2003). Without limitations of nutrient and light, the optimal temperature for many algae is in
range of 28-35°C (Soeder et al., 1985), and varies among algal species. In conditions of
limiting nutrients and light, the optimal temperature of the same algal specie may vary.
Sudden temperature change often results in altering environmental factors (water ionic
equilibria, pH and gas solubility), and generally decreasing algal growth at different levels
(Harris, 1978).
2.1.4.3 pH
The pH affects many bio-chemcal processes that are associated with algal growth and
metabolism, including the bio-availability of CO2 for photosynthesis, the availability and
uptake of nutrient ions and the presence of some inhibition factors (free ammonia and nitrite).
Algal growth in turn may contribute to changing of pH in culture medium. Algal
photosynthesis consumes soluble inorganic carbon and nitrate, resulting in increased pH
which can climb up to pH>11(Craggs, 2005, Park and Craggs, 2010). The elevated pH
consequently enhances NH4+-N removal from aquatic culture via free ammonia (FA)
volatilization and phosphorus removal through phosphate precipitation with Fe3+, Ca2+, Mg2+
available in environment. For instance, at pH 9.5 (20-25°C ), FA concentrations of 34-51g/m3
could reduce the photosynthesis of freshwater algae, Scedenesmus obliquus, by 50-90%
(Azov and Goldman, 1982). The optimal range of pH varies from algal species. Many
freshwater algae have an optimal pH around 8 (Kong et al., 2010) while some have higher
optimal pH value (i.e., between 9 and 10 for Spirulina platensis (Boussiba, 1989). A pH
below or above the optimal value will negatively affect algal growth. For instance,
16
MSc Thesis
productivity of Chlorella sp. was reduced by 22% when pH increased from 8 to 9 (Weissman
et al., 2004).
2.1.4.4 Dissolved oxygen (DO)
High DO can generate photo-oxidative damage on algal cells and hence decrease productivity
as well as treatment efficiency (Oswald, 1988b). For instance, Molina et al. (2001) observed a
reduction by 17-25% at 200-300% DO saturation respectively during photosynthetic activity.
Matsumoto et al. (1996) reported a 98% decrease in the photosynthetic O2 production rate
when DO increased from 0 to 29 mg/L (≈350%). The supersaturation DO level in an enclosed
photobioreactor can be as high as 400%, inhibiting microalgal growth (Kumar et al., 2010a).
2.1.4.5 Carbon source
Algae can grown under autotrophic, heterotrophic and mixotrophic conditions, and types of
carbon source may affect the algal growth rate (Chojnacka and Noworyta, 2004). Among
them, mixotrophic growth is believed to be the fastest growth of algal biomass (Lee et al.,
1989). Ogbonna et al. (1997) reported that Chlorella grown in heterotrophic culture yielded a
higher biomass concentration compared to autotrophic culture. The role of exogenous CO2
injection has been reported inconsistently. Theoretically, exogenous CO2 stimulates
photoautotrophic cultivation and higher algal biomass production is achieved. It has been
reported that CO2 addition in HRAP reduces the loss of total nitrogen from solution, mostly
by decreasing ammonia volatilization due to decreased pH, which can be assimilated by algae
and therefore enhanced algal productivity (Park and Craggs, 2010, Park and Craggs, 2011).
However, when studying the effect of exogenous levels CO2 on the cultivation of Chlorella sp.
in organic carbon rich centrate, Min et al. (2011) did not observe such positive results. In
contrast, following this study, excessive CO2 could decrease pH level severely, creating
unfavorable condition for algal growth and reducing TKN removal.
2.1.4.6 Nutrients (N, P and micronutrients)
The typical formula of alga (C106H181O45N16P) implies an N/P ratio of 16N/P (7.3gN/1gP) in
culture media would be required (Craggs, 2005). However, that ratio can vary from about 4/1
to 40/1 depending on algal species and nutrient availability(Craggs et al., 2011). The optimal
N/P ratio for freshwater algae growth was suggested to be in the range of 6.8-10 (Martin et al.,
1985), where as the typical C/N for algal biomass is in between 6-15 (Benemann, 2003).
Nitrogen is an essential factor for regulating lipid content in algal cell. When nitrogen is
limited, algal growth decreases and lipid content is accumulated from typical value of 20% up
to 40% (Park et al., 2011). It has been documented that algae prefer nitrogen species in the
order: NH4+-N>NO3--N> NO2--N>simple organic-N such as urea and simple amino acids
(Syrett, 1981).
2.1.4.7 Operational conditions
Long HRT may inhibit microalgal growth due to excess DO production (Valigore et al., 2012)
or increased effect of light mutual shading by the higher algal concentration (Park and Craggs,
2010) while too short HRT may result in algal wash out. The effect of mutual shading and
dark respiration can be alleviated by proper mixing of culture medium (Lau et al., 1995).
Mixing promotes uniform distribution of light and nutrients to cells and protect them from
effluent toxicity (Munoz and Guieysse, 2006). However, violent mixing can create shear
stress on algal production (Leupold et al., 2012). Air aeration can be a good approach to attain
both shear stress reduction and cultural mixing (Gudin and Chaumont, 1991).
Thanh Tung Nguyen
17
2.1.4.8 Free ammonia (FA) and Nitrite
Although NH4+-N is the main nitrogen source for algal growth, too high NH4+-N, however,
has been shown to be toxic to algae due to free ammonia(FA) accumulation (Yuan et al.,
2011). The equilibrium of NH4+-N and FA strongly depends on pH, therefore algae may be
significantly inhibited by free ammonia at pH of 9.0 or higher while at low pH, ammonia
inhibition may be reduced by adding CO2 to maintain low pH <8 (Azov and Goldman, 1982).
At pH 9.5 (20-25°C ), high FA concentrations of 34 and 51g/m3 has reduced the
photosynthesis of freshwater algae, Scedenesmus obliquus, by 50% and 90% respectively
(Azov and Goldman, 1982).
When NH4+-N and NO3--N are absent, algae may uptake NO2--N (Chen et al., 2012).
However, high level of nitrite is toxic to alga; the inhibition of high nitrite concentration on
the growth of alga has been demonstrated by Chen et al. (2011). Studying on Microcystis
aeruginosa showed that the high extracellular NO3--N could result in the accumulation of
intracellular NO2--N, which is believed to be the major responsible for nitrite toxicity to alga
(Chen et al., 2011). The same study also found that the existence of nitrate NO3--N could
affect the toxicity of nitrite NO2--N to alga: low NO3--N level might diminish the total
intracellular NO2--N content, but high nitrate level would increase the total intracellular nitrite
content.
2.1.4.9 Biomass density
The optimum biomass concentration in the culture is mainly dependent on the light intensity
and the reactor configuration: at low light intensity, the biomass concentration should be
optimized to avoid mutual shading and dark respiration (Lau et al., 1995), whereas at high
light intensity, a high biomass concentration can be useful to protect microalgae from light
inhibition (Munoz and Guieysse, 2006). Intensive algal culture may result in some negative
effects: limitation of CO2 or availability of inorganic carbon, rapid depletion of certain
nutrients, decrease of Chlorophyll content on a per cell basis due to low light uptake,
accumulation of auto-inhibitory substances chlorellin in very dense algae (Lau et al., 1995).
2.1.4.10 Predators and pathogens
Algae are susceptible to capturing by zooplankton grazers which can decrease the population
of alga rapidly (Benemann, 2008a, Lau et al., 1995). Fungal parasitism and viral infection can
also decrease algal production and trigger changes in algal cell structure, diversity and
succession (Kagami et al., 2007). Zooplankton predator may be controlled by physical
(filtration, centrifugation, low DO concentration/high organic loading) and chemical treatment
(increase pH and FA concentration) (Park et al., 2011).
2.2 Photobioreactors
Basically, there are two main commercial cultivation systems: open raceway ponds
(stabilization ponds or high rate algal ponds) and closed photobioreactors (dome, tubular, flat
plate systems). Compared to closed photobioreactors, open raceway ponds are cheap, suitable
for large scale production but prone to contamination by other microorganisms and more
susceptible to environmental influences than the closed. The best configuration to select will
be determined by factors such as process safety, land cost, and biomass use. High rate algal
pond (HRAP) is effective in treating municipal, industrial and agricultural wastewaters while
at the same time can capture solar energy to produce up to 50 t/ha/year of algal biomass for
biofuel production (Rawat et al., 2011). However, HRAPs face some drawbacks that may lead
to system failure, such as, low light penetration, the presence of algal predators and pathogens
18
MSc Thesis
(Benemann, 2008a). Closed photobioreactors allow for a better species control and should
therefore be preferred in situations where slow growing algae are required.
HRAPs are the most typical for open systems. They are shallow (0.1-0.3 m), wide (2-3 m),
open raceway ponds and range from 1000 to 5000 m2 in large-scale applications (Abeliovich,
1986). Large scale production of algal using wastewater treatment HRAPs was first proposed
by Oswald and Golueke (1960). Ideal HRAPs can treat up to 35 gBOD/m2/d (175 gBOD/m3/d
in a 0.2m deep pond) compared to 5-10 g BOD/m2/d (5-10 g BOD/m3/d in a 1 m deep pond)
in waste stabilization ponds (Racault and Boutin, 2005). HRAPs could further improve
wastewater treatment, particularly nutrient removal, compared with conventional wastewater
stabilization ponds. Especially, they are very appropriate for sanitation purpose in small rural
communities because of their simplicity of operation in comparison to conventional
technologies such as activated sludge facilities (Garcia et al., 2006).
Compared to open systems, closed photobioreactors could offer higher photosynthetic
efficiency and better control (less potential risk of pollutant volatilization and predation).
They can also be built vertically in order to minimize square requirement (Tredici, 2002) and
minimize water looses by evaporation which can be very significant in open system. However,
closed systems are more expensive to construct (need for transparent materials such as glass,
PVC, etc.), difficult to operate and scale up. Tubular photobioreactors are the easiest to scale
up by increasing the length and number of tubes and by the connection of several units via
manifolds (Borowitzka, 1999). Compared to other types, tubular reactor has some advantages:
large illumination surface, a high surface/volume ratio, high biomass productivity and suitable
for outdoor application (Posten, 2009).
For the present study, algal bacterial consortium will be cultured in bench-scale airlift
photobioreactors (Yuan et al., 2011) and 1L glass bottles respectively in different experiments.
An airlift photobioreactor is a column-like reactor that consists of a riser (upflow region) and
a downcomer. It is typically constructed from polyethylene or glass tubing that is sufficiently
transparent to allow good light penetration. Sunlight or artificial light is usually provided from
outside of the reactor; however, light can also be provide from inside to minimize light
attenuation. An air or CO2/air mixture is bubbled into the reactor at the base of the riser to
separate the reactor volume into gassed and ungassed regions. Flue gases from power plants
have also been proposed to provide a more sustainable supply of CO2 to the algae and reduce
greenhouse gas emissions (Kumar et al., 2010a). Liquid circulation is induced by the
hydrostatic disequilibrium caused by the density difference between the riser and the
downcomer.
2.3 Nitrification process
2.3.1 Principle of nitrification
Nitrification is a biological process where ionized NH4+-N and unionized NH3-N (FA) are
oxidized to NO2--N and NO3--N by nitrifying bacteria. It takes place in two sequential
oxidation steps by autotrophic nitrifying bacteria, AOB and NOB, obtaining carbon source
from dissolved CO2 and energy for biomass synthesis from oxidizing ammonia to nitrite
(AOB) and nitrite to nitrate (NOB). The nitrification steps is generally the rate determining
step in biological nitrogen removal because of their low growth rate and sensitivity to
environment changes (Choi et al., 2010). Apart from well-known Nitrosomonas (AOB) and
Thanh Tung Nguyen
19
Nitrobacter (NOB), several genera of nitrifying organisms are also found to be able to
facilitate nitrification process (Henze, 2008).
2.3.2 Kinetic of nitrification
Two basic reactions in nitrification are:
NH4+ + 3/2O2 (AOB)  NO2- + H2O + 2H+
NO2- + 1/2O2 (NOB)  NO3-
(2.4)
(2.5)
Stoichiometrically, the oxygen consumption for ammonia oxidation reaction and nitrite
oxidation reaction are 3.43 and 1.14 mgO2/mgN. The total oxygen consumption for the
stoichiometric conversion of ammonia to nitrate is 4.57 mgO2/mgN. Taking into account the
NH4+-N for biomass uptake, the oxygen requirement per mgN nitrified might be slightly less.
Normally, ammonia oxidizing reaction is the rate limiting step of nitrification process.
2.3.3 Factors influencing nitrification process
Nitrification is affected by various parameters individually or simultaneously, including: pH,
temperature, DO, characteristic of influent, inhibitors such as NH4+/NH3 (FA) and NO2/HNO2 (FNA), SRT, HRT. Their inhibitions can slow or complete, and may reconfigure
bacterial communities (Zhou et al., 2011).
2.3.3.1 Influent source
The characteristics of wastewater and also different batches of the same wastewater type
greatly affect the maximum specific growth rate µAm of nitrifying bacteria. This influence is
so significant that Henze (2008) proposed to classify µAm as wastewater characteristic rather
than a kinetic constant. Besides providing substrates for bacterial growth, the influent
wastewater may contain some inhibitors, which mostly originated from industrial wastewater.
2.3.3.2 Temperature
Nitrification reactions are very sensitive to temperature due to strong effect of temperature on
the maximum specific growth rate µAm of nitrifiers. Approximately, every 6°C drop the µAm
halves, which means that the SRT min required to prevent nitrifiers from “wash out” of the
system, doubles (Henze, 2008). Therefore, design of nitrification systems should be based on
the minimum expected system temperature (Henze, 2008). The relation between the growth
rates of the nitrifiers executing the two constituting steps of nitrification, the AOB and NOB,
may also change with temperature. Mulder and Van Kempen (1997) claimed that at higher
temperatures, the growth rate of the NOB is lower than that of the AOB. According to
Equation 2.7 and 2.8 below, temperature is closely associated to the equilibrium of NH3N/NH4+ and of HNO2-N/NO2- in the water phase. And it is the inhibition of higher FA
concentrations (>1mgNH3-N/mgVAS) at higher temperature that differentially affects AOB
and NOB in favor of the former over the latter, allowing nitrite accumulations (Fdz-Polanco et
al., 1994). Knowles et al. (1965) observed a significantly higher maximum specific growth
rate of Nitrobacter over Nitrosomonas between about 10 and 20°C. At temperatures of 2025°C, however, a slowing of the nitrating activity was observed together with an activation of
the nitritating activity (Balmelle et al., 1992). Beyond temperature of 25°C, the inhibiting
effect of FA outweighed that of temperature with respect to Nitrosomonas (Anthonisen et al.,
1976), and the maximum specific growth rate of Nitrobacter is approximately in the same
range as that of Nitrosomonas (Balmelle et al., 1992), possibly leading to nitrite accumulation.
Nitrite accumulation has been created deliberately in SHARON-ANAMMOX process, a
20
MSc Thesis
shortcut to nitrogen removal, but should be avoided in complete nitrification due to its
toxicity to nitrifiers.
2.3.3.3 Dissolved oxygen (DO)
In nitrification process, oxygen plays as an electron acceptor. Therefore, sufficient oxygen is
utmost important for both AOB and NOB. Theoretically, the nitrogenous oxygen demand
(NOD) is 4.57mg per mg NH3-N according to Equation 2.4 and 2.5. High DO concentrations,
up to 33 mgO2/L, do not appear to affect nitrification rates significantly (Henze, 2008), while
low oxygen concentrations inversely suppress the nitrification. Stenstrom and Poduska (1980)
have formulated this effect by Monod kinetic:
(2.6)
Where: O2 is dissolved oxygen concentration (mgO2/L), Ko is half saturation constant
(mgO2/L), µAm0 is maximum specific growth rate (/d), µA0 is specific growth rate at DO of
0(mg/L). Further analysis, they found that at high SRT, nitrification can occur at DO as low
as 0.5-1.0 mgO2/L while at low SRT, higher DO is required. According to Henze (2008), in
nitrifying reactors with bubble aeration, a popular DO lower limit required to ensure
unimpeded nitrification is 2 mgO2/L at the surface of the mixed liquor. Low DO induces a
significant decrease nitritation rate of pure cultures of Nitrosomonas sp. (Goreau et al., 1980),
whereas NOB seem to be even more sensitive than AOB at low DO (Leu et al., 1998). In
other words, oxygen deficiency due to low DO affects NOB more significantly than AOB
(Hanaki et al., 1990). DO below 1.0 mg/L is supposed to be sufficient for the selective
dominance of AOB (Sinha and Annachhatre, 2007). This is illustrated by the experiment by
Hanaki et al. (1990) with a suspended growth reactor at 25°C in which nitrite oxidation was
strongly inhibited by DO less than 0.5 mg/L. Some authors even tried to control the oxidation
of nitrite to nitrate to achieve nitrite accumulation by maintaining low DO (Sinha and
Annachhatre, 2007). However, this approach may inhibit the overall nitrification because of
the toxicity of high nitrite to nitrifiers (Kuai and Verstraete, 1998).
2.3.3.4 pH and alkalinity
According to the research by Anthonisen et al. (1976), pH drives the equilibrium and
distribution of both NH4+/NH3 (FA) and NO2-/HNO2 (FNA) in water phase. And because of
the toxicity of FA and FNA that directly inhibit both AOB and NOB (Sinha and Annachhatre,
2007), the role of pH is important. Hawkins et al. (2010) also stressed that pH changes are
more important factors limiting NOB rather than the FA. The optimal pH range for the overall
nitrification has been reported in between 7-8.5 (Henze, 2008), with sharp decline of
nitrification rate outside this range. Going further, Alleman (1984) (cited by (Sinha and
Annachhatre, 2007)) found a slightly less basic pH optimum of Nitrobacter (7.2-7.6)
compared to Nitrosomonas (7.9-8.2). Villaverde et al. (1997) explained the influence of pH as
follow: increasing pH, FNA decreases while FA increases, and vice versa when decreasing
pH. At pH=6, the share of FNA in system NO2-/HNO2 is 0.2%, whereas at pH=7.5, no FNA is
present. Inversely, at pH=7, FA is absent, whereas at pH=8.5, the share of FA in system
NH4+/NH3 is more than 10%. Glass and Silverstein (1998) observed an increase of nitrite
accumulation when increasing pH from 7.5 to 9. Similarly, Villaverde et al. (1997) found
nitrite accumulation started at pH above 7.5, increasing up to 85% at pH=8.5. Tokutomi (2004)
even claimed that almost complete nitritation was achieved at pH=8.5.
According to Sinha and Annachhatre (2007), at pH>8, FA is the main inhibitor of nitrification,
while at pH<7.5, FNA is the main inhibitor. Zhou et al. (2011) described that increasing pH
from 7 to 8 can reduce FNA by 90%, while Han et al. (2010) reported a pH=8 to be beneficial
for nitrification.
Thanh Tung Nguyen
21
From the ovearall stoichiometric Equation 2.4, nitrification releases H+ which in turn
decreases alkalinity. According to calculation of Henze (2008), for every 1 mgNH4+-N
nitrified, 7.14 mg alkalinity as CaCO3 is consumed. Also when the alkalinity falls below
about 40mg/L as CaCO3, irrespective of the CO2 concentration, the pH becomes unstable and
decreases to low values. If nitrification causes the alkalinity to drop below that level,
problems associated with low pH will arise.
2.3.3.5 Free ammonia (FA) and nitrite
It is well documented that FA and FNA, rather than NH4+ and NO2-, are responsible for
inhibitory effect on nitrifiers (Anthonisen et al., 1976). Paradoxically, FA was believed to be
the substrate of AOB (Nitrosomonas europaea and N. eutropha) present in the SHARON
reactor (Claros et al., 2010). AOB and NOB are sensitive to their own substrate and more so
to the substrate of the other. However, FA and FNA are found to have selective inhibition on
NOB (Anthonisen et al., 1976) and minimal effect on the AOB (Sinha and Annachhatre,
2007). The selective inhibition by FA on NOB could be the major factor for nitrite
accumulation (Yun and Kim, 2003). Mauret et al. (1996) showed that high FA inhibits
Nitrobacter in the range of 6.6 and 8.9 mg FA/L, while Neufeld et al. (1986) found nitritation
inhibition begins at 10mg FA/L. The AOB was reported to be inhibited at FA in the range of
10-150 mg/L and NOB above 0.1-1.0 mg/L, respectively, which leads to nitrite accumulation
(Anthonisen et al., 1976). The availability of FA strongly depends on pH, NH4+-N and
temperature to a lesser extend, described by Anthonisen et al. (1976):
(2.7)
Where: FA is the free ammonia concentration; TAN, total ammoniacal nitrogen=NH3-N +
NH4+-N; Kb, the ionization constant for ammonium; Kw, the ionization constant for water;
; T: temperature in °C. According to Equation 2.7, the higher pH and
temperature may inhibit nitrification by increasing the levels of inhibitory FA. At
concentrations of around 20mg FA/L there was very little nitrification activity (Sinha and
Annachhatre, 2007). An NH4+-N level of less than 100mg/L was suggested to start up the
nitrification (Li and Zhao, 1999). Anthonisen et al. (1976) first reported the inhibitory effect
of nitrite on nitrification process taking into account of pH and temperature change:
(2.8)
Where: FNA is the free nitrous acid concentration; Ka, the ionization constant for nitrous acid;
; T: temperature in °C. They found that the inhibition on nitrification was
initiated at an FNA concentration of 0.22-2.8 mg/L. Later studies have report inconsistent
inhibitory thresholds. The effect of FNA toxicity varies from microbes to microbes and may
reconfigure the functional structure of the microbial community (Zhou et al., 2011). Many
studies have shown that NOB is more sensitive to FNA than AOB by approximately one
order of magnitude (Zhou et al., 2011, Silva et al., 2011). While very high concentrations of
FNA may cause the wash out of both AOB and NOB from nitrifying systems, a lower FNA
concentration range (0.02-0.03 mgFNA/L) is appropriate to wash out NOB in nitritationbased systems (Zhou et al., 2011). The range of FNA concentrations affecting NOB activity
was observed at 0.011-0.07 mg FNA/L, while complete inhibition was in range of 0.026-0.22
mgFNA/L (Zhang et al., 2010). In a study by Silva et al. (2011), nitrification was successfully
carried out at 25 mg NO2--N/L, while at higher concentration (100 and 200 mg NO2--N/L),
nitrification activity was severely inhibited but nitritation activity did not significantly change.
22
MSc Thesis
2.3.3.6 Light
There have been some studies reporting that light might inhibit the activity of nitrifying
bacteria. Guerrero and Jones (1996) claimed that the effect of light depends on the type of
nitrifiers and environmental conditions. Olson (1981) found that NOB was more sensitive to
sunlight than AOB: 50% reduction of NOB under light intensity of 6.64 µmol/m2/s compared
to 18,26 µmol/m2/s in the case of AOB. Nitrification was believed to proceed faster in dark
(cited by (Sinha and Annachhatre, 2007)) while complete inhibition of Nitrosomonas was
observed under light intensity of 420 lux (Hooper and Terry, 1973).
2.4 Microalgal-bacterial consortium
2.4.1 Interaction between algae and bacteria
The concept of algal-bacterial consortium has been showing promise as an engineered system
in wastewater treatment over the past few years (Munoz and Guieysse, 2006, Su et al., 2012).
Compared to only algae or only bacteria systems, the combination of algae and bacteria has
some advantages. Algae generate DO photosynthetically, which is required for bacterial
respiration to degrade COD and TKN. In turn, bacteria convert COD into CO2, which is
needed for algal growth. In such way, the algal-bacterial consortium provides a cheaper, safer
and environmentally friendly alternative to mechanical aeration and contributes to CO2
mitigation (Munoz and Guieysse, 2006). Photosynthetic oxygen production reduces the need
for external aeration, which is especially advantageous for treatment of hazardous pollutants
that must be biodegraded aerobically but might volatilize during mechanical aeration
(Safonova et al., 2004). Recent studies have shown that it is possible to use algal-bacterial
system to biodegrade hazardous pollutants such as PAH, phenolics, and organic solvents
(Munoz and Guieysse, 2006). In addition, N and P in wastewater, the main cause of water
body eutrophication, are also effectively removed via accumulation into algal-baterial
biomass or via pH elevated ammonia stripping and phosphate precipitation with Fe3+, Ca2+,
Mg2+ in water phase. Experimentally, Su et al. (2011) found that assimilation was the main P
removal mechanism for algal-bacterial culture. In term of energy and resource recovery, the
algal-bacterial biomass, accumulated during wastewater treatment, is the potential source for
fertilizer in agriculture or biofuel production (Benemann, 2008b). Moreover, the algal
photosynthesis favors the elimination of many pathogens and viruses by increasing the
temperature, pH and DO of the treated effluent (Muñoz et al., 2003, Oswald, 1988b). Algalbacterial biomass also have a better settleablility compared to algal biomass only, which can
reduce the biomass harvesting cost (Medina and Neis, 2007). When applying different algae
and sludge inoculation ratios treating municipal wastewater, Su et al. (2012) observed with
5:1 ( algae/sludge inoculation ratio), the culture showed the highest nutrient removal
efficiency and the best settleability.
Zooming into the symbiotic relationship of algae and bacteria, Croft et al. (2006) found that
algae could enhance bacterial activity by releasing extracellular compounds or by producing
sheaths, wherein bacteria were associated with, allowing a rapid and efficient exchange of
substrates. Bacteria can also again enhance algal by releasing growth promoting factors, by
reducing O2 in the medium (Mouget et al., 1995), by removing toxic compounds released
from algae (Ho et al., 2006), or by nitrification removing high ammonia that may be
inhibitory to algal growth (Källqvist and Svenson, 2003). The high algal concentration in
waste stabilization ponds provides light attenuation and more attaching surfaces for bacteria,
and hence preventing them from photo-inhibition and wash out (Zimmo et al., 2004).
Thanh Tung Nguyen
23
Besides those advantages above, the relationship between algae and bacteria can be
antagonistic, negatively affecting the wastewater treatment process. Due to their large size,
some microalgae generally grow at slower rates than heterotrophic bacteria (Fenchel, 1974).
Algal growth may inhibit bacteria by increasing the DO, pH (pH could be increased to 10.6 as
a result of algal respiration) (Green et al., 1996), the temperature of cultivation media, or by
excreting inhibitory metabolites (Munoz and Guieysse, 2006). Cyanotoxins released by
cyanobacteria, which may inhibit nitrification is an example (Makarewicz et al., 2009).
Bacterial growth may also inhibit algae by producing algicidal extracellular metabolites
(Fukami et al., 1997). In sediments, where AOB and benthic microalgae contend for the
available nitrogen, the latter outcompete the former due to their fast growth rate and N uptake
(Risgaard-Petersen et al., 2004). Choi et al. (2010) observed a nitrification inhibition in the
presence of algae and cyanobacteria due to competition for common carbon source and
nitrogen source. Since phototrophs use ammonium as nitrogen source while AOB use it as
energy source, the nitrification process by nitrifiers has been reported to compete with the
ammonium uptake by algae (Su et al., 2012, Zhang et al., 2011).
2.4.2 Algal-bacterial community analysis
To evaluate the performance of algal-bacterial consortium, it is important to know the
composition of biomass, the fractions of algae and bacteria contributing to the total biomass,
the dominant genera or strains of algae and bacteria in the biomass.
Some authors have reported to successfully separate algae and bacteria from the mixture of
biomass. In a study of Choi et al. (2010), gravity sedimentation was used to separate the
nitrifying bacteria from algae. According to the author, biomass separation by natural
sedimentation was described to be fast and effective because nitrifying bacteria easily formed
flocs and quickly settled down at the bottom, whereas algae were still largely suspended. In
another way, Min et al. (2011) have proposed using centrifugal method to separate bacteria
from an algal-bacterial mixture. Since bacteria cells are smaller and lighter than algal cells.
After centrifugation, the bacteria cells are loosely packed at the top layer above algae cells.
By shaking the sample jar, bacteria layer returns to solution while algal cells remain packed
and the bacteria only solution is achieved. Once separation of algae and bacteria is gained, the
measurements of TSS and VSS of the bacteria only indicate the proportion of bacteria in the
total biomass solid and hence the proportion of algae contributing to the biomass mixture is
also known. In cases when it is difficult or impossible to separate algae and bacteria
individually, the composition of biomass mixture can be identified via algal biomass. The
amount of bacteria is consequently approximated by the difference between the amount of
biomass mixture and algal biomass. The growth of algal biomass can be quantified in the
increment of biomass, the number of cells, the amount of protein or pigments, etc (Becker,
2007). Based on them, there are some methods to measure the biomass production, such as
dry weight estimation, optical density, cell number, Chlorophyll content... Among them,
Chlorophyll a method is mostly used to evaluate the amount of algal biomass because
Chlorophyll a, a principal photosynthetic pigment, is easily measured, reproducible and the
expression of algal biomass in term of Chlorophyll a is more meaningful (Mara, 2004). In
HRAPs, Chlorophyll a the is assumed to take 1.5% of the dry weight algal biomass, and the
proportion of algal biomass is then estimated as following (Raschke, 1993):
(2.9)
To identify microorganisms in biomass mixture, the cloning and sequence analysis techniques
are mostly applied. The bacterial communities can be determined by the 16S rRNA gene
24
MSc Thesis
analysis (Choi et al., 2010, Su et al., 2012) while the algal communities can be identified by
amplifying 18S rRNA genes with the primers NS1 and NS2 (Choi et al., 2010).
2.5 Anaerobically digested swine wastewater for algal cultivation
The nutrient components of wastewater greatly affect the growth of microalgae, their
composition and production. González-Fernández et al. (2011a) found that the algal-bacteria
consortia could perform higher biomass productivity and COD removal when grown in fresh
rather than in AD swine slurry, most probably due to the recalcitrant nature of the latter.
Compared to other types of wastewater, AD effluent has relatively low carbon content
because easily biodegradable carbon source was vastly converted to CH4 during digestion
process. Also, ammonification occurs simultaneously owing to the degradation of organic
nitrogen compounds to generate total ammonia nitrogen (TAN). Consequently, AD effluent
contains high NH4+-N and relatively low C/N. This trait makes AD swine waste more
appropriate for algal growth than for nitrification-denitrification process which requires high
bio-available organic carbon content for denitrification. The VFA normally found in AD
effluent has been reported to enhance the algal growth (Hu et al., 2012). In addition, AD
swine waste has a good balance of macro (N, P,...) and micro (Mg, Mn, Si,...) nutrients algae
need for their growth. However, there are some factors that negatively affect the algal growth,
such as: variable in compositions of digested swine waste, its high turbidity and strong
coloration (De la Noüe and Basseres, 1989), high free ammnonia (FA) and heavy metals (As,
Cu, Zn) (Marcato et al., 2008). Normally, dilution of AD effluent is usually needed before
feeding to algae to avoid high NH3 inhibition and turbidity. Wang et al. (2010a) reported
faster growth rates and the increases of total fatty acid content in Chlorella sp. with the
increasing dilution ratios of inlet digested manure. On the other hand, there is a significant
amount of bacteria in AD effluent. Baumgarten et al. (1999) observed the oxidation of NH4+N to NO2--N when cultivating Chlorella sp. only in AD swine waste, suggesting the presence
of nitrifiers naturally living in liquid manure. Many researchers reported a complete NH4+-N
removal when growing algae or combination of algae-bacteria in this type of swine waste, but
the complete TN and COD removals were hardly achieved. Accordingly, the main pathways
of N removal were NH3 stripping, nitrification-denitrification and biomass uptake (GonzálezFernández et al., 2011a, De Godos et al., 2009, Molinuevo-Salces et al., 2010, De la Noüe and
Basseres, 1989).
Table 2.1: Characteristics of AD swine waste applied in some algal studies
Parameters
Unit
TS
SS
COD
TKN
NH4+-N
NO2--N, NO3--N
TP
PO43--P
Cu2+
Zn2+
pH
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
-
Thanh Tung Nguyen
(De la Noüe and
Basseres, 1989)
8 900
2 600
7 723
3 294
111
277
7
28
8
(Kumar et al.,
2010b)
7100
4400
12 152
3304
<2.5
192
7.6
(Molinuevo-Salces et
al., 2010)
6.1
2.5
3858
1664
-
25
3 Materials and methods
3.1 Culture medium
Prior to Experiment 1 with airlift reactors and Experiment 2 with 1L glass bottles, algae were
initially cultivated with synthetic swine wastewater, containing (mg/L): NH4HCO3(6054),
NH4Cl(1.9), KHCO3(3429), NaHCO3(1534), CaCl2.2H2O(956), MgSO4.7H2O(2707),
K2HPO4(55), K2SO4(18), Trace elements and Vitamin B12 (provided based on Bold 1NV
medium).
To favour algal growth, the original synthetic swine wastewater was diluted to desired
concentration (230-250 mg/L) by ground water. The ground water was taken from Botanical
Garden at USF, Tampa-FL, US, with typical composition as follow:
Table 3.1: Characteristic of ground water obtained at Botanical Garden
pH
7.18
Alkalinity
(mg
CaCO3/L)
152
TSS
COD NH4+-N NO2--N
(mg/L) (mg/L) (mg/L) (mg/L)
0
0
0
0
NO3--N
(mg/L)
0
PO43--P Ca2+
Mg2+
(mg/L) (mg/L) (mg/L)
17.0
117.4
5.2
3.2 Real swine waste centrate
The real swine centrate was collected three times per week on Monday, Wednesday and
Friday, from 4 Anaerobic Digestion (AD) reactors in the lab. The four AD reactors were
operated at mesophilic temperature (35˚C) with different SRT 14d, 21d, 28d and 42d. Typical
compositions of their effluents are as follow:
Table 3.2: Typical characteristics of AD swine centrate obtained from week 7 to week 12
Reactor
pH
Alkalinity (g/L)
NH4+-N (g/L)
Soluble TN (g/L)
Soluble TP
(mg/L)
Soluble COD g/L
Total COD g/L
SRT 14d
6.98±3
4.65±1
0.25±0.15
1.62±0.6
SRT 21d
7.07±4
4.93±0.86
0.31±0.11
1.62±0.24
SRT 28d
7.13±5
6.12±2
0.41±0.15
1.56±0.62
SRT 42d
7.27±5
5.9±1
0.55±0.15
1.86±0.74
302.5±150
196±66
142±75
96±26
3.03±0.83
38.2±5.1
2.56±0.4
37.4±3.1
2.62±0.47
36.1±3.1
2.88±0.70
40.6±3.9
The obtained AD was further treated by first centrifuging at a speed of 3500 rpm in 10
minutes. The separated liquid fraction was then filtered through an anthracite filter column
(
). The filtered solutions were analyzed to determine NH4+-N concentration
and diluted to desired NH4+-N concentration (230-250 mg/L). Since real swine centrate
contained a lot of Mg2+, PO43- and NH4+, the struvite (NH4MgPO4.6H2O) precipitation
process may occur, leading to decrease in NH4+-N concentration. Therefore, the feed solutions
were analyzed daily to ensure the desired loading rate.
26
MSc Thesis
Compared to synthetic swine waste, the real one has better nutrient that favors algal growth.
However, there may also have some disadvantages. First, its high turbidity and low light
transmittance greatly affect the algal photosynthetic activity. Second, the real swine centrate
may contain bacteria (both nitrifying autotrophs and heterotrophs) and protozoa. Despite of
the benefits of cohabitation among algae and bacteria, the antagonism may also occur, such as
competition in nutrient (carbon source, nitrogen source...) or excretion of some toxic
compounds. Protozoa may also greatly affect algal population by predating algae. Finally, AD
real swine centrate may contain recalcitrants or toxic compounds, such as heavy metals.
Transmittance (%)
100
80
60
40
20
0
350
400
450
500
550
600
650
700
Wave length (nm)
Feed19.06.13
Feed23.06.13
Feed25.06.13
Figure 3.1: Anaerobically digested swine wastewater after pre-treatment by centrifuge and anthracite
filtration. Its low light transmittance could negatively affect the algal growth
According to Mara (2004), green algae mainly absorb photons of violet-indigo-blue and
orange-red light of wavelengths 350-450 and 650-700 nm (absorption of light of wave lengths
450-650 nm, which is mainly green light, is very weak). From the diagram above, the feed
solution had very low light transmittance at range 350-450 nm. Hence algae grown in real
swine centrate mainly absorbed orange red light at range of 600-750 nm. Low light
transmittance can be a growth inhibitor to algae.
3.3 Microalgae and nitrifying bacteria cultivation
Alga strain Chlorella sp. was obtained from the Laboratory at Department of Civil
Engineering, University of South Florida, Tampa-FL, US. The algae were initially enriched in
250 mL Erlenmeyer flasks containing about 200 mL synthetic swine wastewater (as described
in Section 3.1) and aerated by ambient air. In the batch culture under light illumination of 95101 µmol/m2/s, algal biomass was allowed to increase to 900-1100 mgTSS/L, which occurred
after about 1 week. Two algal flasks were remained for future use while the others were
transferred to two 1.5 L airlift reactors. This two airlift reactors were topped up to 1.5 L by
synthetic swine wastewater and continued to operate in 3 weeks under batch condition at
ambient air flow rate 513 mL/min. Water loss by evaporation when culturing was offset by
adding synthetic swine wastewater. Three days before the main experiment was started, the
two algal airlift reactors were switched from batch to continuous cultivation, fed with real
swine with aim at acclimating the algae to real experimental conditions.
The aerobic nitrifier liquor, collected from Howard Curren Advanced Wastewater Treatment
Plant, Tampa-FL, US, was first allowed to settle for 2 hours, half of the supernatant was then
Thanh Tung Nguyen
27
replaced by synthetic swine wastewater diluted to 240-300 mg NH4+-N/L. The bacteria seeds
were cultured in a 1.5L bubble column under sequence batch condition: 46 hours for
operation and 2 hours for settlement. Every cycle, after settlement, half of the supernatant was
withdrawn and replaced by new synthetic feed. To prevent any heterotrophic growth, no
organic carbon source was added to the feed. The nitrifying bacteria column was operated in
15 batch cycles, lasting 30 days before the main experiment began.
3.4 Reactor setup
3.4.1 Experiment 1 – Continuous culture in airlift reactors
Experiment 1 was conducted with two identical airlift photobioreactors, Algae only Reactor
and Algal-Nitrifier Mixture Reactor. The experiment lasted 29 days from May 11th to June 9th
2013. The reactors were made from two transparent acrylic columns, the inner (
) was centrally installed inside the outer (
). The effective volume of
2
reactor was 1.5 L and illuminated surface area was about 0.08m . Continuous flow regime
was adopted in which influent was pumped into the bottom while effluent came out from the
top side of reactor (Figure 3.2).
igure
Figure 3.2: Schematic of airlift reactor and reactor arrangement
Figure 3.3: Layout of reactor arrangement (CW refers to Cool White Fluorescent Lamp)
28
MSc Thesis
To keep biomass in suspension, compressed house air was injected at a flow rate of 156-500
ml/min to the base of the reactor using porous air diffuser. Two reactors were fed
continuously by a peristaltic pump (Masterflex C/L – Cole Palmer) with flow rate about 0.070.1 ml/min to achieve SRT of 15 d and 10 d respectively. Sixteen fluorescent tubes (Philips
Cool White-20W, 24 inches) were placed in 2 parallel rows, constantly illuminating 2 reactors
in the middle (Figure 3.2&3.3). Total light intensity reaching each reactor was about 70007500 lux (95- 100 µmol/m2/s). Throughout the experiment, two airlift reactors were kept in
constant temperature at around 23°C.
As described in Section 3.3, the incubation of Chlorella sp. was gradually switched from
growing in synthetic to real swine wastewater under continuous regime. Prior to the main
experiment, the algal biomass densities in two reactors reached 2075 mgTSS/L and 2161
mgTSS/L respectively. In order to keep the initial algal concentrations in two reactors as
identical as possible, the algal liquors were mixed together and put back to each reactor. The
obtained concentration in each reactor after mixing was 2118 mgTSS/L. On the other hand, to
avoid the confusion of NO3--N from nitrifier culturing medium with the NO3--N generated
after mixing nitrifier with algae, the bubble column growing nitrifier seeds was allowed to
settle down and the supernatant was withdrawn to achieve condensed liquor. The biomass
density of this concentrated nitrifier liquor was 14056 mgTSS/L. The mixture of algaenitrifier was then fulfilled as follow: take out 250 ml of 2118 mgTSS/L algal liquor and
replace by 38 ml of 14056 mgTSS/L nitrifier liquor and finally top up to 1.5L with
groundwater. After mixing, in theory and by calculation, we have:
- Initial inoculating concentrations of biomass in both reactors were closely equal: 2118
mgTSS/L in Algae reactor and 2121 mgTSS/L in Mixture reactor.
- Initial inoculating ratio of Algae to Nitrifier was nearly equal to 5:1 (≈
). This
ratio was reported to be most efficient to remove nutrients from municipal wastewater (Su et
al., 2011).
Experiment 1 was divided into 3 Phases operating in 30 days: the first 15 days running two
reactors at SRT 15d, air flow rate 336 mL/min; the next 5 days running Mixture reactor at
SRT 10d, air flow rate 336 mL/min; and the last 10 days running mixture reactor at SRT 10d,
air flow rate 156 mL/min. Due to problems with swine waste centrate supplement, the algae
only reactor was stopped after initial 15 days in operation whereas the mixture reactor was
carried out till end of experiment (day 29). The overall operating schedule is as below:
Table 3.3: Operational parameters applied in Experiment 1 with airlift reactors
Phase
Reactor
Period
(day)
SRT
(day)
NH4+N inlet
(mg/l)
200250
200250
200250
Feed
flow rate
(ml/d)
NH4+-N
loading rate
(mg/l/d)
Air flow
rate
(ml/min)
Light intensity
(lux) or
(µmol.m-2.s-1)
7000-7500
(95-101)
7000-7500
(95-101)
7000-7500
(95-101)
Light
period
(h)
05.11.1315
15
100
13.3-16.7
336
24
05.25.13
05.26.132
Mixture
5
10
150
20-25
336
24
05.31.13
06.01.133
Mixture
10
10
150
20-25
156
24
06.10.13
* In this study, HRT equals to SRT.
* The conversion from PPF (µmol/m2/s) to Lux varies under different light sources. For Cool-White fluorescent lamps (215W),
the conversion factor is PPF/Lux = 1/74. Source: http://www.apogeeinstruments.com/conversion-ppf-to-lux/)
1
Algae,
Mixture
Date
Thanh Tung Nguyen
29
3.4.2 Experiment 2 – Semi-continuous culture in glass bottles
Eperiment 2 was conduct with three identical 1L glass bottle reactors (Pyrex
1000mL,
), algae only reactor, nitrifier only reactor and algal-nitrifier
mixture reactor, within 28 days from June 23rd to July 21st 2013. The illuminated surface area
of 1L glass bottle reactor was about 0.045m2. Semi-continuous flow regime was adopted in
which 100 mL of reactor solution was daily withdrawn and replaced by the same amount of
fresh feed solution to maintain an SRT of 10 days.
Figure 3.4: Experiment 2 with three 1L-glass bottles growing algae only, nitrifier only and mixture of
algal-nitrifiers under semi-continuous culture
To keep biomass in suspension, magnetic stirrers (Vortex Genie - 2, Scientific Industries)
with stirring bars (
) were used. In algae and mixture reactors, the stirring
speeds were kept constantly at 200 rpm throughout experiment. Meanwhile, in nitrifier reactor,
the stirring speed was maintained initially at 600 rpm (from day 1, June 23rd, to day 14, July
6th, 2013) and then reduced to 200 rpm (from day 15, July 7th, to end of experiment) to have a
comparable background with the other two reactors. The illuminating condition was
maintained similarly as used in Experiment 1 with sixteen Cool White fluorescent tubes
providing light intensity of about 7000-7500 lux (95- 100 µmol/m2/s). Only algae and mixture
reactors were illuminated while the nitrifier reactor was placed in the dark. Throughout the
experiment, glass bottle reactors were kept in constant temperature at around 23°C.
The same algae specie Chlorella sp. was initially inoculated in algae and mixture reactors. All
preparing procedures were repeated similarly as done in Experiment 1. The initial inoculating
concentrations in three reactors were made closely equal to each other: 987 mgTSS/L in
Nitrifier reactor, 926 mgTSS/L in algae reactor and 1020 mgTSS/L in mixture reactor. The
initial inoculating ratio of algae to nitrifier in mixture reactor was also kept at a ratio of 5:1.
Table 3.4: Operating parameters applied in Experiment 2 with glass bottle
No
Reactor
Date
Period
(day)
SRT
(day)
NH4+-N
inlet
(mg/l)
Feeding
rate
(ml/d)
NH4+-N
loading rate
(mg/l/d)
Stirring
speed
(rpm)
Light intensity
(lux) or
(µmol.m-2.s-1)
7000-7500
(95-101)
7000-7500
(95-101)
7000-7500
(95-101)
Light
period
(h)
06.23.1330
10
230-300
100
23-30
600, 200
24
07.22.13
06.23.132
Algae
30
10
230-300
100
23-30
200
24
07.22.13
06.23.133
Mixture
30
10
230-300
100
23-30
200
24
07.22.13
th
* After day 14 (July 6 , 2013), the stirring speed in Nitrifier reactor was reduced from 600 to 200 rpm.
* The conversion from PPF (µmol/m2/s) to Lux varies under different light sources. For Cool-White fluorescent lamps (215W),
the conversion factor is PPF/Lux = 1/74. Source: http://www.apogeeinstruments.com/conversion-ppf-to-lux/
1
30
Nitrifier
MSc Thesis
3.5 Sampling
In Experiment 1 with airlift reactors operated under continuous regime, effluent samples were
collected daily at the same time for immediate analysis. In Experiment 2 with 1L-glass bottles
run under semi-continuous regime, effluent samples were taken at the end of each cycle
before adding new feed solution. The pH and DO were measured in situ right before sampling.
Nitrogenous species (NH4+-N, NO2--N, NO3--N), dry weight (TSS, VSS), and alkalinity were
measured daily. The Chlorophyll a content was measured every 2 days. The TN and soluble
COD were measured occasionally to make a profile shortcut of the systems. The biomass
settleability were measured on the day right before the dark test was conducted by the end of
experiment. For all nitrogenous, alkalinity and soluble TN, soluble COD analysis, samples
were filtered directly using 0.45 µm filters after collection and stored in the fridge (4°C) if
neccessary.
3.6 Analytical methods
3.6.1 pH, DO, alkalinity and light intensity
The pH and DO were measured in situ using calibrated Orion GS9156 pH and DO electrodes
meter (Thermo Fisher Scientific Inc., Waltham, MA). In Experiment 1 with airlift reactors,
the DO was measured near the effluent area of outer annular column. In Experiment 2 with
glass bottles, the DO was measured at the midway of bottle.
Standard method 2320 B (Apha, 2005) was used for measuring alkalinity using Automatic
Titrator (Metrohm 865 Titroprocessor, Dosimat Plus). Titration was done using a 0.011 N
H2SO4 solution to reach a pH end point of 4.5 for alkalinity determination. Calculation for
alkalinity:
(3.1)
where:
+ D: dilution ratio
+ N: normality of standard acid
+ A: volume of acid used, mL
+ V: volume of diluted sample titrated, mL
Light intensity was measured using an ExTech Easyview 30 light meter (ExTech Inc,
Waltham, MA).
3.6.2 Transmittance
According to Tchobanoglous et al. (2003), Transmittance T, % =
where: Io=initial
detector reading for the blank (DI water) after passing through a solution of known depth;
I=final detector reading after passing through solution containing constituents of interest.
Also, according to Mara (2004), green algae mainly absorb photons of violet-indigo-blue and
orange-red light of wavelengths 350-450 and 650-700 nm (absorption of light of wave lengths
450-650 nm, which is mainly green light, is very weak). In this study, transmittance was
measured by Thermo Scientific 335906-000 Genesys 10 UV Spectrophotometer at different
Thanh Tung Nguyen
31
wave lengths in range of 350-450 nm and 650-700 nm against DI water. The sample was pretreated by filtering through 0.45µm filters.
3.6.3 Chlorophyll a
Chlorophyll a was measured using ethanol extraction method according to NEN 6520 –
Dutch Standard. 3-5 ml of samples was filtered by using glass fiber filter (0.45 µm) and
transferred to test tube with cover. 20 ml of Ethanol 80% (v/v) was added to test tubes
containing filtered sample to extract Chlorophyll a content. To obtain complete pigment
extraction, test tubes were first vortexed on shaker in 1 minute and then heated in a water bath
at temperature of 75°C in 3 minutes. This process was repeated again before all test tubes
were immediately cooled in refrigerator for 15 minutes. The extracted solutions were
centrifuged at 3500 rpm in 10 minutes. After centrifugation, the supernatants were read at
wavelength of 665 nm (Ex) and 750 nm (Eo) against 80% ethanol. After obtaining the
absorbance data, 0.1 ml of HCl 0.4M was added to each sample. after later, the acidified
samples were allowed to stay still in 5 -30 minutes and then read again at 665 (Exa) and 750
nm (Eoa) respectively. Chlorophyll a of all samples was measured in duplicates. The
following equations were used:
-
The corrected absorbance of the non-acidified extract:
The corrected absorbance of the acidified extract:
-
(3.2)
(3.3)
(3.4)
where:
+ 296: factor based on the specific absorption coefficient of Chl a
+ V1 : volume of 80% ethanol added to the filter with algae, mL
+ Vo : sample volume which is filtered, L
+ p : path length of the cuvette, mm
3.6.4 Dry weight (DW)
Biomass dry weight (Total suspended solid (TSS) and Volatile suspended solid (VSS)) was
measured using standard methods 2540 D (Apha, 2005). To measure TSS, sample volume
was chosen to yield between 2.5 and 200 mg dried residue. Sample was well mixed and
filtered through weighed standard glass-fiber filters and the residue retained on the filter was
dried to a constant weight at 103 to 105°C in an oven. The increase in weight of the filter
represented the TSS. The glass-fiber filter was first washed with a lot of DI water by applying
vacuum. The filter was then transferred to an inert aluminium weighing dish, dried in an oven
at 103 to 105°C for 1h before being ignited at 550°C for 15 min in a muffle furnace. It was
finally stored in an oven at 105°C or in a desiccator to balance temperature and weigh.
(3.5)
where:
+ A: weight of filter + dried residue, mg
+ B: weight of filter, mg
32
MSc Thesis
To measure VSS, residue produced by TSS measurement was ignited to constant weight in a
muffle furnace at a temperature of 550°C for 3 hours and cooled in a desiccator before
weighing. The difference in weight before and after ignition represented the VSS.
(3.6)
where:
+ A: weight of filter + dried residue before ignition, mg
+ B: weight of filter + ash residue after ignition, mg
3.6.5 Soluble COD
The closed reflux colorimetric Standard Method 5200B (Apha, 2005) was used for COD
using Orbeco TR125 (Sarasota, FL) heating block and Hach DR/400 spectrophotometer. The
method detection limit for COD was 30.0 mg COD/L. Orbeco high range COD reagent tubes
(Reagent number: TT20712) were used for COD analysis. A stock solution was prepared
using potassium hydrogen phthalate according to Standard Method 5200B, to achieve
maximum concentrations up to 15.0 g COD/L (test tube maximum limit). Spectrophotometer
wavelength for COD was 620 nm.
3.6.6 Nitrite NO2-, Nitrate NO3- and Ortho-Phosphate PO43Throughout Experiment 1, anions (NO2-, NO3- and PO43-) were measured by using Metrohm
Peak 850 Professional AnCat ion chromatography (IC) system (Metrohm Inc., Swizerland).
Samples were pre-processed by filtering through 0.45 µm filters immediately after sampling.
Filtered samples were then diluted to detectable range of 1 to 100 ppm.
In Experiment 2, the wet chemical method was used instead to analyze NO2-, NO3- due to
some problems with the anion IC machine.
Quantitative determination of NO3- concentration was based on nitration of resorcinol in
acidified samples with addition of concentrated H2SO4 acid and HCl acid, resulting in a color
product (maximum absorption at 505 nm) (Zhang and Fischer, 2006). This method had a
detection limit of 0.5 µM and was linear up to 400 µM for NO3-. Samples were pre-processed
by filtering through 0.45 µm filters. A 5 mL of filtered sample was added to a 25 mL
volumetric flask. Next then, 0.5 mL of sulfamic acid (which helped reduce NO2- to N2 with no
effect on NO3- in the original sample), 0.5 mL of 2M HCl, 0.6 mL of 2% (w/v) resorcinol
solution were respectively added. The flasks were swirled again to mix the resorcinol with
sample and 5 ml of H2SO4 concentrated acid was continued adding to the flasks. The flask
was allowed to stand in the dark for 30 min. The solution was topped to the mark with DI
water and finally measured the absorbance of the sample at 505 nm with a 1 cm cuvette
against a reagent blank.
Quantitative determination of NO2-concentration was based on standard colorimetric method
(Apha, 2005). NO2- in water solution coupled diazotized sulphanilamide with N-(1-naphthyl)ethyl-enediamine dihydrochloride (NED dihydrochloride) to form a reddish purple azo dye
solution at pH 2.0 to 2.5. The applicable range of this method for spectrophotometric
measurements was 10 to 1000 µg NO2--N/L (absorption at 543 nm). Samples were preprocessed by filtering through 0.45 µm filters. After then, to 50 mL of filtered sample, or to a
portion diluted to 50 mL, 2 mL color reagent was added and mixed thoroughly. The sample
was allowed to react for 10 min (maximum of 2 hours), and then measured absorbance at 543
nm with a 1 cm cuvette against a reagent blank.
Thanh Tung Nguyen
33
3.6.7 Ammonium
Throughout Experiment 1 and 2, cation NH4+ was measured by using Metrohm Peak 850
Professional AnCat ion chromatography (IC) system (Metrohm Inc., Swizerland). Samples
were pre-processed by filtration through 0.45 µm immediately after sampling. Normally,
filtered samples were diluted to detectable range of 1 to 100 mg/L.
3.6.8 Total Nitrogen (TN)
TN of feed solution and liquid effluent sample were measured using Hach Total Nitrogen
Reagent set TNT 828 (Hach Inc., Loveland, CO). This method can detect TN in range of 20100 mgN/L. TN of biomass was determined as the difference between the TN of
homogenized mixed liquor samples and the TN of filtered soluble liquid samples.
3.6.9 Microscopic observation
Algae and algal-nitrifier biomasses were observed under microscope Nikon Eclipse E200 at a
magnification of x40.
3.6.10 Settleability of biomass
Since the high coloration of reactor solution, it was difficult to visually identify the interface
of the settled biomass and the supernatant in the measurement cylinder when using standard
SVI method. Instead, the settleability was evaluated according to the method suggested by Su
et al. (2011). At the end of the test, 1L mixed sample was transferred to 1L measurement
cylinder to investigate the settleability of the biomass. The TSS samples were taken at time 0,
5, 10, 15 and 30 min at the midway (500 ml mark) of the cylinder. Achieved TSS data were
plotted against time to have a trending curve of TSS still unsettled and remained in
suspension.
3.7 Calculation
3.7.1 Estimating theoretical oxygen balance - continuous flow airlift reactors
The oxygen mass balance is performed using Chemostat system model:
Q, Ci
rauto, rhet,
ralgae, Co
Q, Ce
Ce=Co
Fair, Kla, Cs,
T=22°C
Where:
- Q, Fair: feed flow rate (L/d) and air flow rate (mL/min)
- Cs:saturated dissolved oxygen (mL/min) at T°C
- Ci, Ce, Co : dissolved oxygen (mg/L) of influent, effluent, and inside reactor
34
MSc Thesis
- rauto, rhet, ralgae: oxygen saturated, utilization and generation rate (mg/L/d) of nitrifying
autotrophs, aerobic heterotrophs, and algae respectively
- Kla: oxygen transfer coefficient (min-1). At flow rate of 336 ml/min (25 in scale reading),
Kla = 0.2602 (min-1)
Oxygen balance equation:
(3.7)
Compute the oxygen requirement from a steady-state mass balance on O2 equivalent. Ignoring
the soluble microbial product (SMP) and assuming that there is no microbial biomass in the
feed, we have:
For heterotrophs:
- VSS production rate:
(3.8)
Where typical kinetic parameters for aerobic heterotrophs (Bruce and Perry, 2001):
;
;
d-1;
- Oxygen required:
=Input-Output =
(3.9)
For nitrifying autotrophs:
NH4++1.815O2+0.134CO2C5H7NO2+0.973NO3-+0.921H2O+1.973H+
- VSS production rate:
(3.10)
(3.11)
Where typical kinetic parameters for aerobic heterotrophs (Bruce and Perry, 2001):
;
;
d-1;
;
- Oxygen required:
=Input-Output =
(3.12)
- VSSi : inert VSS, the factor 1.98 mg COD/mg VSS is used to account for the 20
electron equivalents per mole of C5H7O2N in the C and the 8 electron equivalents in the
N,
; the factor 4.57 mgCOD/NH4-N is calculated from the 8
electron equivalents in the NH4-N,
For algae:
106CO2 + 236H2O + 16NH4+ + HPO42-  C106H181O45N16P + 118O2 + 171H2O + 14H+ (3.13)
- From stoichiometric equation, we have:
- Oxygen generated: =
=
(3.14)
- The factor 50.17 is derived based on stoichiometric equation and the assumption that
Chla contributes 3.1% of dry weight algae (based on empirical measurement in this
study, data not shown),
For oxygen transfer:
Thanh Tung Nguyen
35
- Oxygen transfer rate:
(3.15)
where:
+ Kla(156 ml/min, tap water) : 0.1377 (min-1) (based on empirical measurement)
+ Kla(336 ml/min, tap water) : 0.2602 (min-1) (based on empirical measurement)
+ Cs (22°C) : 9.088 mgO2/L
To sum up, we have:
-
(3.16)
in which:
-
(3.17)
-
(3.18)
-
(3.19)
The above equations can only be estimated if these assumptions are met:
- The system is at steady state
- No biomass (bacteria or algae) in the feed
- Algae uptake NH4+-N only, do not uptake other N source
- Algae do not consume CO2 in the air
- Algae perform autotrophic growth only, no mixotrophic and heterotrophic growth
- Algae have the priority over nitrifier in uptaking NH4+-N, which means, only the
NH4+-N that algae cannot uptake anymore will be oxidized to NO3--N
- Nitrification process is complete, no NO2--N is formed
- The inert VSSi in the feed is negligible
3.7.2 Ammonium conversion rate
The NH4+-N conversion rate in reactor was estimated by the slope of the trending line when
plotting NH4+-N concentration against the time.
3.7.3 Nitrogen balance
TN (mixed liquor) = soluble TN + biomass TN.
TN (mixed liquor, beginning cycle) = TN (mixed liquor, end cycle) + volatilized NH3-N +
denitrified N2-N.
3.7.4 Volumentric biomass productivity
The volumetric biomass productivity P (mgVSS/L/d) was calculated as :
(3.20)
where
-
36
Xn : biomass density on day n (mgVSS/L/d)
Vr : reactor volume (L)
Ve : effluent volume on day n (L)
MSc Thesis
3.7.5 Areal biomass productivity
The areal biomass productivity P (gVSS/m2/d) was calculated as (Yuan et al., 2011) :
(3.21)
where
-
Xn : biomass density on day n (gVSS/m3/d)
Vr : reactor volume (m3)
Ve : effluent volume on day n (m3)
Ai : illuminated surface area of the reactors (m2), equals 0.045m2 for 1L glass
bottle and 0.08m2 for 1.5L airlift reactor
3.8 Data processing
Data were processed by using Microsoft Excel and R-studio statistical software. The TSS,
VSS, Chlorophyll a of algae and mixture reactors in Experiment 2, the N species
concentrations in dark test and the COD of three reactors (algae, nitrifier and mixture) were
the factors considered for statistical analysis. Significance was determined using t-tests.
Thanh Tung Nguyen
37
4 Experiment 1 – Continuous culture in airlift reactor
4.1 Comparison between algae and mixture reactors
4.1.1 Results – Comparison between algae & mixture reactors
Algae and mixture of algae and nitrifiers were cultured in two separate airlift reactors under
continuous flow, SRT 15 days and air flow rate 336 ml/min. Unfortunately, the shortage of
feed source did not allow running two reactors in parallel for longer time and the algae reactor
was stopped on day 15, completing one SRT. Therefore, the comparison between algae and
mixture reactors derived from 15 days in operation may not fully reflect the actual situation.
(a) DO profile
(b) pH profile
10.00
9.00
pH-Alg
pH
DO (mg/L)
pH-Mix
9.50
8.50
8.00
7.50
9.00
8.50
DO-Alg
DO_Mix
8.00
7.00
0
2
4
6
8 10 12
Elapsed time (day)
14
0
16
2
(c) Alkalinity profile
Dry weight (mg/L)
Alkalinity (mg/L)
700
600
500
400
300
200
Alkalinity-Alg
100
Alkalinty-Mix
2
4
6
8 10 12
Elapsed time (day)
14
2200
2000
1800
TSS-Alg
1600
TSS-Mix
VSS-Alg
1400
VSS-Mix
0
(e) Chlorophyll a profile
BiomassProductivity(mgVSS/L/d)
Chl a (mg/L)
50
40
30
20
Chla-Alg
Chla-Mix
0
2
4
6
8 10 12
Elapsed time (day)
14
2
4
6
8 10 12
Elapsed time (day)
14
16
(f) Biomass productivity
60
0
16
2400
16
70
10
14
1200
0
0
6
8 10 12
Elapsed time (day)
(d) Dry weight profile
2600
800
4
16
400
350
300
250
200
150
100
50
0
-50
-100
VSS productivity-Alg
VSS productivity-Mix
0
2
4
6
8 10 12
Elapsed time (day)
14
16
Figure 4.1: DO(a), pH (b), alkalinity (c), dry weight (d), Chlorophyll a (e) and biomass productivity (f)
profiles of algae and mixture cultures in airlift reactors during the first 15 day experiment
38
MSc Thesis
(a) Nitrogenous in algae reactor
N species (mg/L)
300
250
NH4-N_Feed
200
NH4-N_Alg
150
NO2-N_Alg
100
NO3-N_Alg
50
0
0
2
4
6
8
10
Elapsed time (day)
12
14
16
(b) Nitrogenous in mixture reactor
N species (mg/L)
300
250
NH4-N_Feed
200
NH4-N_Mix
150
NO2-N_Mix
100
NO3-N_Mix
50
0
0
2
4
6
8
10
Elapsed time (day)
12
14
16
Figure 4.2: Ammonium (NH4+-N), nitrate (NO3--N) and nitrite (NO2--N) profiles of algae (a) and
mixture (b) cultures in airlift reactors during the first 15 day experiment.
4.1.2 Discussion – Comparison between algae and mixture reactors
Dissolved oxygen
The developing trends of DO in two reactors after 15 days in operation are shown in Figure
4.1(a). Since the activity of aerobic bacteria consumed oxygen, the DO was expected to be
lower in mixture reactor than in algae reactor. However, it did not happen. Throughout the
period, the DO in both reactors performed quite similarly, varied within 7-9 mg/L and never
reached saturated or oversaturated levels as reported elsewhere (Kumar et al., 2010a). Maybe,
the time period was not long enough to allow the oversaturation to occur. However, it was
more likely that the exogenous air injection had affected the DO which was measured near the
outlet area where the aerated riser came out from the inner column. This effect was also
reported by Sánchez Mirón et al. (2002). The exogenous aeration could influence the
availability of DO in reactors by either stripping off the surplus oxygen generated by algal
photosynthesis or replenishing oxygen needed by bacterial activity if the algal photosynthesis
could not produce enough oxygen. In such way, the DO levels in both reactors were kept
relatively constant and close to each other regardless of microbial activities.
pH and alkalinity
As shown in Figure 4.1(b), the increasing trend of pH in mixture reactor was significant
whereas that of algae was not until day 10. The highest pH values were measured on day 14
Thanh Tung Nguyen
39
and 15, respectively 9.2 in algae reactor and 9.6 in mixture reactor. In algal-nitrifier system,
nitrification releases H+ resulting in pH decrease (Henze, 2008) whereas algal photosynthesis
consumes dissolved CO2 and helps increase pH (Craggs, 2005). It should be noted that the
final pH is determined by the combination of two factors, but not one of them alone. Probably
this reasoned for why in both reactors, the pH was still high despite of the sharp decreases of
algae number as shown in Figure 4.1(e). This was completely different from the observed in
Phase 3 of mixture airlift reactor (Section 4.2) and in Experiment 2 (Section 5) when the
decrease/increase of algae population agreed with the decrease/increase of pH. From pH
profile, despite of starting at lower pH due to difference in initial biomass inoculation, the
mixture reactor had reached (on day 8 and 9) and surpassed (after day 9) the algae reactor.
Probably the difference in Chlorophyll a developing trend after day 9 (slight increase in
mixture reactor compared to decrease in algae reactor) accounted for that observed. In term of
alkalinity as shown in Figure 4.1(c), no big difference was observed between two reactors
except the slightly higher alkalinity in algae reactor over mixture reactor. The alkalinities in
two reactors were rather high, about 400-600 mgCaCO3/L, suggesting that the alkalinity was
not the limiting factor for both algal and nitrifier growth.
Biomass density and biomass productivity
The overall biomass (Figure 4.1(d)) in both reactors had continuously declined during the first
12 days and got recovered a bit after then. The initial decreases of biomass could be explained
by the fact that the systems were still unstable and on the way to achieve steady state. Before
growing under continuous flow, both reactors had been under batch cultivation for a long time.
In general, with identical growth conditions, biomass growth is believed to be higher under
batch regime than under continuous condition because of no biomass loss from effluent.
After day 12, both TSS and VSS stopped decreasing and started to rise. However, the TSS
was observed to grow faster than the VSS. This could be explained by the relatively high pH
(Figure 4.1(b)) in both reactors during this period. At high pH, some soluble salts of Ca2+,
Mg2+ rich in swine waste, could form precipitates, i.e struvite (Karakashev et al., 2008),
contributing to the overall increase of TSS.
The biomass productivity was expressed by the change in VSS concentration per day (Eq 3.20
in Section 3.7.4). The negative value on a certain day suggested that the biomass growth had
decreased and the amount yielded on that day could not offset the biomass loss from the
effluent. On the other hand, the positive value suggested that the biomass growth had
increased or remained nearly unchanged and the amount yielded on that day could offset the
biomass loss from effluent. On the first 12 days, the biomass (VSS) productivities in two
reactors were unstable. After then, from day 12 to day 15, the biomass yields became positive,
suggesting that the biomass growth kept increasing continuously.
Chlorophyll a
From Figure 4.1(e), despite of starting at different initial algae inoculations, both reactors
finally ended up on day 15 with similar Chlorophyll a contents. Concomitant with the
biomass dry weight, the Chlorophyll a curves also showed a decreasing trend. More
specifically, the Chlorophyll a in algae reactor kept decreasing continually while in mixture
reactor, it stopped decreasing on day 10 and went horizontally after then till day 15. The
Chlorophyll a decrease observed in both reactors could be due to:
a) Unsteady state. The systems were still unstable and on the way to reach steady state.
Probably, longer observation period is needed before further conclusions are made.
b) Change in N source. It has been documented that algae prefer nitrogen species in an order:
NH4+-N>NO3--N>NO2--N>simple organic-N (Syrett, 1981). Since all NH4+-N was completely
oxidized to NO2--N (algae reactor) and NO3--N (mixture reactor) as shown in Figure 4.2, the
40
MSc Thesis
algal growth mainly based on the latter two would be lower. Consequently, the algal produced
could not offset the algal loss from the effluent.
c) Toxicity of NO2--N accumulation. Probably, the NO2--N enrichment found in algae reactor
explained why the decrease of algae number was more drastic in this reactor than was in
mixture reactor. When studying algae grown in swine waste, Baumgarten et al. (1999) also
mentioned NO2--N toxicity and observed that the batch cultivation with Chlorella sp. in
mineral medium with 110 mM showed no growth.
d) Toxicity of heavy metals. In the same study, Baumgarten et al. (1999) also listed the
toxicity of heavy metals in swine waste as one of the negative factors to algal growth. And
indeed, the heavy metals in AD swine slurry could be relatively high, reported by (Marcato et
al., 2008).
e) Low light transmittance of swine waste. The feed solution in this study showed low light
transmittance (as mentioned in Section 3.2). This characteristic was also described by other
authors (Baumgarten et al., 1999, González-Fernández et al., 2011b). Light is crucial for
photosynthetic process and lack of light exposure could greatly inhibit the algal growth.
f) Mutual inhibition between algae and bacteria if cultivating for a long time (Baumgarten et
al., 1999). Since both algae and nitrifier can harness inorganic carbon and NH4+-N for their
activities, the competition between two species is very likely and has been reported elsewhere
(Su et al., 2012, Zhang et al., 2011). Due to their larger size, some microalgae generally grow
at slower rates than bacteria do (Fenchel, 1974). Accordingly, the more favored growth of
bacteria could inhibit algal growth. It was observed when using algae-bacteria consortium to
treat swine slurry (González-Fernández et al., 2011).
Ammonium convesion
After mixing algae with nitrifier, on day 2, all NH4+-N in mixture reactor was completely
converted. On the next 10 days, from day 2 to day 12, mixture reactor had made a constant
increasing curve before reaching a plateau from day 12 to day 15. No NO 2--N was found in
this reactor and the nitrification process was complete. For algae reactor, it took 5 days after
operation to see the disappearance of NH4+-N. Nitrification process was observed in algae
reactor with the main nitrification product of NO2--N and only small amount of NO3--N.
Throughout the period, the NO2--N was developing from zero, and then rising continually
from day 4 to day 10 after reaching steady state from day 11 to day 15. A small amount of
NO3--N in this reactor appeared from day 2 and remained constant until day 15. The complete
nitrification in mixture reactor was predictable but partial nitrification observed in algae
reactor was interesting. The bacteria coming from the AD swine manure effluent may be the
cause. Despite of experiencing liquid-solid fractions separation by centrifuge and filtration,
those microorganisms could not be completely removed. The presence of NO2--N (Figure
4.2(a)), the ever-decrease of Chlorophyll a (Figure 4.1(e)) compared to the slight recovery of
VSS (Figure 4.1(d)) of algae reactor on day 12 to 15 revealed that the bacteria were indeed
enriched in this reactor. Along the time, nitrifiers were accumulated in algal reactor and
gradually developed to the extent that partial nitrification occurred. Some studies also
reported the nitrification process when cultivating algae in real swine waste and dairy
manures even not adding exogenous nitrifier seeds (Baumgarten et al., 1999, Mulbry and
Wilkie, 2001)
4.2 Mixture reactor when varying SRT, air flow rate
The mixture reactor was operated in parallel with algae reactor in Phase 1 (the first 15 days
with SRT 15d, air flow rate 336 ml/min). After then, algae reactor was stopped and only
Thanh Tung Nguyen
41
mixture reactor was allowed to continue running in Phase 2 (SRT 10d, air flow rate 336
ml/min) and Phase 3 (SRT 10d, air flow rate 156 ml/min).
4.2.1 Results – Mixture reactor when varying SRT, air flow rate
Mixture reactor was operated within three phases under different SRTs and air flow rates.
Except DO and the NO3--N concentration were not affected much from changes in operating
conditions throughout three phases, other parameters had varied at different degrees.
Especially, in Phase 3, the number of algae in mixture reactor was decreased dramatically, the
algal color changed from green to brownish yellow, a sign of dying algae. Despite of that, the
NO3--N still remained rather stable.
Table 4.1: Theoretical oxygen balance in mixture reactor based on calculation in Section 3.7.1
Day
16
18
25
28
(Ci-Ce)/Ɵx
(mg/L/d)
-0.70
-0.70
-0.69
-0.68
r_auto
(mg/L/d)
33
34
36
37
r_het
(mg/L/d)
74
103
83
81
r_algae
(mg/L/d)
157
145
100
77
r_transfer
(mg/L/d)
222
208
232
263
Table 4.2: Soluble COD and COD removal in mixture reactor
Day
16
18
25
28
42
Soluble COD –
feed (mg/L)
2099
2614
2225
2225
Soluble COD –
effluent (mg/L)
892
945
876
916
Soluble COD
removal (%)
57.5
63.9
60.6
58.9
MSc Thesis
PHASE 1
SRT15, 336 ml/min
PHASE 2
PHASE 3
SRT10
336 ml/min SRT10, 156 ml/min
(a) pH & DO - mix
10
10
9.5
pH
6
8.5
4
8
pH
DO
7.5
7
Dry weight (mg/l)
0
10
15
20
25
30
25
30
(b) Alkalinity - mix
1000
900
800
700
600
500
400
300
0
5
10
15
20
(c) Dry weight & Chl a - mix
2600
2400
2200
2000
1800
1600
1400
1200
1000
60
50
40
30
TSS
VSS
Chl a
0
Productivity (mgVSS/l/d)
5
2
5
20
Chl a (mg/l)
Alkalinity (mgCaCO3/l)
0
10
0
10
15
20
25
30
(d) Biomass productivity - mix
400
300
200
100
0
-100
0
5
10
15
20
25
30
(e) Nitrogen species - mix
300
N species (mg/l)
DO (mg/L)
8
9
250
NH4-N_Mix
200
NO2-N_Mix
150
NO3-N_Mix
100
NH4-N_Feed
50
0
0
5
10
15
20
Elapsed time (day)
25
30
Figure 4.3: Performance of mixture reactor when varying SRT, air flow rate over Phases
Thanh Tung Nguyen
43
4.2.2 Discussions – Mixture reactor when varying SRT, air flow rate
4.2.2.1 Overview performance of mixture reactor
pH, DO and alkalinity
Figure 4.3(a) gives an overview of the DO and pH variations in mixture reactor throughout
different Phases. Except a slight decrease in Phase 3, no significant change in DO was
observed. Comparing the developing trends of DO and Chlorophyll a (Figure 4.3(c)) revealed
that the DO was rather independent from the algal activity. Again, as discussed in 4.1.2, the
relatively constant DO concentration suggested that very likely, the exogenous aeration, that
helped maintain biomass in suspension, had played a dominant role in providing oxygen to
the nitrification process, not the algal photosynthetic process. Theoretical calculation of
oxygen production in Table 4.1 also confirms this hypothesis when showing a much higher
contribution of oxygen transfer over oxygen generated by algal photosynthesis. The slight
decrease of DO in Phase 3 could be due to the shorter SRT and the dying out of algae
observed in this phase, resulting in the decrease of algae number. However, it was more likely
due to the decrease in air flow rate directly linked to the reduction of oxygen transfer rather
than those two factors.
Meanwhile, the pH varied in response with varying operating conditions. Under long SRT
and high air flow rate during Phase 1 (the first 15 days), the pH steadily increased and hit the
highest at 9.55 on day 15. In Phase 2 (the next 5 days), the pH seemed to reduce slightly.
However, it was not until Phase 3 (the last 10 days) that the pH declined drastically. The
steady increase in pH in Phase 1 could be owing to the fact that the photosynthetic process
was more active than the nitrification process (as discussed in Section 4.1.2). Moving on to
Phase 2, shorter SRT resulted in more biomass loss from effluent in both reactors. However,
the effect seemed to be more severe on algae than on nitrifiers, because the nitrifier loss from
effluent could be partially replenished by the indigenous nitrifiers observed in the feed
solution (as discussed in Section 5.2.2&5.2.4). Therefore, the photosynthetic process in Phase
2 was less active than Phase 1 and the pH stopped increasing, maintained at around 9 to 9.5.
Similarly, the quick decrease of pH in Phase 3 could be explained when the dying out of algae
was observed.
Similar to pH, alkalinity changed under different phases. At high SRT and high air flow rate
in Phase 1, the alkalinity did not change much. However, after switching to lower SRT and air
flow rate in Phase 2 and 3, it was continually climbing up and hit maximum at the end day of
Phase 3. It seemed to be reasonable since algal photosynthesis consumes more alkalinity than
the nitrification does, according to the stoichiometric equations 3.10 and 3.13. The rapid
decrease of algae number in Phase 3 also meant less alkalinity consumed by algae. In addition,
the nitrifiers could not utilize more alkalinity due to limitation of ammonium source.
Therefore, alkalinity kept rising up in Phase 2 and 3.
Biomass density and productivity
The varying patterns of TSS, VSS and Chlorophyll a were rather similar: markedly decreased
at the beginning of Phase 1, gradually recovered by end of this phase, then continued
increasing (except Chlorophyll a remained nearly constant) in Phase 2 and eventually
decreased rapidly in Phase 3. In Phase 1, as discussed in Section 4.1.2, the decrease of
biomass in the first 10 days was probably the consequence of shifting from batch to
continuous cultivation that made the system unstable. After the acclimation period, the reactor
became stable on day 11-12 and the biomass started to increase. In Phase 2, it seemed that the
effect of changing SRT from 15d to 10d on the biomass density was obscure. The VSS
fluctuated in range of 1800-1900 mgVSS/L whereas the Chlorophyll a was stable at around
30mg/L. Maybe, longer operational time rather than 5 day period was needed to better
44
MSc Thesis
understand the system. In Phase 3, the biomass kept steadily decreasing. At the same time, the
biomass colour turned from green to brownish yellow. Microscopic observation revealed that
rotifers flourished in reactor. It was unclear whether the decrease in biomass was due to the
effects of rotifer predation or the reduction of air flow rate or both of them (further discussed
in Section 4.2.2.2).
Figure 4.4: Microscopic photos (x40) of algae flocs (left) and rotifers (right, in red circle) in mixture
airlift reactor on day 28. Number of algae was reduced markedly and algae cells aggregated to form
bigger flocs. New type of algae (filamentous algae) was also observed
The biomass productivity had reflected rather closely the variation of biomass density. On the
first 12 days in Phase 1, the biomass (VSS) productivity was unstable, varying from day to
day. Following from day 12 to day 15, the biomass yields showed positive values, suggesting
that the biomass growth kept increasing continuously. Continuing with Phase 1 was the high
biomass yielding period in Phase 2 and 3, lasting from day 16 to day 23.
Ammonium conversion
Figure 4.3(e) shows the developing trends of nitrogenous species in mixture reactor under
different phases. After mixing algae with nitrifiers, the NH4+-N was quickly removed,
completely oxidized into NO3--N. The NO3--N had increased gradually until day 10 when it
stabilized at around 80-90 mg/L. The NO2--N was not detected until Phase 3 after both SRT
and air flow rate were adjusted to 10 days and 156 ml/min. Despite of high DO concentration
measured near the draining outlet, there could be low DO zones in reactor. Due to poor
mixing condition when air flow rate was reduced to 156 ml/min, the DO in reactor varies at
different depth: higher at the upper part and gradually decreased to the lower part of outer
annular. This may be the explanation for the presence of partial nitrification (Sinha and
Annachhatre, 2007). At the end of Phase 3, the NO2--N was enriched up to significant level, at
around 18 mg/L. Also, since more biomass was settled and piled up at the bottom, forming
some dead zones that water was not well-exchanged with the bulk solution. Low DO in
microzones must have favoured the partial nitrification process (Hanaki et al., 1990).
4.2.2.2 The failure of the system
The mixture reactor was operated for nearly 1 month before the sharp decline of algae and the
invasion of rotifers occurred. During Phase 1 and Phase 2, by visual observation, the algae
still looked green. However, after shifting to Phase 3, a gradual change in population,
morphology and color of algal cells, from green, evenly distributed single cell to brownish
yellow aggregated flocs, was observed microscopically. Also, the number of algae had
dramatically reduced, reflected by the marked decrease in Chlorophyll a. There are a number
of possible reasons for the disappearance of algae which resulted in collapse of the system.
Thanh Tung Nguyen
45
a) Presence of rotifers. Lots of rotifers predating algal cells were observed. The rotifers were
very likely coming from either the feed solution or the inoculated nitrifier seeds. The swine
centrate was collected from the four AD reactors operated at 35°C and according to Mara
(2004), mesophilic anaerobic digestion may not completely remove protozoa cysts and eggs
from wastewater. The rotifers probably presented initially in forms of cysts and eggs. Until
the environmental conditions were favourable, they started to develop and flourish. Since
airlift reactor relied on exogenous aeration to maintain mixing, the reactor solution was
always aerated. Such oxygen-rich environment favoured rotifers to multiply since they are
mainly aerobic heterotrophic organisms. NH4+-N was reported to be toxic to rotifers, however,
NH4+-N in the feed was quickly oxidized to NO3--N as soon as it entered reactor. The
temperature was maintained constantly at around 23°C, and the pH in Phase 3 started to
decrease, no longer as high as before. All these conditions could actually support the rotifer
growth.
b) Lower air flow rate. Lower air flow rate reduced circulation flow rate, the chance for algae
to stay in outer annular column to expose to light accordingly became less. Without light for
photosynthesis, algae could not produce Chlorophyll and started to die. In addition, algae and
bacteria grown in real swine waste, for a certain reason, tended to stick together forming
heavy, big and easily settleable flocs. The presence of rotifers enmeshed into these flocs made
them even heavier and quickly settled down. A small settling test with 20 ml reactor solution
in a 20 ml test tube indeed showed that the algal-nitrifier biomass almost settled down only
within 5 minutes. Lower air flow rate was unable to keep algal-nitrifier biomass in suspension.
As a result, they settled down and piled up in the bottom of reactor. This phenomenon was
observed right after changing to Phase 3.
c) Drawback of reactor design in this study. Owing to the difference in liquid densities
between inner and outer annular column of the airlift reactor, a circulation flow was created
and the biomass was kept in suspension. The higher the air flow rate, the better the solution
and the biomass were mixed. However, practical operation showed that there were still
biomasses settled down at the far corner of the outer annular column. It was because after
long time in operation, the biomasses tended to grow in flocs and biofilms that were heavier
and more settleable. Also, the openings on the foot of inner column, that allow water
interchanging between inner and outer annular columns, were small. After a certain time,
biomass biofilm developed and made them even smaller, hindering the circulation flow.
When algae in the reactor were not well-mixed, they started to die quickly due to lack of light
exposure.
e) Shorter SRT: Reducing SRT from 15 to 10 days means more biomass being lost from
effluent. The decrease of algae population should have been observed in Phase 2 after SRT
was reduced. However, probably, 5 day period of Phase 2 was not long enough to see the
effect, which appeared in Phase 3.
4.3 Conclusions and recommendations
Experiment 1 with airlift reactors was only partly successful. The two research questions were
not answered due to insufficient operating period, the disturbance of exogenous aeration in
airlift reactors, the disappearance of algae before the system could reach steady state, and the
appearance of nitrifier in the feed solution that resulted in nitrification process in both reactors.
46
MSc Thesis
To find the answer for these two research questions, another approaches and methodology
should be considered. Some recommendations for improving the performance of airlift reactor
could be:
+ Periodical clean-up is necessary to remove algal biofilm growing onto the reactor wall and
surround the exchange openings on the lower part of inner column.
+ Using CO2 injection instead of ambient air injection could be considered. CO2 from flue gas
could be utilized to enhance the algal growth (Kumar et al., 2010a) and to some extent, it
helps inhibit the growth of algal enemies (rotifers, protozoa,...) by creating poor oxygen
culture. However, the low oxygen environment also inhibits nitrifiers which could result in
ammonia residue in effluent. Depending on the purpose of the application inclined to nutrient
recovery or nutrient removal, more experimental work could be executed to evaluate the pros
and cons.
+ Control zooplankton predators by keeping low DO, increasing pH and FA concentration.
According to Arauzo (2003), an un-ionized ammonia level over 2.5 mg/L could effectively
reduce the zooplankton community biomass.
Thanh Tung Nguyen
47
5 Experiment 2 – Semi-continuous culture in glass bottles
5.1 Results – Semi-continuous culture in glass bottles
DO (mg/L)
Experiment 2 was conducted with three 1L-glass bottles growing algae, nitrifier, and an algalnitrifier mixture respectively. Throughout the experiment, a mixing speed of 200 rpm was
maintained in algae and mixture reactors. The nitrifier reactor was initially stirred at a speed
of 600 rpm and then reduced to 200 rpm on day 15, to keep similar to the other two reactors.
The reactors were fed at two different NH4+-N loading rates: 230-300 mg/L (day 1 to day 22)
and 530-600 mg/L (day 23 to day 28, end of experiment). The switch was to identify the
differences in algae and mixture reactors at higher NH4+-N load. During the experimental
period, the rotifers predating on algae were observed in mixture reactor on day 15, and they
became bloomed 3 days after then, on day 18.
(a) DO profile
16
14
12
10
8
6
4
2
0
pH
0
5
10
15
20
25
30
20
25
30
(b) pH profile
10
9.5
9
8.5
8
7.5
7
6.5
6
0
Alkalinity (mgCaCO3/L)
Double inlet
NH4+-N
5
10
15
(c) Alkalinity profile
4500
4000
3500
3000
2500
2000
1500
1000
500
0
nit
alg
mix
feed
0
5
10
15
20
Elapsed time (d)
25
30
Figure 5.1: Developing trends of DO, pH and alkalinity of three reactors nitrifiers, algae and mixture
respectively over experimental period
48
MSc Thesis
Chlorophyll a (mg/L)
(a) Chlorophyll a profile
45
40
35
30
25
20
15
10
5
0
VSS (mg/L)
0
TSS (mg/L)
10
15
20
25
30
20
25
30
25
30
(b) VSS profile
2300
2100
1900
1700
1500
1300
1100
900
700
500
0
5
10
15
(c) TSS profile
2400
2200
2000
1800
1600
1400
1200
1000
800
600
0
Productivity (mgVSS/L/d)
5
Double inlet
NH4+-N
5
10
15
20
(d) Biomass productivity
500
400
300
200
nit
100
alg
0
mix
-100
-200
0
5
10
15
20
Elapsed time (d)
25
30
Figure 5.2: Developing trends of Chlorophyll a (a), VSS (b), TSS (c) and biomass productivity (d) of
nitrifiers, algae and mixture reactors respectively over experimental period
Thanh Tung Nguyen
49
N species concentration (mg/L)
Double inlet
NH4+-N
(a) NH4+-N conversion - Nitrifier
700
600
NH4-N_feed
500
600 rpm
400
N species concentration (mg/L)
NH4-N_nit
300
NO2-N_nit
200
NO3-N_nit
100
0
0
5
10
15
20
25
30
(b) NH4+-N conversion - Algae
700
600
500
NH4-N_feed
400
NH4-N_alg
300
NO2-N_alg
200
NO3-N_alg
100
0
0
N species concentration (mg/L)
200 rpm
5
10
15
20
25
30
(c) NH4+-N conversion - Mixture
700
600
500
NH4-N_feed
400
NH4-N_mix
300
NO2-N_mix
200
NO3-N_mix
100
0
0
5
10
15
20
Elapsed time (d)
25
30
Figure 5.3: Ammonium conversions of nitrifier (a), algae (b) and mixture (c) reactors over
experimental period
50
MSc Thesis
(a) NH4+-N conversion rate - Algae
180
16
160
14
140
12
120
10
100
8
80
6
60
NH4-N
NO2-N
NO3-N
DO
4
40
k (alg) = -3.3339 (mg/L/h)
20
2
0
0
0
4
8
12
16
20
24
(b) NH4+-N conversion rate- Mixture
120
N species concentration (mg/L)
DO (mg/L)
18
3.5
3
100
2.5
80
2
60
1.5
40
1
k (mix) = -1.7334 (mg/L/h)
20
0.5
0
0
0
4
8
12
16
20
NH4-N
DO (mg/L)
N species concentration (mg/L)
200
NO2-N
NO3-N
DO
24
Elapsed time (h) - Day 21 (Jul 13, 2013)
Figure 5.4: Ammonium conversion rates in algae and mixture reactors on day 21
DO in algae reactor - Dark test
DO (mg/L)
8
6
4
2
0
0
5
10
15
20
25
30
Elapsed time (min)
35
40
45
Figure 5.5: DO variation in algae reactor during dark test on day 28
Thanh Tung Nguyen
51
N species concentration (mg/L)
(a) Algae reactor - Dark test
220
200
180
160
140
120
100
80
60
40
20
0
NH4-N
NO2-N
NO3-N
N species concentration (mg/L)
0
2
4
6
8
10 12 14 16
Elapsed time (h)
18
20
22
24
(b) Mixture reactor - Dark test
160
140
120
NH4-N
100
NO2-N
80
NO3-N
60
40
20
0
0
2
4
6
8
10 12 14 16
Elapsed time (h)
18
20
22
24
Figure 5.6: Variation of nitrogen species in algae (a) and mixture (b) reactors in dark test on day 28
Table 5.1: Total nitrogen and nitrogen balance in nitrifier, algae and mixture reactors
TN
TN
TN
NH4+-N
NO2--N NO3--N
liquor,
biomass, soluble,
Day
R
effluent
effluent effluent
effluent
effluent
effluent
(mg/L)
(mg/L) (mg/L)
(mg/L)
(mg/L)
(mg/L)
Alg
350
241.9
178.3
19.3
137.5
12.5
5
Mix
350
241.9
174.6
14.0
96.9
60.0
Nit
350
241.9
117.8
0
0
106.8
Alg
334
234.0
186.2
0
158.0
17.3
11
Mix
334
234.0
171.6
0
103.0
68.9
Nit
334
234.0
173.4
0
0
172.2
Alg
284
245.4
307
117
190.0
0
165.3
15.5
16
Mix
284
245.4
244
80
164.0
0
92.5
68.0
Nit
284
245.4
242
42
200.0
18.4
2.5
179.1
Alg
618
596.3
348
344
88
256.0
0
212.5
14.4
24
Mix
618
596.3
370
106
264.0
45.1
119.0
83.9
Nit
618
596.3
334
24
310.0
77.4
12.8
156.7
Alg
638
530.0
357
360
94
266.0
6.0
216.8
50.1
28
Mix
638
530.0
372
96
276.0
31.6
150.0
91.0
Nit
638
530.0
370
14
356.0
147.3
28.9
139.8
* TN in biomass effluent calculated based on the difference between TN in mixed liquor of effluent and TN
soluble in effluent.
* N loss from the system calculated based on the difference between TN in mixed liquor of reactor solution at the
beginning of cycle right after adding the feed solution and TN in mixed liquor of effluent at the end of cycle.
* Dissolved organic nitrogen (DON) calculated based on the difference between soluble TN effluent and sum of
NH4+-N , NO2--N and NO3--N in effluent.
TN
feed
(mg/L)
52
NH4+N feed
(mg/L)
TN liquor,
begin cycle
(mg/L)
DON
effluent
(mg/L)
MSc Thesis
9.0
3.7
11.0
10.9
-0.3
1.2
9.2
3.5
0.0
29.1
16.0
63.1
-6.9
3.4
40.0
Soluble COD removal on average
Soluble COD removal (%)
70
60
50
40
30
20
10
0
Algae
Mixture
Nitrifier
Figure 5.7: Soluble COD removal on average of samples collected on day 13, 18, 24 and 27
Transmittance (%)
Light transmittance of feed and effluents
Feed_day14,15,16
90
80
70
60
50
40
30
20
10
0
Nit14
Alg14
Mix14
Nit15
Alg15
Mix15
Nit16
Alg16
350
400
450
500
550
600
650
Mix16
700
Wave length (nm)
Figure 5.8: Typical light transmittances of feed and effluents from three reactors
%TSS unsettled
Settleability - TSS unsettled over time
100
90
80
70
60
50
40
30
20
10
0
Alg
Mix
Nit
0
5
10
15
20
Elapsed time (min)
25
30
Figure 5.9: Settleability characteristics of three reactors measured on day 27, expressed by the
percentage of TSS remained in solution over time
Thanh Tung Nguyen
53
5.2 Discussion – Semi-continuous culture in glass bottles
5.2.1 Nitrifier reactor
In the nitrifier reactor, a stirring speed at 600 rpm was actually enough to aerate the reactor
and provide oxygen for the complete nitrification process. The DO during the first 15 days
was rather high, at around 5-6 mg/L. After then, in order to make a comparison with the other
two reactors, the stirring speed in nitrifier reactors was reduced to 200 rpm. Since then, the
DO fell immediately to less than 1mg/L, making the reactor always under anoxic status. The
air diffusion induced by mixing stirrer was unable to provide enough oxygen for bacterial
respiration. The nitrifying bacteria became idle, utilized less alkalinity and NH4+-N. The
availability of alkalinity could protect or buffer against rapid pH changes. That probably
explained why pH in nitrifier reactor was rather constant during experimental period.
Obviously, since the low DO in this reactor did not support the aerobic bacterial growth, the
biomass density started to decrease rapidly in response to new stirring speed (Fig 5.2(b)&(c)).
The nitrogen incorporated in the biomass therefore also reduced (Table 5.1). However, the
low DO environment in reactor could become favourable for facultative bacterial growth.
That could be the reason why the biomass density did not fall too far. In addition, the
indigenous bacteria and TSS already available in the feed solution also helped maintain the
biomass density in the reactor. The variation of nitrogenous species is illustrated in Figure
5.3(a). At a stirring speed of 600 rpm, no NH4+-N or NO2--N were detected, the nitrification
was complete. The NO3--N gradually went up, achieving the maximum of around 200 mg/L
on day 14. After switching to 200 rpm, the oxygen for nitrifier activity became limited, the
NH4+-N and NO2--N started to rise in reactor whereas the NO3--N inversely went down.
Probably, the decrease of NO3--N was mostly due to draining out from effluent and maybe a
small contribution of denitrification process. The increase of NH4+-N suggested that the
stirring speed of 200 rpm could not sustain the complete NH4+-N removal. Or in other word, it
could not meet the oxygen demand for complete nitrification process in this reactor. As a
result, after NH4Cl was added to the feed on day 23, the NH4+-N was built up even faster
because the reactor was no longer effective in NH4+-N removal.
5.2.2 Algae reactor
From the beginning to the end of the experiment, the algae reactor showed a very good
performance. As recorded in Figure 5.3(b), the NH4+-N in solution was quickly depleted only
few days after the experiment started and nearly no NH4+-N was detected till the end of
experiment. At the same time, the NO2--N appeared and steadily increased, showing that a
large portion of initial NH4+-N was converted into NO2--N in reactor (no NO2--N was found in
the feed solution, data not shown). Together with results observed in Experiment 1 with airlift
reactors and in measurement of NH4+-N conversion rate, we can speculate that the unexpected
nitrification process in algae reactor was due to the indigenous nitrifiers coming from the feed
solution. Over time, the nitrifiers were enriched in reactor and their activities became
significant. Hence, in reality, the algae reactor no longer contained algae only, but a mixture
of algae and nitrifier. This agrees with the results observed by González-Fernández et al.
(2011b). The presence of nitrifiers could help remove NH4+-N but nearly did not contribute to
the overall TN removal. In contrast, they hampered the algal assimilation by converting
readily usable form of nitrogen source (NH4+-N) to less available forms (NO2--N and NO3--N)
for algal growth. Since the contribution of NH3 volatilization and precipitation was
insignificant (Table 5.1), the NH4+-N removal in this reactor was as results of biomass uptake
and nitrification process.
54
MSc Thesis
At early stage, there was a sudden increase in TSS and VSS to a higher level (from day 1 to 4,
Figure 5.2(b)&(c)), which correlated with sudden increases in pH (Figure 5.1(b)) and DO
(Figure 5.1(a)). This could be owing to the high growth rate of algae when shifting from
culturing in synthetic to real swine wastewater. In synthetic wastewater with only inorganic
carbon source, algae mainly performed autotrophic growth. Whereas, in real swine
wastewater which contains both organic and inorganic carbon sources; algae could grow both
heterotrophically and autotrophically (Chojnacka and Noworyta, 2004, Lee et al., 1989). The
heterotrophic growth has been reported to be faster than the autotrophic one (Ogbonna et al.,
1997). In addition, real swine waste has a well-balanced composition of nutrients and
micronutrients that favour algal growth compared to synthetic one only containing the main
elements. Therefore, despite of low light transmittance of feed solution, the algae still showed
a fast growth when changing to real centrate. After several days acclimating to new culture,
the reactor continued experiencing high growth rate period, from day 10 to day 22, thanks to
the slight increase of NH4+-N (Figure 5.3(b)) and alkalinity (Figure 5.1(c)) in feed solution.
By end of day 22, the reactor was at very high pH (9.38), oversaturated DO (14.79 mg/L),
high Chlorophyll a content (38.5mg/L), high TSS (2204 mg/L) and VSS (1816 mg/L).
Probably, the oxygen rich environment provided by algal photosynthetic process during this
high growth rate period had favoured nitrification process and the NO2--N had increased
continually. It was interesting that despite of very high DO level, the NO3--N did not develop
but still remained at low level. For this, there could be some speculations: the feed solution
did not contain NOB but only AOB or the high pH in reactor did not favour NOB growth
(Glass and Silverstein, 1998). When the NH4+-N in feed solution was doubled on day 23, the
reactor quickly responded in a series of changes. The pH, DO and alkalinity experienced
sharp decreases. TSS, VSS, Chlorophyll a, NO2--N continued to increase until day 25 when
they started to decrease. And on day 25, for the first time, the NO3--N started to rise gradually.
There could be some possible explanations. After long time in operation, the nitrifier
population had been growing significantly. Benefitting from the surplus oxygen generated by
algal photosynthesis, they became very active. When NH4Cl was added to feed solution with
no additional alkalinity source, the nitrifers quickly oxidized NH4+-N to NO2--N. Because the
NH4+-N removal by algae uptake is lower than that of nitrification process, the NH4+-N was
mostly oxidized to NO2--N and NO3--N rather than uptaken by algae. To convert two times
bigger amount of NH4+-N than before, nitrifiers required more oxygen, alkalinity and
produced more H+. That was probably the direct reason resulting in the drop of pH, DO and
alkalinity in reactor. Besides, since there was no additional alkalinity source to the reactor,
alkalinity had reduced rapidly, the pH buffer capacity became weaker. As a result, pH
dropped quickly. Two days after that, TSS, VSS, Chlorophyll a, NO2--N also decreased due to
lack of alkalinity for autotrophic growth by algae and for nitrification process. The reason
why the decreases happened later than the cases of pH, DO and alkalinity could be: the
formers were more transient and easily affected by environmental changes than the latter that
were more resistant to changes from outside. For the presence of NO3--N observed by the end
days of experiment, there could be some reasons. Firstly, pH was no longer high, supporting
the activity of NOBs. Secondly, despite of the drop of DO, the DO was still at around 5-7
mg/L, enough for complete nitrification process to occur. Thirdly, the nitratation converting
NO2--N to NO3--N does not require alkalinity, which was in shortage in reactor at that
moment.
5.2.3 Mixture reactor
The development of a mixture reactor basically could be divided into three periods: beginning
of experiment to day 18, day 18 to day 23, and day 23 till end of experiment.
Thanh Tung Nguyen
55
During the first period from day 1 to day 18, the mixture culture had performed quite
similarly to the algae culture. The NH4+-N coming into reactor was quickly removed and
nitrified to NO2--N and NO3--N (Figure 5.3(c)). After experiencing the acclimation period on
the first 5 days, the reactor showed a high growth rate period, from day 10 to day 22. The
trending curves of pH, DO, alkalinity, TSS, VSS, Chlorophyll a and biomass productivity
respectively resembled those in algae reactor (Figure 5.1&5.2). However, closer looking into
each case, there were still some differences. The pH and DO in mixture bottle were always
lower than the algae bottle. This sounded reasonable because in algal-nitrifer system,
nitrification activity and NH4+-N consumption were stronger and higher than in algae only,
which probably resulted in lower pH in the former than the latter. Besides, during this period,
the VSS in mixture culture was observed to be slightly higher than was in algae reactor,
whereas the TSS and Chlorophyll a were very similar. It is also the nitrifiers seeds initially
added to the mixture reactor that was mainly responsible for the higher NO3--N/NO2--N ratio
in this reactor compared to algae reactor. NO2--N reached steady state at around 100 mg/L on
day 8 while the NO3--N kept increasing slowly over time and always presented at slightly
lower concentration than the NO2--N did. Unlike in Experiment 1 with airlift reactor where
only NO3--N presented in mixture reactor, in this experiment, both NO2--N and NO3--N were
detected at a relatively high concentration in mixture culture. Probably, the higher DO
concentration in airlift reactor accounted for that complete nitrification.
Figure 5.10: Microscopic observation (x40) illustrates the development of rotifers and the decrease of
algae population in mixture reactors during experiment period
56
MSc Thesis
In the next period from day 18 to day 23, the behaviour of mixture bottle varied greatly from
that of algae reactor. This period was characterized by a slight reduction of TSS, VSS (Figure
5.2(b)&(c)) and the rapid decrease in algae population (Figure 5.2(a)). The rotifers were
observed to present in mixture reactor on day 15. However, not until day 18 did they flourish.
Most probably, the rotifers were originated from the nitrifier seeds collected in the wastewater
treatment plant. It was hard to eliminate them from nitrifier seeds because they prey on
bacteria and are also aerobic microorganisms as nitrifiers. Less likely, the rotifers could also
come from the feed solution as discussed in Section 4.2.2.2. The presence of rotifers was most
likely responsible for the sudden decrease of algae population since they can also prey on
algae and take lots of food for their growth. They also have high metabolism and can quickly
deplete the oxygen in the water. Consequently, the dual-effects, decrease in algae and
appearance of new oxygen consumer, could be the reason that led to the steep fall of DO
(Figure 5.1(a)). When algal photosynthetic activity decreased, the nitrification process became
dominant in mixture reactor, resulting in the decrease of pH (Figure 5.1(b)) and a slight
increase of NO2--N (Figure 5.3(c)). However, the nitrification process was also affected by
these effects because of less oxygen produced by algae and the competition by the rotifers in
that limited oxygen source. As a result, the nitrification rate also decreased, leading to the
slower decreasing rate of pH than that of the DO (Figure 5.1(a)&(b)). A reduction of both
algae and nitrifiers activities means less alkalinity consumed and more alkalinity available
(Figure 5.1(c)) for pH buffer. This is also a possible reason for the lower decreasing rate of
pH compared to DO.
The third period, from day 23 to the end of experiment, was depicted by a small recovery of
algae population on day 26, 27 (Figure 5.2(a)) after increasing inlet NH4+-N. Prior to day 23,
the complete nitrification process (despite of difference in nitrification products) were
observed in both algae and mixture reactors. Therefore, in order to indentify the difference in
NH4+-N removal potentials between two reactors, NH4Cl was added to the feed solution to
increase inlet NH4+-N. Since no additional alkalinity source was added, the effluent alkalinity
markedly dropped as observed in algae reactor. On day 24, 25, the DO in mixture reactor
reached very low level (less than 1 mg/L). The results in nitrifier reactors (Section 5.2.1) and
from the dark test revealed that only magnetic stirrer at 200 rpm could not fully aerate the
reactor. Therefore, the DO detected in this reactor was mostly owing to the algal
photosynthesis. However, since the presence of rotifers, the algae number had decreased
dramatically and on day 24, the Chlorophyll a measured was only 13.8 mg Chl a/L (Figure
5.2(a)). The low algae population was directly linked to the low DO level measured. The
competition from rotifers made the oxygen available for nitrifier even less. Consequently, this
low DO had limited the nitrification process, resulting in the increase of NH4+-N in the
effluent starting from day 23 (Figure 5.3(c)). The lack of alkalinity could also be responsible
for the presence of NH4+-N. However, it might not be the main reason. Algae reactor was also
grown under same feed quality but nearly no NH4+-N was detected even though the alkalinity
had decreased a lot. The decrease in algae population leading to less NH4+-N incorporated in
biomass could also be a reason. But again, it was minor because the TN nitrogen balance in
Table 5.1 and measurement of NH4+-N conversion rate showed that only few proportion of
NH4+-N was uptaken by biomass and the main was oxidized into NO2--N, NO3--N. On day 26,
the DO and Chl a were observed to get recovered slightly. This could be owing to the
declined number of rotifers in the reactors. Microscopic observation showed that the rotifer
density had reduced substantially, starting from day 19. This phenomenon could be explained
as follow. Since the outbreak of rotifers leading to the quick decline of algae, the DO in
solution also fell down sharply and maintained at low level from day 19 to day 25, especially
lowest on day 24, 25. DO and algae were no longer abundant for rotifers growth, the rotifer
Thanh Tung Nguyen
57
population hence decreased gradually. It was also reported that the presence of NH4+-N could
inhibit the rotifers. Therefore, adding NH4+-N on day 23 unintentionally had helped inhibit the
rotifers. The decreasing number of rotifers inversely favoured the recovery of algae. The DO
therefore increased slightly and supported nitrification process. As a result, the NH4+-N
decreased slightly and more NO2--N was produced on day 26, 27 (Figure 5.3(c)).
5.2.4 Ammonium conversion rate
After reducing the stirring rate to 200 rpm, the nitrifier bottle performed very low DO
concentration and the NH4+-N kept accumulating in the reactor. For this reason, only the
NH4+-N conversion rate of algae and mixture reactors were measured and illustrated in Figure
5.4. The measurement was conducted on day 21, three days after the outbreak of rotifers on
day 18 in mixture reactors that was believed to be responsible for the sharp decrease of algae.
Therefore, the results obtained from mixture reactor to some extent were greatly affected by
this phenomenon.
As shown in Figure 5.4(a), in algae reactor, the initial NH4+-N of about 30 mg/L was
completely removed after 8 hours. The NO3--N was nearly unchanged, at around 12-12.5
mg/L, throughout the test and the NO2--N increased together with the decrease of NH4+-N,
from initially 160 to around 180 mg/L. The net increase in NO2--N nearly equals to the net
decrease of NH4+-N, suggesting that nitritation process was probably the main route of NH4+N conversion (about 75%). The remaining could come from biomass uptake, precipitation,
NH3 volatilization or denitrification. Unlike algae reactor, in mixture reactor (Figure 5.4(b)),
both NO2--N and NO3--N were observed to increase simultaneously, together with the
decrease in NH4+-N. After 18 hours, complete NH4+-N removal was achieved. The sum of net
increases in NO2--N and NO3--N was lower but rather close to the net decrease of NH4+-N,
suggesting that nitritation and nitratation processes were the main route of NH4+-N conversion
(about 78%). The remaining could be attributed to biomass uptake, precipitation, NH3
volatilization or denitrification.
Two reactors exhibited similar developing patterns of DO and NH4+-N (Figure 5.4). Right
after new feed solution was added to reactors, the NH4+-N was promptly diluted to around 30
mg/L and the DO immediately dropped to below 1mg/L (probably due to the activities of
aerobic microorganisms, especially nitrification by nitrifiers). After then, the NH4+-N
decreased linearly, whereas the DO went horizontally at low level. When the NH4+-N
decreased to a certain level, the DO started to go up rapidly. Finally, when the NH4+-N hit
zero, the DO also simultaneously approached its climax and slightly decreased after that,
making a hump curve. The hump curve appeared in both algae and mixture cultures but at
different heights and positions in time axis. For algae reactor, it appeared after 11 hours and
reached supersaturated level of 17.01 mg/L. For mixture reactor, it gained maximum after 19,
20 hours at lower level of about 3.25 mg/L. There were several possible reasons for these
differences. The mixture reactors had been under deep decrease of DO for several days. On
day 20, the DO was only 4.86 mg/L. The low DO was closely related to the rapid decrease of
algae population, as discussed in Section 5.2.3. in algae reactor, the over saturated oxygen
level was due to the algal photosynthetic process as widely reported (Kumar et al., 2010a).
Also, no rotifers were observed in algae reactor, it can be assumed that the aerobic bacteria
were the only oxygen consumers in this culture. Meanwhile, in mixture reactor, rotifers could
also utilize oxygen. Rotifers maybe competed with aerobic bacteria in limited oxygen source
provided by the decreasing number of algae. Less oxygen available for nitrifying bacteria
became a limiting factor for the nitrification process, and hence the ammonium conversion
58
MSc Thesis
process was longer in mixture reactor than in algae reactor. This is probably why the DO peak
in mixture reactor presented later than did in algae reactor.
Normally, the NH4+-N conversion rate was expected to be faster in mixture reactor than in
algae reactor. However, actual results show the opposite with higher in algae reactor (3.33
mgNH4+-N/L/h or 1.94 mgNH4+-N/gVSS/h) and lower in mixture reactor (1.73 mgNH4+N/L/h or 0.95 mgNH4+-N/gVSS/h). These numbers were smaller than the conversion rate of
7.7 mgNH4+-N/L/h observed by Karya et al. (2013) when using algal-nitrifier treating
synthetic wastewater, but comparable to 3.71 mgNH4+-N/L/h reported by De Godos et al.
(2009) when treating pretreated swine slurry with algae and activated sludge. In both reactors,
the NH4+-N conversion could be owing to the nitrification process (converting into NO2--N
and NO3--N), the biomass uptake (mainly by algae and bacteria), the precipitation process (i.e,
struvite precipitation at high pH, especially in swine centrate which is rich in Mg and PO43-),
the NH3 volatilization and the denitrification process (converting into N2 gas). The nitrogen
balance as in Table 5.1 shows that the nitrogen loss by NH3 volatilization and denitrification
was small and insignificant (1.16%). Therefore, the NH4+-N conversion process could be
assumed to be mainly determined by the nitrification process, the biomass uptake and the
precipitation process. Accordingly, the higher NH4+-N conversion rate in algae reactor than
that in mixture reactor could be owing to:
a) Higher biomass density of algae culture, as shown in Figure 5.2. Probably, the higher TSS,
VSS and Chlorophyll a helped the algae reactor have faster NH4+-N removal by assimilating
in biomass.
b) The negative effect by rotifers. The rotifers killed the algae and competed with nitrifying
bacteria in oxygen, resulting in less oxygen for nitrification process. As a result, the NH4+-N
conversion rate in algae reactor was higher than in mixture reactor.
5.2.5 Dark test
The dark test was carried out to evaluate the oxygen production by air diffusion induced by
mixing stirrers. On day 28, both nitrifier and mixture reactors were at low DO, less than 1
mg/L (Figure 5.1(a)). Therefore, it was not necessary to measure DO of these two reactors,
only DO profile of algae reactor was recorded during dark test. After switching off all the
lamps and coating the algae bottle with aluminium foils to prevent any light source from
getting into reactor, the DO in algae reactor immediately decreased linearly, as shown in
Figure 5.5, from initially 7.52 mg/L to less than 1 mg/L. Without light, algae cannot perform
photosynthesis to produce oxygen. The reactor therefore could only rely on the oxygen
diffusion. However, the oxygen transfer by diffusion induced by stirrer at a speed of 200 rpm
case was quite low and could not meet the oxygen consumption by aerobic microorganisms.
Consequently, the DO quickly dropped to nearly zero and the reactor became anoxic.
Together with results observed in nitrifier reactors, it is clear that only mixing stirrer at a
speed of 200 rpm was unable to meet the oxygen demand by aerobic microorganisms. In other
words, it confirmed that the high DO in reactors was owing to the photosynthetic oxygen
production, not by oxygen diffusion.
One hour after completing DO measurement, algae and mixture reactors were fed with new
feed solution as usual: 100 ml of reactor solution was withdrawn and replaced by the same
amount of feed solution. After that, samples were collected every two hours to investigate the
development of nitrogenous species in one cycle. The obtained results, described in Figure 5.6,
showed that no statistically significant difference between NH4+-N in the first half cycle (12
hours) and the last half cycle (12 hours) in algae reactor (t = -2.2586, df = 5, p-value =
Thanh Tung Nguyen
59
0.07348) and mixture reactor (t = -3.7594, df = 5, p-value = 0.10317) were observed.
Similarly, statistical t-test did not reveal any differences of NO2--N and NO3--N during the
first and second half cycles in both reactors (Appendix 9). This suggested that without light,
there be no photosynthetic oxygen production to support nitrification process.
5.2.6 Comparison among three reactors
Due to the disturbance of rotifers, the mixture reactor could not fulfil the NH4+-N removal
during the end days of experiment, therefore, the evaluation of this reactor was biased. Also,
in reality, the algae reactor was not a pure algal culture, but a mixture of Chlorella sp. algae
and indigenous bacteria from the feed. Therefore, it is difficult to claim algae and mixture
culture, which one was superior to the other. However, based on overall results observed
throughout the experiment, among three reactors, algae reactor was probably the most stable
and nitrifier reactor was the least effective.
Ammonium removal
Throughout the experiment, the algae reactor could completely remove NH4+-N. Even when
the NH4+-N loading rate was doubled, this reactor still showed perfect removal efficiency.
Prior to the invasion of rotifers, the mixture culture also succeeded in complete NH4+-N
removal and performed similarly to algae reactor. After the rotifers appeared, this reactor
became unstable and could not completely remove NH4+-N. In nitrifier reactor, the system
could only achieve 100% NH4+-N removal when working at stirring speed of 600 rpm. When
reducing to 200 rpm to become comparable with the other two reactors, the system rapidly
lost its effectiveness.
Table 5.2: Comparison of ammonium removal by algae and bacteria found in some studies
Substrate
Species
Reactor
Operating conditions
NH4+-N
removal
(%)
References
Fresh swine
waste
Chlorella sp. +
indigenous nitrifiers
in feed
Bubble
column
Continuous flow 240 ml/d,
illumination 3000-7000 lux,
20˚C HRT
100
Baumgarten et al.
(1999)
AD swine
waste
Oocystis sp.,
Scenedesmus sp.+
Activated sludge
Open
pond,
enclosed
Tubular
Continuous flow HRT 8.5d
and illumination 12000 lux,
30-35˚C
100
Molinuevo-Salces et
al. (2010)
Fresh and
AD swine
slurry
C.vulgaris,
Scenedesmus
obliquus
Open
pond
HRT 10d, Harsh 16˚C & 9h
and optimum 30˚C &24h
Fresh: 91.294.8
AD: 92.393.9
GonzálezFernández et al.
(2011b)
AD swine
waste
Chlorella sp. +
ntirifier
1L glass
bottle
HRT 10d, 23˚C, continuous
illumination, semi continuous
flow
100
This study
Total nitrogen removal
The overall total nitrogen balances of three reactors are shown in Table 5.1. Despite of high
NH4+-N removal, the TN in their effluents were still high. In general, NH3 volatilization,
denitrification and biomass uptake are the main nitrogen removal pathways. At low pH, the
NH3 volatilization is negligible, however, at high pH, it becomes significant. Data of TN at
the beginning and end of cycle on day 24 and 28 showed that the nitrogen loss from the algae
reactor by NH3 volatilization and denitrification processes was small and insignificant (348
mg/L at the beginning compared to 344 mg/L at the end of day 24, and 357 mg/L at the
beginning compared to 360 mg/L at the end of day 28). Therefore, the TN removal in algae
60
MSc Thesis
reactor was mainly based on biomass uptake.In this experiment, the pH in mixture and
nitrifier reactors was usually lower than algae reactors, so similar conclusions with two
reactors could be drawn. Table 5.1 shows the TN incorporated in biomasses of algae and
mixture reactors was much higher than that in nitrifier reactor, suggesting that growing algae
could be potential for nutrient recovery. However, only about 26-38% of total nitrogen in
effluents of these two reactors was captured in biomass, the majority was in form of soluble
nitrogen, mainly in NO2--N and NO3--N. Hence, further TN removal step is necessary, such as
denitrification process.
Table 5.3: Comparison of TN removal by biomass uptake by algae and bacteria found in some studies
Substrate
Species
Reactor
Operating conditions
TN
removal by
biomass
uptake(%)
AD swine
waste
Oocystis sp.,
Scenedesmus sp.+
Activated sludge
Open
pond,
enclosed
Tubular
Continuous flow HRT 8.5d
and illumination 12000 lux,
30-35˚C
Open: 38-47
Close: 31
Molinuevo-Salces et
al. (2010)
Fresh and
AD swine
slurry
C.vulgaris,
Scenedesmus
obliquus + bacteria
Open
pond
HRT 10d, light period
16h&24h and tempreature
30˚C
Fresh: 9.540.8
AD: 12.126.9
GonzálezFernández et al.
(2011a)
AD swine
waste
Chlorella sp. +
ntirifier
1L glass
bottle
HRT 10d, 23˚C, continuous
illumination, semi continuous
flow
26-38
This study
References
Soluble COD removal
The soluble COD removal efficiencies for all reactors are demonstrated in Table 5.2. It should
be noted that the easily degradable COD was greatly utilized during AD process, and the
remaining COD in the AD effluent was rather recalcitrant for three reactors. The average
removal efficiencies were respectively 45% in algae culture, 55% in mixture cultures and 56%
in nitrifier culture. Statistic results also show no significant differences among three reactors,
suggesting that the role of aerobic bacteria in COD removal was unclear. The COD removal
results in this study were comparable to results obtained by González-Fernández et al. (2011b)
(30-40%) when applying microalgae-bacteria consortia to treat AD swine slurry.
Table 5.4: Comparison of COD removal by algae and bacteria found in some studies
Substrate
Species
Reactor
Operating conditions
AD swine
waste
Oocystis sp.,
Scenedesmus sp.+
Activated sludge
Open
pond,
enclosed
Tubular
Continuous flow HRT 8.5d
and illumination 12000 lux,
30-35˚C
Fresh and
AD swine
slurry
C.vulgaris,
Scenedesmus
obliquus
Open
pond
HRT 10d, Harsh 16˚C & 9h
and optimum 30˚C &24h
AD swine
waste
Chlorella sp. +
ntirifier
1L glass
bottle
HRT 10d, 23˚C, continuous
illumination, semi continuous
flow
COD
removal
(%)
Open: 36.860.2
Close: 40.953.2
Fresh: 63.587.8
AD: 29.141.1
Alg: 45
Mix: 55
References
Molinuevo-Salces et
al. (2010)
GonzálezFernández et al.
(2011b)
This study
Light transmittance of effluent
Figure 5.7 shows the characteristics of light transmittance of three effluents in comparison
with initial feed solution in three consecutive days. Overall, the effluents from three reactors
significantly showed an improvement in light penetration. However, at low range wave length
Thanh Tung Nguyen
61
(350-450 nm) of violet-indigo-blue colour, the effluents did not show much improvement.
Only at green light wave lengths (450-650 nm) and orange-red light wave lengths (650-700
nm), the difference became significant. No clear differences were found among three types of
effluents.
Biomass density and productivity
Figure 5.2 describes the developing trends of biomass density and biomass productivity in
three bottles. Three reactors started with nearly equal biomass density. However, over time,
they had developed differently. In nitrifier bottle, the biomass density was always lower than
algae and mixture bottles. It became even lower when the stirring speed reduced to 200 rpm
on day 15. In algae and mixture cultre, the biomass densities kept growing steadily and
together reached high VSS levels of about 1700-1800 mg/L after 15 days in operation. The
increasing trend of mixture reactor was stopped on day 18 when the rotifer flourished,
whereas the algae reactor continued to increase till the end of the experiment. In term of
biomass productivity, algae and mixture reactors also exhibited higher and more stable daily
productivity than nitrifier reactor did. The average areal biomass densities of algae and
mixture reactors were 4.3±0.37 and 4.2±0.42 (gVSS/m2/d) and the average volumentric areal
biomass densities were 0.195±0.0167 and 0.189±0.0190 (gVSS/L/d) respectively. These
results showed good biomass productivities and could be comparable with other studies
(Table 5.2) when using algae and bacteria to treat swine waste centrate. The high yield
biomass generated could be a promising source for biofuel production. For instance, in a
simple way, the biomass can be further condensed and brought back to AD reactor to perform
co-digestion with the fresh pig manures (Figure 5.10). At the same time, a high quality of
discharged effluent from the treatment cycle will also be achieved.
Table 5.5: Comparison of biomass productivity by algae and bacteria found in some studies
Substrate
Species
Reactor
AD swine
waste
Oocystis sp.,
Scenedesmus sp.+
Activated sludge
Open
pond,
enclosed
Tubular
C.sorokiniana +
Activated sludge
Tubular
C.vulgaris,
Scenedesmus
obliquus
Open
pond
Chlorella sp. +
ntirifier
1L glass
bottle
Pretreated
swine
slurry
Fresh and
AD swine
slurry
AD swine
waste
62
Operating
conditions
Continuous flow
HRT 8.5d and
illumination 12000
lux, 30-35˚C
Continuous flow and
illumination, HRT
7d
HRT 10d, Harsh
16˚C & 9h and
optimum 30˚C &24h
HRT 10d, 23˚C,
continuous
illumination, semi
continuous flow
Areal
biomass
productivity
(gVSS/m2/d)
Volumetric
biomass
productivity
(gVSS/L/d)
Open: 0.0510.332
Close: 0.0010.007
0.153-0.410
MolinuevoSalces et al.
(2010)
De Godos et
al. (2009)
GonzálezFernández et
al. (2011b)
Fresh: 4.9-9
AD: 2.2-4.9
Alg: 4.3±0.37
Mix: 4.2±0.42
References
Alg:
0.195±0.0167
Mix:
0.189±0.0190
This study
MSc Thesis
Figure 5.11: Schematic diagram of co-digestion process combining algae (and bacteria) biomass
cultured in AD swine centrate with fresh swine manure to increase energy production
Biomass composition
Microscopic observation revealed that after 28 days in operation, Chlorella sp. in algae
reactor still maintained its purity although algal community and morphology had changed
from scattered single algal cells to aggregated flocs consisted of algae and bacteria. In mixture
reactor, the algal biomass was no longer pure Chlorella sp. algal specie, but aggregated flocs
of Chlorella sp. algal cells, bacteria, rotifers and filamentous algae (very likely were cyano
bacteria).
Biomass settleability
The settling characteristics of biomass in three bottle reactors were characterized by the TSS
unsettled over time as shown in Figure 5.8. All three reactors showed good settleability, in
which 50 % of biomass TSS could settle down only within 10 min. The trending curve of
nitrifiers was always below that of algae, suggesting that the nitrifier culture showed better
settleability than the algae culture. It was interesting that the mixture culture showed the best
settleability. This could be due to the contribution of rotifers in this culture which are bigger
and heavier than algae and bacteria. After 10 min, the TSS unsettled in each reactor was 36.19%
in mixture culture, 41.12% in nitrifier culture and 49.81% in algae culture. And after 30 min,
Thanh Tung Nguyen
63
the TSSs unsettled in each reactor were only 18.26% in mixture culture, 20.67% in nitrifier
culture and 32.38% in algae culture.
5.3 Conclusion – Semi-continuous culture in glass bottle
In succession to study of Karya et al. (2013) using algae-nitrifier consortium to remove NH4+N in synthetic wastewater, this research continues working with algae and nitrifying bacteria
application to remove NH4+-N in high strength wastewater. The specific objectives were to
evaluate the NH4+-N removal using Chlorella sp. algal specie and nitrifiers cultured in
anaerobically digested swine manure by focusing on:
a) NH4+-N removal efficiency and the main NH4+-N removal pathways.
b) The nitrification process in photo-bioreactors without exogenous aeration.
c) The overall performance of a mixture of algae-nitrifier in comparison with algae only
in terms of NH4+-N removal and biomass production.
Experiment with 1L glass bottles under semi-continuous regime, SRT 10d, light intensity 95101 µmol/m2/s and magnetic stirrer mixing at 200 rpm, showed that:
a) Complete NH4+-N removal from swine centrate with NH4+-N loading rate of 23-30
mg/L/d was achieved in both algae and mixture reactors. The NH4+-N removal mostly
relied on nitrification process, producing NO2--N, NO3--N in effluent, rather than by
biomass uptake.
b) Algal photo-oxygenation could support nitrification in both algae and mixture reactors
treating swine centrate. The nitrification products in algae reactor were mainly NO2--N,
whereas in mixture reactor were both NO2--N and NO3--N with significant
concentrations.
c) In general, algae culture was more stable and effective than mixture reactor was.
Throughout experiment, algae reactor maintained complete NH4+-N removal capacity,
even when the inlet NH4+-N loading rate was doubled. In mixture reactor, the system
could not perform complete NH4+-N removal when the rotifers appeared. Both
reactors could show good potential in produce high-yield and well-settleable
biomasses.
64
MSc Thesis
6 Recommendations for future works
Some recommendations after this research are:
a) Investigate in treating the residue TN in effluent coming out from the photobioreactors.
Even though both algae and mixture reactors could perform complete NH4+-N removal,
their effluents still contained high NO2--N, NO3--N and they should be removed.
b) Investigate in utilizing CO2 injection to enhance N incorporation in biomass
production.
c) Investigate in NH4+-N removal capacity and biomass production of the systems when
further increasing inlet NH4+-N loading rate.
d) Quantify the amount of oxygen photosynthetically generated by algae and establish
kinetic model for the process.
e) Investigate in NH4+-N removal capacity and biomass production of the systems when
changing light period to mimic diurnal change.
f) Repeat the experiment with different types of reactors to find out the most suitable and
scalable.
g) Investigate the energy value of biomass generated.
Thanh Tung Nguyen
65
7 References
ABELIOVICH, A. 1986. Algae in wastewater oxidation ponds. Handbook of Microalgal
Mass Culture, 35, 331-338.
ANTHONISEN, A., LOEHR, R., PRAKASAM, T. & SRINATH, E. 1976. Inhibition of
nitrification by ammonia and nitrous acid. Journal (Water Pollution Control
Federation), 835-852.
APHA, A. 2005. WPCF (2005) Standard methods for the examination of water and
wastewater. Public Health Association, Washington, DC.
ARAUZO, M. 2003. Harmful effects of un-ionised ammonia on the zooplankton community
in a deep waste treatment pond. Water Research, 37, 1048-1054.
AZOV, Y. & GOLDMAN, J. C. 1982. Free ammonia inhibition of algal photosynthesis in
intensive cultures. Applied and environmental microbiology, 43, 735-739.
BALMELLE, B., NGUYEN, K., CAPDEVILLE, B., CORNIER, J. & DEGUIN, A. 1992.
Study of factors controlling nitrite build-up in biological processes for water
nitrification. Water Science & Technology, 26, 1017-1025.
BAUMGARTEN, E., NAGEL, M. & TISCHNER, R. 1999. Reduction of the nitrogen and
carbon content in swine waste with algae and bacteria. Applied microbiology and
biotechnology, 52, 281-284.
BECKER, E. 2007. Micro-algae as a source of protein. Biotechnology Advances, 25, 207-210.
BENEMANN, J. R. 2003. Bio-fixation of CO2 and Greenhouse Gas Abatement with Microalgae–Technology Roadmap. Final report to the US Department of Energy, National
Energy Technology Laboratory, 55.
BENEMANN, J. R. Open ponds and closed photobioreactors–comparative economics. 5th
Annual World Congress on Industrial Biotechnology and Bioprocessing. Chicago,
2008a.
BENEMANN, J. R. 2008b. Opportunities and challenges in algae biofuels production. A
Position Paper, Algae World, 2, 216-226.
BENEMANN, J. R. & OSWALD, W. J. 1994. Systems and economic analysis of microalgae
ponds for conversion of CO2 to biomass. NASA STI/Recon Technical Report N, 95,
19554.
BOELEE, N. C., TEMMINK, H., JANSSEN, M., BUISMAN, C. J. N. & WIJFFELS, R. H.
2012. Scenario Analysis of Nutrient Removal from Municipal Wastewater by
Microalgal Biofilms. Water, 4, 460-473.
BOROWITZKA, M. A. 1999. Commercial production of microalgae: ponds, tanks, and
fermenters. Progress in Industrial Microbiology, 35, 313-321.
BOUSSIBA, S. 1989. Ammonia uptake in the alkalophilic cyanobacterium Spirulina platensis.
Plant and cell physiology, 30, 303-308.
BRUCE, E. R. & PERRY, L. M. 2001. Environmental biotechnology: principles and
applications. New York: McGrawHill, 400.
CHAN, K.-Y., WONG, K. H. & WONG, P. K. 1979. Nitrogen and phosphorus removal from
sewage effluent with high salinity by Chlorella salina. Environmental Pollution
(1970), 18, 139-146.
CHEN, W., LIU, H., ZHANG, Q. & DAI, S. 2011. Effect of nitrite on growth and
microcystins production of Microcystis aeruginosa PCC7806. Journal of applied
phycology, 23, 665-671.
66
MSc Thesis
CHEN, W., TONG, H. & LIU, H. 2012. Effects of nitrate on nitrite toxicity to Microcystis
aeruginosa. Marine Pollution Bulletin, 64, 1106-1111.
CHOI, O., DAS, A., YU, C. P. & HU, Z. 2010. Nitrifying bacterial growth inhibition in the
presence of algae and cyanobacteria. Biotechnology and bioengineering, 107, 10041011.
CHOJNACKA, K. & NOWORYTA, A. 2004. Evaluation of growth yield of Spirulina
(Arthrospira) sp. growth in photoautotrophic, heterotrophic and mixotrophic cultures.
Enzyme and Microbial Technology, 34, 461-465.
CHRISTENSON, L. & SIMS, R. 2011. Production and harvesting of microalgae for
wastewater treatment, biofuels, and bioproducts. Biotechnology advances, 29, 686-702.
CLAROS, J., JIMÉNEZ, E., BORRÁS, L., AGUADO, D., SECO, A., FERRER, J. &
SERRALTA, J. 2010. Short-term effect of ammonia concentration and salinity on
activity of ammonia oxidizing bacteria. Water science and technology: a journal of the
International Association on Water Pollution Research, 61, 3008.
CRAGGS, R. 2005. Advanced integrated wastewater ponds. Pond Treatment Technology,
IWA Scientific and Technical Report Series, IWA, London, UK, 282-310.
CRAGGS, R. J., HEUBECK, S., LUNDQUIST, T. J. & BENEMANN, J. R. 2011. Algal
biofuels from wastewater treatment high rate algal ponds. Water science and
technology: A journal of the International Association on Water Pollution Research,
63, 660.
CROFT, M. T., WARREN, M. J. & SMITH, A. G. 2006. Algae need their vitamins.
Eukaryotic cell, 5, 1175-1183.
DE GODOS, I., GONZÁLEZ, C., BECARES, E., GARCÍA-ENCINA, P. A. & MUÑOZ, R.
2009. Simultaneous nutrients and carbon removal during pretreated swine slurry
degradation in a tubular biofilm photobioreactor. Applied microbiology and
biotechnology, 82, 187-194.
DE KREUK, M. K., HEIJNEN, J. J. & VAN LOOSDRECHT, M. C. M. 2005. Simultaneous
COD, nitrogen, and phosphate removal by aerobic granular sludge. Biotechnology and
Bioengineering, 90, 761-769.
DE LA NOÜE, J. & BASSERES, A. 1989. Biotreatment of anaerobically digested swine
manure with microalgae. Biological wastes, 29, 17-31.
FDZ-POLANCO, F., VILLAVERDE, S. & GARCIA, P. 1994. Temperature effect on
nitrifying bacteria activity in biofilters: activation and free ammonia inhibition. Water
Science and Technology, 30, 121-130.
FENCHEL, T. 1974. Intrinsic rate of natural increase: the relationship with body size.
Oecologia, 14, 317-326.
FUKAMI, K., NISHIJIMA, T. & ISHIDA, Y. 1997. Stimulative and inhibitory effects of
bacteria on the growth of microalgae. Hydrobiologia, 358, 185-191.
GARCIA, J., GREEN, B. F., LUNDQUIST, T., MUJERIEGO, R., HERNÁNDEZ-MARINÉ,
M. & OSWALD, W. J. 2006. Long term diurnal variations in contaminant removal in
high rate ponds treating urban wastewater. Bioresource technology, 97, 1709-1715.
GARCIA, J., HERNANDEZ-MARINE, M. & MUJERIEGO, R. 2000. Influence of
phytoplankton composition on biomass removal from high-rate oxidation lagoons by
means of sedimentation and spontaneous flocculation. Water environment research,
72, 230-237.
GLASS, C. & SILVERSTEIN, J. 1998. Denitrification kinetics of high nitrate concentration
water: pH effect on inhibition and nitrite accumulation. Water Research, 32, 831-839.
GONZÁLEZ-FERNÁNDEZ, C., MOLINUEVO-SALCES, B. & GARCÍA-GONZÁLEZ, M.
C. 2011a. Nitrogen transformations under different conditions in open ponds by means
Thanh Tung Nguyen
67
of microalgae–bacteria consortium treating pig slurry. Bioresource technology, 102,
960-966.
GONZÁLEZ-FERNÁNDEZ, C., RIAÑO-IRAZÁBAL, B., MOLINUEVO-SALCES, B.,
BLANCO, S. & GARCÍA-GONZÁLEZ, M. C. 2011b. Effect of operational
conditions on the degradation of organic matter and development of microalgae–
bacteria consortia when treating swine slurry. Applied microbiology and
biotechnology, 90, 1147-1153.
GOREAU, T. J., KAPLAN, W. A., WOFSY, S. C., MCELROY, M. B., VALOIS, F. W. &
WATSON, S. W. 1980. Production of NO2-and N2O by nitrifying bacteria at reduced
concentrations of oxygen. Applied and environmental microbiology, 40, 526-532.
GREEN, F., BERNSTONE, L., LUNDQUIST, T. & OSWALD, W. 1996. Advanced
integrated wastewater pond systems for nitrogen removal. Water Science &
Technology, 33, 207-217.
GUDIN, C. & CHAUMONT, D. 1991. Cell fragility—the key problem of microalgae mass
production in closed photobioreactors. Bioresource Technology, 38, 145-151.
GUERRERO, M. & JONES, R. 1996. Photoinhibition of marine nitrifying bacteria. I.
Wavelength-dependent response. Marine ecology progress series. Oldendorf, 141,
183-192.
HAN, X. L., HAI, R. T. & WANG, W. X. 2010. Nitrification in Vertical Flow Constructed
Wetlands with Different Substrate and COD: N Ratio. Advanced Materials Research,
96, 117-120.
HANAKI, K., WANTAWIN, C. & OHGAKI, S. 1990. Nitrification at low levels of dissolved
oxygen with and without organic loading in a suspended-growth reactor. Water
Research, 24, 297-302.
HARRIS, G. P. 1978. Photosynthesis, productivity and growth, the physiological ecology of
phytoplankton, Schweizerbart.
HAWKINS, S., ROBINSON, K., LAYTON, A. & SAYLER, G. 2010. Limited impact of free
ammonia on Nitrobacter spp. inhibition assessed by chemical and molecular
techniques. Bioresource technology, 101, 4513-4519.
HENZE, M. 2008. Biological wastewater treatment: principles, modelling and design,
International Water Assn.
HERNÁNDEZ, D., RIAÑO, B., COCA, M. & GARCÍA-GONZÁLEZ, M. C. 2012.
Treatment of Agro-industrial wastewater using microalgae-bacteria consortium
combined with anaerobic digestion of the produced biomass. Bioresource Technology.
HO, L., HOEFEL, D., AUNKOFER, W., MEYN, T., KEEGAN, A., BROOKES, J., SAINT,
C. & NEWCOMBE, G. 2006. Biological filtration for the removal of algal metabolites
from drinking water. Water science and technology: water supply, 6, 153-159.
HOFFMANN, J. P. 2002. Wastewater treatment with suspended and nonsuspended algae.
Journal of Phycology, 34, 757-763.
HOOPER, A. B. & TERRY, K. R. 1973. Specific inhibitors of ammonia oxidation in
Nitrosomonas. Journal of Bacteriology, 115, 480-485.
HU, B., MIN, M., ZHOU, W., DU, Z., MOHR, M., CHEN, P., ZHU, J., CHENG, Y., LIU, Y.
& RUAN, R. 2012. Enhanced mixotrophic growth of microalga< i> Chlorella</i> sp<
i>.</i> on pretreated swine manure for simultaneous biofuel feedstock production and
nutrient removal. Bioresource technology.
HU, Q., GUTERMAN, H. & RICHMOND, A. 2000. A flat inclined modular photobioreactor
for outdoor mass cultivation of photoautotrophs. Biotechnology and bioengineering,
51, 51-60.
68
MSc Thesis
IMASE, M., WATANABE, K., AOYAGI, H. & TANAKA, H. 2008. Construction of an
artificial symbiotic community using a Chlorella–symbiont association as a model.
FEMS microbiology ecology, 63, 273-282.
KAGAMI, M., DE BRUIN, A., IBELINGS, B. W. & VAN DONK, E. 2007. Parasitic
chytrids: their effects on phytoplankton communities and food-web dynamics.
Hydrobiologia, 578, 113-129.
KÄLLQVIST, T. & SVENSON, A. 2003. Assessment of ammonia toxicity in tests with the
microalga, Nephroselmis pyriformis , Chlorophyta. Water research, 37, 477-484.
KARAKASHEV, D., SCHMIDT, J. E. & ANGELIDAKI, I. 2008. Innovative process scheme
for removal of organic matter, phosphorus and nitrogen from pig manure. Water
Research, 42, 4083-4090.
KARYA, N., VAN DER STEEN, N. & LENS, P. 2013. Photo-oxygenation to support
nitrification in an algal-bacterial consortium treating artificial wastewater. Bioresource
technology.
KIM, M. K., PARK, J. W., PARK, C. S., KIM, S. J., JEUNE, K. H., CHANG, M. U. &
ACREMAN, J. 2007. Enhanced production of Scenedesmus spp.(green microalgae)
using a new medium containing fermented swine wastewater. Bioresource technology,
98, 2220-2228.
KINYUA, MAUREEN NJOKI. 2013. Effect of Solids Retention Time on the Denitrification
Potential of Anaerobically Digested Swine Waste. Graduate School Theses and
Dissertations. Department of Civil and Environmental Engineering, University of
South Florida. http://scholarcommons.usf.edu/etd/4520.
KNOWLES, G., DOWNING, A. L. & BARRETT, M. 1965. Determination of kinetic
constants for nitrifying bacteria in mixed culture, with the aid of an electronic
computer. Journal of General Microbiology, 38, 263-278.
KONG, Q.-X., LI, L., MARTINEZ, B., CHEN, P. & RUAN, R. 2010. Culture of microalgae
Chlamydomonas reinhardtii in wastewater for biomass feedstock production. Applied
biochemistry and biotechnology, 160, 9-18.
KUAI, L. & VERSTRAETE, W. 1998. Ammonium removal by the oxygen-limited
autotrophic nitrification-denitrification system. Applied and Environmental
Microbiology, 64, 4500-4506.
KUMAR, A., ERGAS, S., YUAN, X., SAHU, A., ZHANG, Q., DEWULF, J., MALCATA, F.
& VAN LANGENHOVE, H. 2010a. Enhanced CO 2 fixation and biofuel production
via microalgae: recent developments and future directions. Trends in Biotechnology,
28, 371-380.
KUMAR, M. S., MIAO, Z. H. & WYATT, S. K. 2010b. Influence of nutrient loads, feeding
frequency and inoculum source on growth of< i> Chlorella vulgaris</i> in digested
piggery effluent culture medium. Bioresource technology, 101, 6012-6018.
LAU, P., TAM, N. & WONG, Y. 1995. Effect of algal density on nutrient removal from
primary settled wastewater. Environmental Pollution, 89, 59-66.
LEE, H. Y., LEE, S. Y. & PARK, B. K. 1989. The estimation of algal yield parameters
associated with mixotrophic and photoheterotrophic growth under batch cultivation.
Biomass, 18, 153-160.
LEU, H.-G., LEE, C.-D., OUYANG, C. & TSENG, H.-T. 1998. Effects of organic matter on
the conversion rates of nitrogenous compounds in a channel reactor under various
flow conditions. Water Research, 32, 891-899.
LEUPOLD, M., HINDERSIN, S., GUST, G., KERNER, M. & HANELT, D. 2012. Influence
of mixing and shear stress on Chlorella vulgaris, Scenedesmus obliquus, and
Chlamydomonas reinhardtii. Journal of applied phycology, 1-11.
Thanh Tung Nguyen
69
LI, X. Z. & ZHAO, Q. L. 1999. Inhibition of microbial activity of activated sludge by
ammonia in leachate. Environment International, 25, 961-968.
MAKAREWICZ, J. C., BOYER, G. L., LEWIS, T. W., GUENTHER, W., ATKINSON, J. &
ARNOLD, M. 2009. Spatial and temporal distribution of the cyanotoxin microcystinLR in the Lake Ontario ecosystem: Coastal embayments, rivers, nearshore and
offshore, and upland lakes. Journal of Great Lakes Research, 35, 83-89.
MARA, D. D. 2004. Domestic wastewater treatment in developing countries, Earthscan.
MARCATO, C. E., PINELLI, E., POUECH, P., WINTERTON, P. & GUIRESSE, M. 2008.
Particle size and metal distributions in anaerobically digested pig slurry. Bioresource
Technology, 99, 2340-2348.
MARTIN, C., DE LA NOÜE, J. & PICARD, G. 1985. Intensive cultivation of freshwater
microalgae on aerated pig manure. Biomass, 7, 245-259.
MATSUMOTO, H., HAMASAKI, A., SHIOJI, N. & IKUTA, Y. 1996. Influence of
dissolved oxygen on photosynthetic rate of microalgae. Journal of chemical
engineering of Japan, 29, 711-714.
MAURET, M., PAUL, E., PUECH-COSTES, E., MAURETTE, M. & BAPTISTE, P. 1996.
Application of experimental research methodology to the study of nitrification in
mixed culture. Water Science and Technology, 34, 245-252.
MAYER, P., CUHEL, R. & NYHOLM, N. 1997. A simple in vitro fluorescence method for
biomass measurements in algal growth inhibition tests. Water research, 31, 2525-2531.
MEDINA, M. & NEIS, U. 2007. Symbiotic algal bacterial wastewater treatment: effect of
food to microorganism ratio and hydraulic retention time on the process performance.
MIN, M., WANG, L., LI, Y., MOHR, M. J., HU, B., ZHOU, W., CHEN, P. & RUAN, R.
2011. Cultivating Chlorella sp. in a pilot-scale photobioreactor using centrate
wastewater for microalgae biomass production and wastewater nutrient removal.
Applied biochemistry and biotechnology, 165, 123-137.
MOLINA, E., FERNÁNDEZ, J., ACIÉN, F. & CHISTI, Y. 2001. Tubular photobioreactor
design for algal cultures. Journal of Biotechnology, 92, 113-131.
MOLINUEVO-SALCES, B., GARCÍA-GONZÁLEZ, M. C. & GONZÁLEZ-FERNÁNDEZ,
C. 2010. Performance comparison of two photobioreactors configurations (open and
closed to the atmosphere) treating anaerobically degraded swine slurry. Bioresource
technology, 101, 5144-5149.
MORAINE, R., SHELEF, G., MEYDAN, A. & LEVI, A. 1979. Algal single cell protein from
wastewater treatment and renovation process. Biotechnology and Bioengineering, 21,
1191-1207.
MOUGET, J. L., DAKHAMA, A., LAVOIE, M. C. & NOÜE, J. 1995. Algal growth
enhancement by bacteria: Is consumption of photosynthetic oxygen involved? FEMS
microbiology ecology, 18, 35-43.
MULBRY, W. W. & WILKIE, A. C. 2001. Growth of benthic freshwater algae on dairy
manures. Journal of Applied Phycology, 13, 301-306.
MULDER, J. W. & VAN KEMPEN, R. 1997. N-removal by SHARON. Water Quality
International, 30-31.
MUNOZ, R. & GUIEYSSE, B. 2006. Algal–bacterial processes for the treatment of
hazardous contaminants: a review. Water research, 40, 2799-2815.
MUÑOZ, R., KÖLLNER, C., GUIEYSSE, B. & MATTIASSON, B. 2003. Salicylate
biodegradation by various algal-bacterial consortia under photosynthetic oxygenation.
Biotechnology letters, 25, 1905-1911.
NEUFELD, R., GREENFIELD, J. & RIEDER, B. 1986. Temperature, cyanide and phenolic
nitrification inhibition. Water Research, 20, 633-642.
70
MSc Thesis
OGBONNA, J. C., MASUI, H. & TANAKA, H. 1997. Sequential heterotrophic/autotrophic
cultivation–An efficient method of producing Chlorella biomass for health food and
animal feed. Journal of applied phycology, 9, 359-366.
OGBONNA, J. C. & TANAKA, H. 2000. Light requirement and photosynthetic cell
cultivation–Development of processes for efficient light utilization in photobioreactors.
Journal of applied phycology, 12, 207-218.
OLSON, R. J. 1981. Differential photoinhibition of marine nitrifying bacteria: a possible
mechanism for the formation of the primary nitrite maximum. J. mar. Res, 39, 227238.
OSWALD, W. J. 1988a. Large-scale algal culture systems (engineering aspects). Micro-algal
biotechnology, 357-394.
OSWALD, W. J. 1988b. Micro-algae and waste-water treatment.
OSWALD, W. J. & GOLUEKE, C. G. 1960. Biological transformation of solar energy. Adv.
Appl. Microbiol, 11, 223-242.
PARK, J. & CRAGGS, R. 2010. Wastewater treatment and algal production in high rate algal
ponds with carbon dioxide addition. Water Science and Technology, 61, 633.
PARK, J. & CRAGGS, R. 2011. Nutrient removal in wastewater treatment high rate algal
ponds with carbon dioxide addition. Water Science and Technology, 63, 1758.
PARK, J. B. K., CRAGGS, R. J. & SHILTON, A. N. 2011. Wastewater treatment high rate
algal ponds for biofuel production. Bioresource Technology, 102, 35-42.
POSTEN, C. 2009. Design principles of photo‐ bioreactors for cultivation of microalgae.
Engineering in Life Sciences, 9, 165-177.
RACAULT, Y. & BOUTIN, C. 2005. Waste stabilisation ponds in France: state of the art and
recent trends. Water Science & Technology, 51, 1-9.
RASCHKE, R. L. 1993. Diatom (Bacillariophyta) community response to phosphorus in the
Everglades National Park, USA. Phycologia, 32, 48-58.
RAWAT, I., RANJITH KUMAR, R., MUTANDA, T. & BUX, F. 2011. Dual role of
microalgae: Phycoremediation of domestic wastewater and biomass production for
sustainable biofuels production. Applied Energy, 88, 3411-3424.
RICHMOND, A. 2000. Microalgal biotechnology at the turn of the millennium: A personal
view. Journal of applied phycology, 12, 441-451.
RICHMOND, A. 2008. Handbook of microalgal culture: biotechnology and applied
phycology, Wiley-Blackwell.
RICHMOND, A., CHENG-WU, Z. & ZARMI, Y. 2003. Efficient use of strong light for high
photosynthetic productivity: interrelationships between the optical path, the optimal
population density and cell-growth inhibition. Biomolecular Engineering, 20, 229-236.
RISGAARD-PETERSEN, N., NICOLAISEN, M. H., REVSBECH, N. P. & LOMSTEIN, B.
A. 2004. Competition between ammonia-oxidizing bacteria and benthic microalgae.
Applied and environmental microbiology, 70, 5528-5537.
SAFONOVA, E., KVITKO, K., IANKEVITCH, M., SURGKO, L., AFTI, I. & REISSER, W.
2004. Biotreatment of Industrial Wastewater by Selected Algal‐ Bacterial Consortia.
Engineering in life sciences, 4, 347-353.
SÁNCHEZ MIRÓN, A., CERÓN GARC A, .-C., ARC A CA ACHO, F., MOLINA
GRIMA, E. & CHISTI, Y. 2002. Growth and biochemical characterization of
microalgal biomass produced in bubble column and airlift photobioreactors: studies in
fed-batch culture. Enzyme and microbial technology, 31, 1015-1023.
SCHUMACHER, G., BLUME, T. & SEKOULOV, I. 2003. Bacteria reduction and nutrient
removal in small wastewater treatment plants by an algal biofilm. Water science and
technology: a journal of the International Association on Water Pollution Research,
47, 195.
Thanh Tung Nguyen
71
SILVA, C., CUERVO-LÓPEZ, F., GÓMEZ, J. & TEXIER, A.-C. 2011. Nitrite effect on
ammonium and nitrite oxidizing processes in a nitrifying sludge. World Journal of
Microbiology and Biotechnology, 27, 1241-1245.
SINHA, B. & ANNACHHATRE, A. P. 2007. Partial nitrification—operational parameters
and microorganisms involved. Reviews in environmental science and biotechnology, 6,
285-313.
SOEDER, C., HEGEWALD, E., FIOLITAKIS, E. & GROBBELAAR, J. 1985. Temperature
dependence of population growth in a green microalga: thermodynamic characteristics
of growth intensity and the influence of cell concentration. Zeitschrift fur
Naturforschung. Section C, Biosciences, 40, 227-233.
STENSTROM, M. K. & PODUSKA, R. A. 1980. The effect of dissolved oxygen
concentration on nitrification. Water Research, 14, 643-649.
SU, Y., MENNERICH, A. & URBAN, B. 2011. Municipal wastewater treatment and biomass
accumulation with a wastewater-born and settleable algal-bacterial culture. water
research, 45, 3351-3358.
SU, Y., MENNERICH, A. & URBAN, B. 2012. Synergistic cooperation between wastewaterborn algae and activated sludge for wastewater treatment: Influence of algae and
sludge inoculation ratios. Bioresource Technology, 105, 67-73.
SYRETT, P. 1981. Nitrogen metabolism of microalgae. Can. Bull. Fish. Aquat. Sci, 210, 182210.
TCHOBANOGLOUS, G., BURTON, F. L. & STENSEL, H. D. 2003. Wastewater
Engineering Treatment and Reuse, 4th Edn. Metcalf and Eddy. Inc. McGraw-Hill
Company.
TOKUTOMI, T. 2004. Operation of a nitrite-type airlift reactor at low DO concentration.
Water Science & Technology, 49, 81-88.
TORZILLO, G., PUSHPARAJ, B., MASOJIDEK, J. & VONSHAK, A. 2003. Biological
constraints in algal biotechnology. Biotechnology and Bioprocess Engineering, 8, 338348.
TREDICI, M. R. 2002. Bioreactors, photo. Encyclopedia of Bioprocess Technology.
VALI ORE, J. ., OSTO SKI, P. A., WAREHA , D. . & O’SULLIVAN, A. D. 2012.
Effects of hydraulic and solids retention times on productivity and settleability of
microbial (microalgal-bacterial) biomass grown on primary treated wastewater as a
biofuel feedstock. Water Research, 46, 2957-2964.
VILLAVERDE, S., GARCIA-ENCINA, P. & FDZ-POLANCO, F. 1997. Influence of pH
over nitrifying biofilm activity in submerged biofilters. Water Research, 31, 11801186.
WANG, L., LI, Y., CHEN, P., MIN, M., CHEN, Y., ZHU, J. & RUAN, R. R. 2010a.
Anaerobic digested dairy manure as a nutrient supplement for cultivation of oil-rich
green microalgae< i> Chlorella</i> sp. Bioresource Technology, 101, 2623-2628.
WANG, L., MIN, M., LI, Y., CHEN, P., CHEN, Y., LIU, Y., WANG, Y. & RUAN, R. 2010b.
Cultivation of green algae Chlorella sp. in different wastewaters from municipal
wastewater treatment plant. Applied biochemistry and biotechnology, 162, 1174-1186.
WEISSMAN, J. C., GOEBEL, R. P. & BENEMANN, J. R. 2004. Photobioreactor design:
mixing, carbon utilization, and oxygen accumulation. Biotechnology and
bioengineering, 31, 336-344.
YUAN, X., KUMAR, A., SAHU, A. K. & ERGAS, S. J. 2011. Impact of ammonia
concentration on Spirulina platensis growth in an airlift photobioreactor. Bioresource
Technology, 102, 3234-3239.
72
MSc Thesis
YUN, H. J. & KIM, D. J. 2003. Nitrite accumulation characteristics of high strength ammonia
wastewater in an autotrophic nitrifying biofilm reactor. Journal of Chemical
Technology and Biotechnology, 78, 377-383.
ZHANG, J.-Z. & FISCHER, C. J. 2006. A simplified resorcinol method for direct
spectrophotometric determination of nitrate in seawater. Marine chemistry, 99, 220226.
ZHANG, L., YANG, J. & FURUKAWA, K. 2010. Stable and high-rate nitrogen removal
from reject water by partial nitrification and subsequent anammox. Journal of
bioscience and bioengineering, 110, 441-448.
ZHANG, Y., NOORI, J. S. & ANGELIDAKI, I. 2011. Simultaneous organic carbon,
nutrients removal and energy production in a photomicrobial fuel cell (PFC). Energy
& Environmental Science, 4, 4340-4346.
ZHOU, Y., OEHMEN, A., LIM, M., VADIVELU, V. & NG, W. J. 2011. The role of nitrite
and free nitrous acid (FNA) in wastewater treatment plants. Water Research, 45, 46724682.
ZIMMO, O., VAN DER STEEN, N. & GIJZEN, H. 2004. Quantification of nitrification and
denitrification rates in algae and duckweed based wastewater treatment systems.
Environmental technology, 25, 273-282.
Thanh Tung Nguyen
73
8 Appendixes
74
MSc Thesis
8.1 Appendix 1 – Parameters of airlift reactors in Experiment 1
Table 8.1: Characteristics of feed and effluents of continuous cultures in Experiment 1
Day
NH4+-N Inlet (mg/L)
Algae
pH
Mixure
Algae
DO (mg/L)
Mixure
Algae
Alkalinity
(mgCaCO3/L)
Mixure
Algae
TSS (mg/L)
Mixure
Algae
VSS (mg/L)
Mixure
Biomass
Algae
productivity
Mixure
(mgVSS/L/d)
Areal
Algae
productivity
Mixture
(gVSS/m2/d)
Algae
Chl a (mg/L)
Mixure
Algae
NH4+-N (mg/l)
Mixure
Algae
NO2--N (mg/l)
Mixure
Algae
NO3--N (mg/l)
Mixure
Thanh Tung Nguyen
1
(05.11)
234.9
8.9
8.4
8.7
8.7
553
460
2198
2360
31
19
0.1
0.1
1
18
2
(05.12)
234.9
8.9
8.3
8.7
8.7
579
370
2161
2413
24
1.9
0
0.2
9
45
3
(05.13)
234.9
9.1
8.7
8.9
8.7
636
442
2141
2344
2030
2160
62.6
53.5
19
1
1.4
1.7
9
43
4
(05.14)
165.5
8.9
8.6
8.5
8.4
668
523
2125
2325
2016
2138
120.4
5
(05.15)
165.5
8.9
8.6
8.4
8.1
569
509
2075
2153
1958
1990
72.5
6
(05.16)
265.6
8.8
8.7
7.6
7.4
598
474
1928
2083
1826
1902
-10.3
7
(05.17)
250.2
8.8
8.7
8.0
7.8
574
509
1964
2032
1854
1878
151.6
8
(05.18)
250.4
8.9
8.9
8.2
8.1
588
458
1816
1875
1712
1738
-27.9
9
(05.19)
238.2
8.8
8.9
8.1
8.2
536
456
1725
1917
1604
1716
-1.1
10
(05.20)
238.2
8.7
9.0
8.5
8.6
604
472
1622
1780
1464
1566
-42.4
11
(05.21)
244.2
8.9
9.2
8.5
8.5
582
437
1626
1842
1452
1630
84.8
12
(05.22)
241.3
8.9
9.2
8.5
8.4
582
512
1523
1698
1388
1520
28.5
13
(05.23)
241.3
8.9
9.2
7.8
7.8
515
550
1670
1956
1494
1752
205.6
14
(05.24)
241.3
9.2
9.5
8.1
7.4
588
531
1703
2012
1484
1770
88.9
15
(05.25)
219.2
9.2
9.6
8.1
8.3
563
558
1639
2060
1436
1774
47.7
16
(05.28)
219.2
17
(05.29)
219.2
9.4
9.2
8.0
7.9
625
738
2123
2181
1870
1912
120.5
-15.3
38.8
101.2
-24.1
92.4
-45.6
172.7
-8.7
348.8
136.0
122.3
283.0
233.2
2.3
1.4
-0.2
2.8
-0.5
0.0
-0.8
1.6
0.5
3.9
1.7
0.9
2.3
-0.3
0.7
1.9
-0.5
1.7
-0.9
3.2
-0.2
6.5
2.6
2.3
5.3
4.4
14
1.6
0.8
1.0
9
44
59.3
48.2
3.7
0.7
27
0.2
8
49
2.8
0.7
42
0
9
54
50.2
34.4
1.1
0.5
50
0
8
66
0.9
0.9
55
0
9
72
42.1
28.6
1.2
0.8
62
0
10
81
0.6
1.6
82
29.6
28.8
0.6
0.9
74
0.6
0.6
80
28.6
28.8
0.6
0.6
79
0.9
0.6
74
0
10
86
75
34.6
27.3
0.6
0.6
71
0
9
87
0
0
10
89
0
9
89
0
9
88
31.2
0.8
0
8
86
1.0
0
80
82
Day
NH4+-N Inlet (mg/L)
Algae
pH
Mixure
Algae
DO (mg/L)
Mixure
Algae
Alkalinity
(mgCaCO3/L)
Mixure
Algae
TSS (mg/L)
Mixure
Algae
VSS (mg/L)
Mixure
Biomass
Algae
productivity
Mixure
(mgVSS/L/d)
Areal
Algae
productivity
Mixture
(gVSS/m2/d)
Algae
Chl a (mg/L)
Mixure
Algae
NH4+-N (mg/l)
Mixure
Algae
NO2--N (mg/l)
Mixure
Algae
NO3--N (mg/l)
Mixure
76
18
(05.30)
243.4
19
(05.31)
243.4
20
(06.01)
240.3
21
(06.02)
238.6
22
(06.03)
215.8
23
(06.04)
219.4
24
(06.05)
225.2
25
(06.06)
229.8
26
(06.07)
229.8
27
(06.08)
229.8
28
(06.09)
203.3
29
(06.10)
230.1
9.4
9.2
9.3
9.2
9.3
9.1
9.0
8.9
8.9
8.7
8.6
8.4
8.0
8.0
8.1
7.9
7.7
7.5
7.8
7.9
7.2
7.4
7.3
7.2
755
814
755
738
679
765
798
895
836
900
912
921
2245
2176
2131
1996
2154
2066
1969
1865
1797
1688
1588
1386
1950
1876
1870
1872
1884
1792
1620
1506
1452
1384
1290
1106
233.0
113.6
181.0
189.2
200.4
87.2
-10.0
36.6
91.2
70.4
35.0
-73.4
4.4
2.1
3.4
3.5
3.8
1.6
-0.2
0.7
1.7
1.3
0.7
-1.4
29
30.6
27.1
23.6
16.4
13.4
0.9
1.1
0.8
1.3
0.9
1.1
1.1
2.2
1.5
0.9
1.2
1.1
0
1.4
0
7.2
3.0
1.1
0
4.6
12
14
15
18
83
81
88
87
89
85
91
89
80
83
81
76
MSc Thesis
8.2 Appendix 2 – Parameters of airlift reactors in Experiment 2
Table 8.2: Characteristics of feed and effluents of semi-continuous cultures in Experiment 2
Day
NH4+-N Inlet (mg/L)
Nitrifier
pH
Algae
Mixure
Nitrifier
DO (mg/L)
Algae
Mixure
Feed
Nitrifier
Alkalinity
(mgCaCO3/L)
Algae
Mixure
Nitrifier
TSS (mg/L)
Algae
Mixure
Nitrifier
VSS (mg/L)
Algae
Mixure
Nitrifier
Biomass
productivity
Algae
(mgVSS/L/d)
Mixure
Areal
Algae
productivity
Mixture
(gVSS/m2/d)
Algae
Chl a (mg/L)
Mixure
Nitrifier
NH4+-N (mg/l)
Algae
Mixure
Nitrifier
NO2--N (mg/l)
Algae
Mixure
Nitrifier
NO3--N (mg/l)
Algae
Mixure
Thanh Tung Nguyen
1
(06.23)
244.5
2
(06.24)
244.5
987
926
1020
1166
1156
1178
996
996
1034
0
112
104
3
(06.25)
243.1
7.68
7.29
7.72
5.07
8.14
7.14
2580
398.9
161.7
129.4
1308
1194
1347
1120
1140
1246
236
258
337
5.73
4
(06.26)
241.9
7.60
7.55
7.81
5.38
8.90
7.02
2587
328.8
161.7
150.9
1450
1166
1500
1240
1022
1426
244
-16
323
-0.4
5
(06.27)
241.9
7.63
8.02
7.92
5.67
8.74
7.71
2587
361.1
194
172.5
1182
1160
1466
958
1104
1360
-186
192
70
4.28
6
(06.28)
221.2
7.78
8.14
7.98
6.19
8.16
7.80
2560
345.0
172.5
172.5
1234
1218
1334
960
1154
1292
98
165
61.2
3.68
7
(06.29)
221.5
7.76
8.06
7.87
6.13
8.18
7.58
2561
377.3
183.3
172.5
1146
1226
1444
920
1136
1340
52
95.6
182
2.12
8
(06.30)
220.7
7.74
8.03
7.74
6.01
8.02
6.96
2560
442
172.5
161.7
1120
1312
1454
890
1218
1356
59
204
152
4.53
9
(07.01)
219.5
7.56
8.07
7.82
5.34
8.23
7.41
2480
409.6
194
183.3
1164
1302
1484
936
1232
1392
140
137
175
3.05
10
(07.02)
235.5
7.06
7.98
7.88
6.41
8.87
8.04
2868
463.5
247.9
172.5
1022
1484
1582
876
1364
1494
27.6
268
251
5.96
11
(07.03)
234.0
7.48
8.12
8.07
6.65
8.56
8.24
2860
566
312.6
204.8
1118
1618
1648
932
1434
1548
149
213
209
4.74
12
(07.04)
230.0
7.60
8.28
8.25
6.78
8.76
8.35
2855
625.2
495.9
366.5
1272
1690
1789
996
1490
1626
164
205
241
4.56
13
(07.05
219.1
7.82
8.35
8.29
6.23
8.69
8.42
2695
625.2
517.4
409.6
1158
1850
1980
950
1558
1736
49
224
284
4.97
14
(07.06)
218.4
7.78
8.46
8.37
5.96
8.65
8.58
2650
700.7
566
648.8
1186
1932
2066
904
1616
1800
44.4
220
244
4.88
15
(07.07)
228.7
7.75
8.58
8.42
4.30
8.71
8.64
2911
700.7
619.9
808.5
1136
2024
2058
884
1678
1802
68.4
230
182
5.11
16
(07.08)
227.8
7.82
9.05
8.71
2.10
8.75
8.70
2890
1132
916.3
733
1002
2046
2130
792
1696
1844
-13
188
226
4.17
17
(07.09)
245.4
7.94
9.11
9.00
1.98
9.01
8.72
2908
1149
1078
916.3
1078
2072
2060
846
1706
1788
139
181
123
4.01
7.48
7.17
1.56
1.36
4.04
3.37
3.89
5.59
4.64
5.35
6.3
5.42
4.05
5.03
2.73
0
25.1
34.7
0
122.5
84.6
104.5
10.3
31
27.7
30.1
0
35.6
29.1
0
128.9
101
100.3
13.1
45
0
19.3
14
0
137.5
96.9
106.8
12.5
60
28.6
27.3
0
10.4
10.6
0
151.2
112
127.3
17.4
53.8
0
12.6
11.2
0
135.4
119
141
18.5
43
28.3
27.5
0
13.2
10.6
0
147.8
108
138.1
19.7
56.1
0
1.8
3.2
0
156.2
107
162.6
19.2
68.4
29.1
28.5
0
0
0
0
153.5
104
166
18.9
71.2
0
0
0
0
158
103
172.2
17.3
68.9
27.8
31.7
0
0
0
0
161.3
98.2
172.3
18.6
73
0
0
0
0
164.7
97.2
173.2
16
55.5
31.1
33.6
0
0
0
0
163.3
99.2
197.7
15.4
69.2
10.3
0
0
2.1
163.3
95.6
182.3
16.5
67.5
30.7
31.2
12.2
0
0
2.2
164.3
92.6
173.4
14.8
69.3
18.4
0
0
2.5
165.3
92.5
179.1
15.5
68
77
Day
NH4+-N Inlet (mg/L)
Nitrifier
pH
Algae
Mixure
Nitrifier
DO (mg/L)
Algae
Mixure
Feed
Nitrifier
Alkalinity
(mgCaCO3/L)
Algae
Mixure
Nitrifier
TSS (mg/L)
Algae
Mixure
Nitrifier
VSS (mg/L)
Algae
Mixure
Nitrifier
Biomass
productivity
Algae
(mgVSS/L/d)
Mixure
Areal
Algae
productivity
Mixture
2
(gVSS/m /d)
Algae
Chl a (mg/L)
Mixure
Nitrifier
NH4+-N (mg/l)
Algae
Mixure
Nitrifier
NO2--N (mg/l)
Algae
Mixure
Nitrifier
NO3--N (mg/l)
Algae
Mixure
78
18
(07.10)
235.5
7.71
9.12
8.88
1.23
9.15
7.64
2894
1294
970.2
957
1098
2070
2158
852
1718
1828
91.2
184
223
4.08
19
(07.11)
232.2
8.04
9.15
8.30
1.30
10.49
6.58
2903
1275
946
1046
1020
2108
2178
808
1780
1908
36.8
240
271
5.33
20
(07.12)
275.6
8.12
9.20
8.23
0.98
12.53
4.86
2980
1290
890
1127
862
2050
2050
730
1780
1814
-5
178
87.4
3.96
21
(07.13)
274.8
8.02
9.19
8.07
0.79
13.75
3.25
3056
1336
1073
1269
864
2272
2100
742
1886
1802
86.2
295
168
6.55
22
(07.14)
317.1
7.84
9.38
7.95
0.65
14.79
2.27
3015
1334
1085
1257
842
2204
1870
600
1816
1684
-82
112
50.4
2.48
23
(07.15)
599.9
7.90
8.69
7.91
0.73
14.93
1.86
3264
1386
1183
1296
882
2076
1892
700
1790
1728
170
153
217
3.4
24
(07.16)
596.3
7.71
8.56
7.54
0.52
13.20
0.85
3450
1178
754.6
916.3
902
2200
1782
708
1902
1542
78.8
302
-32
6.72
25
(07.17)
590.0
7.78
6.94
7.26
0.56
9.09
0.72
3436
1009
256
592.9
924
2318
1970
750
2130
1756
117
441
390
9.8
26
(07.18)
536.6
7.67
6.66
7.21
0.43
8.10
1.38
4204
1062
256
431.2
856
2118
1882
664
2002
1692
-20
72.2
105
1.6
27
(07.19)
535.0
7.74
6.44
7.01
0.55
7.85
1.27
4104
1016
404.3
485.1
890
2088
1818
690
1980
1674
95
176
149
3.91
28
(07.20)
530.0
7.78
6.58
6.87
0.59
7.52
1.24
4040
1041
323.4
425.8
916
2058
1940
710
1938
1720
91
152
218
3.37
4.95
6.02
1.94
3.74
1.12
4.82
-0.7
8.66
2.34
3.32
4.84
38.9
0
0
2.4
174.3
95.7
158.6
12
73.2
35.5
24.5
47.6
0
0
2
178.4
95
150
12.7
85.3
48.5
0
0
2.1
180.2
98.3
159.4
12.1
92.2
38.5
16.2
49.7
0
0
2.8
178.7
99.6
150.6
14.1
93.1
61.1
0
35.6
8.2
200.1
101
155.8
16.5
93.4
38.7
13.8
77.4
0
45.1
12.8
212.5
119
156.7
14.4
83.9
98.3
4
48.8
16.9
227.7
138
147.1
22.8
86.8
41
21.7
108.7
6
39.8
21.3
225.2
160
146.4
34.1
89.8
38.3
23.3
127.5
9
36.7
24.3
221.9
162
141
46.3
95.2
35.3
20.3
147.3
6
31.6
28.9
216.8
150
139.8
50.1
91
33.5
27.7
26.3
0
0
3
170.7
95
170.7
12.3
70.9
MSc Thesis
8.3 Appendix 3 – Soluble COD profile in Experiment 2
Table 8.3: Soluble COD of feed and effluents of semi-continuous cultures in Experiment 2
Day 13
Type
Alg
Mix
Nit
Feed
(mg/L)
1470.5
1116.5
966.5
2945.0
Day 18
%
removal
50.07
62.09
67.18
Day 24
%
removal
55.05
60.12
61.48
(mg/L)
1348.5
1196.5
1155.5
3000.0
Day 27
%
removal
46.67
59.38
44.72
(mg/L)
1368.0
1042.0
1418.0
2565.0
(mg/L)
1656.5
1472.0
1200.0
2350.0
%
removal
29.51
37.36
48.94
Average
%
removal
45.32
54.74
55.58
8.4 Appendix 4 – Ammonium conversion rate in Experiment 2
Table 8.4: Nitrogen species when measuring ammonium conversion rates in algae and
mixture cultures during Experiment 2
+
-
-
+
-
-
Time
Alg-NH4 -N
Alg-NO2 -N
Alg-NO3 -N
Alg-DO
Mix-NH4 -N
Mix-NO2 -N
Mix-NO3 -N
Mix-DO
h
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
mg/L
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
18
20
22
24
26.7
26
22.6
18.4
13.7
10.6
5.7
2.9
1.7
0
0
0
0
0
0
0
0
0
0
0
0
161
162.6
168.7
167
170.4
173.3
179.5
180.8
183.7
181.2
182
183.5
181.7
178.2
180.6
182.4
173.3
175
177.9
180.8
180.2
11.2
13.2
13
12.7
13.5
13.2
13
15.7
11.8
13.6
12.4
12.1
12.9
12.2
13.3
12.7
10.6
11.8
13
12.4
12.5
0.72
0.78
0.81
0.79
0.82
0.88
1.74
4.9
9.44
14.84
15
15.33
16.29
16.62
16.46
15.76
15.48
15.15
14.96
14.99
14.78
29.7
28.7
27
24.7
22.8
21.5
19.2
15.8
14.9
13.9
11.8
10.1
8
5.7
5.2
4
2.9
0
0
0
0
87.5
88.6
90.6
91
89
91.7
94.4
94.3
95.2
98
99.8
103.2
101.9
103.3
102.3
99.6
101.6
97.7
101.7
101.6
98.3
67.8
68.9
76.7
70.8
77.4
75.8
74.3
83.6
81.5
83.3
74.6
85.6
77.3
82.7
80
86.8
88.6
89.7
83.7
83.9
92.2
0.41
0.48
0.53
0.45
0.47
0.43
0.46
0.49
0.47
0.48
0.49
0.51
0.56
0.59
0.71
0.87
1.26
2.66
3.02
3.25
2.27
Thanh Tung Nguyen
79
8.5 Appendix 5 – Dark test in Experiment 2 with glass bottle
Table 8.5: DO profile of algae reactor in Experiment 2 when it was put in the dark
Time(min) 0
5
10
15
20
25
30
35
DO(mg/L) 7.52
6.26
4.86
3.31
2.24
1.22
0.73
0.41
40
0.33
Table 8.6: Nitrogen species algae and mixture cultures during dark test
+
-
-
+
-
-
Time
Algae-NH4 -N
Algae-NO2 -N
Algae-NO3 -N
Mixture-NH4 -N
Mixture-NO2 -N
Mixture-NO3 -N
h
0
2
4
6
8
10
12
14
16
18
20
22
24
mg/L
66.96
67.39
68.57
66.61
65.96
67.78
68.04
65.83
64.53
64.01
65.71
64.28
64.80
mg/L
198.65
197.22
196.23
198.94
198.22
197.22
197.51
196.23
197.37
195.94
194.78
195.66
194.09
mg/L
52.49
56.34
53.08
56.34
44.20
46.57
51.60
51.60
56.63
48.35
49.40
46.57
53.08
mg/L
80.02
80.81
81.07
80.02
78.98
80.02
78.85
76.25
80.28
73.38
75.47
73.65
73.78
mg/L
146.05
144.34
141.63
142.49
140.63
142.49
140.06
139.78
137.21
141.35
141.50
140.78
141.35
mg/L
101.92
103.10
98.07
98.07
82.98
86.82
99.85
96.00
102.51
89.78
92.56
97.78
95.70
8.6 Appendix 6 – Settleability of biomasses in Experiment 2 with
glass bottle
Table 8.7: TSS unsettled and its percentage in reactors
Time
min
0
5
10
15
30
Algae
mgTSS/L % unsettled
2088
100
1654
79.21
1040
49.81
900
43.10
676
32.38
Mixture
mgTSS/L % unsettled
1818
100
1518
83.50
658
36.19
438
24.09
332
18.26
Nitrifier
mgTSS/L % unsettled
890
100
606
68.09
366
41.12
280
31.46
184
20.67
8.7 Appendix 7 – Transmittance of effluents in Experiment 2 with
glass bottle
80
MSc Thesis
Table 8.8: Light transmittance of feed and effluents on day 14,15, 16 in Experiment 2
nm
Feed,
day14,15,16
%
Alg,
day16
%
Mix,
day 16
%
Nit,
day 16
%
Alg,
day 15
%
Mix,
day 15
%
Nit,
day 15
%
Alg,
day 14
%
Mix,
day 14
%
Nit,
day 14
%
350
370
390
410
430
450
500
550
600
650
660
670
680
690
700
0.1
0.1
0.5
1.2
2.4
4
10.4
22
37.3
51.5
54.5
56.8
59.9
62.2
64.3
0.1
0.1
0.9
2.1
4.3
7.4
20.3
36.3
52.9
66.9
69.6
71.4
73.8
75.9
78.1
0.1
0.1
1.3
2.9
5.6
9.2
23.5
39.7
56.4
69.7
72.3
73.9
76.2
77.4
80.2
0.1
0.1
0.9
2.1
4.1
6.9
18.8
33.8
50.5
64.7
67.5
69.7
72.1
74.3
76.9
0.1
0.1
1.1
2.5
4.7
7.8
20.1
35.4
51.2
63.3
65.5
66.8
68.1
70.6
73.6
0.1
0.3
2.3
4.9
8.7
13.1
28.7
44.2
60.1
72.5
74.1
74.3
78.4
80.3
81.1
0.1
0.5
2.6
5.1
8.7
13.4
29.5
46.2
62.6
75
77.1
79.1
80.8
82.4
84.5
0.1
0.1
1.2
2.9
5.5
9
23
39.4
56.2
69.8
71.9
73.1
76
78.2
80.1
0.1
0.2
1.6
3.5
6.5
10.6
25
41.3
57.4
71.2
72.5
74.9
77.4
77.3
80.4
0.1
0.3
2
4.1
6.9
11.1
25.7
41.5
57.7
71.5
73.1
73.8
77.3
79.2
80.7
λ
8.8 Appendix 8 – Test for differences between algae and mixture
reactors during the first 18 days in Experiment 2
No differences in TSS(t-test, t = -1.0634, df = 31.463, p-value = 0.2957), VSS (t-test, t=1.8001, d.f.=32, P=0.08128) and Chlorophyll a (t-test, t=-0.9194, d.f.=12, P=0.376) could
be found between algae and mixture reactors from day 2 to day 18 during Experiment 2
with glass bottle.
# Read file: TSS difference between algae and mixture day 2-18 #
data<-read.table("Exp2_TSS.txt",header=TRUE)
algae<-data$TSS[data$reactor=="algae"]
mixture<-data$TSS[data$reactor=="mixture"]
# Test for normal distribution #
shapiro.test(algae)
shapiro.test(mixture)
reactor
W-value
P-value
algae
0.8422
0.008177
mixture
0.8963
0.05896
# QQ-plots, histograms and boxplot #
layout(matrix(c(1:6),nrow=3,ncol=2,byrow = TRUE))
qqnorm(algae,main="Normal Q-Q Plot of algae");qqline(algae)
qqnorm(mixture,main="Normal Q-Q Plot of mixture");qqline(mixture)
hist(algae,freq=FALSE)
class_f<-seq(min(algae),max(algae),length.out=100)
lines(class_f,dnorm(class_f,mean(algae),sd(algae)))
hist(mixture,freq=FALSE)
class_m<-seq(min(mixture),max(mixture),length.out=100)
lines(class_m,dnorm(class_m,mean(mixture),sd(mixture)))
Thanh Tung Nguyen
81
boxplot(TSS~reactor,data=data,ylab="TSS(mg/L)",xlab="reactor")
# Test for equal variances #
var.test(TSS~reactor,data=data)
# “P value is greater than α=0.05 means that variances are equal (F = 1.3005, num df = 16,
denom df = 16, p-value = 0.6054)#
# t-tests for equal and unequal variances #
t.test(TSS~reactor,data=data,var.equal=TRUE)
t.test(TSS~reactor,data=data,var.equal=FALSE)
# “P value is greater than α=0.05 means that no significant difference between VSS in two
reactors (t = -1.0634, df = 31.463, p-value = 0.2957) #
-1
0
1
2000
1600
2
-2
-1
0
1
Theoretical Quantiles
Theoretical Quantiles
Histogram of algae
Histogram of mixture
2
0.0000
0.0010
Density
0.0006
0.0000
Density
0.0012
-2
1200
1600
Sample Quantiles
2000
Normal Q-Q Plot of mixture
1200
Sample Quantiles
Normal Q-Q Plot of algae
1000
1200
1400
1600
1800
2000
2200
1000
1200
1400
1800
2000
2200
1600
2000
mixture
1200
TSS(mg/L)
algae
1600
algae
mixture
reactor
# Read file: VSS difference between algae and mixture day 2-18 #
algae<-data$VSS[data$reactor=="algae"]
mixture<-data$VSS[data$reactor=="mixture"]
# Test for normal distribution #
shapiro.test(algae)
shapiro.test(mixture)
reactor
W-value
P-value
82
MSc Thesis
algae
0.9002
0.06858
mixture
0.9239
0.1717
# QQ-plots, histograms and boxplot #
layout(matrix(c(1:6),nrow=3,ncol=2,byrow = TRUE))
qqnorm(algae,main="Normal Q-Q Plot of algae");qqline(algae)
qqnorm(mixture,main="Normal Q-Q Plot of mixture");qqline(mixture)
hist(algae,freq=FALSE)
class_f<-seq(min(algae),max(algae),length.out=100)
lines(class_f,dnorm(class_f,mean(algae),sd(algae)))
hist(mixture,freq=FALSE)
class_m<-seq(min(mixture),max(mixture),length.out=100)
lines(class_m,dnorm(class_m,mean(mixture),sd(mixture)))
boxplot(VSS~reactor,data=data,ylab="VSS(mg/L)",xlab="reactor")
# Test for equal variances #
var.test(VSS~reactor,data=data)
# “P value is greater than α=0.05 means that variances are equal (F = 1.1202, num df = 16,
denom df = 16, p-value = 0.8231)#
# t-tests for equal and unequal variances #
t.test(VSS~reactor,data=data,var.equal=TRUE)
t.test(VSS~reactor,data=data,var.equal=FALSE)
# “P value is greater than α=0.05 means that no significant difference between VSS in two
reactors (t = -1.8001, df = 31.897, p-value = 0.08131) #
Thanh Tung Nguyen
83
0
1
1600
2
-2
-1
0
1
Theoretical Quantiles
Histogram of algae
Histogram of mixture
1200
1400
1600
1000
1200
1400
1600
1800
mixture
1400
1000
VSS(mg/L)
1800
algae
1800
2
0.0000 0.0010 0.0020
Theoretical Quantiles
Density
1000
1200
Sample Quantiles
1400
-1
0.0000 0.0010 0.0020
-2
Density
Normal Q-Q Plot of mixture
1000
Sample Quantiles
Normal Q-Q Plot of algae
algae
mixture
reactor
# Read file: Experiment 2_Chl a difference between algae and mixture day 2-18 #
data<-read.table("Exp2_Chla1_18.txt",header=TRUE)
algae<-data$Chla[data$reactor=="algae"]
mixture<-data$Chla[data$reactor=="mixture"]
# Test for normal distribution #
shapiro.test(algae)
shapiro.test(mixture)
reactor
W-value
P-value
algae
0.8733
0.1983
mixture
0.9408
0.6456
# QQ-plots, histograms and boxplot #
layout(matrix(c(1:6),nrow=3,ncol=2,byrow = TRUE))
qqnorm(algae,main="Normal Q-Q Plot of algae");qqline(algae)
qqnorm(mixture,main="Normal Q-Q Plot of mixture");qqline(mixture)
hist(algae,freq=FALSE)
class_f<-seq(min(algae),max(algae),length.out=100)
lines(class_f,dnorm(class_f,mean(algae),sd(algae)))
hist(mixture,freq=FALSE)
84
MSc Thesis
class_m<-seq(min(mixture),max(mixture),length.out=100)
lines(class_m,dnorm(class_m,mean(mixture),sd(mixture)))
boxplot(Chla~reactor,data=data,ylab="Chla(mg/L)",xlab="reactor")
# Test for equal variances #
var.test(Chla~reactor,data=data)
# “P value is greater than α=0.05 means that variances are equal (F = 0.3348, num df = 6,
denom df = 6, p-value = 0.2087)#
# t-tests for equal and unequal variances #
t.test(Chla~reactor,data=data,var.equal=TRUE)
t.test(Chla~reactor,data=data,var.equal=FALSE)
# “P value is greater than α=0.05 means that no significant difference between Chlorophyll a
in two reactors (t = -0.9194, df = 9.612, p-value = 0.3804) #
Thanh Tung Nguyen
85
8.9 Appendix 9 – Test for differences of nitrogen species in algae
and mixture reactors during dark test
# Read file: NH4+-N, NO3--N, NO2--N in algae reactor between first half (12 hours) and
second half (the next 12 hours) cycles during dark test #
data<-read.table("DarkAlg.txt",header=TRUE)
NH4before<-data$before[data$specie=="NH4"]
NH4after<-data$after[data$specie=="NH4"]
NO2before<-data$before[data$specie=="NO2"]
NO2after<-data$after[data$specie=="NO2"]
NO3before<-data$before[data$specie=="NO3"]
NO3after<-data$after[data$specie=="NO3"]
# Test for normal distribution #
shapiro.test(NH4before)
shapiro.test(NH4after)
shapiro.test(NH4after-NH4before)
shapiro.test(NO2before)
shapiro.test(NO2after)
shapiro.test(NO2after-NO2before)
shapiro.test(NO3before)
shapiro.test(NO3after)
shapiro.test(NO3after-NO3before)
type
W-value
P-value
NH4before
0.9972
0.9995
NH4after
0.8754
0.2487
NH4after-NH4before
0.9562
0.7903
NO2before
0.9367
0.6327
NO2after
0.9384
0.6461
NO2after- NO2before
0.9401
0.6598
NO3before
0.8784
0.2618
NO3after
0.9398
0.6574
NO3after- NO3before
0.9652
0.8587
# Quantile plots, histogram and boxplot #
layout(matrix(c(1,2,3,4,5,6),3,2,byrow = TRUE))
qqnorm(NH4before,main="Normal Q-Q Plot of NH4-N before");qqline(NH4before)
qqnorm(NH4after,main="Normal Q-Q Plot of NH4-N after");qqline(NH4after)
qqnorm(NH4after-NH4before,main="Normal Q-Q Plot of NH4-Nafter-NH4-Nbefore");
qqline(NH4after-NH4before)
qqnorm(NO2before,main="Normal Q-Q Plot of NO2-N before");qqline(NO2before)
qqnorm(NO2after,main="Normal Q-Q Plot of NO2-N after");qqline(NO2after)
qqnorm(NO2after-NO2before,main="Normal Q-Q Plot of NO2-N after-before");
qqline(NO2after-NO2before)
qqnorm(NO3before,main="Normal Q-Q Plot of NO3-N before");qqline(NO3before)
qqnorm(NO3after,main="Normal Q-Q Plot of NO3-N after");qqline(NO3after)
qqnorm(NO3after-NO3before,main="Normal
Q-Q
Plot
of
NH4
after-before");
qqline(NO3after-NO3before)
hist(NH4after-NH4before,freq=FALSE)
class<-seq(from=min(NH4after-NH4before),to=max(NH4after-NH4before),length.out=100)
86
MSc Thesis
lines(class,dnorm(class,mean(NH4after-NH4before),sd(NH4after-NH4before)))
hist(NO2after-NO2before,freq=FALSE)
class<-seq(from=min(NO2after-NO2before),to=max(NO2after-NO2before),length.out=100)
lines(class,dnorm(class,mean(NO2after-NO2before),sd(NO2after-NO2before)))
hist(NO3after-NO3before,freq=FALSE)
class<-seq(from=min(NO3after-NO3before),to=max(NO3after-NO3before),length.out=100)
lines(class,dnorm(class,mean(NO3after-NO3before),sd(NO3after-NO3before)))
# Boxplot #
boxplot(data$before,data$after)
# t-test for paired (=dependent) samples #
t.test(NH4after,NH4before,paired=TRUE)
# “P value is greater than α=0.05 means that no significant difference between NH4before and
NH4after in algae reactor during dark test (t = -2.2586, df = 5, p-value = 0.07348) #
t.test(NO2after,NO2before,paired=TRUE)
# “P value is greater than α=0.05 means that no significant difference between NO2before and
NO2after in algae reactor during dark test (t = -2.2439, df = 5, p-value = 0.07485) #
t.test(NO3after,NO3before,paired=TRUE)
# “P value is greater than α=0.05 means that no significant difference between NO2before and
NO2after in algae reactor during dark test (t = -0.4012, df = 5, p-value = 0.7048) #
# Read file: NH4+-N, NO3--N, NO2--N in mixture reactor between first half (12 hours)
and second half (the next 12 hours) cycles during dark test #
data<-read.table("DarkMix.txt",header=TRUE)
NH4before<-data$before[data$specie=="NH4"]
NH4after<-data$after[data$specie=="NH4"]
NO2before<-data$before[data$specie=="NO2"]
NO2after<-data$after[data$specie=="NO2"]
NO3before<-data$before[data$specie=="NO3"]
NO3after<-data$after[data$specie=="NO3"]
# Test for normal distribution #
shapiro.test(NH4before)
shapiro.test(NH4after)
shapiro.test(NH4after-NH4before)
shapiro.test(NO2before)
shapiro.test(NO2after)
shapiro.test(NO2after-NO2before)
shapiro.test(NO3before)
shapiro.test(NO3after)
shapiro.test(NO3after-NO3before)
type
W-value
P-value
NH4before
0.9073
0.4187
NH4after
0.9222
0.5216
NH4after-NH4before
0.9021
0.3866
NO2before
0.9443
0.6937
NO2after
0.8557
0.1748
NO2after- NO2before
0.9449
0.6989
NO3before
0.8586
0.1843
NO3after
0.9823
0.9623
NO3after- NO3before
0.8997
0.3724
Thanh Tung Nguyen
87
# Quantile plots, histogram and boxplot #
layout(matrix(c(1,2,3,4,5,6),3,2,byrow = TRUE))
qqnorm(NH4before,main="Normal Q-Q Plot of NH4-N before");qqline(NH4before)
qqnorm(NH4after,main="Normal Q-Q Plot of NH4-N after");qqline(NH4after)
qqnorm(NH4after-NH4before,main="Normal Q-Q Plot of NH4-Nafter-NH4-Nbefore");
qqline(NH4after-NH4before)
qqnorm(NO2before,main="Normal Q-Q Plot of NO2-N before");qqline(NO2before)
qqnorm(NO2after,main="Normal Q-Q Plot of NO2-N after");qqline(NO2after)
qqnorm(NO2after-NO2before,main="Normal Q-Q Plot of NO2-N after-before");
qqline(NO2after-NO2before)
qqnorm(NO3before,main="Normal Q-Q Plot of NO3-N before");qqline(NO3before)
qqnorm(NO3after,main="Normal Q-Q Plot of NO3-N after");qqline(NO3after)
qqnorm(NO3after-NO3before,main="Normal
Q-Q
Plot
of
NH4
after-before");
qqline(NO3after-NO3before)
hist(NH4after-NH4before,freq=FALSE)
class<-seq(from=min(NH4after-NH4before),to=max(NH4after-NH4before),length.out=100)
lines(class,dnorm(class,mean(NH4after-NH4before),sd(NH4after-NH4before)))
hist(NO2after-NO2before,freq=FALSE)
class<-seq(from=min(NO2after-NO2before),to=max(NO2after-NO2before),length.out=100)
lines(class,dnorm(class,mean(NO2after-NO2before),sd(NO2after-NO2before)))
hist(NO3after-NO3before,freq=FALSE)
class<-seq(from=min(NO3after-NO3before),to=max(NO3after-NO3before),length.out=100)
lines(class,dnorm(class,mean(NO3after-NO3before),sd(NO3after-NO3before)))
# Boxplot #
boxplot(data$before,data$after)
# t-test for paired (=dependent) samples #
t.test(NH4after,NH4before,paired=TRUE)
# “P value is greater than α=0.05 means that no significant difference between NH4before and
NH4after in mixture reactor during dark test (t = -3.7594, df = 5, p-value = 0.10317) #
t.test(NO2after,NO2before,paired=TRUE)
# “P value is greater than α=0.05 means that no significant difference between NO2before and
NO2after in mixture reactor during dark test (t = -2.6811, df = 5, p-value = 0.43765) #
t.test(NO3after,NO3before,paired=TRUE)
# “P value is greater than α=0.05 means that no significant difference between NO2before and
NO2after in mixture reactor during dark test t = 0.3693, df = 5, p-value = 0.7271) #
88
MSc Thesis
Normal Q-Q Plot of NH4-N after
67
66
64
-1.0
-0.5
0.0
0.5
1.0
-1.0
-0.5
0.0
0.5
1.0
Normal Q-Q Plot of NH4-Nafter-NH4-Nbefore
Histogram of NH4after - NH4before
Density
0
-1.0
-0.5
0.0
0.5
0.00 0.05 0.10 0.15
Theoretical Quantiles
1
Theoretical Quantiles
-4 -3 -2 -1
Sample Quantiles
65
Sample Quantiles
68.0
67.0
66.0
Sample Quantiles
68
Normal Q-Q Plot of NH4-N before
1.0
-5
-4
-2
-1
0
1
2
NH4after - NH4before
50
100
150
200
Theoretical Quantiles
-3
1
2
8.10 Appendix 10 – Test for differences of COD removal among
three reactors in Experiment 2
rm(list=ls(all.names=TRUE))
#Read file#
data<-read.table("scod.txt",header=TRUE)
data
reactor code COD reactor code
alg
1
50.1
mix
2
alg
1
55.1
mix
2
alg
1
46.7
mix
2
alg
1
29.5
mix
2
COD
62.1
60.1
59.4
37.4
reactor
nit
nit
nit
nit
code
3
3
3
3
COD
67.2
61.5
44.7
48.9
algae<-data$cod[data$code==1]
mixture<-data$cod[data$code==2]
Thanh Tung Nguyen
89
nitrifier<-data$cod[data$code==3]
# Test for normal distribution #
shapiro.test(algae)
shapiro.test(mixture)
shapiro.test(nitrifier)
reactor code
W-value
Algae
1
0.8897
Mixture
2
0.7208
Nitrifier
3
0.9257
P-value
0.3815
0.01977
0.5695
# QQ-plots, histograms and boxplot #
layout(matrix(c(1:6),nrow=3,ncol=2,byrow = TRUE))
qqnorm(algae,main="Normal Q-Q Plot of algae");qqline(algae)
qqnorm(mixture,main="Normal Q-Q Plot of mixture");qqline(mixture)
qqnorm(mixture,main="Normal Q-Q Plot of nitrifier");qqline(nitrifier)
hist(algae,freq=FALSE)
class_f<-seq(min(algae),max(algae),length.out=100)
hist(mixture,freq=FALSE)
class_m<-seq(min(mixture),max(mixture),length.out=100)
lines(class_m,dnorm(class_m,mean(mixture),sd(mixture)))
hist(nitrifier,freq=FALSE)
class_m<-seq(min(nitrifier),max(nitrifier),length.out=100)
lines(class_m,dnorm(class_m,mean(nitrifier),sd(nitrifier)))
boxplot(algae,mixture,nitrifier,ylab="COD(mg/L)")
# Test for homogeneity of variances #
bartlett.test(cod~code,data=data)
# “P value is greater than α=0.05 means that variances are homogeneity (Bartlett's K-squared
= 0.0251, df = 2, p-value = 0.9875)#
# Statistic of 1 variable in 3 factors (cod in 3 independent reactors), then One-way
ANOVA test is adopted#
oneway.test(cod~code,data=data,var.equal=TRUE)
anova(lm(cod~code,data=data))
fit<-aov(cod~code,data=data)
summary(fit)
# “P value is greater than α=0.05 means that no significant difference among three reactors (F
= 1.0474, num df = 2, denom df = 9, p-value = 0.39) #
# Post-hoc comparisons between groups, find out which groups differ from other#
TukeyHSD(fit)
pairwise.t.test(data$cod,data$code,p.adj="bonferroni",pool.sd=TRUE)
Pair
1-2 (alg-mix)
2-3 (mix-nit)
3-1 (nit-alg)
P value
0.79
1.00
0.68
# “P value is greater than α=0.05 means that no significant difference among every two
reactors in terms of COD removal #
90
MSc Thesis
-0.5
0.5
40
55
Normal Q-Q Plot of mixture
1.0
-1.0
-0.5
0.0
0.5
Normal Q-Q Plot of nitrifier
Histogram of algae
0.0
0.5
1.0
20
30
40
50
Histogram of mixture
Histogram of nitrifier
50
60
70
40
45
50
55
60
65
70
nitrifier
30
50
mixture
0.00
Density
40
60
0.04
algae
0.04
Theoretical Quantiles
0.00
30
0.00
55
-0.5
1.0
0.04
Theoretical Quantiles
Density
Theoretical Quantiles
-1.0
Density
0.0
40
Sample Quantiles
-1.0
COD(mg/L)
Sample Quantiles
45
30
Sample Quantiles
Normal Q-Q Plot of algae
Alg
Thanh Tung Nguyen
Mix
Nit
91