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7-2013
Nutrient and Carbon-Dioxide Requirements for
Large-Scale Microalgae Biofuel Production
Benjamin K. Shurtz
Utah State University
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NUTRIENT AND CARBON-DIOXIDE REQUIREMENTS FOR
LARGE-SCALE MICROALGAE BIOFUEL PRODUCTION
by
Benjamin K. Shurtz
A thesis submitted in partial fulfillment
of the requirements for the degree
of
MASTER OF SCIENCE
in
Mechanical Engineering
Approved:
________________________
Dr. Byard D. Wood
Co-Major Professor
________________________
Dr. Jason C. Quinn
Co-Major Professor
________________________
Dr. Rees R. Fullmer
Committee Member
________________________
Dr. Mark R. McLellan
Vice President for Research and
Dean of the School of Graduate Studies
UTAH STATE UNIVERSITY
Logan, Utah
2013
ii
Copyright © Benjamin K. Shurtz 2013
All Rights Reserved
iii
ABSTRACT
Nutrient and Carbon-Dioxide Requirements for
Large-Scale Microalgae Biofuel Production
by
Benjamin K. Shurtz, Master of Science
Utah State University, 2013
Co-Major Professors: Byard D. Wood, Jason C. Quinn
Department: Mechanical & Aerospace Engineering
Growing demand for energy worldwide has increased interest in the production of
renewable fuels, with microalgae representing a promising feedstock. The large-scale
feasibility of microalgae-based biofuels has previously been evaluated through
technoeconomic and environmental impact assessments, with limited work performed on
resource requirements. This study presents the use of a modular engineering system
process model, founded on literature, to evaluate the nutrient (nitrogen and phosphorus)
and carbon dioxide resource demand of five large-scale microalgae to biofuels production
systems. The baseline scenario, representative of a near-term large-scale production
system, includes process models of growth, dewater, lipid extraction, anaerobic digestion,
and biofuel conversion. Optimistic and conservative process scenarios are simulated to
represent practical best and worst case system performance to bound the total resource
demand of large-scale production. Baseline modeling results combined with current US
nutrient availability from fertilizer and wastewater are used to perform a scalability
iv
assessment. Results show nutrient requirements represent a major barrier to the
development of microalgae-based biofuels to meet the US Department of Energy 2030
renewable fuel goal of 30% of transportation fuel, or 60 billion gallons per year.
Specifically, results from the baseline and optimistic fuel production systems show
wastewater sources can provide sufficient nutrients to produce 3.8 billion gallons and 13
billion gallons of fuel per year, corresponding to 6% and 22% of the DOE goal,
respectively. High resource demand necessitates nutrient recovery from the lipidextracted algae, thus limiting its use as a value-added co-product. Discussion focuses on
system scalability, comparison of results to previous resource assessments, and model
sensitivity of nutrient and carbon dioxide resource requirements to system parameter
inputs.
(79 pages)
v
PUBLIC ABSTRACT
Nutrient and Carbon-Dioxide Requirements for
Large-Scale Microalgae Biofuel Production
by
Benjamin K. Shurtz
Ever-increasing fuel prices and a limited supply of oil worldwide are threatening
our economy and way of life in both the near and distant future. In order to reduce oil
dependence, the US Department of Energy (DOE) has established a goal that by the year
2030, 30% of the US transportation fuel will be renewable. The goal equates to
approximately 60 billion gallons per year of renewable fuel.
In an effort to reach the DOE renewable fuel goal, numerous types of plants are
being studied, whose oil can be harvested and refined to serve as replacement fuel.
Among the various plants under consideration for fuel production, microalgae have the
greatest known potential yield in gallons of oil per acre of land. This study evaluates the
nutrient requirements for large-scale microalgae production, sufficient to meet the DOE
fuel goal. Five different production scenarios are considered, and the nutrient
requirements for the baseline, or most expected scenario, are then compared to the
availability of various nutrient sources. Results suggest that nutrient demand represents a
major barrier to achievement of the DOE fuel production goal. A sensitivity analysis is
used to determine which aspects of the fuel production process have the greatest impact
on nutrient demand, which guides suggestions for future research.
vi
DEDICATION
To my wonderful wife, Kyla, who keeps me motivated to persevere.
vii
ACKNOWLEDGMENTS
The author gratefully acknowledges funding support from the Department of Energy,
DE-EE0003114, Utah Science and Technology Initiative for Economic Development,
and Utah State University.
Thank you to my supervisory committee - Byard D. Wood, Jason C. Quinn, and Rees R.
Fullmer - for their guidance throughout this research project. A special thanks to Byard
and Jason for their numerous hours dedicated to the accomplishment of this work as coadvisors.
Ben Shurtz
viii
CONTENTS
Page
ABSTRACT ....................................................................................................................... iii
PUBLIC ABSTRACT ........................................................................................................ v
DEDICATION ................................................................................................................... vi
ACKNOWLEDGMENTS ................................................................................................ vii
LIST OF TABLES .............................................................................................................. x
LIST OF FIGURES ........................................................................................................... xi
LIST OF ABBREVIATIONS ........................................................................................... xii
BACKGROUND AND LITERATURE REVIEW ............................................................ 1
Introduction ................................................................................................................... 1
Previous Assessments ................................................................................................... 1
Statement of Need ......................................................................................................... 3
METHODS: MODEL DEVELOPMENT AND VALIDATION ....................................... 5
Baseline, Optimistic, and Conservative Scenarios ....................................................... 6
Growth .................................................................................................................... 7
Dewatering .............................................................................................................. 8
Lipid Extraction and Conversion ............................................................................ 8
Recycling – Anaerobic Digestion ........................................................................... 9
ix
Alternative Process Scenarios ..................................................................................... 10
Lipid-extracted Algae (LEA) Co-product ............................................................. 10
Hydrothermal Liquefaction ................................................................................... 11
RESULTS AND DISCUSSION ....................................................................................... 14
Total Resource Requirements ..................................................................................... 14
Scalability Assessment Based on Nutrient Availability ............................................. 15
Comparison to Previous Nutrient Assessments .......................................................... 17
Sensitivity Analysis .................................................................................................... 19
CONCLUSION ................................................................................................................. 22
REFERENCES ................................................................................................................. 23
APPENDICES .................................................................................................................. 28
Appendix A. Research Proposal ................................................................................. 29
Appendix B. Detailed Process Block Diagrams ......................................................... 41
Appendix C. Model Calculations in Mathcad............................................................. 45
Appendix D. Complete Table of Model Parameter Values ........................................ 56
Appendix E. Calculations in Mathcad for Nutrient Source Availability .................... 59
x
LIST OF TABLES
Table
Page
1
Summary of system parameter values for the five microalgae to
biofuel production scenarios ......................................................................................13
2
Total biomass and corresponding nutrient requirements for the five
scenarios at the DOE 60 BGY fuel production level .................................................15
3
Scalability assessment: Comparison of seawater, wastewater, and
fertilizer availability and requirements to meet baseline system
nutrient needs at a biofuel production level of 60 BGY.............................................16
A.1 Planned dates for completion of the proposed milestones .........................................39
D.1 Complete summary of system parameter values used for model mass
balance calculations for the five production scenarios ...............................................58
xi
LIST OF FIGURES
Figure
Page
1
Systems process model of the microalgae to biofuel production
system, used to model five production scenarios. ........................................................5
2
Process model for baseline, optimistic, and conservative scenarios ............................6
3
Process model for the LEA co-product scenario ........................................................11
4
Process model for the HTL scenario ..........................................................................12
5
Results for (A) Nitrogen demand and (B) Phosphorus demand from
various literature sources ............................................................................................18
6
Model output sensitivity to system parameters for (A) Nitrogen
requirement, (B) Phosphorus requirement, and (C) CO2 requirement .......................20
A.1 Model of the process studied by Pate et al. (2011) ....................................................33
A.2 Model of the process to be studied .............................................................................36
B.1 Mass flow schematic of the complete microalgae to biofuel
production process ......................................................................................................42
B.2 Growth process input/output diagram ........................................................................43
B.3 Harvest process input/output diagram ........................................................................43
B.4 Recycling process input/output diagram ....................................................................43
B.5 Extraction process input/output diagram ....................................................................44
B.6 Transesterification and end products process input/output diagram ..........................44
xii
LIST OF ABBREVIATIONS
AD
Anaerobic Digestion
BGY
Billion gallons per year
CO2
Carbon dioxide
C:N:P
Carbon:Nitrogen:Phosphorus
DOE
Department of Energy
HTL
Hydrothermal liquefaction
LCA
Life-cycle assessment
LEA
Lipid-extracted algae
Mmt
Million metric tons
TEA
Techno-economic analysis
TGY
Trillion gallons per year
TS
Total solids
VS
Volatile solids
BACKGROUND AND LITERATURE REVIEW
Introduction
Increasing volatility in energy availability and cost, as well as the environmental
impacts of fossil fuel use, has led to an elevated interest in the domestic production of
renewable fuel. The US Department of Energy (DOE) has quantified the level of interest
in, and need for, renewable fuel through the establishment of a goal for the year 2030 that
30% of US transportation fuel will be renewable, which corresponds to approximately 60
billion gallons per year (BGY) [1]. A variety of second and third generation terrestrial
based feedstock options exist, but microalgae have several advantages, including higher
solar energy conversion efficiency, abilities to be grown on non-arable land, utilization of
salt or saline water, and integration with various low value nutrient (nitrogen and
phosphorus) sources including seawater, wastewater, and point source carbon dioxide
(CO2) [2,3]. Furthermore, microalgae are of interest in wastewater treatment for the
removal of nitrogen and phosphorus [4-6]. The many advantages make microalgae one of
the more promising feedstocks for production of renewable biofuels.
Previous Assessments
Researchers have evaluated the requirements, implications, and barriers to
microalgae biofuel production at the scale required for the DOE 2030 goal via
technoeconomic analysis (TEA), lifecycle assessment (LCA), and resource assessment,
with discussion regarding the utilization of wastewater as a nutrient source. The majority
2
of the TEAs and LCAs to date have developed system models in an effort to quantify the
energy and mass requirements, but limit results to financial costs and environmental
impact without considering nutrient requirements at large-scale [7-22]. Results from
TEAs have suggested that higher value co-products from the lipid extracted algae (LEA)
could improve the economics of biofuel production, but fail to address the impact of this
mass flow on the nutrient demand and availability [7,8]. Published LCAs contain results
focused on energy use and carbon emissions, but include only minimal analysis and
discussion regarding scale-up limitations based on nutrient requirements [9,10]. Resource
assessments have evaluated the impact and availability of land, water, CO2, and nutrient
requirements on the scalability of microalgae based biofuels with results derived from
simplified models. Studies have shown that land does not currently represent a resource
limitation, while water will be a limiting resource in some regions of the US [23-25].
Limitations on the economic transportation of CO2 combined with land availability
represent another potential barrier for scale-up [24]. Pate et al. [26] perform a high-level,
low-granularity evaluation of the water, land, CO2 and nutrient requirements for largescale production based on a mass balance assessment. Model details include only
microalgae cultivation and omit all other steps of the biofuel production system. Their
results provide the order of magnitude to expect for resource demand, but are inadequate
due to the limited model scope. Additional nutrient assessments make claims that
seawater and wastewater can potentially provide the nutrients necessary for large-scale
microalgae production, but provide no justification for these claims [27,28]. The
feasibility of integrating wastewater with microalgae cultivation as a source of water and
nutrients for microalgae growth, as well as effective wastewater treatment has been
3
established [2-3, 29-31], but such studies have not extended results to scalability. Despite
the knowledge gained through previous TEAs, LCAs, and resource assessments, a need
remains for thorough evaluation of the nutrient demand of large-scale biofuel production.
Statement of Need
Based on the current state of the field, there exists a need to quantify the nutrient
and CO2 requirements of the microalgae to biofuels system based on large-scale
representative models and evaluate the potential for various nutrient sources such as
fertilizer and wastewater to meet nutrient needs. This study assesses the large-scale
resource requirements through the development of a large-scale engineering process
model of the microalgae to biofuel production system. The baseline model,
representative of a near-term large-scale system, includes growth, dewatering, lipid
extraction, anaerobic digestion (AD), and biofuel conversion processes for a system
boundary that is consistent with the “strain to pump.” Optimistic and conservative
scenarios were developed to encompass the variability of process efficiencies within the
baseline system, account for advancements in processing technology, and provide bounds
for the resource requirements. Alternative production processes were assessed which
include a system based on the production of fuel with LEA co-products and the
utilization of an alternative extraction and nutrient recycling process through
hydrothermal liquefaction (HTL). Total nutrient and CO2 input requirements were
determined for an annual fuel production level of 60 billion gallons corresponding to the
DOE renewable fuel goal. Nutrient requirement results were compared to US resource
availability to evaluate system scalability. Discussion focuses on the results for resource
4
requirements for the five production scenarios, system scalability within the US, a
comparison of results to previous resource assessments, and sensitivity of resource results
to model inputs. This thesis reflects the completion of work proposed and approved, as
contained in Appendix A.
5
METHODS: MODEL DEVELOPMENT AND VALIDATION
A modular engineering system model, designed to track mass flow, was
developed and validated in order to quantify the resource requirements of a large-scale
microalgae to biofuels production system. The process model, shown in Fig. 1, includes
24 inputs used to characterize the production system with process losses and efficiencies
in each step included. More detailed mass flow diagrams for each step are contained in
Appendix B. The mass flow diagrams were used in the development of the calculations
for the mass flow model, which are contained in Appendix C. Five production scenarios
were modeled, with the following sections outlining the input values and justification for
each system parameter.
1
Systems process model of the microalgae to biofuel production system, used to model five production scenarios.
Fig. 1. Systems process model of the microalgae to biofuel production system, used to
model five production scenarios
6
Baseline, Optimistic, and Conservative Scenarios
The baseline scenario is intended to represent a near-term large-scale microalgae
to biofuels production system, Fig. 2. The model includes growth, dewater, lipid
extraction, biofuel conversion, and resource recycling through AD as the process steps
[7,9,11,32]. In order to establish bounds on resource demand, optimistic and conservative
scenarios are modeled. The optimistic scenario is comprised of the most efficient values
reported in literature for each process, typically at bench or lab-scale, and represents the
biofuel production system with the lowest possible resource consumption. The
conservative scenario represents current technology, and results in the highest possible
resource consumption. It is recognized that at commercial scale, processes will
inherently have a loss to the environment or a waste stream, so a loss is included in each
process and standardized due to uncertainty. The baseline process model loss was
assumed to be 1% and was cut in half for the optimistic case and doubled for the
conservative case, corresponding to 0.5% and 2%, respectively.
2
Process model for baseline, optimistic, and conservative scenarios
Fig. 2. Process model for baseline, optimistic, and conservative scenarios
7
Growth
The elemental composition of the microalgae is represented through the carbon :
nitrogen : phosphorus (C:N:P) ratio, where the Redfield ratio, 106:16:1, was used for the
baseline scenario [33]. The C:N:P ratio was assumed to be 166:20:1 for the optimistic
case, as presented by Smith [34], which increases the C:N ratio by approximately 20%
from the baseline. For the conservative case, 79:16:1 was selected to provide an equal but
opposite deviation from the baseline as the optimistic scenario. A biomass carbon content
of 50% was assumed for the baseline and unchanged for the optimistic and conservative
scenarios as it is a generally accepted value [9,26,35,36]. The hydrogen content of the
microalgae was selected to be 7.5%, 6%, and 9% for the baseline, optimistic, and
conservative values, respectively [37]. Published values for lipid content cover a wide
range. A lipid percentage of 30% was selected for the baseline as a mid-range, realistic
value to expect from an actual large-scale growth system, with 40% and 20%
representing the optimistic and conservative cases, respectively [7,9,26,38,39]. The
percent of lipids that are phospholipids was assumed to be 25% for the baseline, the
average value reported by Chen et al. [40], and was taken as 5% for the optimistic
scenario and 30% for the conservative scenario [9,40].
Nutrient absorption in the growth phase is assumed to be 99% for nitrogen uptake,
with the remainder being absorbed by competitive species or converted to nitrous oxide
[41,42]. Nitrogen uptake in the optimistic and conservative scenarios was assumed to be
100% and 98%, respectively [41]. The growth system assumes additional carbon will be
supplied to the growth medium in the form of gaseous CO2, and an associated utilization
efficiency of 85% was selected for the baseline [8,9]. Sheehan et al. [43] report 90% CO2
8
absorption into the growth medium, which was used for the optimistic scenario. The
conservative case was calculated based on the operation of outdoor cultivation systems
and laboratory experimental work supporting a utilization of 2% [38,44-46].
Dewatering
Multiple possible dewatering processes exist, and the baseline scenario of this
study assumed dissolved air flotation with a 10% biomass loss followed by centrifugation
with a 5% biomass loss, which were combined for an overall 14.5% biomass loss going
from 1% solids to 20% solids [9]. The lost biomass was assumed to be caught by a filter
and sent to the AD. The optimistic scenario considered only a centrifuge with a minimal
biomass loss of 2%, and the conservative scenario assumed a 20% loss, increased based
on low-energy harvesting technologies such as bio-flocculation.
Lipid Extraction and Conversion
The lipid extraction process contains two fundamental avenues for losses, first in
biomass oil that is un-extracted and second in the incomplete recovery of the extracted
lipid. For the baseline, the percent of biomass passing unaffected through the extraction
process was assumed to be 10%. It is assumed the oil that is extracted but not recovered
is 5% [9,43]. The unaffected biomass was assumed to be caught by a filter and sent to the
AD. Both of the extraction parameters were varied by ±50% of their baseline values for
the optimistic and conservative scenarios, which corresponds to 5% and 15% for the
biomass passing through, and 2% and 8% for un-extracted oil [9].
For the conversion of extracted lipid to biofuel, transesterification was modeled
with 99% of the lipid entering the process converted to biofuel [32,47]. It is important to
note here that it was assumed that ‘lipid content’ in the biomass is referring to the lipids
9
only, and not to triacylglycerides (TAG), which means that any non-fuel output of
transesterification is already accounted for. Thus, it was assumed that the fuel output
from transesterification is equal to the lipid input with a standard process loss to the
environment.
Recycling – Anaerobic Digestion
The baseline recycling process utilizes an AD for nutrient recovery and biogas
generation from the LEA and whole biomass recovered from the filtration of the centrate
in the dewatering step [32]. In describing the baseline performance of the AD, it was
assumed that 76% of the nitrogen is recovered which includes a 5% volatilization [9].
Phosphorus is assumed to be recovered with a 70% efficiency for the baseline scenario
[48]. For the optimistic and conservative cases, nitrogen and phosphorus recovery via AD
was assumed to be 85% and 60%, respectively [9,48].
The biogas production of the AD system was modeled based on the volatile solids
(VS), total solids (TS), methane yield per gram of volatile solids (g-VS), and percent
methane (CH4) content in the biogas. A characteristic of 0.94 g-VS g- TS-1 for whole
biomass, and 0.90 g-VS g- TS-1 for LEA was assumed and kept constant for all scenarios
[49,50]. The biogas yield from whole biomass was assumed to be 0.43 L-CH4 g-VS-1 at
67% CH4, 0.80 L-CH4 g-VS-1 at 62% CH4, and 0.25 L-CH4 g-VS-1 at 72% CH4 for the
baseline, optimistic, and conservative scenarios, respectively [50,51]. Similarly for the
digestion of LEA, biogas yield and composition for the baseline, optimistic, and
conservative scenarios were assumed to be 0.22 L-CH4 g-VS-1 at 59% CH4, 0.31 L-CH4
g-VS-1 at 49% CH4, and 0.14 L-CH4 g-VS-1 at 69% CH4, respectively [49,52,53]. It was
assumed that all biogas would be burned in an on-site combined heat and power unit and
10
the produced CO2 reused in the growth phase, with a 10% CO2 loss assumed for the
baseline scenario, then halved for the optimistic case and doubled for the conservative
case to account for losses in recovery and transport.
Alternative Process Scenarios
Two alternative process scenarios were modeled and evaluated: a) the use of LEA
as a co-product and b) hydrothermal processing of the biomass to bio-oil through HTL.
The LEA co-product scenario provides further insight to claims made by previous TEAs
in regard to the economic value of the LEA, and HTL represents a promising alternative
processing technology due to the ability to process wet biomass and recover nutrients.
For each of the alternative scenarios, the baseline assumptions are used unless otherwise
detailed.
Lipid-extracted Algae (LEA) Co-product
Analyses of the economics of biofuel production typically include discussion in
regard to higher-valued co-products derived from the LEA to improve the cost of biofuel
production [7,8]. While the generation of revenue supplemental to that of biofuel through
LEA co-products initially appears economically advantageous, its implications on
resource demand must be assessed. In this study, an alternative scenario was modeled,
considering the use of LEA as a co-product to evaluate the associated resource
requirements, Fig. 3. The LEA co-product scenario assumes all of the lipid-extracted
biomass is removed from the process stream as a product after extraction. It is assumed
the minimal whole biomass recovered in the filtration of the centrate in the dewater step
is added to the LEA product stream and not processed through anaerobic digestion.
11
3
Process model for the LEA co-product scenario
Fig. 3. Process model for the LEA co-product scenario
Hydrothermal Liquefaction
Hydrothermal liquefaction (HTL) is a technology currently being evaluated which
breaks down biological matter into lipid and elemental components, providing the
advantages of processing wet biomass and the conversion of proteins and carbohydrates
to bio-oil [54-57]. The outputs of this process are bio-oil (or biogas if a catalyst is used),
nitrogen and potassium-rich water, and precipitated phosphorus and sulfur [57]. The
modeled process replaces the extraction and the AD steps of the baseline production
system, Fig. 4.
The bio-oil yield from the process is assumed to be 50% based on recovery of the
30% algal lipid content and 20% additional bio-oil through the conversion of
carbohydrates and proteins [54-57]. The percent phospholipid content was set to 0%
because HTL strips the phosphates from any phospholipids in the processing of the
feedstock [56,57]. The biomass caught by the filter in the dewater process is sent into the
HTL process.
Similar to AD, the literature describing the experimental performance of HTL
report the amount of oil obtained in terms of output per input, which inherently accounts
12
4
Process model for the HTL scenario
Fig. 4. Process model for the HTL scenario
for any losses to the environment [54-57]. Therefore, as with AD in the previous
scenarios, HTL was modeled utilizing recovery efficiencies with no additional process
losses. Nutrient recovery was assumed to be 75% for nitrogen, with the remaining
nitrogen being contained in the bio-oil, and 95% for precipitated phosphorus recovery
and processing [57].
Table 1 provides a summary of the system parameters and assumptions for each
of the five microalgae to biofuel production scenarios used in this study, presented in the
order in which they were discussed previously. A more thorough table, with parameters
presented in the order which they appear in the model, is contained in Appendix D.
13
1
Summary of system parameter values for the five microalgae to biofuel production scenarios
Table 1
Summary of system parameter values for the five microalgae to biofuel production
scenarios
Parameter
Process Loss (%)
C:N:P Ratio
Hydrogen (%)
Lipid Content (%)
Phospholipids (%)
N Consumed by Microalgae (%)
CO2 Absorbed (%)
Biomass Unharvested (%)
Biomass Unextracted (%)
Extracted Lipid Lost (%)
N Recovery (AD) (%)
P Recovery (AD) (%)
CH4 Prod. –Biomass (L-CH4 g-VS-1)
CH4 Prod. – LEA (L-CH4 g-VS-1)
CH4 in Biogas – Biomass (%)
CH4 in Biogas – LEA (%)
CO2 Combustion Loss (%)
Baseline
1
106:16:1
7.5
30
25
99
85
14.5
10
5
76
70
0.43
0.22
67
59
10
Optimistic
0.5
166:20:1
6
40
5
100
90
2.0
5
2
85
85
0.80
0.31
62
49
5
Conservative
2
79:16:1
9
20
30
98
2
20.0
15
8
60
60
0.25
0.14
72
69
20
LEA coproduct
1
106:16:1
7.5
30
25
99
85
14.5
10
5
0
0
N/A
N/A
N/A
N/A
N/A
HTL
1
106:16:1
7.5
30
0
99
85
2.0
0
0
66
95
N/A
N/A
N/A
N/A
N/A
14
RESULTS AND DISCUSSION
Total Resource Requirements
Mass flow calculations were performed for the five biofuel production scenarios,
each at the DOE 60 BGY fuel production level, to quantify the supply of resources to be
added to the system. In addition to nutrients and CO2 demand, water consumption was
also calculated based on the hydrogen content of the grown biomass, which in this study
is only referring to the fixation of hydrogen in the biomass and does not include
evaporation or process losses. The total system requirements for each scenario are
reported in Table 2.
The lower and upper bounds for nutrient and CO2 requirements for large-scale
microalgae production are illustrated through the optimistic and conservative results, with
the baseline scenario portraying the expected resource demand from production that is
realizable in the near-term. The mass balance calculations show that one billion metric
tons of biomass will need to be grown in the baseline scenario to meet the 60 BGY fuel
goal corresponding to a land area of 21 million hectares if a growth rate of 13 g m-2 day-1
is assumed [32]. This land requirement does not represent a resource constraint as
concluded by previous assessments [23,39]. The total nutrient need for the optimistic
scenario is approximately one-fourth that of the baseline scenario, with the conservative
scenario requiring three times the phosphorus and four times the nitrogen compared to
the baseline scenario. The large variation in nutrient demand can be attributed primarily
to the range in efficiency of nutrient recovery in the AD. The LEA co-product scenario,
characterized as the baseline scenario without AD, has roughly the same nutrient
requirements as the conservative scenario. Alternatively, HTL has approximately half the
15
2
Total biomass and corresponding nutrient requirements for the five scenarios at the DOE 60 BGY fuel production level
Table 2
Total biomass and corresponding nutrient requirements for the five scenarios at the DOE
60 BGY fuel production level. The unit Mmt represents millions of metric tons.
Biomass produced (Mmt)
Nitrogen required (Mmt)
Phosphorus required (Mmt)
CO2 required (Mmt)
H20 Consumption (BGY)
Baseline
Optimistic
Conservative
LEA coproduct
HTL
1002
23.1
5.2
1540
179
587
6.7
1.1
738
84
1850
96
14.4
161000
395
1002
89
12.2
2060
180
435
10.4
0.4
896
78
required biomass production and nitrogen demand from the baseline, and a phosphorus
requirement less than one-tenth of the baseline, similar to the optimistic scenario. The
ratio of nitrogen input to phosphorus input is much higher in the HTL scenario because of
the high nitrogen content in the bio-oil, an issue that necessitates further downstream
processing [55]. The system CO2 requirement shows higher variability across the
scenarios than the other resources due to the wide range of utilization efficiency. Through
the variation apparent in Table 2, bounds have been determined for the resource
requirements to meet the DOE 2030 fuel goal via microalgae biodiesel with the baseline
scenario representing a near-term realizable production facility and the optimistic and
conservative cases used to bound the analysis.
Scalability Assessment Based on Nutrient Availability
System scalability was assessed through a comparison of the total input
requirements and current nutrient resource availability. Growth nutrients for the
cultivation of microalgae can be in the form of fertilizer or by utilizing a growth medium
that already contains nutrients such as wastewater or seawater [26,27]. Total source
16
3
Scalability assessment: Comparison of seawater, wastewater, and fertilizer availability and requirements to meet baseline system nutrient needs at a biofuel production level of 60 BGY
Table 3
Scalability assessment: Comparison of seawater, wastewater, and fertilizer availability
and requirements to meet baseline system nutrient needs at a biofuel production level of
60 BGY. The unit TGY is trillion gallons per year.
Seawater
Wastewater
Fertilizer
Wastewater
& Fertilizer
Input Needed
% of the Great Salt Lake[58]
Nitrogen
19 TGY
280%
Phosphorus
28 TGY
430%
Input Needed
% of total Available WW in U.S.[59,60]
186 TGY
1600%
173 TGY
1500%
Input Needed
% of U.S. Consumption[61]
23.1 Mmt
98%
5.2 Mmt
125%
92%
117%
% of U.S. Fertilizer Consumption after
use of 100% of U.S. Wastewater[58-61]
supply to meet the baseline nutrient requirements was determined for three nutrient
sources - seawater, wastewater, and fertilizer, Table 3. In the case of seawater, since the
supply is seemingly limitless, a volumetric comparison is provided. Detailed calculations
related to the results in Table 3 are provided in Appendix E.
The scalability assessment shows nutrient requirements are a current barrier to
meeting DOE 2030 production goals strictly through microalgae based biofuels. If
seawater is used as the nutrient source, it would be necessary in amounts equivalent to
more than four times the volume of the Great Salt Lake in Utah. This would represent a
major economic barrier due to pumping requirements, and limit production facility
locations geographically. The nutrients available from wastewater correspond to a fuel
production level of 3.8 BGY or 6% of the DOE fuel goal. Meeting the DOE goal would
require sixteen times the available wastewater in the US, therefore illustrating wastewater
to be a possible supplement, but not the ultimate source for nutrients. Commercial
17
fertilizer represents a viable nutrient supply, but scaling to the levels discussed would
require the entire current US fertilizer market.
Technological advancement in the various processes can improve performance
and the corresponding scalability. If achieved, the optimistic scenario would bring the
nutrient requirements to within the availably supply, with a fertilizer demand of 28% of
the current US market to meet the DOE goal. A production level of 13 BGY could be
achieved from wastewater nutrients, with 17% of the US fertilizer market supplementing
the wastewater to achieve 60 BGY total fuel production. Additionally, HTL as an
extraction and nutrient recycling process shows potential value in biofuel production due
to the inherent ability to process non-lipid organic material into bio-oil while recovering
nutrients. The nutrient demands for the LEA co-product scenario are an order of
magnitude greater than availability and show that for biofuel production sufficient to
meet the DOE 2030 goal, nutrient from the LEA must be recycled. Results from the
scalability assessment show that nutrient management represents an important aspect of
microalgae scalability.
Comparison to Previous Nutrient Assessments
A variety of microalgae based TEA and LCA studies have reported nutrient
requirements for the cultivation of microalgae but have failed to evaluate the large-scale
demand [9,10,12,13,32]. The majority of the previous assessments are founded on models
which are constructed with small-scale laboratory-based growth and lipid production data
[10,13,27,28], but this type of analysis has been shown to misrepresent microalgae
production at large-scale [11]. The simplistic scaling of small-scale laboratory based data
18
leads to erroneous assumptions about industrial growth facility function, and is a source
of uncertainty in microalgae biofuels economic, sustainability, and resource assessments.
Incomplete resource assessments have led to a large uncertainty in the current
requirements for large scale production [9,10,12,13,26-28,32]. Results from previous
studies were normalized to the baseline units of this study, 60 billion gallons of fuel
production annually from 1002 Mmt of biomass. Normalized values reported in surveyed
literature range from 14 Mmt of nitrogen and 4.1 Mmt of phosphorus [9,32] to 147 Mmt
of nitrogen and 21 Mmt of phosphorus [10,27] as shown in Fig. 5.
The nutrient requirements in this study are 1.6 and 1.3 times the lowest values and
0.16 and 0.25 times the highest values reported in the literature surveyed for nitrogen and
phosphorous requirements, respectively. Interestingly, when compared to the high
5
Results for (A) Nitrogen demand and (B) Phosphorus demand from various literature sources
140
120
100
80
60
40
20
0
25
(B) Phosphorus Input (Mmt)
(A) Nitrogen Input (Mmt)
160
20
15
10
5
0
Fig. 5. Results for (A) Nitrogen demand and (B) Phosphorus demand from various
literature sources. The black dotted line represents the baseline requirement from this
study.
19
productivity scenario from Pate et al. [26], the most comparable scenario to this study,
the baseline requirements are 63% and 105% of their results for nitrogen and phosphorus.
The differences and similarities in results occur through a combination of factors,
specifically that Pate et al. [26] only consider the growth phase, neglect losses, and
assume high algal productivity which introduces uncertainty. Compared to literature, the
baseline results obtained in this study are similar with results from previous studies,
falling between the high and low values reported. Results from this study, however, are
derived from an improved model which describes a complete production system
including process details and system losses.
Sensitivity Analysis
A sensitivity analysis was performed to illustrate which process parameters have
the greatest effect on resource demand and to ultimately guide future research. The
sensitivity analysis was performed by independently varying each system parameter by
±20% from the baseline and recording the nitrogen, phosphorus, and CO2 requirements.
Fig. 6 shows the results from the sensitivity to process parameters in order of greatest
impact on resource requirements. Results were analyzed using a one tail distribution with
a 95% confidence interval, resulting in a critical t-ratio of 2.1. The critical t-ratio provides
the boundary through which system parameters are deemed as sensitive or non-sensitive.
The parameters whose t-ratio magnitude are above the critical, and thus whose bar
extends beyond the confidence interval in Fig. 6, were deemed sensitive. Although
twenty-four system parameters were varied, only those parameters deemed as sensitive
according to their t-ratio were included in the figure.
20
N Recovered from AD (76%, -47)
P Recovered from AD (70%, -29)
C:N:P Ratio - N (16, 16)
C:N:P Ratio - P (1, 16)
Carbon Content (50%, 16)
Carbon Content (50%, 16)
C:N:P Ratio - C (116, -17)
C:N:P Ratio - C (116, -17)
Lipid Content (30%, -17)
Lipid Content (30%, -17)
N Consumed by Algae (99%, -13)
Phospholipids (25%, 5.8)
Biomass Unharvested (14.5%, 2.7)
Biomass Unharvested (14.5%, 2.7)
Process Loss (1%, 2.4)
Process Loss (1%, 2.3)
-60
-30
0
30
60
-60
0
30
60
(B) Percent Deviation from Baseline
Input of 5.2 Million Metric Tons
(A) Percent Deviation from Baseline
Input of 23.1 Million Metric Tons
Legend
Parameter (x,y) –
x- Baseline parameter value
y- t-ratio of parameter
Carbon Content (50%, 20)
CO2 Absorbtion (85%, -20)
Lipid Content (30%, -15)
Output for parameter
deviation of +20% from
baseline value
CH4 Content - Whole (62%, 2.7)
CH4 Content - LEA (50%, 2.4)
CH4 Prod. - Whole (0.43 L g-VS-1, -2.6)
Output for parameter
deviation of -20% from
baseline value
CH4 Prod. - LEA (0.22 L g-VS-1, -2.3)
-60
-30
-30
0
30
60
95% Confidence Interval
(C) Percent Deviation from Baseline
Input of 1540 Million Metric Tons
Fig. 6. Model output sensitivity to system parameters for (A) Nitrogen requirement, (B)
Phosphorus requirement, and (C) CO2 requirement
6
Model output sensitivity to system parameters for (A) Nitrogen requirement, (B) Phosphorus requirement, and (C) CO2 requirement
The two parameters whose variation has the greatest impact on system nutrient
demand are the nitrogen and phosphorus recovery rates of the AD. Any advancement in
nutrient recovery in recycling, whether it be improvement in anaerobic digester
21
performance or by use of an alternative technology such as HTL, will decrease the
nitrogen and phosphorus supply requirements. The next group of parameters most
capable of driving down resource demand are those describing the composition of the
microalgae strain. This demonstrates that the microalgae strain characteristics including
C:N:P ratio, lipid content, phospholipid content, and carbon content must be thoroughly
understood and even optimized through synthetic biology or other means for efficient
nutrient use in microalgae growth. The utilization efficiency of CO2 also has a large
effect on the input requirement, which becomes especially important when coupled with
the variability of this parameter. As explained in the methods section, and shown in Table
1, the CO2 absorption rate has varied from as low as 2% to as high as 90% in built
systems. There is high variability in the absorption, and the input requirement is highly
sensitive to this variance, so it will be imperative to maximize the utilization efficiency
when designing a commercial-scale production system. Resource requirements for largescale microalgae production can be reduced through technological improvements
affecting these sensitive system parameters.
22
CONCLUSION
A detailed and modular engineering system model was developed, validated, and
used to determine total system resource requirements for five microalgae to biofuel
production scenarios at a production level of 60 BGY. Optimistic and conservative
process scenarios were modeled in an effort to bound the nutrient and CO2 requirements
for large-scale microalgae production with a baseline scenario intended to represent a
near-term commercial system. Baseline nutrient requirements for attaining the DOE 2030
goal exceed the nutrients available in wastewater and are equivalent to the current
fertilizer consumption in the US. Utilization of wastewater as the sole source of nutrients
limits biofuel production to 3.8 BGY for the baseline scenario and 13 BGY for the
optimistic scenario. Wastewater alone cannot provide the necessary nutrient supply to
meet the DOE 2030 goal based on a microalgae to biofuel production system. The heavy
nutrient demand for large-scale production requires nutrients to be recovered from the
LEA, and thus LEA cannot be used as an economically beneficial co-product. Sensitivity
analysis shows that resource demand can be impacted through improvements in nutrient
recycling, microalgae strain characteristics, and the CO2 utilization efficiency. Current
nutrient requirements for microalgae production limit the ability to meet the DOE 2030
renewable fuel goal, but system improvements can potentially make the goal attainable,
as illustrated with the optimistic scenario.
23
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APPENDICES
29
Appendix A. Research Proposal
30
Abstract
Current research is focused on the evaluation and advancement of microalgae
biofuels in an effort to economically produce transportation fuels at commercial-scale.
Microalgae cultivation requires a variety of resources with land, water, and nutrients.
Nutrient requirements have been assessed for large-scale cultivation, but rely on lab-scale
research data which is not applicable. The use of laboratory data introduces a large
uncertainty regarding the resource requirements for large-scale production. The proposed
research will aim to fill the knowledge gap associated with resource requirements for the
scaling of microalgae biofuels to large-scale levels. Sensitivity analysis will be performed
on a systems level to identify areas for further research and development. Results from
this study will provide values for nutrient input requirements and water consumption of a
commercial-scale microalgae production system. Results will then be extended into a
scalability assessment through a comparison of resource requirements to the geographic
resolution of resource availability. This information will be essential for future economic
and environmental analyses related to large-scale microalgae production.
Background
Ever-growing demand for fuel world-wide has generated instability in the supply
chain. The increase in fuel demand –and thus fuel prices – has increased the volatility of
economies worldwide. One option for decreasing market variability is to find alternative
forms of fuel, whose added supply and competition will naturally drive prices down.
Additionally, fossil fuels are a limited resource that will eventually be depleted, which
creates a long-term need for alternative fuel sources. These immediate and long-term
fuel needs have sparked much interest and research in finding possible alternatives to
31
fossil fuel. There are many potential sources of fuel, and the final solution will likely use
multiple sources.
A variety of alternative fuel feedstocks are actively being investigated. One
promising option is growing microalgae and harvesting its oil, which can be refined to
serve as a drop in diesel fuel replacement. Microalgae is especially appealing because of
its abilities to be grown on non-arable land, to use water sources other than freshwater,
and to absorb and use CO2. These advantages combined with uncertainty in global energy
supply have led to interest in microalgae as an alternative fuel feedstock.
Research has provided much scientific insight about the possibilities as well as the
foreseeable obstacles to be overcome for commercial-scale biofuel production via
microalgae. When considering resource requirements, much of the focus of the research
to date has been into sunlight availability, associated productivity rates, land
consumption, and water consumption. The nutrient requirement for algae growth,
however, is a topic often neglected or minimalized in available literature and in
scalability assessments. Two key nutrients microalgae require are nitrogen and
phosphorus. Terrestrial crops require these nutrients also, as fertilizer, so microalgae and
food crops could potentially have to compete for the same supply of fertilizers. This
makes understanding the required nutrient inputs a key component for accurate
scalability assessments that needs to be addressed.
In order to accurately assess a large-scale growth facility to meet production
goals, and in order to predict the cost of biofuel production, the required system input for
the desired production must be accurately known. This presents an opportunity and a
need for further research.
32
Current State of Applicable Research
Introduction
Extensive research has already been done regarding the scalability of microalgae
production, but knowledge is still lacking regarding resource requirements. The proposed
research aims to fill this knowledge gap, and the information that has become available
through previous research will serve as an essential foundation for the proposed research.
The previous research that will be utilized can be broken into three categories: 1. Nutrient
Requirements, 2. Process Losses and Efficiencies, and 3. Wastewater and Nutrient
Removal. The current state of research in each category is presented here.
Nutrient Requirements
Some studies discuss the nutrient and water requirements for algae growth, but
conclusions are only discussed at a laboratory scale (Yang et al., 2011; Woertz et al.
2009). Pate et al. (2011) took a first step towards understanding the implications of
large-scale microalgae production and the corresponding nutrient requirements. In their
study, percentages (15%, 30%, 80%, 150%) of DOE goals for lipid production were used
as a reference point to determine how much microalgae would need to be grown annually
for the different scenarios. Each production scenario was considered in four different
regions of the United States to determine the necessary land, water, and nutrients. It will
be shown that their study provides useful information, but is lacking in scope due to
crude assumptions.
Figure A.1 was created as a visual representation of the system studied by Pate et
al. (2011). Each numbered location in the figure has a mass flow associated with it that
33
will be discussed. The desired level of lipid production at location 3 was determined as a
percentage of DOE goals. The results of the study describe the level of resources
required at location 2. Using freshwater supplemented with the required nutrients, and
with no considerations made for losses and recycling, the results found to describe
location 2 are assumed to also be the requirements at location 1. The conclusion is that
the amount of resources required for large-scale biofuel production through microalgae
growth is unattainably high. This model is simple, and provides a good starting point for
understanding the implications of large-scale algae production, but the results are highly
inaccurate for an actual process when discussing the resource inputs at location 1 because
production systems will include recycle processes.
Fig. A.1. Model of the process studied by Pate et al. (2011)
7A.1
Model of the process studied by Pate, et al. (2011)
Process Losses and Efficiencies
There will be losses and efficiencies at each step throughout the complete process,
but only a few will be discussed in any detail here. First, some nutrients will be
contained in the extracted lipids. Frank et al. (2011), in their life-cycle report, assumed
that the phosphorus and nitrogen content within the lipids is small enough to be neglected
without affecting calculations. They do recognize, though, that there will be a small
34
amount of nutrients contained in the lipids. In a separate study, Li-Beisson (2012)
discusses the biomass composition of various strains of microalgae, as well as TAG
makeup. Next, nutrients can be recycled through anaerobic digestion or hydrothermal
liquefaction, each with efficiency rates. Frank et al. (2011) provide several references
for nutrient recovery rates through anaerobic digesters, and conclude to use 80% nutrient
recovery in their study. Currently, information regarding each of the process steps is
available, but scattered. This information needs to be compiled and utilized to
understand the nutrient flow throughout the entire process, which is the goal of the
proposed research.
Wastewater and Nutrient Removal
Wastewater is considered a potentially valuable resource by those interested in
biofuel production via microalgae (Yang et al., 2011; Menetrez, 2012; Pittman et al.,
2011). Wastewater contains both water and nutrients that are necessary for algae growth,
but these nutrients are considered pollutants in wastewater treatment that are expensive to
remove (Abdel-Raouf et al., 2012). As a result of this, many researchers in wastewater
treatment are interested in using microalgae as a water treatment process (Abdel-Raouf et
al., 2012; Woertz et al., 2009). Microalgae present a potentially less-expensive method
of pollutant removal, combined with the advantage of producing bioproducts from
harvested lipids. The studies done on microalgae use for wastewater cleanup have
produced a range of values for the percentage of pollutant removal from the water.
Abdel-Raouf et al. (2012) cites several studies using Chlorella vulgaris which reported
50.2% - 86% nitrogen removal and 70% - 97.8% phosphorus removal. Woertz et al.
35
(2009) were able to remove >98% of ammonium and >96% of phosphorus with
microalgae. Although information is available on wastewater composition and
nutrient/pollutant removal rates, whether wastewater has the potential to be the sole
nutrient source for microalgae growth is still unknown because the exact nutrient input
required for the complete production process is unknown.
Summary
Information is available on different stages of the production process, showing
what the resource consumption and losses will be. This information, however, is only
available for individual stages and research has not been done which combines these
pieces of information into a process evaluation. A study has conducted a zeroth order
assessment which reported the nutrients contained in a given quantity of microalgae, but
did not include any process factors. Finally, wastewater has great potential as a nutrient
source for microalgae production, but whether this source alone will be sufficient is
unknown because the nutrient input requirements are unknown.
Proposal of Thesis Topic and Completion Steps
Overview
Based on the inconsistency and low level of detail in previous studies, the
proposed research is to move beyond a simple model like the one utilized by Pate et al.
(2011). The proposed model will include more of the process details that will be
involved in an actual system. The study will focus on nutrient consumption and losses,
along with water consumption in order to provide detailed information for this key aspect
36
of a scalability assessment. The model architecture is presented in Figure A.2. This
model includes nutrient losses during growth and harvesting, losses in lipid extraction,
losses in the recycling process, and nutrient recovery through recycling. The model will
be constructed in such a way to critically evaluate a variety of nutrient sources, system
processes, microalgae strains, and end products from biofuel to bioproducts.
8A.2
Model of the process to be studied
Fig. A.2. Model of the process to be studied
The numbered locations in Figure A.2 correspond to the same numbered locations
in Figure 1. The results obtained by Pate et al. (2011) give nutrient levels at location 2
based on a given level of lipid production at location 3. The proposed research will then
use this information, along with information about each step of the process, to determine
the required nutrient input at location 1. A sensitivity analysis will be performed to
determine the variability that can be expected from the obtained results, and especially
which steps of the process most greatly impact input requirements. The results will then
be compared to the amount of available nutrients and nutrient sources at various locations
37
throughout the country. Evaluations will also be made for different recycling processes,
microalgae strains, and additional bioproducts.
Research Challenge and Question
Challenge: The Energy Independence and Security Act of 2007 stipulates a 36
billion gallon biofuel production by 2022. Researchers have shown that scalability of
microalgae biofuels is dependent on, and potentially limited by, available resources. In
order to quantify the exact limits of large-scale microalgae production imposed by
nutrient availability, lab-scale models of algae growth must be connected to system-scale
modeling. This challenge inspires a key question to be answered by this research:
What is the required nutrient input for a commercial-scale, microalgae production process
when considering losses, efficiencies, and recycling?
Additional Discussion Points:
a. How does this input requirement compare to nutrient availability via wastewater
and other sources?
b. How do different system processes affect the overall system performance and
nutrient input?
c. How will additional end products affect the nutrient requirement of the system?
d. Will different strains of microalgae significantly change the nutrient input
requirements?
e. What is the non-recoverable, elemental water consumption due to microalgae
growth?
38
Description of Tasks for Completion
In order to answer the research question proposed, the following information will
need to be gathered or determined:
1. Nutrient losses during growth and harvesting. This includes nutrients not
absorbed from the water during growth as well as algae missed during harvest that
escapes.
2. Nutrient content in lipids
3. Biomass losses during extraction
4. Losses in the recycling process. This includes biomass losses throughout the
process and the nutrient recovery rate.
5. Wastewater composition and supply at locations throughout the country
6. Characteristics of recycling processes (AD, Hydrothermal Liquefaction)
7. C:N:P Ratios for microalgae strains
8. Any other systems validation data
The first step will be to generate mass balance calculations which reproduce the
results found by Pate et al. (2011). This can be used to evaluate different C:N:P ratios.
The next step will be to generate mass balance calculations for the full system model.
The information listed above will be gathered, completed in parallel with the
creation of mass balance equations. The mass balance equations will then be used to
determine which aspects of the process will have great impact or little impact on the
system input requirements. Those pieces of the process that have the greatest effect will
39
then be analyzed in greater detail to determine how much variability can be expected and
to identify needs for future research. Four production levels will be used - the same
levels used by Pate et al. (2011). A written summary of the process and results will be
provided, as well as tables and graphs containing the complete results. Condensed
results, displaying maximum, minimum, and mid-range results will also be provided.
Work Plan
4A.1
Planned dates for completion of the proposed milestones
Table A.1
Planned dates for completion of the proposed milestones
Task
Completion Date
Determine Process Values
11/15/2012
Write Mass Balance Equations to Mathcad
11/15/2012
Run Scenarios and Gather Results
12/15/2012
Create Tables and Graphs for Results
1/15/2013
Write Summary of Results for Publication (First Draft)
2/5/2013
Write Complete Report for Thesis (First Draft)
3/20/2013
Submit Publication
4/10/2013
Thesis Defense
4/20/2013
40
References
Abdel-Raouf, N., Al-Homaidan, A.A., Ibraheem, I.B.M.. 2012. Microalgae and
wastewater treatment. Saudi Journal of Biological Sciences. 19, 257-275.
Davis, Ryan, Aden, Andy, Pienkos, Philip T.. 2011. Techno-economic analysis of
autotrophic microalgae for fuel production. Applied Technology. 88, 3524-3531.
Frank, E.D., Han, J., Palou-Rivera, I., Elgowainy, A., Wang, M.Q.. 2011. Life-Cycle
Analysis of Algal Lipid Fuels with the GREET Model. ANL Report.
Li-Beisson,Yonghua. 2012. Triacylglycerol Biosynthesis in Eukaryotic Microalgae.
AOCS. http://lipidlibrary.aocs.org/plantbio/tag_algae/index.htm. Accessed
9/18/2012.
Lundquist, T.J., Woertz, I.C., Quinn, N.T.W., Benemann, J.R.. 2010. A Realistic
Technology and Engineering Assessment of Algae Biofuel Production.
Menetrez, Marc Y. 2012. An Overview of Algae Biofuel Production and Potential
Environmental Impact. Environmental Science & Technology. 46, 7073-7085.
Park, J.B.K., Craggs, R.J., Shilton, A.N.. 2011. Recycling algae to improve species
control and harvest efficiency from a high rate algal pond. Water Research.45,
6637-6649.
Pate, Ron, Klise, Geoff, Wu, Ben. 2011. Resource demand implications for US algae
biofuels production scale-up. Applied Energy. 88, 3377-3388.
Pittman, Jon K., Dean, Andrew P., Osundeko, Olumayowa. 2011. The potential of
sustainable algal biofuel production using wastewater resources. Bioresource
Technology. 102, 17-25.
Wijffels, Rene H., Barbosa, Maria J. 2010. An Outlook on Microalgal Biofuels. Science.
329, 796-799.
Woertz, Ian, Fulton, Laura, Lundquist, Tryg. 2009. Nutrient Removal & Greenhouse Gas
Abatement with CO2 Supplemented Algal High Rate Ponds. WEFTEC. 79247936.
Yang, Jia, Xu, Ming, Zhang, Xuezhi, Hu, Qiang, Sommerfeld, Milton, Chen, Yongsheng.
2011. Life-cycle analysis on biodiesel production from microalgae: Water
footprint and nutrients balance. Bioresource Technology. 102, 159-165.
41
Appendix B. Detailed Process Block Diagrams
42
This appendix contains input/output diagrams for each process within the
microalgae to biofuel production system that was modeled. The individual block
diagrams were used to generate the mass balance calculations of the model, which appear
in Appendix C. Figure B.1 shows the system as a whole, with each flow stream
numbered, and corresponding numbers are contained in each subdiagram. There is one
subdiagram for each process block within the system, which comprise Figures B.2
through B.6.
Legend
N - Nutrients (N,P,C/CO2)
L - Lipid
B - Biomass
Leaving the system
Input/output that remains within the system
6
Inputs
Growth
4
Harvest
Extraction
5
3
Filte
r
9
2
2
End Product
7
Filter
8
9
10
1
Recycling
Fig. B.1. Mass flow schematic of the complete microalgae to biofuel production
process
9B.1
Mass flow schematic of the complete microalgae to biofuel production process
43
System Inputs: N
Growth
1.From Recycling:
N
2.From Harvest: N
Loss to Env.: N,B
4.To Harvest: N,B
3.From Extraction:
N
Fig. B.2. Growth process input/output diagram
10B.2
Growth process input/output diagram
Harvest
Loss to Env.: N,B
4.From Growth: N,B
5.To Extraction: N,B
6.To End Product: B
9.To Recycle: B
Filter1: N,B
2.To Growth: N
Fig. B.3. Harvest process input/output diagram
11B.3
Harvest process input/output diagram
8.From Extraction: B
Recycling
Solids: N
9.From Filter1: B
10.From Filter2: B,L
1.To Growth: N
Fig. B.4. Recycling process input/output diagram
12B.4
Recycling process input/output diagram
44
Loss to Env.: N,B,L
Extraction
7.To End Product: L
5.From Harvest: N,B
Ash: B
8.To Recycling: B
Filter2:
N,B,L
10.To Recycling: B,L
3.To Growth: N
Fig. B.5. Extraction process input/output diagram
14B.6
Transesterification and end products process input/output diagram
6.From Harvest: B
End Products
7.From Extraction: L
Biomass Product
Glycerine
Transesterification: L
Loss to Env.: L
Fuel Product
Fig. B.6. Transesterification and end products process input/output diagram
13B.5
Extraction process input/output diagram
45
Appendix C. Model Calculations in Mathcad
46
47
48
49
50
51
52
53
54
55
56
Appendix D. Complete Table of Model Parameter Values
57
Table D.1 is intended to serve as a supplement to Table 1 of the main text and
also as an aid in understanding and utilizing the model developed (Appendix C). Whereas
Table 1 provides a summary of the parameter values used for each scenario in terms
consistent with the description of work contained in the text body and omits any
information that is unessential for conceptual understanding, Table D.1 provides a
complete summary of all model variables in terms consistent with the mass balance
calculations in Appendix C. The variables listed in Table D.1 correspond to all of the
terms highlighted in blue in the model.
T
58
5D.1
Complete summary of system parameter values used for model mass balance calculations for the five production scenarios
Table D.1
Complete summary of system parameter values used for model mass balance calculations
for the five production scenarios
CNP
Lip_cont (%)
H_perc (%)
C_perc (%)
Fuel_prod (BGY)
Trans_Loss (%)
NF_Lip (BGY)
PL_Perc (%)
NL_Biom (Mmt)
Extr_Lip (%)
Extr_Paste (%)
Lip_extr_water (%)
Biom_extr_water (%)
Harv_Loss (%)
Har_Biom (%)
N_Rec_Eff (%)
P_Rec_Eff (%)
Whole_VS_Rat (g-VS g-TS-1)
LEA_VS_Rat (g-VS g-TS-1)
CH4_WholeVS (L g-VS-1)
CH4_LEAVS (L g-VS-1)
CH4_Burn (%)
CO2_Conv
Whole_CH4% (%)
LEA_CH4% (%)
CO2_Loss (%)
Gro_Loss (%)
N_Alg (%)
CO2_abs (%)
Baseline
Optimistic
Conservative
106:16:1
30
7.5
50
60
1
0
25
0
1
1
5
10
1
14.5
76
70
0.94
0.90
0.43
0.22
100
1
67
59
10
1
99
85
166:20:1
40
6
50
60
0.5
0
5
0
0.5
0.5
2
5
0.5
2
60
60
0.94
0.90
0.80
0.31
100
1
62
49
5
0.5
100
90
79:16:1
20
9
50
60
2
0
30
0
2
2
8
15
2
20
85
85
0.94
0.90
0.25
0.14
100
1
72
69
20
2
98
2
LEA coproduct
106:16:1
30
7.5
50
60
1
0
25
0
1
1
5
10
1
14.5
0
0
0.94
0.90
0.43
0.22
100
1
67
59
100
1
99
85
HTL
106:16:1
50
7.5
50
60
1
0
0
0
0
0
0
0
2
0
75
95
0.94
0.90
0.43
0.22
100
1
67
59
100
1
99
85
59
Appendix E. Calculations in Mathcad for Nutrient Source Availability
60
This appendix contains calculations to determine the level of nutrient source
supply required to meet baseline system demands. These calculations serve as the
intermediate step between the information supplied from the sources [59-61], and the
values reported in Table 3. Throughout the research and writing process, several
comparisons were attempted (i.e. Seawater required was originally reported as % of
Colorado River annual flow, then later reported as % of Great Salt Lake). The
information in the ultimately unused calculations could potentially be of value in future
work, so all of the various calculations are included here.
61
62
63
64
65
66
67