Integration of Biological Wastewater Treatment and Algal Growth for

Philippa Uttley, S. Wilkinson, S. Palmer (MWH), K. Bangert
Department of Chemical and Biological Engineering, Sir Robert Hadfield
Building, Mappin Street, Sheffield S1 3JD
Email: [email protected] | Web: http://e-futures.group.shef.ac.uk
Introduction
Aims
Biological wastewater treatment is an established process that harnesses
microorganisms to reduce the contaminants in domestic and industrial effluents to
acceptable levels for discharge or further processing. Photosynthetic microalgae are
autotrophic organisms that fix carbon dioxide from the atmosphere in order to grow.
They are actively under consideration for large scale biodiesel production since, under
the right conditions, some strains of algae produce high levels of lipid that can be
separated and converted to biodiesel by a single chemical step (transesterification).
By coupling our computational model for integrated
wastewater treatment and algal biodiesel production to a
mathematical optimisation algorithm, we can investigate
the economics of massive scale production of microalgae
using wastewater-derived nutrients, as well as enhanced
production of biogas from anaerobic digestion of algae and
activated sludge.
Aeration Tank Simulation
CO2
Electricity
CHP
System
CO2
O2-rich Off-gas
Wastewater
A.S.
Aeration
Tank
Algal Pond
Settler
Lipid-rich
Biomass
Spent Algal Biomass
Sludge
CH4 / CO2
Liquid Digestate
Anaerobic
Digester
Figure 1. Schematic Diagram of an Integrated WWTP
Activated Sludge and Algal Pond Models
Activated Sludge Model No. 3 will be used for the biological processes involved in the
aeration tank. The components of the system are divided into two categories: soluble
components and particulate components, denoted by S and X. Mass balances in the
aeration tank can be described
π‘Žπ‘‘
𝑖𝑛
π‘Ÿπ‘ 
π‘Žπ‘‘
𝑑𝑋𝑖
𝑄𝑖𝑛 𝑋𝑖 + π‘„π‘Ÿπ‘  𝑋𝑖 βˆ’ 𝑄𝑖𝑛 + π‘„π‘Ÿπ‘  𝑋𝑖
by [3]:
=
+𝑅
𝑑𝑑
The production of
algal biomass can
be calculated using
the specific growth
rate, ΞΌ:
πœ‡ = πœ‡π‘šπ‘Žπ‘₯
𝑖
π‘‰π‘Žπ‘‘
+
𝑁𝑂32βˆ’
Methodology
β€’
𝑁𝑂32βˆ’
𝑁𝑂3
𝐾𝑀
Figure 2. ASM3_2N using CellDesigner [1,2]
𝐢𝑂2
𝐢𝑂2
𝐾𝑀
𝑂2
+ 𝐢𝑂2 +
𝐾𝐼
1βˆ’
𝑋
𝑋 π‘šπ‘Žπ‘₯
β€’
β€’
Connect Activated Sludge, Anaerobic Digestion and
Algal Pond models
Conduct bench-scale experiments using microalgae
Gather data to calculate missing parameter values in
algal growth model.
Results
A proof-of-concept model of an integrated WWTP has been built using simplified models
of activated sludge and algal growth, utilising Monod kinetics. Future versions will
include AD and CHP as producers of CO2. The final version will employ a mathematical
optimisation algorithm to find cost-optimal designs. The following results illustrate the
benefits of mass transfer of gases between the separate units.
Design Variables
Sludge Air Sludge OffIn
Gas
(kg d-1 m-3) (kg d-1 m-3)
Scenario 1 5.0
5.0
Scenario 2 1.0
0.0
Output after 100 Days
Rate of Algal
Pond Air In Pond Off-Gas Gas Exchange – Gas Exchange – COD
(kg d-1 m-3) (kg d-1 m-3) Sludge to Pond Pond to Sludge Removal Biomass Production
(g d-1 m-3)
-1
-3
-1
-3
(kg d m )
(kg d m )
(%)
5.0
5.0
0.0
0.0
64.8
0.2
0.0
1.0
11.0
10.0
86.1
68.6
Future Work
It is envisaged that the final model will be applied to an
industrial-scale development, by using the model of an
established WWTP with an algal pond bolt-on. The
removal of nitrogen and phosphorus should be decoupled
from secondary treatment for the provision of nutrients to
microalgal tertiary treatment. These nutrients will then be
recycled for further use in the form of liquid digestate from
anaerobic digestion.
References:
1. IACOPOZZI, I., INNOCENTI, V., MARSILI-LIBELLI, S. & GIUSTI, E. 2007. A modified Activated Sludge Model No. 3 (ASM3) with two-step nitrification-denitrification. Environmental Modelling & Software, 22, 847-861
2. FUNAHASHI, A., TANIMURA, N., MOROHASHI, M. & KITANO, H. 2010. CellDesigner. 4.1 ed. Tokyo, The Systems Biology Institute.
3. BALKU, S. & BERBER, R. 2006. Dynamics of an activated sludge process with nitrification and denitrification: Start-up simulation and optimization using evolutionary algorithm. Computers & Chemical Engineering, 30, 490-499.