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Microalgae growth and biomass-to-SNG
conversion through HTG:
dynamic modeling of cultivation phase in open pond and closed reactor
Master Thesis Project
Student: Matteo Marsullo
Supervisor: Professor Andrea Lazzaretto
Co-supervisors: Professor François Maréchal
Professor Adriano Ensinas
PhD Alberto Mian
Content of the thesis
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1. Creation of a dynamic model for the microalgae
cultivation phase in open and closed photobioreactors
2. Evaluation of the impact of the most important design and operating parameters which affect microalgae growth.
Aims of the thesis
Microalgae utilization for energy production:
From biomass cultivation to Synthetic Natural Gas (SNG)
Overview of the presentation
1. Microalgae for energy production
2. Hydrothermal Gasification for microalgae conversion into Synthetic Natural Gas
3. Microalgae cultivation technologies
4. Model description
5. Results
6. Conclusions
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What are microalgae?
• Single cells microorganisms
• Photosynthetic ability in a single cell
• Photosynthesis is the main energy generating process
used by microalgae
𝐶𝑂2 + 𝐻2𝑂 + 𝑙𝑖𝑔ℎ𝑡 → 𝐶𝐻2𝑂 𝑛 + 𝑂2
• Maximum photosynthetic efficiency is higher than for
terrestrial plants, reaching values of 12%.
• They are able to live in a wide range of different habitats,
from fresh to marine and hyper-saline environments.
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Why microalgae?
• No competition with food
• High productivity and efficiency
(maximum photosynthetic efficiency being 12% instead of 4%)*
• Possibility to absorb CO2 from flue gases
• Possibility to remove contaminants and pollutants
from wastewater
• No requirement for arable land
• Less requirement for freshwater
5 * Williams et al. , 2010, “Microalgae as biodiesel & biomass feedstocks: Review and analysis of the biochemistry, energetics & economics”
Why hydrothermal gasification?
• Hydrothermal gasification (HTG) of biomass is the thermochemical conversion of biomass into gases by
processing in a hot, pressurized water environment.
• Since algae are extremely diluted in the water, the drying phase, that is necessary for biodiesel production through
transesterification, is energy intensive, requiring sometimes 40%
of global energy demand for microalgae production*
• HTG allows processing a diluted stream feedstock with water
content between 50% and 90%.
6 * Borowitzka, 1999, “Commercial production of microalgae: ponds, tanks, tubes and fermenters”
Microalgae cultivation technologies
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Efficiency
CO2 capture
potential
Investments and
maintenance costs
Open Systems Low Low Low
Closed Systems High High High
Open systems Closed systems
Open raceway pond Flat panel PBR
Tubular PBR
Microalgae cultivation phase modeling
System boundaries
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Microalgae cultivation phase modeling
Model structure
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Microalgae growth model
Growth rate 𝑟𝑔𝐴 = 𝜇𝐴𝑋𝐴 where:
XA = mass concentration of microalgae [g/m3]
μA = specific growth rate [1/h]
𝜇𝐴 = 𝜇 𝐴𝐶𝑂2𝐷
𝐾𝐶 + 𝐶𝑂2𝐷
𝑁𝑇𝐾𝑁𝐴 + 𝑁𝑇
𝑓𝐼𝑓𝑇
10
0
0.2
0.4
0.6
0.8
1
1.2
0 100 200 300 400 500
Irradiation [W/m2]
Irradiation factor
0
0.2
0.4
0.6
0.8
1
1.2
-10 0 10 20 30 40 50
Water temperature [°C]
Temperature factor
Mass and energy balances
• Time dependent mass balance for microalgae
𝑑𝑋𝐴𝑑𝑡
= 𝜇𝐴𝑋𝐴 − 𝑘𝑑𝐴𝑋𝐴
• Time dependent mass balance for nutrients and oxygen
𝑑𝑀
𝑑𝑡= 𝜇𝐴𝑋𝐴𝑌𝐴𝑀 − 𝑘𝐿𝑔𝛼 𝑀𝑔 −𝑀𝑔
∗
• Time dependent thermal balance (open raceway pond)
𝑉𝑅𝑐𝑝𝑊𝜌𝑊𝑑𝑇𝑊𝑑𝑡
= 𝑄𝑖𝑟𝑟 − 𝑄𝑎𝑙𝑔𝑎𝑒 − 𝑄𝑟𝑎𝑑 − 𝑄𝑒𝑣𝑎𝑝 − 𝑄𝑐𝑜𝑛𝑣 − 𝑄𝑐𝑜𝑛𝑑 11
Numerical method for
systems of differential equations
• The equations presented are all linked together
• An easy way to solve the system of differential equations without losing too much accuracy is through finite difference method
• To have a stable numerical method, instead of using the forward difference formula, the backwards difference formula has been implemented
𝑑𝑥
𝑑𝑡= 𝑓 𝑡 →
𝑥 𝑡 − 𝑥(𝑡 − 1)
∆𝑡= 𝑓(𝑡)
• If applied to matrices and arrays
𝐴 𝑡 = 𝑓 𝑡 ∗ ∆𝑡 + 𝐴(𝑡 − 1)
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Microalgae cultivation technologies
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Open raceway pond Characteristics:
• Paddle wheel for mixing
• Bubbling system for CO2
injection: the injected gas
could be air or stack gases
Operating strategy:
• Repeated batch cultivation*
• Raceway pond covers 1 ha
• Harvesting and refilling of the pond
during one night
• Each pond has a cylindrical settler
for a first algae separation
* Radmann et al. 2007, “Optimization of the repeated batch cultivation of Spirulina platensis in open raceway pond”
Microalgae cultivation technologies
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Flat panel photobioreactor Characteristics:
• Parallel lines of vertical flat panels
• Bubbling system for CO2 injection,
O2 removal and mixing
• Temperature control system
Operating strategy:
• Repeated batch cultivation*
• Flat panels cover 1 ha
• Harvesting and refilling of the
reactor during one night
• Each reactor has a cylindrical
settler for a first algae separation
* Radmann et al. 2007, “Optimization of the repeated batch cultivation of Spirulina platensis in open raceway pond”
Results – Inputs of the simulations
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Location Sevilla (SPA)
Petrolina (BRA)
Tipology P. Tricurnutum
T. pseudonana
Xa_init [g/m3] 100
Xa_target [g/m3] 490
Tw_in [°C] 15
CO2 rate [%] 0.04
Z_pond [m] 0.3
LW [-] 10
Location Sevilla
Petrolina
Tipology P. Tricurnutum
T. pseudonana
azimuth [°] 0
-90
slope [°] 90
Xa_init [g/m3] 3000
Xa_target [g/m3] 6000
CO2 rate [%] 0.02
h [m] 1.5
s [m] 0.05
d [m] 0.5
Open raceway pond Flat panel PBR
Results – Open raceway pond
• [1] Slegers et al. 2013 • [2] Brennan et al. 2010 • [3] Jorquera et al. 2010 16
Parameters to evaluate reactor performance
Mass input
Energy input and output
From simulations From literature
Petrolina (BRA) Sevilla (SPA)
Mass microalgae [t] 57.29 64.68 → 63.7 [1]
CO2 captured [t] 140 156
CO2 injected [t] 243 260
CO2 ratio losses [%] 32.400 29.800 → 30
N absorbed [t] 5.8 6.55
Water injected [t] 109210 121640
Water evaporation [t] 5.8 5.01
Energy microalgae [kWh] 342610 386790
Electrical energy [kWh] 18497 19024
Thermal energy [kWh] - -
Volumetric productivity [kg/(m3*d)] 0.0523 0.0591 → 0.05 [2]
Areal productivity [kg/(m2*d)] 0.0157 0.0177 → 0.014 [2]
NER 0.108 0.0603 → 0.11 [3]
Photosynthetic efficiency [%] 1.65 2.21 → 1.56 [1]
Results – Flat panel PBR
From simulations From literature
Petrolina Sevilla
Mass microalgae [t] 393 366 → 200 [1]
CO2 captured [t] 1083 1025
N absorbed [t] 45.2 42.77
Water injected [t] 62353 57742
Energy microalgae [kWh] 2350700 2191100
Electrical energy [kWh] 44906 42473
Thermal energy [kWh] 1267400 2093900
Volumetric productivity [kg/(m3*d)] 0.7934 0.7395 → 1.25 [2]
Areal productivity [kg/(m2*d)] 0.1077 0.1004 → 0.55 [1]
NER 1.1165 1.95 → 0.3 [3]
Photosynthetic efficiency [%] 6.58 7.84 → 5
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• [1] Slegers et al. 2011 • [2] Münkel et al. 2013 • [3] Jorquera et al. 2010
Parameters to evaluate reactor performance
Mass input
Energy input and output
Conclusions
• A microalgae cultivation phase dynamic model has been created and validated through values coming from literature
• The model is able to simulate the behavior of the cultivation systems thanks to the possibility to manage time dependent inputs
• The model is an efficient instrument to evaluate the feasibility of a cultivation. It’s flexibility comes from the possibility to operate with many different input values (different locations, microalgae species, geometries)
• The model might be used for the global analysis of the cultivation and transformation process
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