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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