D ATA M ONITORING in both reactors L IQUID M EDIA : pH, Dissolved Oxygen (DO) H EADSPACE G ASES : O...
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DATA MONITORING in both reactors LIQUID MEDIA: pH, Dissolved Oxygen (DO) HEADSPACE GASES: O 2 , CO 2 , Methane (CH 4 ) ALGAL REACTOR MASS BALANCE MODEL A2M: Experimental and Modeling Approaches to Optimize an Algae-to-Methane Coupled Bioreactor System Ruby An 1,2,* , Joseph J. Vallino 2 , Zoe G. Cardon 2 1 The University of Chicago, Chicago, IL; 2 Marine Biological Laboratory, Woods Hole, MA; *[email protected] Why A2M? Electricity generated by solar and wind power depends on available sunlight or sufficient wind and currently cannot be stored efficiently. Algae- to-methane (A2M) has potential as an efficient and practical strategy to provide reliable renewable energy around the clock and on demand. • Algal biomass is a renewable resource that requires minimal land and water usage. • Methane can be efficiently stored and effectively transported using existing infrastructure. • Coupled bioreactors incorporate nutrient recycling which removes the need for expensive fertilizer inputs. In our A2M setup, these components create a closed system that uses the biological processes of photosynthesis and methanogenesis to generate usable energy solely from light and air. Acknowledgements Norsk Institutt for Vannforskning (NIVA) algal culture collection Falmouth Wastewater Treatment Plant & Foreman Lab Zeiss Microscopy Staff & Huber Lab Suzanne Thomas Anonymous Donor & Jeff Metcalf Internship Specific Aims 1.Experimental Develop a closed system with 100% nutrient recycling Maximize methane production 2.Modeling Develop accurate description of A2M system Use model predictions to improve system performance & achieve experimental objectives http://dryas.mbl.edu/A2M/ Exp1/Exp1.html Experimental Results Modeling Results Models were built and numerically solved using the R package deSolve. STATE VARIABLES : 1.c A – concentration of Algae 2.c DIC – concentration of Dissolved Inorganic Carbon 3.c O2(aq) – concentration of Dissolved Oxygen 4.p CO2(g) – pressure of Carbon Dioxide gas 5.p O2(g) – pressure of Oxygen gas ALGAL GROWTH RATE EQUATION : OUR ALGAE: Chlamydomonas reinhardtii CW15, a green algae mutant lacking a cell wall – making them easier to digest hence facilitating both methane production and nutrient recycling (left). OUR MICROBES: We first inoculated with anaerobic groundwater from a sampling well near Little Pond (middle left). Although this established a microbial community, it did not perform methanogenesis. Subsequently, we inoculated again with wastewater sediments from the Falmouth Wastewater Treatment plant (bottom left). Methods 25 μm 12 hr Diel Light Cycle Temperature: 25 °C anaerobic MICROBIAL DIGESTER aerobic ALGAL REACTOR A clump of algae being attacked by microbes (right) The A2M system in real life: microbial digester on left with top of cover removed (for photo visibility) & the algal reactor on the right (above left). Both reactors contain part liquid media (18L in digester, 4L in algal reactor) and part gaseous headspace. The monitoring & control rack measures liquid and gas data (above right). Summary Optimized experimental system conditions for high algal growth Accurately modeled algal reactor dynamics Next Steps Optimize experimental conditions to establish & maximize methane production Develop full coupled bioreactor model to simulate methanogenesis Day 90 Day 1 KEY ASPECTS OF DATA • Diel Cycling (24 hr period) • Strong CO 2 limitation with input at atmospheric level – photosynthesis does not occur through full day • We stimulated high algal growth (see photos on right) by elevating CO 2 input levels to 2% in air. Doubled light levels Elevated CO 2 Input Installed air spargers to increase input airflow Bioreactors Coupled pH crash Bioreactors Decoupled Atmospheric CO 2 •Days 30-33 : Atm. Input •Days 57-60 : Elevated Platt equation for light limitation Monod CO 2 Limitation PARAMETERS : This model contains 35 parameters. Parameter values were determined both from system measurements and standard literature values; however, the following 5 parameters represented unknown system characteristics. These parameters were used to fit the model to experimental data from days 57-60: α, β, κ L O2 A, r A, K CO2 MODEL FIT TO EXPERIMENTAL DATA DATA COMPARISON TO MODEL PREDICTIONS MODEL DESCRIPTION OF EXPERIMENTAL DATA: We were able to attain a decent fit of model output to experimental data under elevated CO 2 conditions by adjusting the variable parameters (bottom left). We then tested the model predictions for atmospheric CO 2 input and compared this to earlier experimental data from days 30-33 (bottom right). The model reasonably reproduces the CO 2 limitation dynamics of the system. Data Model Data Model
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Transcript of D ATA M ONITORING in both reactors L IQUID M EDIA : pH, Dissolved Oxygen (DO) H EADSPACE G ASES : O...
DATA MONITORINGin both reactors
LIQUID MEDIA: pH, Dissolved Oxygen (DO)HEADSPACE GASES: O2 , CO2 , Methane (CH4)
ALGAL REACTOR MASS BALANCE MODEL
A2M: Experimental and Modeling Approaches to Optimize an Algae-to-Methane Coupled Bioreactor SystemRuby An1,2,*, Joseph J. Vallino2, Zoe G. Cardon2
1The University of Chicago, Chicago, IL; 2Marine Biological Laboratory, Woods Hole, MA; *[email protected]
Why A2M?Electricity generated by solar and wind power depends on available sunlight or sufficient wind and currently cannot be stored efficiently. Algae-to-methane (A2M) has potential as an efficient and practical strategy to provide reliable renewable energy around the clock and on demand.• Algal biomass is a renewable resource that requires minimal land and
water usage.
• Methane can be efficiently stored and effectively transported using existing infrastructure.
• Coupled bioreactors incorporate nutrient recycling which removes the need for expensive fertilizer inputs.
In our A2M setup, these components create a closed system that uses the biological processes of photosynthesis and methanogenesis to generate usable energy solely from light and air.
AcknowledgementsNorsk Institutt for Vannforskning (NIVA) algal culture collection
Falmouth Wastewater Treatment Plant & Foreman LabZeiss Microscopy Staff & Huber Lab
Suzanne ThomasAnonymous Donor & Jeff Metcalf Internship
Specific Aims
1. Experimental Develop a closed system with
100% nutrient recycling Maximize methane production
2. Modeling Develop accurate description of
A2M system Use model predictions to improve
system performance & achieve experimental objectives
http://dryas.mbl.edu/A2M/Exp1/Exp1.html
Experimental Results
Modeling ResultsModels were built and numerically solved using the R package deSolve.
STATE VARIABLES : 1.cA – concentration of Algae2.cDIC – concentration of Dissolved Inorganic Carbon3.cO2(aq) – concentration of Dissolved Oxygen4.pCO2(g) – pressure of Carbon Dioxide gas5.pO2(g) – pressure of Oxygen gas
ALGAL GROWTH RATE EQUATION :
OUR ALGAE:Chlamydomonas reinhardtii CW15, a green algae mutant lacking a cell wall – making them easier to digest hence facilitating both methane production and nutrient recycling (left).
OUR MICROBES:We first inoculated with anaerobic groundwater from a sampling well near Little Pond (middle left). Although this established a microbial community, it did not perform methanogenesis. Subsequently, we inoculated again with wastewater sediments from the Falmouth Wastewater Treatment plant (bottom left).
Methods
25 μm
12 h
r Die
l Lig
ht C
ycle
Temperature: 25 °C
anaerobicMICROBIAL DIGESTER
aerobicALGAL REACTOR
A clump of algae being attacked by microbes
(right)
The A2M system in real life: microbial digester on left with top of cover removed (for photo visibility) & the algal reactor on the right (above left). Both reactors contain part liquid media (18L in digester, 4L in algal reactor) and part gaseous headspace. The monitoring & control rack measures liquid and gas data (above right).
Summary Optimized experimental system
conditions for high algal growth Accurately modeled algal reactor
dynamics
Next Steps Optimize experimental
conditions to establish & maximize methane production
Develop full coupled bioreactor model to simulate methanogenesis
Day 90
Day 1
KEY ASPECTS OF DATA• Diel Cycling (24 hr period)• Strong CO2 limitation with input at atmospheric level
– photosynthesis does not occur through full day• We stimulated high algal growth (see photos on right)
by elevating CO2 input levels to 2% in air.
Dou
bled
ligh
t lev
els
Elev
ated
CO
2 Inp
ut
Inst
alle
d ai
r spa
rger
sto
incr
ease
inpu
t airfl
ow
Bioreactors Coupled
pH c
rash
Bioreactors Decoupled
Atm
osph
eric
CO
2
•Days 30-33 : Atm. Input•Days 57-60 : Elevated
Platt equation for light limitationMonod CO2 Limitation
PARAMETERS :This model contains 35 parameters. Parameter values were determined both from system measurements and standard literature values; however, the following 5 parameters represented unknown system characteristics. These parameters were used to fit the model to experimental data from days 57-60: α, β, κL
O2A, rA, KCO2
MODEL FIT TO EXPERIMENTAL DATA DATA COMPARISON TO MODEL PREDICTIONS
MODEL DESCRIPTION OF EXPERIMENTAL DATA:We were able to attain a decent fit of model output to experimental data under elevated CO2 conditions by adjusting the variable parameters (bottom left). We then tested the model predictions for atmospheric CO2
input and compared this to earlier experimental data from days 30-33 (bottom right). The model reasonably reproduces the CO2 limitation dynamics of the system.
DataModel
DataModel
