Post on 21-May-2020
Rotating Algal Biofilm Reactor (RABR) for Biomass Growth and Nutrient Removal
Terence Smith, Ashik Sathish,
Reese Thompson, Dr. Ronald Sims
Algae Biomass Summit
10/1/2013
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Outline
• Background
• Objectives
• Methods/Model
• Results
• Discussion/Conclusion
2
Background Wastewater Treatment
• Logan City Wastewater Treatment Facility – 460 acre facultative lagoon style wastewater treatment plant – Currently releasing excess phosphorus/nitrogen in effluent
• Average of 9.5 mg/L of Ammonia and 3.1 mg/L of Total Phosphorus – Requirements: Phosphorus and Ammonia = 1 mg/L, (Annual average, seasonal standards)
– Conventional retrofit is expensive • ~$110 million (activated sludge, nitrification/denitrification)
– 7,000 similar lagoon style wastewater treatment plants in the US
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Background Wastewater Treatment
• Potential solution: Algae – Grow on excess nutrients
• C106H175O42N16P
• Remove algae, remove problem
– Use algal biomass for different valuable bioproducts
• Challenge: – How to grow and harvest enough
algae (cost effectively) to make this solution viable.
– Conventional system: Raceway
• Difficult/costly to separate algae from the wastewater
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Potential solution: Rotating Algal Biofilm Reactor
(RABR) • Material details
– 74” diameter aluminum irrigation wheels
– ~60 inches in length
– ~4000 ft. of solid braid cotton rope (substratum)
– ~10700 L tank
– Wastewater drawn from final pond of treatment facility
• Data collection
– Biomass growth
– Nutrient removal
– Water temperature, pH, DO
– Weather conditions/Air temperature
• from local Campbell Scientific data logging station
– Photosynthetically active radiation
• from USU sensor
– Tested during different seasons, conditions
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Objectives
• Objective 1: Develop a predictive model of the growth of algal biofilm biomass on the rotating algal biofilm reactor (RABR) • Task 1: Propose model based on variables including light, temperature,
nutrients, and cultivation area
• Task 2: Observe biofilm growth at pilot scale under natural conditions to compare to model
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Objectives
• Objective 2: Develop a predictive model of nutrient removal by the rotating algal biofilm reactor (RABR) for wastewater remediation • Task 1: Propose nutrient removal model based on biofilm uptake of
nitrogen and phosphorus
• Task 2: Observe nutrient removal at pilot scale under natural conditions to compare to model prediction
• Application at other facilities/locations
• Maximize system efficiency
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Model
Conceptual diagram of growth conditions
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Biofilm Model
• Based on EPA Benthic Algae model
• Growth formula (photosynthetic rate):
𝑑𝐵
𝑑𝑡= 𝑢 − 𝑅𝑟 − 𝐷𝑟 𝑆𝑎
𝑢 = 𝑢max ∗ 𝐼 ∗ 𝑇 ∗ 𝑁 ∗ 𝐴
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Variable Identity Units
u specific growth rate (photosynthesis) g/m2/sec
u_max maximum growth rate (photosynthesis) g/m2/sec
I Light attenuation coefficient (photosynthesis) Dimensionless
T Temperature attenuation coefficient (photosynthesis) Dimensionless
N Nutrient attenuation coefficient Dimensionless
A Space attenuation coefficient Dimensionless
Rr Respiration rate 1/sec
Dr Death rate 1/sec
Sa Surface Area m2
Model Biofilm Growth
• Light attenuation coefficient (Steele’s equation)
– 𝐼 =𝐼𝑜
𝐼𝑠∗ exp(1 −
𝐼𝑜
𝐼𝑠)
• Temperature attenuation coefficient (Arrhenius equation)
– 𝑇 = 𝐺𝑡−20
𝑢=𝑢_max∗𝐼∗𝑇∗𝑁∗𝐴
Variable Identity Units
Io Observed PAR intensity umol/m2/sec
Is Optimum PAR intensity umol/m2/sec
Variable Identity Units
G Photosynthesis temperature coefficient Dimensionless
t Observed temperature Dimensionless
• Nutrient equation – Monod equation
– 𝑁 =𝑆
𝐾𝑠+𝑆
• Area equation (logistic)
– 𝐴 = (1 −𝑎
𝑎𝑚𝑎𝑥)
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Model Biofilm Growth
• Light attenuation coefficient (Steele’s equation)
– 𝐼 =𝐼𝑜
𝐼𝑠∗ exp(1 −
𝐼𝑜
𝐼𝑠)
• Temperature attenuation coefficient (Arrhenius equation)
– 𝑇 = 𝐺𝑡−20
𝑢=𝑢_max∗𝐼∗𝑇∗𝑁∗𝐴
Variable Identity Units
Io Observed PAR intensity umol/m2/sec
Is Optimum PAR intensity umol/m2/sec
Variable Identity Units
G Photosynthesis temperature coefficient Dimensionless
t Observed temperature Dimensionless
• Nutrient equation – Monod equation
– 𝑁 =𝑆
𝐾𝑠+𝑆
• Area equation (logistic)
– 𝐴 = (1 −𝑎
𝑎𝑚𝑎𝑥)
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Model Biofilm Growth
• Light attenuation coefficient (Steele’s equation)
– 𝐼 =𝐼𝑜
𝐼𝑠∗ exp(1 −
𝐼𝑜
𝐼𝑠)
• Temperature attenuation coefficient (Arrhenius equation)
– 𝑇 = 𝐺𝑡−20
𝑢=𝑢_max∗𝐼∗𝑇∗𝑁∗𝐴
Variable Identity Units
Io Observed PAR intensity umol/m2/sec
Is Optimum PAR intensity umol/m2/sec
Variable Identity Units
G Photosynthesis temperature coefficient Dimensionless
t Observed temperature Dimensionless
• Nutrient equation – Monod equation
– 𝑁 =𝑆
𝐾𝑠+𝑆
• Area equation (logistic)
– 𝐴 = (1 −𝑎
𝑎𝑚𝑎𝑥)
10
Model Biofilm Growth
• Light attenuation coefficient (Steele’s equation)
– 𝐼 =𝐼𝑜
𝐼𝑠∗ exp(1 −
𝐼𝑜
𝐼𝑠)
• Temperature attenuation coefficient (Arrhenius equation)
– 𝑇 = 𝐺𝑡−20
𝑢=𝑢_max∗𝐼∗𝑇∗𝑁∗𝐴
Variable Identity Units
Io Observed PAR intensity umol/m2/sec
Is Optimum PAR intensity umol/m2/sec
Variable Identity Units
G Photosynthesis temperature coefficient Dimensionless
t Observed temperature Dimensionless
• Nutrient equation – Monod equation
– 𝑁 =𝑆
𝐾𝑠+𝑆
• Area equation (logistic)
– 𝐴 = (1 −𝑎
𝑎𝑚𝑎𝑥)
10
Model Biofilm Growth
• Light attenuation coefficient (Steele’s equation)
– 𝐼 =𝐼𝑜
𝐼𝑠∗ exp(1 −
𝐼𝑜
𝐼𝑠)
• Temperature attenuation coefficient (Arrhenius equation)
– 𝑇 = 𝐺𝑡−20
𝑢=𝑢_max∗𝐼∗𝑇∗𝑁∗𝐴
Variable Identity Units
Io Observed PAR intensity umol/m2/sec
Is Optimum PAR intensity umol/m2/sec
Variable Identity Units
G Photosynthesis temperature coefficient Dimensionless
t Observed temperature Dimensionless
• Nutrient equation – Monod equation
– 𝑁 =𝑆
𝐾𝑠+𝑆
• Area equation (logistic)
– 𝐴 = (1 −𝑎
𝑎𝑚𝑎𝑥)
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Model
• Nutrient removal due to biomass:
•𝑑𝑁
𝑑𝑡= −𝑢𝑛𝑁
𝑆𝑎
𝑉+ 𝑁𝐹𝑖𝑛 − 𝑁𝐹𝑜𝑢𝑡
•𝑑𝑃
𝑑𝑡= −𝑢𝑝𝑃
𝑆𝑎
𝑉+ 𝑃𝐹𝑖𝑛 − 𝑃𝐹𝑜𝑢𝑡
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Variable Identity Units
N Bioavailable nitrogen mg/L
n N content of biofilm biomass Dimensionless
P Bioavailable phosphorus mg/L
p P content of biofilm biomass Dimensionless
F Flow rate L/day
Sa Surface area m2
V Volume of tank L
Results Biofilm Growth
• Natural environmental conditions • Continuous flow • Different retention times
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Results for Objective 1 Modeling of Biomass
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R2=0.907
Results for Objective 1 Modeling of Biomass
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R2=0.145
Results Observed Nutrient Removal
Results for Objective 2 Predicted vs. Measured
Comparison of projected uptake vs. measured uptake (Biofilm)
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Results for Objective 2 Predicted vs. Measured
Comparison of projected uptake vs. measured uptake (Biofilm)
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Results Observed Nutrient Removal
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Results for Objective 2 Predicted vs. Measured
Comparison of measured uptake (Biofilm) vs. nutrient removal from bulk fluid
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Results for Objective 2 Predicted vs. Measured
Environmental factors affecting nutrient removal: -pH (precipitation, volatilization) -DO (denitrification)
Comparison of measured uptake (Biofilm) vs. nutrient removal from bulk fluid
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Energy balance
40 g/m2/d
518.4 kJ/d
(Electricity)
Biomass
* Source: Christenson, L. B., & Sims, R. C. (2012). Rotating algal biofilm reactor and spool harvestor for wastewater treatment with biofuels by-products. Biotechnology and Bioengineering, 109(7), 1674-1684.
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RABR Effective area: 2.5 m2
RABR Productivity: 40 g/m2-day
RABR Power requirement*: 6 watts
Energy Consumption: 5184.00 KJ/kg Algae
RABR Productivity per unit: 100 g dry algae/day
Biomass energy content 21,400.00 KJ/kg Algae
Energy balance 16,216.00 KJ/kg Algae
Algae From RABRs
Discussion/conclusion
• Objective 1 - Develop a predictive model of the growth of algal biofilm biomass on the RABR
– Promising results for modeling biomass growth
• Objective 2 - Develop a predictive model of nutrient removal by the RABR for wastewater remediation
– Good agreement for biological uptake of nutrients into biofilm
– Environmental conditions dominant in observed nutrient removal • Future modeling needs to account for nutrient removal via pH and DO
• Current work – Lifecycle analysis (upstream and downstream)
• Clemson University and Dr. Jason Quinn (USU)
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Acknowledgements
– Special thanks to:
• Logan City Environmental Department
• WesTech Engineering
• Utah Water Research Laboratory
• Carollo Engineering
• US EPA
• Campbell Scientific
• Utah Science Technology and Research (USTAR)
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Sources
1. Ambrose, R. B., Martin, J. L., & Wool, T. A. U.S. Environmental Protection Agency, Office of Research and Development. (2006). Wasp7 benthic algae - model theory and user's guide (R600/R-06/106). Washington DC: U.S. Environmental Protection Agency.
2. Cerucci, M., Jaligama, G. K., & Ambrose, R. B. (2010). Comparison of the monod and droop methods for dynamic water quality simulations. Journal of Environmental Engineering, 136(10), 1009-1019.
3. Christenson, L. B., & Sims, R. C. (2012). Rotating algal biofilm reactor and spool harvestor for wastewater treatment with biofuels by-products. Biotechnology and Bioengineering, 109(7), 1674-1684.
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