Rotating Algal Biofilm Reactor for Biomass Growth and...

Post on 21-May-2020

1 views 0 download

transcript

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

1

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

3

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

4

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

5

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

6

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

7

Model

Conceptual diagram of growth conditions

8

Biofilm Model

• Based on EPA Benthic Algae model

• Growth formula (photosynthetic rate):

𝑑𝐵

𝑑𝑡= 𝑢 − 𝑅𝑟 − 𝐷𝑟 𝑆𝑎

𝑢 = 𝑢max ∗ 𝐼 ∗ 𝑇 ∗ 𝑁 ∗ 𝐴

9

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)

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)

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)

10

Model

• Nutrient removal due to biomass:

•𝑑𝑁

𝑑𝑡= −𝑢𝑛𝑁

𝑆𝑎

𝑉+ 𝑁𝐹𝑖𝑛 − 𝑁𝐹𝑜𝑢𝑡

•𝑑𝑃

𝑑𝑡= −𝑢𝑝𝑃

𝑆𝑎

𝑉+ 𝑃𝐹𝑖𝑛 − 𝑃𝐹𝑜𝑢𝑡

11

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

12

Results for Objective 1 Modeling of Biomass

13

R2=0.907

Results for Objective 1 Modeling of Biomass

14

R2=0.145

Results Observed Nutrient Removal

Results for Objective 2 Predicted vs. Measured

Comparison of projected uptake vs. measured uptake (Biofilm)

16

Results for Objective 2 Predicted vs. Measured

Comparison of projected uptake vs. measured uptake (Biofilm)

17

Results Observed Nutrient Removal

18

Results for Objective 2 Predicted vs. Measured

Comparison of measured uptake (Biofilm) vs. nutrient removal from bulk fluid

19

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

20

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.

21

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)

22

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)

23

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.

24