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A Dynamic Model of Biofiltration for Odor Control Hebi Li, Ron W. Martin Jr., John C. Crittenden, James R. Mihelcic Department of Civil and Environmental.

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Presentation on theme: "A Dynamic Model of Biofiltration for Odor Control Hebi Li, Ron W. Martin Jr., John C. Crittenden, James R. Mihelcic Department of Civil and Environmental."— Presentation transcript:

1 A Dynamic Model of Biofiltration for Odor Control Hebi Li, Ron W. Martin Jr., John C. Crittenden, James R. Mihelcic Department of Civil and Environmental Engineering Michigan Technological University Based on Material Presented to Central States Water Environment Association (CSWEA) Conference, May 14, 2001 Copyright © 2001-2002. Michigan Technological University. All Rights Reserved.

2 Outline  Introduction  Motivation and Objectives  Model Development  Model Calibration and Verification  Model Applications

3 Introduction  Odor Sources l Wastewater collection and treatment, petrochemical, paper, agricultural, et. al. l Odor-causing chemicals: Ammonia, VOCs, reduced sulfur compounds  Hydrogen Sulfide (H 2 S) –Low Odor Threshold: 8.5-1,000 ppb v –Toxicity –Corrosive

4 Introduction (continued)  Odor Control Technologies LPhysicochemical: scrubbing, adsorption, incineration, oxidation, masking. –Disadvantage: high operational cost Biofiltration –Advantages: requires no chemical addition can be operated at room temperature low operational cost

5 Why a Biofiltration Model?  Design and operation of biofilters for control of odor-causing air emissions is not well developed (Hautakangas et al., 1999).  A good mathematical model is useful to aid in biofilter design and operation.  No model had been available for design and optimization of biofilters used for odor control (Card, 1999).

6 Study Objectives  Develop a mathematical model that simulates the processes occurring in a biofilter used for odor control  Calibrate and verify the model using full-scale and pilot-scale data  Evaluate the usefulness of the model as a tool for biofilter design and operation

7 Schematics of a Biofilter Air with H 2 S Clean air Packing Material Supporting Biofilm Biological Reaction Water with nutrients Water with oxidation products

8 Schematics of a Biofilter GasLiquid BiofilmSupport

9 Mechanisms Used in Model  Advective flow in gas- and liquid- phases  Mass transfer at the gas-liquid and liquid-biofilm interfaces  Internal diffusion in the biofilm  Active biomass growth and decay and biological reaction in the biofilm

10 Plant Layout - Cedar Rapids (Iowa) WPCF

11 2 Full-Scale Biofilters in Parallel Surface Area = 2,556 ft 2 each

12 Packing Medium - Lava Rock (Average ~1 inch Diameter) Interior of Cedar Rapids Biofilter 6 ft. media depth

13 Cross Section of Lava Rock Showing Porous Structure

14 Intermittent Rinse Water Feed (Secondary Effluent 5 minutes per hour)

15 Pilot-Scale Biofilter Lava Rock 3.1 ft 2 Area by 6 ft. Depth

16 Model Calibration and Verification  Using H 2 S data from Cedar Rapids  Calibration –pilot data: 03/07/00~03/14/00  Verification –pilot-scale data: 01/17/00~01/28/00 –full-scale data: 10/09/98 - 11/03/98  Minimize Objective Function:

17 Model Calibration with Pilot-scale Data from Cedar Rapids (20°C) Model Calibration with Pilot-scale Data from Cedar Rapids (20°C) (Air residence time 6.5 sec)

18 Model Verification with Pilot-scale Data from Cedar Rapids(11.5°C) Model Verification with Pilot-scale Data from Cedar Rapids (11.5°C) (Air residence time 7.7 sec)

19 Model Verification with Full-scale Data from Cedar Rapids (25°C) Model Verification with Full-scale Data from Cedar Rapids (25°C) (Air residence time 8.4 sec)

20 Outline  Introduction  Motivation and Objectives  Model Development  Model Calibration and Verification  Model Applications

21 Model Application: Effect of Residence Time

22 Model Application: Effect of Influent Conc./Temperature 9

23 Model Application: Effect of Variable Influent Conc.

24 Conclusions from Modeling  The lava rock-based biofilter is efficient for treating odorous H 2 S.  The biofiltration model, which has been incorporated into a friendly software (Biofilter TM ), is capable of predicting the biofilter removal performance of H 2 S.  The model is a useful tool for biofilter design and operation.

25 Further Reading Li, Hebi, John C. Crittenden, James R. Mihelcic, H. Hautakangas, “Optimization of Biofiltration for Odor Control: Model Development and Parameter Sensitivity," Water Environment Research, 74(1):5- 16, 2002. Martin, Ron W., Hebi Li., James R. Mihelcic, John C. Crittenden, Donald R. Lueking, Chris R. Hatch, Pat Ball, “Optimization of Biofiltration for Odor Control: Model Verification and Applications," Water Environment Research, 74(1):17-27, 2002. Li, Hebi, James R. Mihelcic, John C. Crittenden, Keith Anderson, “Application of a Dynamic Biofiltration Model to a Two-Stage Biofilter that treats Hydrogen Sulfide and Organic Sulfur Compounds,” Proceedings of the 75 nd Annual Water Environment Federation Conference & Exposition, September 28-October 2, 2002. Hautakangas, Hannu, James R. Mihelcic, John C. Crittenden, Eric J. Oman, Optimization and Modeling of Biofiltration for Odor Control, Proceedings of the 72nd Annual Water Environment Federation Conference & Exposition, October 10-13, 1999.

26 Acknowledgements  Project Support: –Prof. Donald Lueking (MTU) –Christopher R. Hatch (Cedar Rapids WPCF) –Patrick Ball (Cedar Rapids WPCF)  Financial Support: –Cedar Rapids Water Pollution Control Facility –National Center for Clean Industrial and Treatment Technologies –US Department of Education GAANN Program


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