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Modelling of the removal of livestock-related airborne contaminants via biofiltration Dennis McNevin and John Barford Department of Chemical Engineering University of Sydney Australia
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Biofiltration
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Mathematical model
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Solid filter medium bulk density of the dry solid (g per m 3 dry solid) voidage of the dry solid (m 3 space per m 3 dry solid) water content of the solid (m 3 water per g dry solid) interfacial area available for heat and mass transfer (m 2 per g dry solid) partition coefficient (g.m -3 compound j in the gas phase at equilibrium with 1 g.m -3 compound j adsorbed onto the solid)
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Equations Differential balances or transport equations mass, heat Equilibrium expressions physical, chemical Rate expressions mass & heat transfer, microbial activity Air phase behaviour pressure, density
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Bioconversions aerobic Organic carbon oxidation VOC CO 2 + H2O chemoheterotrophs Nitrification NH 4 + NO 2 - Nitrosomonas spp. NO 2 - NO 3 - Nitrobacter spp. Sulfide oxidation S 2- SO 4 2- Thiobacillus spp.
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Aqueous phase mass balances Aqueous species divided into four groups:
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Volatile, non-dissociating species j = VOC, O 2, N 2 Diffusion Bulk flow microbial production/consumption mass transfer from air/biofilm interface
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Non-volatile, non-dissociating species j = Ca 2+, Cl - Diffusion Bulk flow
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Dissociating species
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Volatile, dissociating species j = NH 3, H 2 S, CO 2 Diffusion Bulk flow microbial production/consumption mass transfer from air/biofilm interface
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Non-volatile, dissociating species j = HNO 2, HNO 3, H 2 SO 4 Diffusion Bulk flow
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Interfacial equilibrium Partition coefficient for mass Antoine equation for temperature
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Chemical equilibrium Dissociation Water Acids Bases
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Chemical equilibrium Electroneutrality
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Mass transfer Air phase Wakao & Kaguei (1982) Aqueous phase (diffusion controlled)
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Heat transfer Air phase Wakao & Kaguei (1982) Aqueous phase (diffusion controlled)
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Gross rate of biomass growth Monod (1942)
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Net rate of biomass growth Endogenous or maintenance metabolism gives a “true” growth rate: k = VOC oxidisers, nitrifiers, sulfide oxidisers
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Microbial substrates For each micro-organism, three substrate requirements are considered: anabolism –carbon source catabolism (energy source) –electron donor –electron acceptor
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Case study Nitrification Anabolism (balanced for carbon) Catabolism
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Bioconversion rates Bioconversion rates are linked to gross biomass growth rates: Y j/x = moles compound j per g biomass
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pH and growth rate
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Temperature and growth rate
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Numerical solution P.D.E.’s converted to O.D.E.’s by discretising the spatial dimension with finite (backward) differences Biofilter height divided into n equal elements. In the ith element:
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Numerical solution (cont.) System of O.D.E.’s and algebraic equations solved by SPEEDUP (Aspen Technology, 1994) Modified Gear’s method integrator selected
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Comparison with experimental data Hodge & Devinny (1995) Compost biofilter for removal of ethanol Solid medium characteristics: = 0.45 W = 60 % = 247 000 g dry compost per m 3 = 0.001 m (a = 0.004 m 2 g -1 ) = 0.0003
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Comparison with experimental data (cont.) Inlet air –u g = 23.7 m.hr -1 –C EtOH = 11 000 ppm Solid medium buffered to pH 7.5 with 0.0251 mol.L -1 total carbonate
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Air phase ethanol concentration
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Carbon dioxide concentration profile
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Aqueous phase pH
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Tuning the model Requires knowledge of: microbiological constants –kinetic –stoichiometric thermodynamic equilibrium constants –physical –chemical rheological properties
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Design variables Choice of solid medium Column dimensions –diameter –height boundary conditions initial conditions
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Reaction vs diffusion limitation Reaction limitation: –low Thiele number, –high solubility, C* –low half-saturation constant, K Diffusion limitation –high Thiele number, –low solubility, C* –high half-saturation constant, K
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Thiele number Indication of relative rates of biological degradation and diffusion through the biofilm = aqueous film characteristic dimension (m) x = biomass concentration (g.m -3 ) = biomass growth rate (hr -1 ) Y = biomass yield from substrate (g.g -1 ) D = diffusion coefficient (m 2 hr -1 )
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In conclusion... Numerical model successfully predicts VOC removal via biofiltration Model reveals information useful for optimising microbial activity Model may be tuned for a particular application
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