Modelling of the removal of livestock-related airborne contaminants via biofiltration Dennis McNevin and John Barford Department of Chemical Engineering University of Sydney Australia

Biofiltration

Mathematical model

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)

Equations Differential balances or transport equations mass, heat Equilibrium expressions physical, chemical Rate expressions mass & heat transfer, microbial activity Air phase behaviour pressure, density

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.

Aqueous phase mass balances Aqueous species divided into four groups:

Volatile, non-dissociating species j = VOC, O 2, N 2 Diffusion Bulk flow microbial production/consumption mass transfer from air/biofilm interface

Non-volatile, non-dissociating species j = Ca 2+, Cl - Diffusion Bulk flow

Dissociating species

Volatile, dissociating species j = NH 3, H 2 S, CO 2 Diffusion Bulk flow microbial production/consumption mass transfer from air/biofilm interface

Non-volatile, dissociating species j = HNO 2, HNO 3, H 2 SO 4 Diffusion Bulk flow

Interfacial equilibrium Partition coefficient for mass Antoine equation for temperature

Chemical equilibrium Dissociation Water Acids Bases

Chemical equilibrium Electroneutrality

Mass transfer Air phase Wakao & Kaguei (1982) Aqueous phase (diffusion controlled)

Heat transfer Air phase Wakao & Kaguei (1982) Aqueous phase (diffusion controlled)

Gross rate of biomass growth Monod (1942)

Net rate of biomass growth Endogenous or maintenance metabolism gives a “true” growth rate: k = VOC oxidisers, nitrifiers, sulfide oxidisers

Microbial substrates For each micro-organism, three substrate requirements are considered: anabolism –carbon source catabolism (energy source) –electron donor –electron acceptor

Case study Nitrification Anabolism (balanced for carbon) Catabolism

Bioconversion rates Bioconversion rates are linked to gross biomass growth rates: Y j/x = moles compound j per g biomass

pH and growth rate

Temperature and growth rate

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:

Numerical solution (cont.) System of O.D.E.’s and algebraic equations solved by SPEEDUP (Aspen Technology, 1994) Modified Gear’s method integrator selected

Comparison with experimental data Hodge & Devinny (1995) Compost biofilter for removal of ethanol Solid medium characteristics: = 0.45 W = 60 % = g dry compost per m 3 = m (a = m 2 g -1 ) =

Comparison with experimental data (cont.) Inlet air –u g = 23.7 m.hr -1 –C EtOH = ppm Solid medium buffered to pH 7.5 with mol.L -1 total carbonate

Air phase ethanol concentration

Carbon dioxide concentration profile

Aqueous phase pH

Tuning the model Requires knowledge of: microbiological constants –kinetic –stoichiometric thermodynamic equilibrium constants –physical –chemical rheological properties

Design variables Choice of solid medium Column dimensions –diameter –height boundary conditions initial conditions

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

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 )

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