Modelling of the removal of livestock-related airborne contaminants via biofiltration

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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 m3 dry
  solid)
• voidage of the dry solid (m3 space per m3 dry
  solid)
• water content of the solid (m3 water per g dry
  solid)
• interfacial area available for heat and mass
  transfer (m2 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 CO2 H2O chemoheterotrophs
• Nitrification
• NH4 NO2- Nitrosomonas spp.
• NO2- NO3- Nitrobacter spp.
• Sulfide oxidation
• S2- SO42- 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, O2, N2
• Diffusion
• Bulk flow
• microbial production/consumption
• mass transfer from air/biofilm interface

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Non-volatile, non-dissociating species

• j Ca2, Cl-
• Diffusion
• Bulk flow

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Dissociating species
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Volatile, dissociating species

• j NH3, H2S, CO2
• Diffusion
• Bulk flow
• microbial production/consumption
• mass transfer from air/biofilm interface

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Non-volatile, dissociating species

• j HNO2, HNO3, H2SO4
• Diffusion
• Bulk flow

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Interfacial equilibrium

• Partition coefficient for mass
• Antoine equation for temperature

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Chemical equilibriumDissociation

• Water
• Acids
• Bases

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Chemical equilibriumElectroneutrality
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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
• Yj/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 Gears 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 m3
• 0.001 m (a 0.004 m2g-1)
• 0.0003

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Comparison with experimental data (cont.)

• Inlet air
• ug 23.7 m.hr-1
• CEtOH 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 (m2hr-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