Biological treatment is driven by microbial kinetics, and biokinetic models — the IWA Activated Sludge Models (ASM) and Anaerobic Digestion Model (ADM1) — capture that behaviour mathematically. They predict effluent quality, sludge production, aeration demand and biogas yield under real, varying conditions.
The questions this modelling discipline answers
ASM1/2d/3 describe carbon, nitrogen and phosphorus removal through growth, decay and conversion of distinct biomass and substrate fractions — the standard for activated-sludge, MBBR and MBR design.
ADM1 represents hydrolysis, acidogenesis, acetogenesis and methanogenesis, predicting biogas yield, VFA accumulation and stability for digesters and high-rate anaerobic reactors.
Substrate-limited growth follows Monod kinetics, μ = μmax·S/(Ks+S); the model strings these rate expressions together across every species and reaction.
Kinetic and stoichiometric parameters are calibrated against plant or pilot data, so the model reflects your biomass and wastewater, not a textbook default.
Typical values at 20°C for municipal wastewater; temperature-corrected for design.
| Parameter | Symbol | Unit | Typical Value | Temperature Correction |
|---|---|---|---|---|
| Max specific growth rate (heterotrophs) | μH,max | d-1 | 4.0–6.0 | θ = 1.07 |
| Half-saturation coefficient (heterotrophs) | KS | g COD/m3 | 10–20 | θ = 1.00 |
| Max specific growth rate (autotrophs) | μA,max | d-1 | 0.5–0.8 | θ = 1.10 |
| Half-saturation coefficient (autotrophs) | KNH | g N/m3 | 0.5–1.0 | θ = 1.14 |
| Decay coefficient (heterotrophs) | bH | d-1 | 0.3–0.6 | θ = 1.04 |
| Decay coefficient (autotrophs) | bA | d-1 | 0.05–0.15 | θ = 1.04 |
| Yield coefficient (heterotrophs) | YH | g COD/g COD | 0.60–0.67 | None |
| Yield coefficient (autotrophs) | YA | g COD/g N | 0.15–0.24 | None |
| Anoxic growth correction factor | ηg | — | 0.6–0.8 | None |
| Anoxic hydrolysis correction factor | ηh | — | 0.4–0.6 | None |
These defaults are a starting point. Reynolds & Bauhm calibrates μmax, KS, Y and b against your pilot or plant data so the model predicts your effluent, not a textbook average.
The standard IWA anaerobic digestion model with 19 biochemical and 3 physico-chemical processes.
Composite particulates break into carbohydrates, proteins and lipids; soluble organics release through hydrolysis. Rate-limiting for high-solids feed.
Sugar-degrading acidogens (Xsu), amino-acid degraders (Xaa) and long-chain fatty acid degraders (Xfa) produce acetate, propionate, butyrate and hydrogen.
Propionate (Xc4) and butyrate (Xpro) oxidisers convert volatile fatty acids to acetate and H2. Thermodynamically inhibited at high H2 partial pressure.
Acetoclastic methanogens (Xac) split acetate to CH4 + CO2; hydrogenotrophic methanogens (Xh2) reduce CO2 with H2. pH inhibition below 6.5.
ADM1 predicts VFA accumulation before pH collapse. In high-rate anaerobic reactors (UASB, EGSB, IC), a slug load can drive propionate above 1,000 mg/L and stall methanogenesis within hours. ADM1 identifies the limiting substrate, the inhibitory threshold, and the required dilution or recycle rate to restore stability. It also predicts biogas yield, methane fraction, and nutrient demand for full digester design.
From default parameters to a plant-specific digital twin.
Review 12–24 months of influent/effluent data, operational logs, and maintenance records. Identify seasonality, shock loads, and control strategies.
Vary each parameter ±20% to rank influence on effluent COD, NH4-N, and sludge production. Focus calibration effort on the sensitive parameters.
Use non-linear regression (e.g. Nelder-Mead, Levenberg-Marquardt) or Bayesian inference to fit μmax, KS, Y, and b to measured data. Report 95% confidence intervals.
Split data into calibration (70%) and validation (30%) sets. Validate against independent data; R2 > 0.85 for COD and NH4-N required for design use.
Run design scenarios: cold-weather nitrification, peak flow, toxic shock, and maintenance shutdown. Use results to size aeration, buffers, and standby capacity.
A removal-efficiency number is a snapshot; a biokinetic model is a mechanism. By representing each microbial group, its substrate and its growth and decay rates, ASM and ADM1 predict how the plant responds to what actually happens — a cold snap that slows nitrification, a slug load that depletes oxygen, a toxic shock that stalls methanogenesis. The core is Monod-type kinetics for substrate-limited growth, wrapped in mass balances for every component across every reactor. Because the parameters are calibrated to measured data, the model becomes a reliable design and troubleshooting tool: sizing aeration for the worst-case nitrification day, predicting biogas for a CHP technical case, or diagnosing why an existing plant misses its consent.
Reynolds & Bauhm applies the right modelling discipline to the question — from a steady-state flowsheet to a calibrated digital twin — so design and operating decisions are made on evidence, not assumption.
Our expertise spans multiple industries with sector-specific water treatment solutions.