A Comparative Model-Analysis on Sulphide Bio-oxidation with Different Electron Acceptors

Tensions the paper itself points to. Each side quotes the passage it comes from.

• Within the paper Model predicts anode potential has lower impact on bioelectrochemical sulphide oxidation than expected, yet anode-based removal is fastest in case study

  One side

  Anode potential exhibits low sensitivity in sulphide concentration (SHS⁻) and plateau effect above 0.1–0.15 V

  “the uptake rate of sulphide increases with an increase in the potential up to a certain threshold (here: 0.1-0.15 V)... Consequently, increasing the potential further would not improve the oxidation rate”

  Other side

  Anode achieves target sulphide removal in 1.7 days vs. 2.4 days (oxygen) and 3.9 days (nitrate), making it fastest pathway

  “the simulation results show that the target concentration is reached within 3.9 days with nitrate, 2.4 days with oxygen, and 1.7 days with an anode potential of 0.1 V”

  Authors’ reading. Authors acknowledge this in Section 3.4: 'the anode potential has a lower impact on the simulated bioelectrochemical sulphide oxidation rate than expected.' The discrepancy arises because the Nernst–Monod term saturates at low potential, making kinetics substrate-limited; the anode's speed advantage comes from stoichiometry and biomass kinetics, not potential control. Authors note this as a model limitation requiring further investigation.

Specific gaps in this paper, with an experiment that would close each.

• High Model does not test or compare different initial biomass concentrations (Xeet, Xsob, Xsnb) systematically; only one base case (0.1 mM) and one sensitivity run (1 mM) are shown

  Section 3.1.1 states 'The effect of the uptake rate is more prominent with a lower concentration of active biomass (results not included)' and Section 3.3 mentions 'by increasing the initial biomass from 0.1 to 1 mM, the target concentration of 0.1 mM sulphide was reached in less than 60 days (results not included).' This suggests biomass concentration is a critical control but is not systematically characterized.

  Try. Vary initial active biomass concentration from 0.01 to 1.0 mM for each electron acceptor pathway; measure time to reach SHS⁻ = 0.1 mM and steady-state removal rate as function of biomass.

• High Model assumes chemolithoautotrophic growth only; does not include heterotrophic competition or inhibition by high nitrate/oxygen concentrations, which are known to occur in real AD systems

  Section 3.4 states: 'To improve the model, other anaerobic microbial processes and competing reactions should be implemented. These processes can affect the oxidation rates through competition for substrates and inhibition.' Authors also note 'supplying high initial concentrations is unrealistic, as the microorganisms can be negatively affected by too high dosages of nitrate or oxygen' but do not model this.

  Try. Add inhibition kinetics (e.g., competitive or non-competitive) for high NO₃⁻ and O₂ concentrations; test km⁰ and Ks values at [NO₃⁻] > 5 mM and [O₂] > 2 mM to identify inhibition thresholds.

• High Anode potential implementation uses simplified Nernst–Monod with single electron transfer (n=1) and no biofilm thickness, mass transfer, or competing electron sinks; model does not capture potential-dependent biofilm dynamics

  Section 3.4 explicitly states: 'To improve the model implementation of bioelectrochemical sulphide oxidation, incorporation of different stoichiometry and other bioanode-related processes would be valuable. This includes biofilm thickness, mass transfer limitations, competing reactions at the bioanode surface, and electron transfer limitations (due to different electron sinks).' The plateau effect at 0.1–0.15 V may be an artifact of oversimplification.

  Try. Implement biofilm-based model with variable thickness and electron transfer resistance; test anode potential from −0.2 to +0.5 V with measured biofilm conductivity and compare to experimental MES data.

• Medium Model does not test C/N ratio or carbon source availability, which are critical for heterotrophic growth and may compete with chemolithoautotrophic sulphide oxidation in real AD

  Paper assumes chemolithoautotrophs only (Section 2, assumption 2) and does not model organic carbon or heterotrophic pathways. In real biogas digesters, organic matter is abundant and may support heterotrophic denitrifiers or aerobic heterotrophs that compete with sulphide oxidizers.

  Try. Add heterotrophic denitrification and aerobic heterotrophy pathways; vary influent COD/NO₃⁻ ratio from 1 to 10 and measure sulphide removal rate and biomass composition.

• Medium Model does not predict or test N2O or NO₂⁻ accumulation during nitrate-based sulphide oxidation; only assumes complete reduction to N₂

  Stoichiometric equations 10–11 assume 100% conversion to N₂ with no intermediate accumulation. Real denitrifying systems often produce N2O, especially under low C/N or high NO₃⁻ conditions. No sensitivity analysis on N2O yield or NO₂⁻ accumulation is provided.

  Try. Implement branched denitrification pathway with NO₃⁻ → NO₂⁻ → NO → N₂O → N₂; vary [NO₃⁻] from 0.1 to 5 mM and measure N2O and NO₂⁻ accumulation as function of SHS⁻ and pH.

• High Model validation is entirely absent; no experimental data from real biogas plants or lab reactors are used to calibrate or validate predictions

  Section 3.4 concludes: 'calibration and validation of the model with data from real plants will improve the model further.' Authors acknowledge 'different kinetic constants and parameters can be found in the literature related to the sulphide routes and the specific microbial groups. The variation will affect the simulation results.' No comparison to published experimental data is provided.

  Try. Conduct batch or continuous-flow experiments with real biogas digestate or enriched sulphide-oxidizing cultures; measure SHS⁻, SNO₃⁻, SO₂, and biomass over time; fit Monod and Nernst–Monod parameters; validate model predictions against independent dataset.