Modelling sulfate reduction in anaerobic digestion: Complexity evaluation and parameter calibration

Area of Science:

• Environmental microbiology
• Biochemical engineering
• Computational modeling

Background:

• Sulfate reduction (SR) is a key process in anaerobic digestion (AD).
• Accurate modeling of SR is crucial for optimizing AD systems.
• Existing SR models vary in complexity and accuracy.

Purpose of the Study:

• To comparatively analyze five different SR model structures for AD.
• To evaluate model accuracy and identify the optimal complexity.
• To provide guidance for model developers and users.

Main Methods:

• Systematic calibration of five SR models against extensive experimental data.
• Utilized a recent parameter calibration technique.
• Compared model performance based on prediction errors.

Main Results:

• The M_AH model, incorporating acetate and hydrogen utilizing SR bacteria, demonstrated a favorable balance between performance and complexity.
• All models performed acceptably, except for the model solely including hydrogen-utilizing SR bacteria.
• Prediction errors were minimized with the M_AH model.

Conclusions:

• The M_AH model is recommended for general AD applications due to its balance of accuracy and simplicity.
• More complex SR models are only necessary for specific experimental scenarios.
• Model selection should consider the trade-off between complexity and predictive performance.