Generalized parameter estimation and calibration for biokinetic models using correlation and single variable...

Area of Science:

• Environmental Engineering
• Biochemical Engineering
• Wastewater Treatment

Background:

• Accurate biokinetic parameters are crucial for reliable anaerobic digestion (AD) models.
• Traditional multi-parameter optimization methods can be computationally intensive and suffer from identifiability issues.
• Existing models often require extensive calibration for specific processes like sulfate reduction.

Purpose of the Study:

• To propose a generalized and efficient method for estimating biokinetic parameters in AD models.
• To reduce the number of parameters requiring direct fitting to experimental data.
• To improve the accuracy and reduce computational cost of AD model calibration.

Main Methods:

• A two-step approach combining mechanistic correlations for initial parameter estimation.
• Sensitivity-based hierarchical and sequential single parameter optimization (SHSSPO) for remaining parameters.
• Application and validation using sulfate reduction parameters within the IWA Anaerobic Digestion Model No. 1 (ADM1).

Main Results:

• The method successfully estimated most biokinetic parameters using mechanistic correlations.
• SHSSPO efficiently calibrated the remaining parameters, identifying only hydrogen sulfide inhibition parameters for optimization.
• The proposed method demonstrated superior performance over multi-dimensional optimization in terms of error and computation time.
• Model predictions showed comparable or improved accuracy compared to traditional methods.

Conclusions:

• The generalized method offers a deterministic, step-by-step approach for biokinetic parameter estimation.
• It significantly decreases identifiability uncertainty and computational effort in AD modeling.
• The method shows potential for broad application to other biokinetic models in wastewater treatment.