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The Anaerobic Digestion Model No.1 (ADM1) application for continuous anaerobic digestion is often constrained by challenges in reliably calibrating model parameters, especially when long-term data are unavailable. This study presents a Bayesian inference-based framework that enables ADM1 calibration using only initial-stage digester performance data. A custom Python implementation was developed, integrating modules for global sensitivity analysis, Bayesian calibration and parameter identifiability evaluation. Key microbial and ionic parameters were refined through Random Balance Designs-Fourier Amplitude Sensitivity Test (RBD-FAST), identifying sugar/acetate degraders and cation/anion levels in the inoculum as critical drivers of steady-state performance. With informative priors derived from similar ADM1 studies, the model was calibrated with less than two hydraulic retention times of data and validated against steady-state performance data. It predicted pH and total chemical oxygen demand (tCOD) with mean percentage errors of 1.10 % and 5.38 % respectively. Biogas production trends were captured within the 95 % credible interval for 63.14 % of observations. Compared to uniform priors, the Bayesian approach with informative priors improved predictive accuracy. Jensen-Shannon divergence revealed that hydrolysis rates is the most identifiable for thermally hydrolysed sludge. Unlike conventional ADM1 calibration approaches that require long-term steady-state data, this Bayesian framework achieves reliable predictions using only early-stage observations. By enabling accurate simulation of organic contaminant degradation and system stability from limited data, the framework supports risk-informed design and operation of anaerobic digesters and offers a solution for data-scarce industrial settings to improve safety, sustainability, and optimisation.
Liu et al. (Thu,) studied this question.