Accurate prediction of Volatile Solids Reduction (VSR) is critical for sizing municipal sludge anaerobic digesters and estimating biogas potential. Existing models often rely on historical data and focus on forecasting performance for operating plants, making them unsuitable for design stages where such data is unavailable. This study introduces a mathematically explicit, pre-calibrated, data-driven model for predicting VSR under mesophilic conditions without requiring prior plant data. This contrasts with mechanistic approaches, which typically need site-specific calibration and lack direct integration into design workflows. The model was developed using process data from six full-scale wastewater treatment plants, covering diverse sludge types and treatment configurations, with time series data ranging from 192 to 4,696 days. Key input variables include hydraulic retention time, solids retention time of the activated sludge, wastewater temperature, primary sludge volatile solids content, and primary sludge fraction. Benchmarking against the Anaerobic Digestion Model No. 1 (ADM1), both calibrated and default, showed the proposed model achieved the lowest average prediction root mean square error (4.2% VSR) and bias (2.3% VSR) when tested on unseen plants, confirming its suitability for design tasks. The model can support informed sizing of biogas networks and ancillary equipment, reducing inefficiencies and operational costs when applied within appropriate constraints. It also enables dynamic activated sludge retention time optimization strategies to enhance methane yield. Future improvements include integrating influent composition data and expanding the dataset to strengthen robustness across climates and process configurations.
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