Forecasting the Methane Yield of a Commercial-Scale Anaerobic Digestor Based on the Biomethane Potential of Feedstocks

Özlem Türker Bayrak, Sibel Uludag-Demirer, Meicai Xu and Wei Liao ()
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Özlem Türker Bayrak: Department of Inter-Curricular Courses, Cankaya University, Ankara 06790, Türkiye
Sibel Uludag-Demirer: Anaerobic Digestion Research and Education Center, Department of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
Meicai Xu: Anaerobic Digestion Research and Education Center, Department of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA
Wei Liao: Anaerobic Digestion Research and Education Center, Department of Biosystems & Agricultural Engineering, Michigan State University, East Lansing, MI 48824, USA

Energies, 2025, vol. 18, issue 22, 1-14

Abstract: With rising energy demand and the need for sustainable waste treatment, anaerobic digestion (AD) has emerged as a key technology for converting organic residues into renewable energy. However, predicting methane yield in full-scale facilities remains challenging due to the complexity of AD processes, the variability of feedstocks, and the impracticality of frequent biochemical methane potential (BMP) testing. In this study, we developed a simple, data-driven approach to forecast methane production in a commercial-scale digester co-digesting manure and food waste. The model employs weekly cumulative BMP of feedstock mixtures, calculated from literature values, as the explanatory variable. The model achieved an R 2 of 0.70 and a forecast mean absolute percentage error (MAPE) of 7.4, indicating its potential for full-scale AD prediction. Importantly, the analysis revealed a long-run equilibrium between BMP and methane yield, with deviations corrected within roughly one month—closely matching the system’s hydraulic retention time. These findings demonstrate that literature-based BMP values can be used to reliably predict methane yield in operating AD systems, offering a low-cost and scalable tool to support decision-making in waste management and biogas plant operations.

Keywords: biomethane potential; co-digestion; manure; food waste; prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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