Machine learning vs. ADM1: Reliable biogas prediction with minimal data requirements in full-scale plants

Tisocco, Sofia, Weinrich, Sören, Møller, Henrik Bjarne, Ward, Alastair James, Kilmartin, Liam, Zhan, Xinmin, & Crosson, Paul. (2026). Machine learning vs. ADM1: Reliable biogas prediction with minimal data requirements in full-scale plants. Environmental Science and Ecotechnology, 29, 100662. https://doi.org/10.1016/j.ese.2026.100662

Abstract

Anaerobic digestion harnesses microbial processes to convert organic wastes into renewable biogas, offering a sustainable pathway for energy production. In agricultural settings, biogas plants often co-digest livestock manure with crop residues, yet seasonal variations in feedstock quality introduce fluctuations that challenge process stability and yield optimization. Mechanistic models such as the Anaerobic Digestion Model No. 1 (ADM1) provide detailed biochemical simulations but require extensive substrate characterization, limiting their practicality for full-scale operations. Here we show that a simplified ADM1, alongside machine learning approaches—random forest and long short-term memory (LSTM) networks—achieves comparable accuracy in predicting daily biogas and methane production from a full-scale plant over 2023–2024. All models yielded Nash-Sutcliffe efficiencies above 0.78, with random forest excelling when incorporating feedstock quantities and maize silage volatile solids. While LSTM proved effective even with minimal inputs, it incurred a training time 141 times greater than ADM1, highlighting critical trade-offs in computational efficiency. These findings advance hybrid modelling strategies for real-time monitoring, enabling operators to balance predictive precision with data requirements to enhance renewable energy integration and agricultural sustainability.

Funder

Teagasc

Publisher

Elsevier

Rights

CC BY

cb

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