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

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. • We compared a simplified ADM1 with RF and LSTM models to simulate biogas production at a full-scale plant. • All three models achieved reliable predictive performance with Nash-Sutcliffe efficiency values above 0.78. • LSTM enabled high-accuracy forecasting using only daily feedstock mass, reducing the need for chemical analyses. • Maize silage was identified as the most critical input feature influencing biogas and methane production across models. • LSTM training time was 141 times higher than ADM1, revealing a significant computational-accuracy trade-off.