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Adaptive ensemble forecasting of industrial-scale biogas production under fluctuating operating conditions
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DOI：10.1016/j.seta.2026.105281.png)
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Abstract
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• Dynamic LSTM-XGB ensemble handles industrial biogas fluctuations. • Interpretable ML revealed core operational drivers of biogas production. • SHAP analysis validates VFA as a key early warning metric for AD. • Adaptive framework reduces prediction errors by 23.6% over baselines. • Dynamic weighting paradigm generalizes to other fluctuating processes.
Keywords:
Industrial-scale biogas production
Anaerobic digestion
Dynamic ensemble forecasting
Data volatility
Machine learning
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