Adaptive Digital Twin with LSTM-Based Forecasting and Model Predictive Control for Biogas Production Optimization

Abstract

The increasing complexity of anaerobic digestion processes, characterized by nonlinear biochemical interactions, delayed dynamics, and sensitivity to feedstock variability, necessitates the development of advanced control and prediction systems. This study proposes an adaptive digital twin framework that integrates a physics-based anaerobic digestion model (ADM1) with a data-driven long short-term memory (LSTM) network and a delay-aware model predictive control (MPC) strategy. The proposed framework incorporates an adaptive residual synchronization mechanism that continuously updates the hybrid model parameters based on deviations between simulated and measured methane production, supported by an Extended Kalman Filter (EKF) for state estimation. Unlike conventional approaches that treat prediction and control as separate tasks, the proposed framework unifies these components within a single cyberphysical architecture, enabling real-time optimization of methane production. Experimental evaluation based on synthetic ADM1-consistent datasets and validated operational profiles demonstrates improved predictive reliability with an F1-score reaching 0.92 and a methane yield increase of up to 23.4% compared to baseline methods. The results confirm that the integration of temporal learning and predictive control significantly enhances process stability and efficiency under uncertain and time-delayed conditions.