Integrating Kinetic Models with Physics-Informed Neural Networks (PINNs) for Predicting Methane Production from Anaerobic Co-Digestion of Enzyme-Modified Biodegradable Plastics and Food Waste Leachate

In the face of increasingly severe water environmental pollution and energy shortages, anaerobic digestion (AD) technology has demonstrated immense potential for the resource recovery of wastewaters such as food waste leachate (FWL). However, the inherent drawback of the long experimental period required for AD severely constrains research efficiency. Existing studies often rely on either kinetic models with high interpretability or machine learning models with strong generalization capabilities, rarely integrating both. To address this, this study innovatively investigated the anaerobic co-digestion of enzyme-modified biodegradable plastics (BPs) and FWL, and constructed a novel Physics-Informed Neural Network (PINN) based on a dataset of 261 experimental observations. The results indicated that, among the three kinetic models, the Modified Gompertz model exhibited the best prediction accuracy (R2 approaching 0.99), stability, and universality. Among the four machine learning models, the Artificial Neural Network (ANN) demonstrated optimal generalization ability (Test set R2 = 0.958). Notably, the constructed Modified Gompertz PINN model achieved superior predictive performance (Test set R2 = 0.994), reducing the Root Mean Square Error (RMSE) by 74.0% compared to the ANN model. Shapley analysis further confirmed the PINN retained strong biological rationality, indicating that the hydrolysis process significantly impacts methane production. This work provides a robust hybrid model for efficient co-digestion prediction and offers a new approach for the resource valorization of enzyme-modified BPs and FWL.