Machine Learning for Enhancing Prediction of Biogas Production and Building a VFA/ALK Soft Sensor in Full-Scale Dry Anaerobic Digestion of Kitchen Food Waste

Abstract

Based on operational data collected over 1.5 years from four industrial-scale dry anaerobic digesters used for kitchen food waste treatment, this study adopted eight typical machine learning algorithms to distinguish the best methane prediction model and to develop a soft sensor based on the VFA/ALK ratio. Among all the eight tested models, the CatBoost (CB) algorithm demonstrated superior performance in terms of prediction accuracy and model fitting. Specifically, the CB model achieved a biogas production prediction accuracy (R2) ranging from 0.604 to 0.915, and a VFA/ALK R2 between 0.618 and 0.768 on the test dataset. Furthermore, the feature importance analysis revealed that biomass amount into the dry anaerobic digester was the primary factor influencing biogas production. Chemical oxygen demand (COD) and total ammonia nitrogen (TAN) were identified as the most significant factors impacting the VFA/ALK indicator during dry anaerobic digestion, collectively contributing to nearly 50% of the influence. Overall, this study verifies the feasibility of using machine learning to predict biogas production in industrial-scale dry anaerobic digestion and provides a crucial foundation for monitoring the stability of dry anaerobic digesters.