An Explainable AI Framework for Circular Bioeconomy Systems: Predicting and Optimizing Biogas Production from Waste Biomass

Abstract

The increasing emphasis on renewable energy generation, sustainable waste management, and circular bioeconomy development has created a growing need for intelligent approaches capable of optimizing biomass conversion processes. This study proposed an Explainable Artificial Intelligence (XAI) framework for predicting and optimizing biogas production from waste biomass. Using the Biogas Production Analysis dataset, machine learning models were developed to evaluate the relationships between biomass characteristics and methane generation performance. The analytical framework integrated predictive modeling, explainability analysis, and sustainability assessment to improve decision-making and resource utilization within waste-to-energy systems. Multiple machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, and Artificial Neural Network models, were evaluated using R², Mean Absolute Error, and Root Mean Square Error metrics. The results demonstrated that ensemble learning approaches achieved superior predictive performance, with XGBoost producing the highest prediction accuracy. Feature importance and SHAP analyses identified the critical variables influencing methane production and enhanced model transparency by providing interpretable insights into prediction outcomes. Furthermore, the optimization framework improved resource efficiency, process performance, and methane yield while reducing resource consumption and environmental impacts. The findings highlight the capability of explainable artificial intelligence to support sustainable bioenergy production, efficient biomass utilization, and data-driven optimization within circular bioeconomy systems. Overall, the proposed framework provides a transparent and effective approach for enhancing biogas production and advancing renewable energy development through intelligent waste-to-energy conversion strategies.