Machine learning for waste-to-energy processes: Resource evaluation, conversion efficiency, and environmental effects

Abstract

Waste-to-energy (WtE) technologies are increasingly important for sustainable waste management and circular economy practices, as they enable recovery of energy from municipal, agricultural, and industrial wastes while reducing landfill use and associated emissions. Despite this relevance, existing research on machine learning (ML) applications in WtE systems remains fragmented, with most studies addressing individual processes, specific algorithms, or isolated performance metrics, and lacking an integrated perspective across the full value chain. The objective of this work is to provide a comprehensive review of machine learning applications in WtE systems, covering resource evaluation, conversion efficiency, and environmental effects within a unified framework. The study is based on a systematic analysis of recent peer-reviewed literature reporting experimental validation or applied modeling in incineration, gasification, pyrolysis, and anaerobic digestion processes. The review indicates that machine learning models successfully capture the nonlinear and time-varying behavior of WtE systems, allowing accurate prediction of waste generation and composition, heating value, biogas yield, process efficiency, and pollutant emissions. Tree-based ensembles and neural networks show strong performance in feedstock assessment and conversion modeling, while data-driven soft sensors and surrogate models support real-time emission prediction and life-cycle impact evaluation. These findings demonstrate that machine learning offers practical benefits for improving operational stability, energy recovery, and environmental compliance in WtE plants, while also highlighting persistent challenges related to data quality, model transferability, and interpretability that should guide future research and deployment.