A mini review on AI-driven thermal treatment of solid waste: Emission control and process optimization

Abstract

The advent of novel waste disposal methodologies, which are energy-efficient and environmentally benign, has created opportunities for the deployment of artificial intelligence technologies in the management of solid waste treatment. This review examines the deployment of AI-optimized control algorithms in processes including pyrolysis, incineration, and gasification. The application of machine learning models, including linear regression (LR), genetic algorithm (GA), support vector machine (SVM), artificial neural networks (ANN), decision trees (DT), and Extreme Gradient Boosting (XGBoost), enables real-time monitoring of performance and dynamic adjustment of parameters to enhance energy recovery and minimize pollution. The implementation of AI-based solutions enables the optimization of key characteristics, such as temperature and oxygen levels, with the objective of achieving optimal energy efficiency while minimizing the emission of harmful substances, including CO, NOx, and dioxins. Notwithstanding these advancements, challenges remain in hyperparameter tuning, probabilistic assessments, and feature generation. A comprehensive understanding of future technologies will necessitate a synthesis of knowledge and data-oriented approaches, the design of autonomous control systems, and the integration of digital twin technologies to bridge the gap between theory and practice.

Keywords

• Artificial intelligence
• Solid waste treatment
• Energy recovery optimization
• Emission control
• Machine learning algorithms