Advancing Sustainability and Efficiency in Coal‐Fired Boilers: A Critical Review of Prediction Models and Optimization Strategies for Emission Reduction

Coal‐fired boilers continue to serve as a primary energy source worldwide, yet their operational efficiency and environmental impact present persistent challenges. This critical review examines recent advancements in performance prediction models and optimization strategies aimed at enhancing the efficiency and sustainability of coal‐fired boilers. A comprehensive analysis is conducted on predictive methodologies, encompassing both conventional thermodynamic models and emerging artificial intelligence (AI)‐driven approaches, including artificial neural networks (ANNs) and machine learning (ML) algorithms. Key optimization strategies related to combustion control, sensor‐based operations, and emissions mitigation are systematically reviewed. Through a detailed evaluation of current research trends, this study identifies critical knowledge gaps and proposes future research directions to advance the environmental performance and operational viability of coal‐fired power generation.