Artificial Intelligence in Industrial Heat Recovery and Waste Heat Utilization

The dynamic nature of industrial processes often leads to variable waste heat generation, posing challenges to achieving consistent and efficient heat recovery. Traditional control systems typically lack the flexibility to adapt to such fluctuations in real time. This chapter explores the application of artificial intelligence (AI), particularly advanced machine learning algorithms and reinforcement learning, in enabling real-time control and adaptive management of industrial waste heat recovery systems. Emphasis is placed on how AI can learn from historical and streaming process data to dynamically adjust system parameters, optimize energy capture, and respond to variations in process load and heat availability. The integration of AI-driven control with existing SCADA (supervisory control and data acquisition) and distributed control system platforms is discussed, alongside the role of digital twins in simulating optimal control strategies. Case studies from high-heat industries such as steel, cement, and glass manufacturing demonstrate measurable improvements in energy efficiency, operational stability, and carbon footprint reduction. The chapter concludes with an outlook on future developments and challenges related to data integration, model generalization, and real-time system validation.