Sustainable Industrial Waste Heat Recovery Systems using IoT and Gradient Boosting Machines

Reducing energy use and greenhouse gas emissions is made possible in large part by waste heat recovery. This paper investigates how to improve energy efficiency and sustainability in industrial operations by integrating Gradient Boosting Machines (GBM) with Internet of Things (IoT) technologies in smart industrial waste heat recovery systems. Real-time sensor data from industrial processes is analyzed using GBM, a program renowned for its ability to handle large datasets and complex, nonlinear relationships. Through the prediction of ideal operating parameters and the identification of potential energy-saving opportunities, the system aims to optimize heat recovery techniques. This highlights the importance of data-driven decision-making in enhancing heat recovery efficiency using GBM’s predictive analytics. The proposed system lessens its impact on the environment while improving operational performance and resource utilization through continuous monitoring and analysis. IoT-enabled sensors for data collection, cloud computing infrastructure for processing and storing data, and GBM algorithms for predictive modeling are important parts of the puzzle. Simulations and case studies demonstrate how well the GBM-based strategy works to achieve sustainable industrial practices. The results highlight the potential for smarter, more effective industrial waste heat recovery systems through the integration of cutting-edge ML techniques with IoT.