Detection Method for Energy Efficiency Data in Shell-and-Tube Heat Exchangers Using Multi-Pipeline Segmentation Algorithm

Abstract

Shell-and-tube heat exchangers are pivotal in thermal engineering, making the accuracy and quality of the heat transfer data obtained from them essential. Current data monitoring technologies face several challenges, such as increased complexity, noise, and inefficiency in handling the dynamic heat transfer process. This paper introduces a novel approach to enhancing the accuracy and precision of energy transfer data segmentation in shell-and-tube heat exchangers using a multi-pipeline segmentation algorithm. Our methodology integrates data collection with the algorithm's hands-on development, employing advanced techniques to segment and categorize energy transfer data based on real-time system parameters. This creates a robust definition of normal and anomalous operating conditions. Our approach was validated through extensive experiments and simulations, demonstrating superior data accuracy and noise detection compared to traditional methods. Moreover, this innovative segmentation algorithm has potential applications in maintenance forecasting and optimization strategies, ultimately improving energy efficiency. In the future, our algorithm could be extended to other types of heat exchangers or industrial systems, further enhancing their energy efficiency and operational lifespan.