OVERCOMING EFFICIENCY BARRIERS IN THE OPTIMIZATION OF HEAT EXCHANGERS FOR ENHANCED ENERGY RECOVERY IN INDUSTRIAL SYSTEMS

Abstract

This study investigates the optimization of heat exchangers for enhanced energy recovery in industrial systems, addressing key efficiency barriers such as fouling, material limitations, and operational design inefficiencies. The research employs a multifaceted approach, incorporating experimental testing, computational simulations, and machine learning-based optimization models to evaluate the performance of various heat exchanger configurations. The results reveal that optimized flow arrangements, along with the use of advanced corrosion-resistant materials, significantly improve heat transfer rates and energy recovery efficiency. Specifically, heat exchangers with optimized designs exhibited up to a 30% increase in heat transfer performance and a 15% reduction in energy losses compared to conventional systems. The system achieves better efficiency and utilizes waste heat recovery specifically that maintains peak operation under diverse energy input conditions.  Results demonstrate machine learning models show strong forecasting abilities to predict optimal system conditions that simultaneously reduce operation expenses while enhancing energy recovery.  Flexible heat exchangers must be developed with scalable characteristics that will match the changing industrial requirements.  The research introduces an entire system to improve heat exchanger efficiency through advanced materials integration with innovative flow structures coupled with predictive data models running in real-time to achieve industrial energy efficiency goals and sustainability advancements.  The experimental results help future industrial efforts by providing essential insights to achieve higher efficiency and reduce environmental impact of their sustainable energy systems