Optimization and Performance Prediction of a CHPW System Using Reinforcement Learning and Exergo-Economic Analysis

This study investigates and analyzes a combined heat, power, and water (CHPW) generation system designed to enhance energy efficiency, reduce thermodynamic losses, and improve exergy performance, thereby contributing to better economic and environmental outcomes. The proposed approach is based on an integrated framework that includes technical system modeling, multi objective optimization, and deep reinforcement learning. Within this framework, the problem is formulated as a sequential decision making process in which the learning agent observes system states—such as electrical and thermal loads, fuel price, ambient temperature, and performance indicators—and selects appropriate control actions. The reward function is designed by simultaneously considering economic, exergetic, and technical criteria in order to guide the agent toward optimal operational policies. The results of the analyses and model structure indicate that applying a reinforcement learning (RL) approach can establish dynamic control strategies that reduce operational costs, decrease exergy destruction, and improve the overall technical performance of the system. These outcomes are particularly valuable for applications requiring the simultaneous supply of electricity, heat, and freshwater, especially in water scarce regions, hot and arid climates, and off grid energy systems. The main contribution of this research lies in the integrated combination of techno exergy analysis, multi objective optimization, and deep reinforcement learning for intelligent control of the CHPW system, providing a novel framework for the operational management of multi purpose energy systems.