Novel heuristic algorithm incorporating dynamic penalty and adaptive evolution strategy for heat integration network design

Designing efficient heat-exchanger networks is essential for the effective use of energy in the process industries, fostering sustainable and low-carbon development. Traditional heuristic algorithms often fail to balance global and local search processes adequately when solving heat exchanger network synthesis. This frequently leads to early structural fixation and limits the exploration of integer variable search spaces. This study presents an innovative structural enhancement approach, incorporating a dynamic penalty for utility operating costs and an adaptive evolution strategy. The developed method increases the number of heat exchangers by dynamically penalizing utility operating costs and allows evolutionary parameters to be adaptively adjusted based on historical solutions. This approach diminishes the dependence on manual parameter tuning while enhancing flow matching diversity. Moreover, novel metrics for assessing structural diversity, based on node-based nonstructural model with stream splitting are established to facilitate the transition between various optimization paths, thereby improving the algorithm's robustness throughout different stages of the optimization process. Finally, computational analysis on five industrial-scale cases yields more optimal solutions with varied structural configurations compared to previous studies, demonstrating the method's feasibility and effectiveness.