Multi-Objective Optimization of a Spherical Thermal Storage Tank Using a Student Psychology-Based Approach

Abstract

Energy storage technologies often store heat, with water as a preferred medium due to its availability and low cost. However, maintaining water in a liquid state at high temperatures requires large pressure vessels, posing significant design challenges. Balancing thermal storage capacity with pressure constraints is essential. This paper explores the dynamics of thermal storage water tanks, aiming to optimize their design using a multi-criteria approach. An equilibrium thermodynamic model was developed to evaluate the impact of geometric structure and operating parameters. The results show that optimizing a single variable is insufficient to minimize pressure swing, reduce heat loss, and maximize storage capacity. To address these trade-offs, a multi-objective student psychology-based optimization (SPBO) algorithm was employed for three-objective optimization, outperforming particle swarm optimization (PSO) in convergence. The technique for order preference by similarity to ideal solution (TOPSIS) method was applied to the Pareto frontier, yielding ideal solutions using both data-driven and manually weighted approaches. Compared with the initial design, the data-driven weighted (entropy-weighted and coefficient of variation methods) optimal designs improved all objectives, reducing pressure swing by 12.8% and 19.8%, respectively. A manually weighted approach reduced pressure swing by up to 86.7%, albeit with a decrease in thermal storage capacity.