Performance Optimization of a Silica Gel–Water Adsorption Chiller Using Grey Wolf-Based Multi-Objective Algorithms and Regression Analysis

The growing need for cooling, combined with the environmental concerns surrounding conventional mechanical vapour compression (MVC) systems, has accelerated research for sustainable cooling solutions driven by low-grade heat. Single-stage dual-bed adsorption chillers (ADCs) using silica gel and water provide a promising approach due to their continuous cooling output, lower complexity, and the use of environmentally safe working fluids. However, limitations in their performance, specifically in the coefficient of performance (COP), cooling capacity (Q_cc), and waste heat recovery efficiency (η_e), necessitate improvement through optimization. This study employs statistically validated regression-based objective functions to optimize ten decision variables using the single Grey Wolf Optimizer (GWO) and its multi-objective variant, Muilti-Objective Grey Wolf Optimization (MOGWO), for a silica gel–water single-stage dual-bed ADC. The results from the single-objective optimization showed a maximum coefficient of performance (COP) of 0.697, cooling capacity (Q_cc) of 20.76 kW, and waste heat recovery efficiency (η_e) of 0.125. The values from the Pareto-optimal solutions for the MOGWO ranged from 0.5123 to 0.6859 for COP, 12.45 to 20.73 kW for Q_cc and 8.24% to 12.48% for η_e, demonstrating superior performance compared to existing benchmarks. A one-at-a-time sensitivity analysis revealed non-linear and non-monotonic impacts of variables, confirming the robustness and physical realism of the MOGWO model. The developed MOGWO framework effectively enhances the performance of the single-stage dual-bed ADC and improves low-grade heat utilization, offering a robust decision-support tool for system design and optimization.