Genetic algorithm based multi-objective optimization of an adsorption cooling system with passive heat recovery mechanism

The present study comprehensively examines the thermodynamic performance of an adsorption refrigeration system considering the effect of wetness fraction and hot flushing. A detailed mathematical model analyses temporal exergy destruction and proposes an internal heat recovery mechanism to reduce the exergy losses. The non-sorting genetic algorithm II (NSGA II) along with the linear programming technique for multi-dimensional analysis of preference (LINMAP) is used to optimize six operating variables, maximizing the coefficient of performance (COP) and minimizing specific exergy destruction (eD) using MATLAB R2021b. The research findings reveal that the precooling and preheating processes together contribute more than half of total exergy destruction in the sorption reactor. The exergy losses by the flushing process, a significant contributor after the cooling tower, precooling, and thermal energy source, are substantially reduced with the implementation of the heat recovery scheme. Further, the multi-objective optimization shows that the optimal values of COP, eD, and chiller efficiency with heat recovery mode are found to be improved by +18.56%, -27.63%, and + 25.37%, respectively, compared to without heat recovery. Finally, the comparison among the optimum design variables has been presented based on NSGA II, PSO, and GA. The research and findings reported in this work have implications for optimizing the cooling performance of adsorption systems with heat recovery applications.