Operational conditions optimization of a proposed solar-powered adsorption cooling system: Experimental, modeling, and optimization algorithm techniques

Abstract

Adsorption cooling systems have low energy efficiency and large sizes compared to traditional cooling systems and still have to be improved and optimized in order to become more competitive. The objective of this study is to enhance and optimize the performance of a solar powered adsorption cooling system (SDACS) by defining its optimal operating conditions. A multi-objective genetic algorithm (MOGA) combing a Kriging based response surface is employed to optimize the operating parameters. Eight operating parameters include hot, cooling, and chilled water temperatures and mass flow rates, and cycle and switching times are considered. An innovative SDACS with three axial finned tubes heat exchangers connected in parallel has been designed and tested. A non-equilibrium lumped parameter model has been developed to predict the system performance. Results from optimization algorithm and simulation are compared with those obtained experimentally and good agreements are obtained with ±10% maximum error. The proposed SDACS is able to produce about 0.56 kW (145 W kg−1) cooling power with a COP of about 0.52 at the rated operating conditions. The optimized operating conditions using MOGA improves the SCP by 51.7% and the system COP by 21% compared to the rated operating conditions at the same design parameters.