Thermodynamic analysis and optimization of double effect absorption refrigeration system using genetic algorithm

Abstract

Abstract World energy demand is continuously increasing due to population growth, extensive urbanization and rapid industrialization as growth in global energy demand will rise by 30% in 2040. This demand can be met by utilizing traditional and alternative energy resources and by enhancing energy efficiency. The energy efficiency of an energy intensive process may be improved by utilization of waste or low grade energy, design and operation optimization, and running the plant in flexible mode with improved control strategies. Waste heat of an industrial plant can be utilized by employing the so called absorption refrigeration systems (ARS). ARS is capable of utilizing the low quality energy or waste heat which otherwise may have been lost as compared to conventional compression refrigeration system (CRS) where huge quantity of electricity is required for compression of the refrigerant. One of the major concerns associated with ARS is its low efficiency. Efficiency improvement of ARS can be achieved by analyzing and optimizing the various configurations of ARS using any mathematical technique. In the present work, the aim is to analyze and maximize the performance of ARS using exergy efficiency instead of conventional coefficient of performance (COP) approach. Both the series and parallel flow configurations have been considered for the analysis and optimization. Thermodynamic model has been implemented in MATLAB® using the best available correlations for phase equilibria and thermodynamic properties. Operating conditions including temperatures of all the units of both configurations of ARS and distribution ratio for parallel configuration have been optimized. Genetic Algorithm (GA) was used to optimize the operating conditions. The effect of distribution ratio and effectiveness of heat exchanger on the maximum allowable temperature of low pressure generator (LPG) and high pressure generator (HPG) to avoid crystallization has also been evaluated. Exergy efficiency of parallel flow configuration came out to be 6.45% more than that of series flow configuration at nominal operating conditions. At optimal operating conditions, exergy efficiency improved by 11.6% and 20.81% for series and parallel flow configurations, respectively. Furthermore, exergy efficiency for parallel flow configuration is 15.1% higher than series flow configuration at optimal operating conditions. The effect of variation of effectiveness of solution heat exchangers on the optimization results has also been evaluated. Effect of weather conditions, type of energy source, application for solvent cooling and variable cooling load also needs to be considered in future.