Absorption cooling systems have been investigated for many years due to their ability to use low-grade heat instead of electricity as the energy source. The aim of this work is to advance the performance of a single-effect Lithium bromide/water absorption cooling system. Taking the generator and evaporator temperatures as variables, the system is optimized to maximize exergetic and energetic efficiencies at different operational conditions using a multi-objective-multi-variable Genetic Algorithm. The Group Method of Data Handling neural network approach is adopted to derive correlations between the design variables and operational parameters. Finally, the system is coupled to evacuated tube solar collectors and compared to a similar system. The results reflect a maximum improvement in energetic and exergetic efficiencies of about 9.1% and 3.0%, respectively. This translates into savings of 187 dollars for every square meter of solar collector at present time. This improvement is achieved by decreasing the mean temperature of the generator by 6.2 °C and increasing the mean temperature of the evaporator by 1.6 °C. In the case of applying low-grade heat such as solar energy, it brings about both an improvement in the thermodynamic performances and a reduction in the generator temperature.
optimization; solar energy; exergy; absorption refrigeration system; nsga ii; gmdh