Forecasting the performance of a diffusion absorption refrigeration system using optimal fluid blend—A neural network approach

Abstract

AbstractIn recent years, the diffusion absorption system has gained special attention in several types of research due to its ability to promote sufficient cooling effect from the use of low‐grade heat. Normally, in the diffusion absorption refrigeration (DAR) system, ammonia works as a refrigerant, helium as an inert gas, and water as an absorbent. These refrigerant combinations cause certain environmental issues such as global warming, ozone layer depletion, toxic gas formation, etc. This work aims to formulate an environment‐friendly DAR system from among six different alternative refrigerant blends. The refrigeration performances such as coefficient of performance (COP), exergy analysis, heat supplied in the generator, mass flow rate, refrigeration effect, and cooling capacity are determined for different evaporator temperatures. Harris hawk optimization (HHO) screened the optimal refrigerant blend and validated using hybrid Elman recurrent neural network (ERNN)‐based HHO methods. It is observed that the performance of the DAR is optimal when isobutane + dimethylformamide + helium is used as the working fluid.