Reformulation, as a Function of Only Working Temperatures, of Performance Parameters of a Solar Driven Ejector-Absorption Cycle Using Artificial Neural Networks

Abstract

Theoretical thermodynamic analysis of the absorption thermal systems is too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations and simulations programs. This article proposes a new approach to performance analysis of solar driven ejector-absorption refrigeration system (EARS) operated aqua/ammonia. Use of artificial neural networks (ANNs) has been proposed to re-determine the performance parameters, as a function of only working temperatures, at different working conditions. Thus, this study is considered to be helpful in predicting the performance of an EARS prior to its setting up in an environment where the temperatures are known. The statistical coefficient of multiple determinations (R 2 -value) equals to 0.976, 0.9825, 0.9855 for the coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F), respectively. These accuracy degrees are acceptable in design of EARS. The present method greatly reduces the time required by design engineers to find optimum solution, and in many cases, reaches a solution that could not be easily obtained from simple modeling programs. The importance of the ANN approach, apart from reducing the whole time required, is that it is possible to find solutions that make solar energy applications more viable, and thus more attractive to potential users such as the solar engineer. Also, this approach has the advantages of computational speed, low cost for feasibility, and rapid turnaround, which is especially important during iterative design phases, and ease of design by operators with little technical experience.