Modeling of a re-heat two-stage adsorption chiller by AI approach

Abstract

A distinct advantage of adsorption chillers is their ability to be driven by heat of near ambient temperature. However the performance of the thermally driven adsorption systems is lower than that of other heat driven heating/cooling systems. It is the result of a poor heat transfer coefficient between the bed and the immersed heating surfaces of a built-in heat exchanger system. The aim of this work is to study the effect of thermal conductance values as well as other design parameters on the performance of a re-heat two-stage adsorption chiller. One of the main energy efficiency factors in cooling production, i.e. cooling capacity (CC) for wide-range of both design and operating parameters is analyzed in the paper. Moreover, the work introduces artificial intelligence (AI) approach for the optimization study of the adsorption cooler. The Adaptive Neuro – Fuzzy Inference System (ANFIS) was employed in the work. The developed ANFIS model can be applied for optimizations purposes and may constitute a submodel or a separate module in engineering calculations, capable to predict the CC of the re-heat two-stage adsorption chiller.