An adaptive neuro-fuzzy model of a re-heat two-stage adsorption chiller

Abstract

Since the adsorption chillers do not use primary energy as driving source the possibility to employ low temperature waste heat sources in cooling energy production receives nowadays much attention of the industry and science community. However, the performance of the thermally driven adsorption systems is lower than that of other heat driven heating/cooling systems. Low coefficients of performance are one of the main disadvantages of adsorption coolers. 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 purpose of this work is to study the effect of thermal conductance values of sorption elements and evaporator 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 for wide-range of both design and operating parameters is analyzed in the paper. Moreover, the work introduces artificial intelligence approach for the optimization study of the adsorption cooler. The ANFIS was employed in the work. The increase in both the bed and evaporator conductance provides better performance of the considered innovative adsorption chiller. The highest obtained value of cooling capacity is 21.7 kW and it can be achieved for the following design and operational parameters of the considered re-heat two-stage adsorption chiller: Msorb = 40 kg, t = 1300 s, T = 80?C, Csorb/Cmet = 50, hAsorb = 4000 W/K, hAevap = 4000 W/K.