Modeling and multi-objective optimization of an M-cycle cross-flow indirect evaporative cooler using the GMDH type neural network

摘要

A model was presented to determine product air properties of dew-point indirect evaporative coolers with cross flow heat exchanger, M-cycle CrFIEC. In this regard, the most powerful statistical method known as the group method of data handling-type neural network (GMDH) was employed. Then the developed GMDH model was implemented for multi-objective optimization of a prototype CrFIEC and the average annual values of coefficient of performance (COP) and cooling capacity (CC) were maximized, simultaneously, while working to air ratio (WAR) and inlet air velocity were decision variables of optimization. Accordingly, features of the proposed system were optimized at twelve diverse climates of the world based on Koppen Geiger's classification. Results implied that the optimized inlet air velocity for all climates varied between 1.796 and 1.957 m.s(-1), while the optimum WAR was 0.318 for "A" class cities. Moreover, the mean values of the COP and CC were improved 8.1% and 6.9%, respectively. (C) 2016 Elsevier Ltd and IIR. All rights reserved.