Optimization and Estimation of the Thermal Energy of an Absorber With Graphite Disks by Using Direct and Inverse Neural Network

Abstract

The most critical component of an absorption heat transformer (AHT) is the absorber, by which the exothermic reaction is carried out, resulting in a useful thermal energy. This article proposed a model based on improving the performance of energy for an absorber with disks of graphite during the exothermic reaction, through an optimal strategy. Two models of artificial neural networks (ANN) were developed to predict the thermal energy, through two important factors: internal heat in the absorber (QAB) and the temperature of the working solution of the absorber outlet (TAB). Confronting the simulated and real data, a satisfactory agreement was appreciated, obtaining a mean absolute percentage error (MAPE) value of 0.24% to calculate QAB and of 0.17% to calculate TAB. Furthermore, from these ANN models, the inverse neural network (ANNi) allowed improves the thermal efficiency of the absorber (QAB and TAB). To find the optimal values, it was necessary to propose an objective function, where the genetic algorithms (GAs) were indicated. Finally, by applying the ANNi–GAs model, the optimized network configuration was to find an optimal value of concentrated solution of LiBr–H2O and the vapor inlet temperature to the absorber. The results obtained from the optimization allowed to reach a value of QAB from 1.77 kW to 2.44 kW, when a concentrated solution of LiBr–H2O at 59% was used and increased the value of TAB from 104.66 °C to 109.2 °C when a vapor inlet temperature of 73 °C was used.