A. Huicochea

Optimal multivariable conditions in the operation of an absorption heat transformer with energy recycling solved by the genetic algorithm in artificial neural network inverse

Abstract This research presents an application of an artificial neural network inverse (ANNi) and genetic algorithm (GA) to propose and solve a multivariable function in order to optimize of an absorption heat transformer (AHT) with energy recycling. The purpose of the research is to provide a method capable of maximizing the coefficient of performance (COP) of the AHT, by finding multiple optimal input variables, with the benefit of minimizing energy in the heat supply of the equipment. AHT use waste heat sources to obtain useful energy, but by recycling part of the useful energy within the same system provides an increase in performance. Therefore, the research is focused on optimizing the heat source in the generator, evaporator and additionally the condenser simultaneously, since the value of the COP is determined through the ratio of the useful heat load between the loads of heat supplied. Consequently, this study is based on modeling the process and then performing the optimization, using the following research methods: artificial neural networks (ANN), ANNi and GA. An ANN model was developed to predict the value of COP, based on experimental data from the equipment. A satisfactory agreement was obtained by comparing the simulated and experimental data. With the ANN model consolidated, an ANNi was applied where the variables to be optimized were: temperature in the generator, temperature in the evaporator and temperature in the condenser. GA was chosen to solve the multivariable function. The results showed that when applying the ANNi-GA methodology, it is possible to carry out the multivariable optimization, since this methodology had only been used to optimize one variable at a time. The temperature in the generator turned out to be the key variable to increase the performance of AHT, followed by the evaporator and condenser, managing to maximize the COP value of a specific test from 0.26 to 0.43 and obtain an energy saving of up to 3 °C with a maximum computation time of 5.38 s. With the results obtained, the research effect provides a feasible method to control multiple input variables of an AHT, from a desired COP value. By properly supplying the waste heat to the AHT, it is possible to minimize the energy consumption and experimentation time, since the proposed method determines the best scenario to obtain good results, instead of performing different experimental tests. Finally the GA solved satisfactorily the ANNi multivariable function proposed, making feasible the use of this tool to optimize different variables at the same time.