Simultaneous increase of parameters of an experimental absorption system: Neural network inverse optimization methodology with multi-inputs

Abstract

The Absorption Heat Transformer (AHT) has become an alternative proposal to weaken the trend of thermal pollution because it contributes to the recovery of residual heat and the use of solar thermal energy for its activation. Recently, compact designs have been developed that share two heat exchangers in one shell to reduce heat losses and save maintenance costs. The evaluation of the performance of AHTs depends on multiple parameters, which have been optimized using models based on ideal conditions. Multivariable optimization with artificial intelligence has considered experimental data to obtain feasible conditions related to the operation of the AHT. However, these models have focused only on determining one parameter at a time, reflecting a limitation. This study aims to develop a new optimization strategy to simultaneously maximize two relevant parameters associated with the efficiency of a compact experimental AHT. The optimization methodology is applied to increment the values of the heat generated in the absorber (QAB) and the Exergy Coefficient of Performance (ECOP) using the same objective function. This objective function is resolved when reaching the desired output parameters by determining multiple optimal conditions of the AHT. Initially, the modeling of the experimental data was carried out using an artificial neural network (ANN) for the diagnosis and prediction of the QAB and the ECOP. The model was validated by comparing the experimental values of QAB and ECOP against predicted values through a linear regression model, with a satisfactory result of R2>0.98. Subsequently, the multivariable inverse artificial neural network methodology for multiple output parameters (ANNim-m) was used and coupled with the Particle Swarm Optimization algorithm (ANNim-m-PSO) to generate the new multivariate optimization strategy and improve QAB and ECOP parameters simultaneously. Finally, 4 random tests with different initial operating conditions were optimized. Based on the optimization of the 4 tests, the QAB heat load was increased by 87.7 %, 54.2 %, 30.79 %, and 22.66 % from initial experimental conditions of 2.7, 9.0, 14.23 and 23.29 kW, respectively. In the case of ECOP, elevations of 28.5 %, 23.4 %, 15.4 %, and 16.18 % were obtained for initial values of 0.3801, 0.455, 0.5180, and 0.5729, respectively. It is determined that the parameters QAB and ECOP have a high sensitivity when the inlet temperature of the absorber of the external circuit (TAB) decreases. The results reveal that it is feasible to use the ANNim-m-PSO model, because the optimization was able to significantly maximize both QAB and ECOP parameters of the 40 kW experimental AHT.