Modeling a Thermochemical Reactor of a Solar Refrigerator by BaCl2-NH3 Sorption Using Artificial Neural Networks and Mathematical Symmetry Groups

Abstract EN

The aim of this work is to present a model for heat transfer, desorbed refrigerant, and pressure of an intermittent solar cooling system’s thermochemical reactor based on backpropagation neural networks and mathematical symmetry groups.

In order to achieve this, a reactor was designed and built based on the reaction of BaCl2-NH3.

Experimental data from this reactor were collected, where barium chloride was used as a solid absorbent and ammonia as a refrigerant.

The neural network was trained using the Levenberg–Marquardt algorithm.

The correlation coefficient between experimental data and data simulated by the neural network was r = 0.9957.

In the neural network’s sensitivity analysis, it was found that the inputs, reactor’s heating temperature and sorption time, influence neural network’s learning by 35% and 20%, respectively.

It was also found that, by applying permutations to experimental data and using multibase mathematical symmetry groups, the neural network training algorithm converges faster.