Modeling a Thermochemical Reactor of a Solar Refrigerator by BaCl 2 -NH 3 Sorption Using Artificial Neural Networks and Mathematical Symmetry Groups

Onesimo Meza-Cruz, Isaac Pilatowsky, Agustín Pérez-Ramírez, Carlos Rivera-Blanco, Youness El Hamzaoui, Miguel Perez-Ramirez and Mauricio A. Sanchez

Mathematical Problems in Engineering, 2020, vol. 2020, 1-11

Abstract:

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 BaCl 2 -NH 3 . 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:9098709

DOI: 10.1155/2020/9098709

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