Artificial Neural Network Analysis For Domestic Refrigerator With Hydrocarbon Refrigerant Mixtures

International Journal of Mechanical Engineering

© 2021 by SSRG - IJME Journal

Volume 8 Issue 6

Year of Publication : 2021

Authors : D.V. Raghunatha Reddy, P. Bhramara, K. Govindarajulu

10.14445/23488360/IJME-V8I6P102

How to Cite?

D.V. Raghunatha Reddy, P. Bhramara, K. Govindarajulu, "Artificial Neural Network Analysis For Domestic Refrigerator With Hydrocarbon Refrigerant Mixtures," SSRG International Journal of Mechanical Engineering, vol. 8,  no. 6, pp. 4-8, 2021. Crossref, https://doi.org/10.14445/23488360/IJME-V8I6P102

Abstract:

This study deals with the usage of Artificial Neural Network (ANN) modeling to predict the domestic refrigerator performances such as refrigeration affects power consumption and the coefficient of performance with hydrocarbon refrigerant mixtures (H.C.M.). Experimental work has conducted to obtain the data to train and test the models. The back-propagation algorithms used as a learning algorithm of ANN in the multilayered feedforward networks. Based on the design of experiments, Taguchi's L25 orthogonal array are used by varying Mass of refrigerant (Mf) length of the capillary tube (Lc) Evaporating temperature(Te) and condenser temperature(Tc) were used as input parameters. To develop ANN model has checked for adequacy and significance to systems performance. Finally, the results of the ANN model were compared and analyzed with experimental values. It is shown that while ANN model was good approaches were deficient in predicting desired output parameters, more accurate results were obtained with experimental results.

Keywords:

H.C.M., Lc, Mf, Artificial Neural Network, Domestic Refrigeration System, Power consumption, and Coefficient of performance.

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