Modeling of Artificial Neural Network for Predicting Specific Heat capacity of working fluid LiBr-H2O used in Vapor Absorption Refrigeration System

Dheerendra Vikram Singh, Govind Maheshwari

Abstract


The objective of this work is to model an artificial neural network (ANN) to predict the value of specific heat capacity of working fluid LiBr-H2O used in vapour absorption refrigeration systems. A feed forward back propagation algorithm is used for the network, which is most popular for ANN. The consistence between experimental and ANN?s approach result was achieved by a mean relative error -0.00573, sum of the squares due to error0.00321, coefficient of multiple determination R-square 0.99961and root mean square error 0.01573 for test data. These results had been achieved in Matlab environment and the use of derived equations in any programmable language for deriving the specific heat capacity of LiBr-H2O solution.

Keywords


Artificial neural network; Refrigeration system; LiBr-H2O

Full Text:

PDF



International Journal of Online and Biomedical Engineering (iJOE) – eISSN: 2626-8493
Creative Commons License
Indexing:
Scopus logo Clarivate Analyatics ESCI logo IET Inspec logo DOAJ logo DBLP logo EBSCO logo Ulrich's logo Google Scholar logo MAS logo