Prediction of thermophysical properties of mixed refrigerants using artificial neural network

Digitalization in Thermodynamics

“…The success was limited, and the initially high interest declined temporarily before surging again after about 2010. Recent applications of machine learning in thermodynamics include solubility or phase equilibria , thermal ( pvT ) properties , , caloric properties , , transport properties , , and surface tension , to cite only a few. A substantial part of the recent work is dedicated to properties of ionic liquids , , , , , that are hard to describe otherwise.…”

Application of artificial neural networks for generation of energetic maps of a variable speed compression system working with R1234yf

“…To mention some examples, Arcaklioglu et al [13] investigated different concentrations of refrigerant mixtures and their performances by using an ANN. In the same way, Arzu Secan [14] presents a new approach using an ANN to determine liquid and vapor thermophysical properties of alternative refrigerants. Hosoz and Ertunc [15] developed an ANN model for experimental air conditioning system using the refrigerant R134a to predict the performance parameters, such as compressor power, heat rejection in the condenser, refrigerant mass flow rate, compressor discharge and coefficient of performance.…”

Toward artificial intelligence-based modeling of vapor liquid equilibria of carbon dioxide and refrigerant binary systems

“…[16][17][18][19][20][21][22][23][24] The ANN-based models have recently found extensive application for the estimation of different properties of refrigerants. [25][26][27][28][29][30][31][32][33] Chouai et al modelled vapour-liquid phase behaviour of some refrigerant fluids namely: R134a, R32, and R143a using neural network in a wide range of temperatures and pressures. 12 Laugier and Richon, used an ANN model to represent pressure, temperature, volume data of some refrigerants in wide ranges of operating conditions.…”