Abstract
In this paper, methodology for finding optimum composition of binary mixture (R290/R1270) for Solar-Assisted Heat Pump (Direct Expansion) is framed. Experiments are conducted on DX-SAHP by using R22 under the metrological condition of Calicut, India. Properties of binary mixture such as vapour pressure, liquid density for mass percentage of R1270 ranging from 0 to 50% is taken from REFPROP 11® software. Artificial Neural Network (ANN) is integrated with Genetic algorithm (GA) in MATLAB 7.7.0® to obtain optimum composition of binary mixture, where maximized EPR (Energy Performance Ratio) and minimized cost of refrigerant are two objective functions. The result shows that, the optimum compositions of binary mixture are 70% of R290 with 30% of R1270. Unique feature of this study is that, ANN integrated with GA technique can be used to find best optimum composition of alternative refrigerant mixture by mass percentage for any given system, provided experimental data of that system should be available for R22 or any other refrigerant.
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Vinoth Kumar, K., Paradeshi, L., Srinivas, M., Jayaraj, S. (2018). Optimum Composition of Alternative Refrigerant Mixture for Direct Expansion Solar-Assisted Heat Pump Using ANN and GA. In: Chandra, L., Dixit, A. (eds) Concentrated Solar Thermal Energy Technologies. Springer Proceedings in Energy. Springer, Singapore. https://doi.org/10.1007/978-981-10-4576-9_18
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DOI: https://doi.org/10.1007/978-981-10-4576-9_18
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