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Application of Soft Computing Techniques for Analysis of Vapour Compression Refrigeration System


Author : D.V.Raghunatha Reddy, P.Bhramara and K.Govindarajulu

Pages : 368-373, DOI:http://Dx.Doi.Org/10.14741/Ijcet/Spl.2.2014.67
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Abstract

In recent years, soft computing (SC) techniques have been widely used in the analysis of vapour compression refrigeration system (VCR). Soft computing is becoming useful as an alternate approach to conventional techniques. Soft computing differs from conventional (hard) computing in that; it is tolerant of imprecision, uncertainty, Partial truth and approximation. The techniques are evolutionary computing such as Artificial Neural Networks (ANN) and Fuzzy Logic (FL) each technique can be used separately, but a powerful advantage of soft computing is the complementary nature of the techniques. Used together they can produce solutions to problems that are too complex can be solved with conventional mathematical methods. The applications of soft computing have proved two main advantages. First one it solving nonlinear problems, in which mathematical models are not available and second, it introduced the human knowledge such as cognition, recognition, understanding, learning, and others into the fields of computing. These results can give the intelligent systems such as autonomous self-tuning systems, and automated designed systems. However the computer simulation method has its advantages over the conventional one. With the computer simulation method, the working conditions and the configuration parameters of the product are given at first, then the performances predicted, and at last the configuration parameters of the product is evaluated based on the performance prediction. If the predicted performance does not meet the requirement, the configuration parameters can be adjusted, and Simulation will be done again. Hence soft computing has been widely used for performance prediction and analysis of vapour compression refrigeration system.

Key words: Soft Computing, Fuzzy Logic, Artificial Neural Network, vapour compression refrigeration

Article published in International Conference on Advances in Mechanical Sciences 2014, Special Issue-2 (Feb 2014)

 

 

 

 

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