Measurement of Refrigeration Capacity of Compressors With Metrological Reliability Using Artificial Neural Networks

This paper proposes and experimentally evaluates a method, which is able to measure the refrigeration capacity of compressors in extremely short times. Traditionally, the measurement of this parameter is done using specialized and expensive test rigs and can take up to 4.5 h for the measurement of a single compressor. To overcome this long measurement time, it is proposed to use a simpler measuring system, in which, instead of measuring the refrigeration capacity directly, the rate of pressure rise imposed by the compressor to a cylinder with fixed volume is measured. This paper shows that both variables have a high correlation factor when the test and compressor conditions are taken into account. As the correlation among the variables is nonlinear, it is not possible to use simple mathematical correlation functions, so neural networks are used for that purpose. By using the proposed method, the refrigeration capacity of a compressor can be determined in less than 7 s, which is approximately 0.05% of the time required by traditional methods. To guarantee the metrological reliability of the results presented by the proposed neural model, this paper proposes a novel approach based on the Monte Carlo and the bootstrap methods.