Prediction of performance parameters of a hermetic reciprocating compressor under different discharge lift limiter heights by machine learning

Abstract

The research examines the complex correlation between discharge valve properties in severe temperature circumstances, ranging from 54.4 degrees C to -23.3 degrees C, in accordance with ASHRAE operational guidelines. The design parameters include examining valve thicknesses of 0.127, 0.152, 0.178, and 0.2 mm, together with lengths of 14.722, 16.222, and 17.722 mm, at compressor speeds of 1300, 2100, and 3000 rpm. An artificial neural network (ANN) is used to replicate the output properties of a hermetic reciprocating compressor, which include the ratio of cooling capacity to compression power and volumetric efficiency. One hundred and eleven numerically recorded datasets are used to train the developed ANN model. The model is trained using 77 datasets, validated using 17 datasets, and tested using 17 datasets. The LM-type ANN approach is used to train the multilayer perception neural network, which consists of a hidden layer with 15 neurons. Given the proximity of the margin of deviations (MoDs) to the 0% deviation line, the variances between the ANN and fluid-structure interaction outcomes for the cooling capacity to compression power ratio and volumetric efficiency are insignificant. The average figures for the MoD output have been calculated as -0.18% and 0.06, respectively. Not only do the data points lie on the line, indicating a 0% error, but they also fall inside the interval, indicating a 10% error. In addition, the mean squared error and correlation coefficient values for the ANN model that was created are 2.04E-03 and 0.99853, respectively.