Hybrid ANN approach for prediction of lubricant retention in compressor suction lines based on analytical model and parametric study

Abstract

A modeling study for prediction of oil retention in compressor suction lines is presented in this article. A new flow-pattern-based analytical model called the double-circle model (DCM) was developed to evaluate the oil retention amount in suction lines. A new consolidated oil retention database was established to develop interfacial friction factor correlation. Parametric study was conducted by means of force balance analysis to determine the influence factors of oil transport. The DCM and parametric study were validated by the database, and the results showed that they display reliable accuracy. Finally, a hybrid artificial neural network (ANN) approach was proposed, which used the DCM and parametric study to produce extrapolation data and determine the input parameters, respectively. The hybrid ANN was optimized for the 6-13-1 configuration with logarithmic sigmoid (logsig) transfer function and the Lavenberg–Marquardt (L-M) algorithm. The hybrid ANN model prediction yields a mean relative error (MRE) and R 2 of 6.25% and 96.86%, respectively.