Artificial Neural Network for Modeling the Extraction of Aromatic Hydrocarbons from Lube Oil Cuts

Abstract

An artificial neural network (ANN) approach was used to obtain a simulation model to predict the rotating disc contactor (RDC) performance during the extraction of aromatic hydrocarbons from lube oil cuts, to produce a lubricating base oil using furfural as solvent. The field data used for training the ANN model was obtained from a lubricating oil production company. The input parameters of the ANN model were the volumetric flow rates of feed and solvent, the temperatures of feed and solvent, and the disc rotation rate. The output parameters were the volumetric flow rate of the raffinate phase and the extraction yield. In this study, a feed-forward multi-layer perceptron neural network was successfully used to demonstrate the complex relationship between the mentioned input and output parameters. It is possible to conduct a parametric study of the complex lubricating oil extraction process in an industrial rotating disc contactor column using the artificial neural network (ANN) procedure. The accuracy of the created ANN model was checked by randomly selected data among the archived operational data set of an industrial lubricating oil producer company. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.