The Prediction of Separation Performance of an In-Line Axial Oil–Water Separator Using Machine Learning and CFD

Abstract

Recently, global energy consumption has increased due to industrial development, resulting in increasing demand for various energy sources. Aside from the increased demand for renewable energy resources, the demand for fossil fuels is also on the rise. Accordingly, the demand for resource development in the deep sea is also increasing. Various systems are required to efficiently develop resources in the deep sea. A study on an in-line type oil–water separator is needed to compensate for the disadvantages of a gravity separator that separates traditional water and oil. In this paper, the separation performance of the axial-flow oil–water separator for five design variables (conical diameter, conical length, number of vanes, angle of vane, and thickness of vane) was analyzed. Numerical calculations for multiphase fluid were performed using the mixture model, one of the Euler–Euler approaches. Additionally, the Reynolds stress model was used to describe the swirling flow. As a result, it was found that the effect on the separation performance was large in the order of angle of vane, conical diameter, number of vanes, the thickness of vane, and conical length. A neural network model for predicting separation performance was developed using numerical calculation results. To predict the oil–water separation performance, five design parameters were considered, and the evaluation of the separation performance prediction model was compared with the multilinear regression (MLR) model. As a result, it was found that the R square was improved by about 74.0% in the neural network model, compared with the MLR model.