The Prediction of the Density of Undersaturated Crude Oil Using Multilayer Feed-Forward Back-Propagation Perceptron

Abstract

Crude oil density is an important thermodynamic property in simulation processes and design of equipment. Using laboratory methods to measure crude oil density is costly and time consuming; thus, predicting the density of crude oil using modeling is cost-effective. In this article, we develop a neural network–based model to predict the density of undersaturated crude oil. We compare our results with previous works and show that our method outperforms them.