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Abstract
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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.
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