A Constrained Machine Learning Surrogate Model To Predict The Distribution Of Water-in-oil Emulsions In Electrostatic Fields

This article discusses the use of a constrained machine learning model to accurately predict water droplet size distribution in crude oil, which is crucial for evaluating water separation efficiency in dehydration systems. The model incorporates key parameters of the electrostatic dehydration process and has been validated through comparison with experimental data and a population balance mathematical model.