@inproceedings{f3f2399ed3ea450e8aa53d554d9d5e46,
title = "Accurate Prediction of Gas Compressibility Factor using Kernel Ridge Regression",
abstract = "The natural gas compressibility factor (z) is one of the critical parameters in the computations used for the upstream and downstream zones of petroleum/chemical industries. The process of obtaining accurate value for physical and thermodynamical properties of hydrocarbons is getting more challenging in the case of multicomponent non ideal systems. The purpose of this work is applying the kernel ridge regression (KRR) in the form of the recently developed truncated regularized kernel ridge regression (TR-KRR) algorithm to estimate z-factor. Compared to the support vector machines (SVM), the KRR algorithm is just as accurate as, but faster than SVM.",
keywords = "gas compressibility factor, kernel ridge regression, truncated-Newton method, z-factor",
author = "Maher Maalouf and Naji Khoury and Dirar Homouz and Kyriaki Polychronopoulou",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019 ; Conference date: 03-07-2019 Through 05-07-2019",
year = "2019",
month = jul,
doi = "10.1109/ACTEA.2019.8851106",
language = "British English",
series = "2019 4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019",
address = "United States",
}