Accurate Prediction of Gas Compressibility Factor using Kernel Ridge Regression

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageBritish English
Title of host publication2019 4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728101309
DOIs
StatePublished - Jul 2019
Event4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019 - Zouk-Mosbeh, Lebanon
Duration: 3 Jul 20195 Jul 2019

Publication series

Name2019 4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019

Conference

Conference4th International Conference on Advances in Computational Tools for Engineering Applications, ACTEA 2019
Country/TerritoryLebanon
CityZouk-Mosbeh
Period3/07/195/07/19

Keywords

  • gas compressibility factor
  • kernel ridge regression
  • truncated-Newton method
  • z-factor

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