eprintid: 14178
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/41/78
datestamp: 2023-11-10 03:28:45
lastmod: 2023-11-10 03:28:45
status_changed: 2023-11-10 01:56:13
type: article
metadata_visibility: show
creators_name: Suresh, S.D.
creators_name: Qasim, A.
creators_name: Lal, B.
creators_name: Imran, S.M.
creators_name: Foo, K.S.
title: Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation
ispublished: pub
note: cited By 1
abstract: The production of oil and natural gas contributes to a significant amount of revenue generation in Malaysia thereby strengthening the country�s economy. The flow assurance industry is faced with impediments during smooth operation of the transmission pipeline in which gas hydrate formation is the most important. It affects the normal operation of the pipeline by plugging it. Under high pressure and low temperature conditions, gas hydrate is a crystalline structure consisting of a network of hydrogen bonds between host molecules of water and guest molecules of the incoming gases. Industry uses different types of chemical inhibitors in pipeline to suppress hydrate formation. To overcome this problem, machine learning algorithm has been introduced as part of risk management strategies. The objective of this paper is to utilize Machine Learning (ML) model which is Gaussian Process Regression (GPR). GPR is a new approach being applied to mitigate the growth of gas hydrate. The input parameters used are concentration and pressure of Carbon Dioxide (CO2) and Methane (CH4) gas hydrates whereas the output parameter is the Average Depression Temperature (ADT). The values for the parameter are taken from available data sets that enable GPR to predict the results accurately in terms of Coefficient of Determination, R2 and Mean Squared Error, MSE. The outcome from the research showed that GPR model provided with highest R2 value for training and testing data of 97.25 and 96.71, respectively. MSE value for GPR was also found to be lowest for training and testing data of 0.019 and 0.023, respectively. © 2021, Journal of Advanced Research in Fluid Mechanics and Thermal Sciences. All Rights Reserved.
date: 2021
publisher: Penerbit Akademia Baru
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85119695627&doi=10.37934%2farfmts.88.2.2737&partnerID=40&md5=3a0ce37f13520bc62417f41c405c74d0
id_number: 10.37934/arfmts.88.2.2737
full_text_status: none
publication: Journal of Advanced Research in Fluid Mechanics and Thermal Sciences
volume: 88
number: 2
pagerange: 27-37
refereed: TRUE
issn: 22897879
citation:   Suresh, S.D. and Qasim, A. and Lal, B. and Imran, S.M. and Foo, K.S.  (2021) Application of Gaussian Process Regression (GPR) in Gas Hydrate Mitigation.  Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 88 (2).  pp. 27-37.  ISSN 22897879