relation: https://khub.utp.edu.my/scholars/6886/
title: An intelligent optimization�based prediction model for natural gas hydrate formation in a deepwater pipeline
creator: Abbasi, A.
creator: Hashim, F.M.
description: Temperature, pressure, and composition of gas mixtures in deepwater pipelines promote rapid formation of gas hydrates. To avert this dilemma, it is more significant to find out the temperature and pressure limits in gas hydrates formation of the deepwater pipeline. The objective of this research is to develop an optimization method that finds the optimal temperature and pressure profile for natural gas hydrate formation conditions and an error calculation method to find the realistic approach of the hydrate formation prediction model. A newly developed correlation model is computing the hydrate formation pressure and temperature for a single component of methane (CH4) gas. The proposed developed prediction model is based on the 2 and 15 constant coefficients and holds a wide range of temperature and pressure data about 2.64 to 46°C and 0.051 to 400 MPa for pure water and methane, respectively. The reducing error discrepancies are 1.2871, 0.35012, and 1.9052, which is assessed by GA, PSO, and GWO algorithms, respectively. The results show the newly developed optimization algorithms are in admirable compliance with the experimental data and standards of empirical models. These correlations are providing the capability to predict gas hydrate forming conditions for a wide range of hydrate formation data. © 2016 Taylor & Francis Group, LLC.
publisher: Taylor and Francis Inc.
date: 2016
type: Article
type: PeerReviewed
identifier:   Abbasi, A. and Hashim, F.M.  (2016) An intelligent optimization�based prediction model for natural gas hydrate formation in a deepwater pipeline.  Petroleum Science and Technology, 34 (15).  pp. 1352-1358.  ISSN 10916466     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986550117&doi=10.1080%2f10916466.2016.1204315&partnerID=40&md5=03396e82e80fd82defb13fb57e628f30
relation: 10.1080/10916466.2016.1204315
identifier: 10.1080/10916466.2016.1204315