eprintid: 6886
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/68/86
datestamp: 2023-11-09 16:18:41
lastmod: 2023-11-09 16:18:41
status_changed: 2023-11-09 16:07:55
type: article
metadata_visibility: show
creators_name: Abbasi, A.
creators_name: Hashim, F.M.
title: An intelligent optimization�based prediction model for natural gas hydrate formation in a deepwater pipeline
ispublished: pub
keywords: Algorithms; Forecasting; Gases; Genetic algorithms; Hydration; Methane; Natural gas; Optimization; Particle swarm optimization (PSO); Pipelines; Regulatory compliance; Water pipelines, Deepwater pipelines; Error calculation methods; Gas hydrates formation; Hydrate forming conditions; Intelligent optimization; Optimization algorithms; Prediction model; Temperature and pressures, Gas hydrates
note: cited By 6
abstract: 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.
date: 2016
publisher: Taylor and Francis Inc.
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84986550117&doi=10.1080%2f10916466.2016.1204315&partnerID=40&md5=03396e82e80fd82defb13fb57e628f30
id_number: 10.1080/10916466.2016.1204315
full_text_status: none
publication: Petroleum Science and Technology
volume: 34
number: 15
pagerange: 1352-1358
refereed: TRUE
issn: 10916466
citation:   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