eprintid: 19960
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/99/60
datestamp: 2024-06-04 14:19:42
lastmod: 2024-06-04 14:19:42
status_changed: 2024-06-04 14:16:17
type: article
metadata_visibility: show
creators_name: Osei, H.
creators_name: Bavoh, C.B.
creators_name: Lal, B.
title: Research Advances in Machine Learning Techniques in Gas Hydrate Applications
ispublished: pub
note: cited By 0
abstract: The complex modeling accuracy of gas hydrate models has been recently improved owing to the existence of data for machine learning tools. In this review, we discuss most of the machine learning tools used in various hydrate-related areas such as phase behavior predictions, hydrate kinetics, CO2 capture, and gas hydrate natural distribution and saturation. The performance comparison between machine learning and conventional gas hydrate models is also discussed in detail. This review shows that machine learning methods have improved hydrate phase property predictions and could be adopted in current and new gas hydrate simulation software for better and more accurate results. © 2024 The Authors. Published by American Chemical Society.
date: 2024
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183033490&doi=10.1021%2facsomega.3c04825&partnerID=40&md5=8d1e5e7ac1af28ff804828999b846f85
id_number: 10.1021/acsomega.3c04825
full_text_status: none
publication: ACS Omega
volume: 9
number: 4
pagerange: 4210-4228
refereed: TRUE
citation:   Osei, H. and Bavoh, C.B. and Lal, B.  (2024) Research Advances in Machine Learning Techniques in Gas Hydrate Applications.  ACS Omega, 9 (4).  pp. 4210-4228.