IF 5.2 3区 工程技术 Q2 ENERGY & FUELS
Haonan Li, Huiru Sun, Jing Chen, Bingbing Chen, Dongliang Zhong* and Mingjun Yang*, 
{"title":"Advances, Applications, and Perspectives of Machine Learning Approaches in Predicting Gas Hydrate Phase Equilibrium","authors":"Haonan Li,&nbsp;Huiru Sun,&nbsp;Jing Chen,&nbsp;Bingbing Chen,&nbsp;Dongliang Zhong* and Mingjun Yang*,&nbsp;","doi":"10.1021/acs.energyfuels.4c0492410.1021/acs.energyfuels.4c04924","DOIUrl":null,"url":null,"abstract":"<p >Given the urgent environmental issues posed by rising carbon emissions and a global temperature increase, the modern world must develop effective solutions. The deployment of technology associated with hydrates represents a viable strategy for the mitigation of environmental degradation. This is achieved by employing methane hydrates as an alternative, more environmentally friendly energy resource and utilizing carbon dioxide hydrates for carbon sequestration and storage. In the study of hydrates, accurately determining hydrate phase equilibrium conditions is crucial for understanding and controlling the gas hydrate formation and stability. With the rise of machine learning, artificial intelligence algorithms have become increasingly relevant to hydrate research, particularly in the development of predictive models for hydrate phase equilibrium. These algorithms offer both high feasibility and a necessity in addressing complex hydrate-related problems. This paper focuses on the application of machine learning, specifically the Gradient Boosted Regression Tree (GBRT) algorithm, to predict hydrate phase equilibrium conditions. The rationale for selecting GBRT, along with the model construction process, training, and validation methods, is discussed in detail. This integration of hydrate research and machine learning techniques promises to advance our predictive capabilities and optimize the extraction and utilization of hydrates as a sustainable energy resource.</p>","PeriodicalId":35,"journal":{"name":"Energy & Fuels","volume":"38 24","pages":"23320–23335 23320–23335"},"PeriodicalIF":5.2000,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy & Fuels","FirstCategoryId":"5","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.energyfuels.4c04924","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

鉴于碳排放量增加和全球气温上升带来的紧迫环境问题,现代世界必须制定有效的解决方案。水合物相关技术的应用是缓解环境恶化的可行战略。具体做法是利用甲烷水合物作为更环保的替代能源,并利用二氧化碳水合物进行碳封存和储存。在水合物研究中,准确确定水合物相平衡条件对于了解和控制天然气水合物的形成和稳定性至关重要。随着机器学习的兴起,人工智能算法与水合物研究的关系日益密切,特别是在开发水合物相平衡预测模型方面。在解决复杂的水合物相关问题时,这些算法具有很高的可行性和必要性。本文重点介绍机器学习,特别是梯度提升回归树(GBRT)算法在预测水合物相平衡条件方面的应用。本文详细讨论了选择 GBRT 的理由、模型构建过程、训练和验证方法。水合物研究与机器学习技术的结合有望提高我们的预测能力,优化水合物作为可持续能源资源的开采和利用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Advances, Applications, and Perspectives of Machine Learning Approaches in Predicting Gas Hydrate Phase Equilibrium

Advances, Applications, and Perspectives of Machine Learning Approaches in Predicting Gas Hydrate Phase Equilibrium

Given the urgent environmental issues posed by rising carbon emissions and a global temperature increase, the modern world must develop effective solutions. The deployment of technology associated with hydrates represents a viable strategy for the mitigation of environmental degradation. This is achieved by employing methane hydrates as an alternative, more environmentally friendly energy resource and utilizing carbon dioxide hydrates for carbon sequestration and storage. In the study of hydrates, accurately determining hydrate phase equilibrium conditions is crucial for understanding and controlling the gas hydrate formation and stability. With the rise of machine learning, artificial intelligence algorithms have become increasingly relevant to hydrate research, particularly in the development of predictive models for hydrate phase equilibrium. These algorithms offer both high feasibility and a necessity in addressing complex hydrate-related problems. This paper focuses on the application of machine learning, specifically the Gradient Boosted Regression Tree (GBRT) algorithm, to predict hydrate phase equilibrium conditions. The rationale for selecting GBRT, along with the model construction process, training, and validation methods, is discussed in detail. This integration of hydrate research and machine learning techniques promises to advance our predictive capabilities and optimize the extraction and utilization of hydrates as a sustainable energy resource.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy & Fuels
Energy & Fuels 工程技术-工程:化工
CiteScore
9.20
自引率
13.20%
发文量
1101
审稿时长
2.1 months
期刊介绍: Energy & Fuels publishes reports of research in the technical area defined by the intersection of the disciplines of chemistry and chemical engineering and the application domain of non-nuclear energy and fuels. This includes research directed at the formation of, exploration for, and production of fossil fuels and biomass; the properties and structure or molecular composition of both raw fuels and refined products; the chemistry involved in the processing and utilization of fuels; fuel cells and their applications; and the analytical and instrumental techniques used in investigations of the foregoing areas.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信