Recent trends in optimization models for industrial decarbonization

IF 6.8 2区 工程技术 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Current Opinion in Chemical Engineering Pub Date : 2025-06-01 Epub Date: 2025-03-11 DOI:10.1016/j.coche.2025.101118
Raymond R Tan , Maria Victoria Migo-Sumagang , Kathleen B Aviso
{"title":"Recent trends in optimization models for industrial decarbonization","authors":"Raymond R Tan ,&nbsp;Maria Victoria Migo-Sumagang ,&nbsp;Kathleen B Aviso","doi":"10.1016/j.coche.2025.101118","DOIUrl":null,"url":null,"abstract":"<div><div>The global call for deep decarbonization poses the critical challenge of cutting greenhouse gas emissions from industrial operations. Decarbonization can be achieved with a mix of strategies and technologies, but decision-support models are needed to help optimize their emissions reduction portfolios. This review surveys the development and use of models to support industrial decarbonization decisions and proposes a research roadmap for the future. Four key modeling challenges are identified: epistemic uncertainties inherent in new technologies, feedback loops between techno-economic performance and technology selection, the interplay between multiple decision-makers, and embedding within a broader decarbonization context.</div></div>","PeriodicalId":292,"journal":{"name":"Current Opinion in Chemical Engineering","volume":"48 ","pages":"Article 101118"},"PeriodicalIF":6.8000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Opinion in Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2211339825000292","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/3/11 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

The global call for deep decarbonization poses the critical challenge of cutting greenhouse gas emissions from industrial operations. Decarbonization can be achieved with a mix of strategies and technologies, but decision-support models are needed to help optimize their emissions reduction portfolios. This review surveys the development and use of models to support industrial decarbonization decisions and proposes a research roadmap for the future. Four key modeling challenges are identified: epistemic uncertainties inherent in new technologies, feedback loops between techno-economic performance and technology selection, the interplay between multiple decision-makers, and embedding within a broader decarbonization context.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
工业脱碳优化模型的最新趋势
全球对深度脱碳的呼吁提出了减少工业运营温室气体排放的关键挑战。脱碳可以通过战略和技术的组合来实现,但需要决策支持模型来帮助优化其减排组合。本文综述了支持工业脱碳决策的模型的发展和使用,并提出了未来的研究路线图。确定了四个关键的建模挑战:新技术固有的认知不确定性,技术经济绩效和技术选择之间的反馈循环,多个决策者之间的相互作用,以及嵌入更广泛的脱碳背景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Current Opinion in Chemical Engineering
Current Opinion in Chemical Engineering BIOTECHNOLOGY & APPLIED MICROBIOLOGYENGINE-ENGINEERING, CHEMICAL
CiteScore
12.80
自引率
3.00%
发文量
114
期刊介绍: Current Opinion in Chemical Engineering is devoted to bringing forth short and focused review articles written by experts on current advances in different areas of chemical engineering. Only invited review articles will be published. The goals of each review article in Current Opinion in Chemical Engineering are: 1. To acquaint the reader/researcher with the most important recent papers in the given topic. 2. To provide the reader with the views/opinions of the expert in each topic. The reviews are short (about 2500 words or 5-10 printed pages with figures) and serve as an invaluable source of information for researchers, teachers, professionals and students. The reviews also aim to stimulate exchange of ideas among experts. Themed sections: Each review will focus on particular aspects of one of the following themed sections of chemical engineering: 1. Nanotechnology 2. Energy and environmental engineering 3. Biotechnology and bioprocess engineering 4. Biological engineering (covering tissue engineering, regenerative medicine, drug delivery) 5. Separation engineering (covering membrane technologies, adsorbents, desalination, distillation etc.) 6. Materials engineering (covering biomaterials, inorganic especially ceramic materials, nanostructured materials). 7. Process systems engineering 8. Reaction engineering and catalysis.
期刊最新文献
Learning catalytic kinetic models from data: current and emerging methods From waste to protein: feedstock selection and sustainability in bacterial single-cell protein production Electrochemical technologies for critical raw materials recovery and circular integration Corrigendum to “Toward consistent thermodynamic modeling of CO2 adsorption on Lewatit VPOC 1065 under dry conditions: isotherm variability, data gaps, and model fitting” [Curr Opin Chem Eng, 51 (March 2026), 101201] Molecular machine learning in chemical process design
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1