From automated to autonomous process operations

IF 3.9 2区 工程技术 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Computers & Chemical Engineering Pub Date : 2025-05-01 Epub Date: 2025-02-16 DOI:10.1016/j.compchemeng.2025.109064
Michael Baldea , Apostolos T. Georgiou , Bhushan Gopaluni , Mehmet Mercangöz , Constantinos C. Pantelides , Kiran Sheth , Victor M. Zavala , Christos Georgakis
{"title":"From automated to autonomous process operations","authors":"Michael Baldea ,&nbsp;Apostolos T. Georgiou ,&nbsp;Bhushan Gopaluni ,&nbsp;Mehmet Mercangöz ,&nbsp;Constantinos C. Pantelides ,&nbsp;Kiran Sheth ,&nbsp;Victor M. Zavala ,&nbsp;Christos Georgakis","doi":"10.1016/j.compchemeng.2025.109064","DOIUrl":null,"url":null,"abstract":"<div><div>This paper considers current trends towards a higher degree of automation of process operations. Often referred to as “autonomous” process operations, these developments involve cyber-physical systems that can automate tasks that have hitherto relied extensively on human plant operators and, in particular, on their accurate assessment of the current plant situation based on a multitude of information sources, and on their ability to devise and implement plans of actions for dealing with often novel situations. The paper analyses the main drivers behind the need for a higher level of automation in process operations, and reviews the industrial applications that have been described in the public domain to date. It also presents a review of advances and potential impact of some of the enabling technologies for autonomy; these include sensors, mathematical modelling abstractions, reinforcement learning, knowledge graphs, and large language models.</div></div>","PeriodicalId":286,"journal":{"name":"Computers & Chemical Engineering","volume":"196 ","pages":"Article 109064"},"PeriodicalIF":3.9000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers & Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0098135425000687","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/16 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

This paper considers current trends towards a higher degree of automation of process operations. Often referred to as “autonomous” process operations, these developments involve cyber-physical systems that can automate tasks that have hitherto relied extensively on human plant operators and, in particular, on their accurate assessment of the current plant situation based on a multitude of information sources, and on their ability to devise and implement plans of actions for dealing with often novel situations. The paper analyses the main drivers behind the need for a higher level of automation in process operations, and reviews the industrial applications that have been described in the public domain to date. It also presents a review of advances and potential impact of some of the enabling technologies for autonomy; these include sensors, mathematical modelling abstractions, reinforcement learning, knowledge graphs, and large language models.
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
从自动化到自主流程操作
本文考虑了目前过程操作自动化程度更高的趋势。这些发展通常被称为“自主”流程操作,涉及网络物理系统,可以自动完成迄今为止广泛依赖于人类工厂操作员的任务,特别是依赖于他们基于大量信息源对当前工厂状况的准确评估,以及他们设计和实施处理新情况的行动计划的能力。本文分析了过程操作中更高水平自动化需求背后的主要驱动因素,并回顾了迄今为止在公共领域描述的工业应用。它还介绍了一些自主技术的进展和潜在影响;这些包括传感器、数学建模抽象、强化学习、知识图和大型语言模型。
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
Computers & Chemical Engineering
Computers & Chemical Engineering 工程技术-工程:化工
CiteScore
8.70
自引率
14.00%
发文量
374
审稿时长
70 days
期刊介绍: Computers & Chemical Engineering is primarily a journal of record for new developments in the application of computing and systems technology to chemical engineering problems.
期刊最新文献
Kolmogorov-Arnold network driven soft sensors for chemical processes with distributed output Reproducibility of GPU-based Large Eddy Simulations for mixing in stirred tank reactors PlantGraphExpert: A knowledge graph-driven tool for chemical plant operator assistance Strategic design of decentralized multi-hub hydrogen supply chains with LNG value chain integration for global trade Adaptive physics-informed neural network-based digital twins integrated with Ensemble Kalman Filter
×
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