{"title":"实施ISO 15746化工过程优化标准。","authors":"Guodong Shao,&nbsp;Albert Jones,&nbsp;Peter Denno,&nbsp;Yan Lu","doi":"10.1115/MSEC2016-8635","DOIUrl":null,"url":null,"abstract":"<p><p>This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization - and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times - each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.</p>","PeriodicalId":91952,"journal":{"name":"Proceedings of the ASME International Conference on Manufacturing Science and Engineering. ASME International Conference on Manufacturing Science and Engineering","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/MSEC2016-8635","citationCount":"2","resultStr":"{\"title\":\"Implementing the ISO 15746 Standard for Chemical Process Optimization.\",\"authors\":\"Guodong Shao,&nbsp;Albert Jones,&nbsp;Peter Denno,&nbsp;Yan Lu\",\"doi\":\"10.1115/MSEC2016-8635\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization - and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times - each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.</p>\",\"PeriodicalId\":91952,\"journal\":{\"name\":\"Proceedings of the ASME International Conference on Manufacturing Science and Engineering. ASME International Conference on Manufacturing Science and Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1115/MSEC2016-8635\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ASME International Conference on Manufacturing Science and Engineering. ASME International Conference on Manufacturing Science and Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/MSEC2016-8635\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ASME International Conference on Manufacturing Science and Engineering. ASME International Conference on Manufacturing Science and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/MSEC2016-8635","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

摘要

本文提出了一种在任何工厂内集成先进过程控制解决方案与优化(APC-O)解决方案的方法,以实现更高效的生产过程。目前,提供实现控制解决方案的软件应用程序的供应商是隔离的,相对独立的。每个这样的解决方案都是为了实现特定的任务而设计的,比如控制、模拟和优化——而且只能实现这个任务。供应商使用不同的数学形式化和建模工具来产生不同的数据表示和格式是很常见的。此外,相同的知识不是只统一建模一次,而是经常建模多次——每次都使用不同的专门抽象。因此,将优化与先进的过程控制相结合是极其困难的。我们相信最近的一个标准,国际标准化组织(ISO) 15746,描述了一个可以促进这种集成的数据模型。在本文中,我们展示了一种利用ISO 15746与数值优化相结合的先进过程控制的新方法。该演示基于一个化学过程优化问题,该问题位于国际自动化学会(ISA) 95体系结构的第2级。该优化问题的输入(在ISO 15746数据模型中捕获)有两种形式:来自级别3的目标和来自级别1的反馈。我们使用该数据模型将这些输入映射到化学过程优化问题的元模型的种群。元模型种群的序列化为优化问题的数值优化代码提供了输入。这个集成过程的结果是自动化的,为最初选择的第2级优化问题提供了解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

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
Implementing the ISO 15746 Standard for Chemical Process Optimization.

This paper proposes an approach to integrating advanced process control solutions with optimization (APC-O) solutions, within any factory, to enable more efficient production processes. Currently, vendors who provide the software applications that implement control solutions are isolated and relatively independent. Each such solution is designed to implement a specific task such as control, simulation, and optimization - and only that task. It is not uncommon for vendors to use different mathematical formalisms and modeling tools that produce different data representations and formats. Moreover, instead of being modeled uniformly only once, the same knowledge is often modeled multiple times - each time using a different, specialized abstraction. As a result, it is extremely difficult to integrate optimization with advanced process control. We believe that a recent standard, International Organization for Standardization (ISO) 15746, describes a data model that can facilitate that integration. In this paper, we demonstrate a novel method of integrating advanced process control using ISO 15746 with numerical optimization. The demonstration is based on a chemical-process-optimization problem, which resides at level 2 of the International Society of Automation (ISA) 95 architecture. The inputs to that optimization problem, which are captured in the ISO 15746 data model, come in two forms: goals from level 3 and feedback from level 1. We map these inputs, using this data model, to a population of a meta-model of the optimization problem for a chemical process. Serialization of the metamodel population provides input to a numerical optimization code of the optimization problem. The results of this integrated process, which is automated, provide the solution to the originally selected, level 2 optimization problem.

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
自引率
0.00%
发文量
0
期刊最新文献
Thelethong Development for Eco Friendly Material Fulfillment of Security Law Principle in Pledge with Stock as it Object Urgency of Notary Engagement as a Limitation on the Freedom of Contracts on the Pre-Project Selling Events Gaps Analysis of Integrating Product Design, Manufacturing, and Quality Data in The Supply Chain Using Model-Based Definition. AUTOMATING ASSET KNOWLEDGE WITH MTCONNECT.
×
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:481959085
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