基于神经网络的显式MPC化学反应器控制

IF 0.9 Q4 CHEMISTRY, MULTIDISCIPLINARY Acta Chimica Slovaca Pub Date : 2019-10-01 DOI:10.2478/acs-2019-0030
Karol Kiš, Martin Klauco
{"title":"基于神经网络的显式MPC化学反应器控制","authors":"Karol Kiš, Martin Klauco","doi":"10.2478/acs-2019-0030","DOIUrl":null,"url":null,"abstract":"Abstract In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.","PeriodicalId":7088,"journal":{"name":"Acta Chimica Slovaca","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Neural network based explicit MPC for chemical reactor control\",\"authors\":\"Karol Kiš, Martin Klauco\",\"doi\":\"10.2478/acs-2019-0030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.\",\"PeriodicalId\":7088,\"journal\":{\"name\":\"Acta Chimica Slovaca\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2019-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Chimica Slovaca\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2478/acs-2019-0030\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Chimica Slovaca","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/acs-2019-0030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 9

摘要

本文介绍了深度神经网络在过程控制中的应用。在我们的方法中,神经网络的训练是基于模型预测控制的,这是受欢迎的,因为它能够通过加权矩阵进行调整,并且尊重系统约束。引入了一种神经网络,通过模拟控制输入轨迹来近似MPC行为,同时状态约束和控制输入约束不受加权矩阵的影响。该方法在一个涉及多组分化学反应发生的连续搅拌槽式反应器的模拟案例研究中得到了证明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Neural network based explicit MPC for chemical reactor control
Abstract In this paper, implementation of deep neural networks applied in process control is presented. In our approach, training of the neural network is based on model predictive control, which is popular for its ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical reaction takes place.
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
Acta Chimica Slovaca
Acta Chimica Slovaca CHEMISTRY, MULTIDISCIPLINARY-
自引率
12.50%
发文量
11
期刊最新文献
Atomic partial charge model in chemistry: chemical accuracy of theoretical approaches for diatomic molecules Substitution effect of phenol derivatives on electrochemical oxidation potentials: Correlation of theoretical reaction Gibbs free energies Thermal- and light-induced SCO effect in Fe(II) complexes and coordination polymers Stability of ferrate during long-term storage Colour masterbatches and their use in polylactic acid fibres dyeing
×
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