Optimal operational control for complex industrial processes

被引：107

作者：

Chai, Tianyou ^{[1
]}

Qin, S. Joe ^{[1
,2
,3
]}

Wang, Hong ^{[1
,4
]}

机构：

[1] Northeastern Univ, State Key Lab Synthet Automat Proc Ind, Shenyang, Peoples R China

[2] Univ So Calif, Ming Hsieh Dept Elect Engn, Los Angeles, CA 90089 USA

[3] Univ So Calif, Mork Family Dept Chem Engn & Mat Sci, Los Angeles, CA 90089 USA

[4] Univ Manchester, Sch Elect & Elect Engn, Manchester M13 9PL, Lancs, England

来源：

ANNUAL REVIEWS IN CONTROL | 2014年 / 38卷 / 01期

基金：

中国国家自然科学基金;

关键词：

MODEL-PREDICTIVE CONTROL; INTELLIGENT CONTROL; OPTIMIZING CONTROL; OPTIMIZATION; SYSTEM;

D O I：

10.1016/j.arcontrol.2014.03.005

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Process control should ensure not only controlled variables to follow their setpoint values, but also the whole process plant to meet operational requirements optimally (e.g., quality, efficiency and consumptions). Process control should also enable that operational indices for quality and efficiency be improved continuously, while keeping the indices related to consumptions at the lowest possible level. This paper starts with a survey on the existing operational optimization and control methodologies and then presents a data-driven hybrid intelligent optimal operational control for complex industrial processes where process operational models are difficult to obtain. Applications via a hybrid simulation system and an industrial roasting process for hematite ore mineral processing are presented to demonstrate the effectiveness of the proposed operational control method. Issues for future research on the optimal operational control for complex industrial processes are outlined before concluding the paper. (C) 2014 Elsevier Ltd. All rights reserved.

引用

页码：81 / 92

页数：12

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