Optimal operational control for complex industrial processes

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

110 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)81-92
Journal / PublicationAnnual Reviews in Control
Volume38
Issue number1
Online published26 Apr 2014
Publication statusPublished - 2014
Externally publishedYes

Abstract

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. © 2014 Elsevier Ltd. All rights reserved.

Citation Format(s)

Optimal operational control for complex industrial processes. / Chai, Tianyou; Qin, S. Joe; Wang, Hong.
In: Annual Reviews in Control, Vol. 38, No. 1, 2014, p. 81-92.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review