Drill-down diagnosis of deficient models in MPC

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)759-764
Journal / PublicationIFAC-PapersOnLine
Volume48
Issue number8
Online published25 Sep 2015
Publication statusPublished - 2015
Externally publishedYes

Conference

Title9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015
PlaceCanada
CityWhistler
Period7 - 10 June 2015

Abstract

Model maintenance is the most time-consuming and cost-intensive in industrial model predictive control. In this paper, a drill-down diagnosis algorithm for deficient models of industrial MPC via a model quality index (MQI) is proposed. The CVs with poor models can be detected first by MQI values with all controlled variables. Then, a leave-one-out algorithm is proposed to further diagnose which sub-models are deficient for the CVs with poor model performance. Thus, the effort and cost of model maintenance can be reduced. The application result to the Wood-Berry distillation column process indicates the effectiveness of the proposed assessment method.

Research Area(s)

  • Drill-down diagnosis, Model quality index, Predictive control, Wood-Berry distillation column

Citation Format(s)

Drill-down diagnosis of deficient models in MPC. / Li, Lijuan; Qin, S. Joe.

In: IFAC-PapersOnLine, Vol. 48, No. 8, 2015, p. 759-764.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review