Drill-down diagnosis of deficient models in MPC

Lijuan LI, S. Joe QIN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)759-764
Number of pages6
Issue number8
Early online date25 Sept 2015
Publication statusPublished - 2015
Externally publishedYes
Event9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 - , Canada
Duration: 7 Jun 201510 Jun 2015

Bibliographical note

This work is supported by National Natural Science Foundation of China (61203072 and 61490700 ), the Six Talent Peak Project of Jiangsu Province, and the Priority Academic Program Development of Jiangsu Higher Education Institutions.


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


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