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.
|Number of pages||6|
|Early online date||25 Sept 2015|
|Publication status||Published - 2015|
|Event||9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 - , Canada|
Duration: 7 Jun 2015 → 10 Jun 2015
Bibliographical noteThis 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