Drill-down diagnosis of deficient models in MPC
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 759-764 |
| Journal / Publication | IFAC-PapersOnLine |
| Volume | 48 |
| Issue number | 8 |
| Online published | 25 Sept 2015 |
| Publication status | Published - 2015 |
| Externally published | Yes |
Conference
| Title | 9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 |
|---|---|
| Place | Canada |
| City | Whistler |
| Period | 7 - 10 June 2015 |
Link(s)
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.
In: IFAC-PapersOnLine, Vol. 48, No. 8, 2015, p. 759-764.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
