Abstract
An efficient model reduction technique for the distillation columns is applied to account for the detail dynamics. This technique utilizes the orthogonal collocation and cubic spline method. Then the extended Kalman filter is applied to identify the model parameters and the feed composition from the measurements of the column. From the simulation, the model reduction technique can account for the detail dynamics of the rigorous distillation model and not only the model parameters, but also the feed composition can be identified by the recursive prediction error method.
| Original language | English |
|---|---|
| Journal | Computers and Chemical Engineering |
| Volume | 21 |
| Issue number | SUPPL.1 |
| Publication status | Published - 1997 Dec 1 |
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ASJC Scopus subject areas
- Chemical Engineering(all)
- Control and Systems Engineering
Cite this
Monitoring of a distillation column using modified extended Kalman filter and a reduced order model. / Yang, Dae Ryook; Lee, Kwang Soon.
In: Computers and Chemical Engineering, Vol. 21, No. SUPPL.1, 01.12.1997.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Monitoring of a distillation column using modified extended Kalman filter and a reduced order model
AU - Yang, Dae Ryook
AU - Lee, Kwang Soon
PY - 1997/12/1
Y1 - 1997/12/1
N2 - An efficient model reduction technique for the distillation columns is applied to account for the detail dynamics. This technique utilizes the orthogonal collocation and cubic spline method. Then the extended Kalman filter is applied to identify the model parameters and the feed composition from the measurements of the column. From the simulation, the model reduction technique can account for the detail dynamics of the rigorous distillation model and not only the model parameters, but also the feed composition can be identified by the recursive prediction error method.
AB - An efficient model reduction technique for the distillation columns is applied to account for the detail dynamics. This technique utilizes the orthogonal collocation and cubic spline method. Then the extended Kalman filter is applied to identify the model parameters and the feed composition from the measurements of the column. From the simulation, the model reduction technique can account for the detail dynamics of the rigorous distillation model and not only the model parameters, but also the feed composition can be identified by the recursive prediction error method.
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UR - http://www.scopus.com/inward/citedby.url?scp=0000142191&partnerID=8YFLogxK
M3 - Article
VL - 21
JO - Computers and Chemical Engineering
JF - Computers and Chemical Engineering
SN - 0098-1354
IS - SUPPL.1
ER -