Recursive data-based prediction and control of product quality for a PMMA batch process

Cited 11 time in webofscience Cited 0 time in scopus
  • Hit : 180
  • Download : 0
In many batch processes, frequent process/feedstock disturbances and unavailability of direct on-line quality measurements make it very difficult to achieve tight control of product quality. Motivated by this, we present a simple data-based method in which measurements of other process variables are related to end product quality using a historical data base. The developed correlation model is used to make on-line predictions of end quality, which can serve as a basis for adjusting the batch condition/time so that desired product quality may be achieved. This strategy is applied to a methyl methacrylate (MMA) polymerization process. Important end quality variables, the weight average molecular weight and the polydispersity, are predicted recursively based on the measurements of reactor cooling rate. Subsequently, a shrinking-horizon model predictive control approach is used to manipulate the reaction temperature. The results in this study show promise for the proposed inferential control method. (C) 2003 Published by Elsevier Ltd.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2003-07
Language
English
Article Type
Article
Citation

CHEMICAL ENGINEERING SCIENCE, v.58, no.14, pp.3215 - 3221

ISSN
0009-2509
DOI
10.1016/S0009-2509(03)00190-8
URI
http://hdl.handle.net/10203/81538
Appears in Collection
CBE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 11 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0