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Online control of molar mass and particle‐size distributions in emulsion polymerization
Author(s) -
Zeaiter J.,
Romagnoli J. A.,
Gomes V. G.
Publication year - 2006
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.10773
Subject(s) - emulsion polymerization , model predictive control , molar mass distribution , polymer , molar mass , process control , process engineering , materials science , computer science , polymerization , control theory (sociology) , process (computing) , control (management) , engineering , composite material , artificial intelligence , operating system
Abstract Product quality control is a complex issue in polymerization than in conventional chemical processes. The key polymer properties are related to the polymer particle‐size distribution (PSD), and molecular‐weight distribution (MWD), which are in turn sensitive to the thermo‐kinetic and operating history of the process. Disturbances in operating conditions may cause irreversible changes in the quality of the polymer formed. Therefore, adjustments must be made during the reaction through effective online control schemes. For this purpose, model predictive control (MPC) algorithm for on‐line control of polymer PSD and MWD was developed and implemented. An intelligent control hierarchy was formulated for the distributed parameter system at three levels: off‐line optimization, on‐line MPC and regulatory control successively. The novelty of our approach includes the incorporation of a validated high‐order dynamic model as a soft‐sensor for on‐line feedback of PSD and MWD. The MPC was implemented successfully on a reactor facility, where off‐line sample analysis, using CHDF and GPC instruments, were used to update the on‐line model predictions. © 2006 American Institute of Chemical Engineers AIChE J, 2006