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Kinetic parameter estimation of HDPE slurry process from molecular weight distribution: Estimability analysis and multistep methodology
Author(s) -
Zhang Chen,
Shao Zhijiang,
Chen Xi,
Yao Zhen,
Gu Xueping,
Biegler Lorenz T.
Publication year - 2014
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.14527
Subject(s) - high density polyethylene , sensitivity (control systems) , eigenvalues and eigenvectors , matching (statistics) , kinetic energy , convergence (economics) , mathematics , process (computing) , estimation theory , mathematical optimization , computer science , algorithm , polyethylene , materials science , statistics , engineering , physics , operating system , quantum mechanics , electronic engineering , economics , composite material , economic growth
This study addresses kinetic parameter estimation for a high‐density polyethylene (HDPE) slurry process based on fitting molecular weight distributions (MWDs). From the process model, we conduct an estimability analysis by assessing the relative sensitivity between output variables and kinetic parameters as well as confidence intervals. This determines which parameters can be estimated. Conversely, a major challenge remains with the solution of an ill‐conditioned parameter estimation problem with MWD as the output variable. To overcome the convergence difficulties with the associated problem, we develop a novel multistep methodology where we first obtain MWD parameters by matching to data and then estimate kinetic parameters by matching to the regressed MWD parameters. Computational results and eigenvalue analysis show this multistep methodology separates an ill‐conditioned problem into two well‐conditioned subproblems. Moreover, we consider simulation‐based and industrial HDPE case studies. These results demonstrate the applicability, potential, and efficiency of this solution procedure. © 2014 American Institute of Chemical Engineers AIChE J , 60: 3442–3459, 2014