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Optimal flowsheet configuration of a polymerization process with embedded molecular weight distributions
Author(s) -
Zhang Chen,
Shao Zhijiang,
Chen Xi,
Gu Xueping,
Feng Lianfang,
Biegler Lorenz T.
Publication year - 2016
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.15040
Subject(s) - continuous stirred tank reactor , superstructure , process (computing) , process engineering , polymerization , nonlinear programming , nonlinear system , slurry , high density polyethylene , computer science , materials science , mathematical optimization , polyethylene , mathematics , engineering , chemical engineering , structural engineering , polymer , physics , quantum mechanics , composite material , operating system
We consider the optimal reactor network synthesis of a polymerization process with detailed molecular weight distributions (MWDs). Based on an industrial high‐density polyethylene (HDPE) slurry process model including an embedded MWD, a fully connected process superstructure of continuous stirred tank reactors (CSTRs) is established through the introduction of splitters. Using this generalized superstructure as a basis, two nonlinear programming (NLP) problem formulations, which simultaneously maximize the monomer conversion and minimize the deviation between the calculated and target MWDs, are developed by applying multiobjective optimization (MO) methods. Different optimal flowsheet configurations are generated by systematically manipulating a set of continuous decision variables. Several case studies that consider different specifications on MWD are conducted to illustrate the effectiveness and efficiency of the proposed synthesis approach. Numerical results show that the optimal flowsheet configurations overcome the limitations of conventional reactor network structures and help to increase reactor productivity at the desired product quality. © 2015 American Institute of Chemical Engineers AIChE J, 62: 131–145, 2016