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Analysis and Decomposition for Improved Convergence of Nonlinear Process Models in Chemical Engineering
Author(s) -
Bublitz Saskia,
Esche Erik,
Tolksdorf Gregor,
Mehrmann Volker,
Repke JensUwe
Publication year - 2017
Publication title -
chemie ingenieur technik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 36
eISSN - 1522-2640
pISSN - 0009-286X
DOI - 10.1002/cite.201700041
Subject(s) - jacobian matrix and determinant , initialization , convergence (economics) , nonlinear system , sensitivity (control systems) , block (permutation group theory) , process (computing) , decomposition , computer science , transformation (genetics) , diagonal , mathematical optimization , mathematics , matrix (chemical analysis) , block matrix , algorithm , engineering , materials science , chemistry , electronic engineering , economic growth , composite material , operating system , biochemistry , geometry , quantum mechanics , programming language , eigenvalues and eigenvectors , physics , organic chemistry , economics , gene
Abstract Solving nonlinear equation systems as they occur in process simulation simultaneously, often fails due to ill‐conditioned models or bad initialization. To counteract these issues two methods have been implemented in the web‐based platform MOSAICmodeling. The Dulmage‐Mendelsohn algorithm forms a block diagonal Jacobian matrix. In addition, the bordered block transformation identifies variables that need to be carefully initialized due to their great influence on the system. Both methods are applied on two process models and their convergence and sensitivity towards initialization is analyzed.