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Iterative Process Design with Surrogate‐Assisted Global Flowsheet Optimization
Author(s) -
Janus Tim,
Engell Sebastian
Publication year - 2021
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.202100095
Subject(s) - workflow , process (computing) , computer science , process optimization , global optimization , process integration , process design , engineering optimization , iterative and incremental development , process engineering , mathematical optimization , optimization problem , engineering , algorithm , mathematics , software engineering , database , environmental engineering , operating system
Flowsheet optimization is an important part of process design where commercial process simulators are widely used, due to their extensive library of models and ease of use. However, the application of a framework for global flowsheet optimization upon them is computationally expensive. Based on machine learning methods, we added mechanisms for rejection and generation of candidates to a framework for global flowsheet optimization. These extensions halve the amount of time needed for optimization such that the integration of the framework in a workflow for iterative process design becomes applicable.