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Scoping and screening complex reaction networks using stochastic optimization
Author(s) -
Marcoulaki E. C.,
Kokossis A. C.
Publication year - 1999
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.690450914
Subject(s) - curse of dimensionality , stochastic optimization , computer science , mathematical optimization , nonlinear system , mathematics , machine learning , physics , quantum mechanics
Abstract A systematic methodology to target the performance of chemical reactors with the use of stochastic optimization is presented. The approach employs a two‐level strategy where targets are followed by the proposition of reactor configurations that match or are near the desired performance. The targets can be used for synthesis and retrofit problems, as they can provide the incentives to modify the operation, and ideas in developing the reactor design. The application of stochastic optimization enables confidence in the optimization results, can afford particularly nonlinear reactor models, and is not restricted by the dimensionality or the size of the problem.