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dc.contributor.authorMohammadi, Yousef
dc.contributor.authorPenlidis, Alexanderen
dc.date.accessioned2020-10-26 19:13:45 (GMT)
dc.date.available2020-10-26 19:13:45 (GMT)
dc.date.issued2018-06-07
dc.identifier.urihttps://doi.org/10.1021/acs.iecr.8b01424
dc.identifier.urihttp://hdl.handle.net/10012/16467
dc.description.abstractThe optimization of reacting systems, including chemical, biological, and macromolecular reactions, is of great importance from both theoretical and practical standpoints. Even though several classical deterministic and stochastic modeling and simulation approaches have been routinely examined to understand and control reacting systems from lab- to industrial-scales, almost all tackling the same problem, i.e., how to predict reaction outputs from any given set of reaction input variables. Development and application of an effective and versatile mathematical tool capable of appropriately connecting preset desired reaction outputs to corresponding inputs have always been the ideal goal for experts in the related fields. Hence, there definitely exists the need to predict a priori optimum reaction conditions in a computationally-demanding multi-variable space for both keeping the chemical and biological reactions in optimal conditions and at the same time satisfying preset desired targets. As a novel and powerful solution, we hereby introduce a robust and functional computational tool capable of simultaneously simulating and optimizing, i.e. ‘optim-ulating’ intricate chemical, biological, and macromolecular reactions via the amalgamation of the Kinetic Monte Carlo (KMC) simulation approach and the multi-objective version of Genetic Algorithms (NSGA-II). The synergistic interplay of KMC and NSGA-II for the optimulation of Oxidative Coupling of Methane (OCM) as an example of a challenging chemical reaction engineering system has clearly demonstrated the outstanding capabilities of the proposed method. Undoubtedly, the proposed novel hybridized technique is very powerful and can address a variety of unsolved optimization questions in chemical, biological, and macromolecular reaction engineering.en
dc.language.isoenen
dc.publisherACSen
dc.subjectComputational Intelligenceen
dc.subjectOCMen
dc.subjectReaction Engineeringen
dc.subjectOptimizationen
dc.subjectKinetic Monte Carloen
dc.subjectSimulationen
dc.title‘Optimulation’ in Chemical Reaction Engineering: The Oxidative Coupling of Methane as a Case Studyen
dc.typeArticleen
dcterms.bibliographicCitation"This is an Accepted Manuscript of an article published by Industrial & Engineering Chemistry Research (I & ECR), doi: 10.1021/acs.iecr.8b01424, accepted in June 2018; vol 57, 8664-8678 (2018).”en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Chemical Engineeringen
uws.typeOfResourceTexten
uws.peerReviewStatusRevieweden
uws.scholarLevelFacultyen
uws.scholarLevelGraduateen


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