Machine learning based suggestions of separation units for process synthesis in process simulation

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dc.contributor.authorOeing, Jonas
dc.contributor.authorHenke, Fabian
dc.contributor.authorKockmann, Norbert
dc.date.accessioned2022-03-16T13:13:22Z
dc.date.available2022-03-16T13:13:22Z
dc.date.issued2021-09-15
dc.description.abstractAs part of Industry 4.0, workflows in the process industry are becoming increasingly digitalized. In this context, artificial intelligence (AI) methods are also finding their way into the process development. In this communication, machine learning (ML) algorithms are used to suggest suitable separation units based on simulated process streams. Simulations that have been performed earlier are used as training data and the information is learned by machine learning models implemented in Python. The trained models show good, reliable results and are connected to a process simulator using a .NET framework. For further optimization, a concept for the implementation of user feedback will be assigned. The results will provide the fundamental basis for future AI-based recommendation systems.en
dc.identifier.urihttp://hdl.handle.net/2003/40799
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22656
dc.language.isoende
dc.relation.ispartofseriesChemie - Ingenieur - Technik;93(12)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectMachine learningen
dc.subjectProcess engineeringen
dc.subjectProcess simulationen
dc.subjectProcess synthesisen
dc.subjectSeparation unitsen
dc.subject.ddc660
dc.titleMachine learning based suggestions of separation units for process synthesis in process simulationen
dc.typeTextde
dc.type.publicationtypearticlede
dcterms.accessRightsopen access
eldorado.secondarypublicationtruede
eldorado.secondarypublication.primarycitationOeing, J., Henke, F. and Kockmann, N. (2021), Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation. Chemie Ingenieur Technik, 93: 1930-1936. https://doi.org/10.1002/cite.202100082de
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202100082de

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