AI in process industries

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dc.contributor.authorBortz, Michael
dc.contributor.authorDadhe, Kai
dc.contributor.authorEngell, Sebastian
dc.contributor.authorGepert, Vanessa
dc.contributor.authorKockmann, Norbert
dc.contributor.authorMüller-Pfefferkorn, Ralph
dc.contributor.authorSchindler, Thorsten
dc.contributor.authorUrbas, Leon
dc.date.accessioned2025-01-24T13:41:19Z
dc.date.available2025-01-24T13:41:19Z
dc.date.issued2023-04-13
dc.description.abstractThe chemical industry is one of the key industrial sectors in Germany and at the same time one of the largest consumers of energy and raw materials. A successful energy transition and the development of a circular economy can only succeed if they are actively supported and shaped by the chemical industry – through the redesign of existing production processes and the exploration and implementation of new process routes. The challenge is to realize this transformation within a very short time and for many production processes, whereby a much larger number of process routes must be explored. Digital technologies are key to master this transformation towards more sustainability, climate, and environmental protection. The KEEN project aims to explore and leverage artificial intelligence (AI) opportunities in process industry. The newly developed AI methods are tested wherever possible in real working environments and production plants to prove the economic benefit, applicability, and reliability of the methods and technologies.en
dc.identifier.urihttp://hdl.handle.net/2003/43385
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-25217
dc.language.isoen
dc.relation.ispartofseriesChemie - Ingenieur - Technik; 95(7)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectData handlingen
dc.subjectEngineering of plants and processesen
dc.subjectOptimization of operationsen
dc.subjectSelf-optimizing plantsen
dc.subjectSurrogate modelsen
dc.subject.ddc660
dc.titleAI in process industriesen
dc.title.alternativecurrent status and future prospectsen
dc.typeText
dc.type.publicationtypeArticle
dcterms.accessRightsopen access
eldorado.secondarypublicationtrue
eldorado.secondarypublication.primarycitationBortz, M., Dadhe, K., Engell, S., Gepert, V., Kockmann, N., Müller-Pfefferkorn, R., Schindler, T. and Urbas, L. (2023), AI in Process Industries – Current Status and Future Prospects . Chemie Ingenieur Technik, 95: 975-988. https://doi.org/10.1002/cite.202200247
eldorado.secondarypublication.primaryidentifierhttps://doi.org/10.1002/cite.202200247

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