Carbon Footprint Reduction of a Petrochemical Process Supported by ML and Digital Twin modelling

Uproszczony widok
dc.contributor.authorKulikowski, Sławomir
dc.contributor.authorRomanowski, Andrzej
dc.contributor.authorSierszeń, Artur
dc.date.accessioned2023-09-21T12:26:44Z
dc.date.available2023-09-21T12:26:44Z
dc.date.issued2023
dc.description.abstractThis article aims to present a concept of an Artificial Intelligence application in the form of pre-trained Machine Learning modules to reduce the carbon footprint of a chemical recycling process. Chemical recycling of plastic is energy-consuming as it requires relatively high temperatures and calibration cycles based on a constantly changing structure of raw materials. Due to that fact, complex process parameters must be tuned to allow the production of the required fraction of gasoline. In general, the designed IoT system enables a massive collection of technology and environmental data and the processing of parameters to feed the Digital Twin of a petrochemical plant. The scientific part of the project consists of Digital Twin modelling, experiments, simulations, and training of machine learning modules to predict the optimal set of production line parameters based on the specific structure of raw materials to reduce the number of calibrations and lower energy consumption indirectly which will lead to carbon footprint reduction. There is an here is an estimate that that deployed solution will allow reduction of energy consumption on a monthly level of 10-15% which could generate direct savings on a cost of energy but also savings in a field of carbon emission and related credits. The project also includes the evaluation of predictions supported by machine learning modules, training techniques and comparison to expert settings to assess the quality of the application.en_EN
dc.identifier.citationKulikowski S., Romanowski A., Sierszeń A., Carbon Footprint Reduction of a Petrochemical Process Supported by ML and Digital Twin modelling. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 157-162, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.23.
dc.identifier.doi10.34658/9788366741928.23
dc.identifier.isbn978-83-66741-92-8
dc.identifier.urihttp://hdl.handle.net/11652/4799
dc.identifier.urihttps://doi.org/10.34658/9788366741928.23
dc.language.isoenen_EN
dc.page.numbers. 157-162
dc.publisherWydawnictwo Politechniki Łódzkiejpl_PL
dc.publisherLodz University of Technology Pressen_EN
dc.relation.ispartofWojciechowski A. (Ed.), Lipiński P. (Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.
dc.rightsDla wszystkich w zakresie dozwolonego użytkupl_PL
dc.rightsFair use conditionen_EN
dc.rights.licenseLicencja PŁpl_PL
dc.rights.licenseLUT Licenseen_EN
dc.subjectinternet of thingsen_EN
dc.subjectdigital twinen_EN
dc.subjectmachine learningen_EN
dc.subjectcarbon footprinten_EN
dc.subjectinternet rzeczypl_PL
dc.subjectcyfrowy bliźniakpl_PL
dc.subjectuczenie maszynowepl_PL
dc.subjectślad węglowypl_PL
dc.titleCarbon Footprint Reduction of a Petrochemical Process Supported by ML and Digital Twin modellingen_EN
dc.typeRozdział - monografiapl_PL
dc.typeChapter - monographen_EN

Pliki

Oryginalne pliki
Teraz wyświetlane 1 - 1 z 1
Brak miniatury
Nazwa:
23. Carbon_footpr_reduct_Kulikowski_Romanowski_2023.pdf
Rozmiar:
517.23 KB
Format:
Adobe Portable Document Format
Opis:
Pobierz
Licencja
Teraz wyświetlane 1 - 1 z 1
Brak miniatury
Nazwa:
license.txt
Rozmiar:
1.71 KB
Format:
Item-specific license agreed upon to submission
Opis:
Pobierz

Kolekcje

Rozdziały
Książki, monografie, podręczniki, rozdziały (WEEIiA)