Carbon Footprint Reduction of a Petrochemical Process Supported by ML and Digital Twin modelling
Ładowanie...
Data
2023
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
This 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.
Opis
Słowa kluczowe
internet of things, digital twin, machine learning, carbon footprint, internet rzeczy, cyfrowy bliźniak, uczenie maszynowe, ślad węglowy
Cytowanie
Kulikowski 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.