Comparison between Physics-Based Approaches and Neural Networks for the Energy Consumption Optimization of an Automotive Production Industrial Process

Francesco Pelella, Luca Viscito, Federico Magnea, Alessandro Zanella, Stanislao Patalano, Alfonso William Mauro () and Nicola Bianco
Additional contact information
Francesco Pelella: Department of Industrial Engineering, Università degli Studi di Napoli Federico II, P.le Tecchio 80, 80125 Naples, Italy
Luca Viscito: Department of Industrial Engineering, Università degli Studi di Napoli Federico II, P.le Tecchio 80, 80125 Naples, Italy
Federico Magnea: Centro Ricerche Fiat, Str. Torino 50, 10043 Orbassano, Italy
Alessandro Zanella: Centro Ricerche Fiat, Str. Torino 50, 10043 Orbassano, Italy
Stanislao Patalano: Department of Industrial Engineering, Università degli Studi di Napoli Federico II, P.le Tecchio 80, 80125 Naples, Italy
Alfonso William Mauro: Department of Industrial Engineering, Università degli Studi di Napoli Federico II, P.le Tecchio 80, 80125 Naples, Italy
Nicola Bianco: Department of Industrial Engineering, Università degli Studi di Napoli Federico II, P.le Tecchio 80, 80125 Naples, Italy

Energies, 2023, vol. 16, issue 19, 1-22

Abstract: The automotive production sector plays a significant role in the energy consumption of all the industrial sphere, which currently represents approximately 38% of the total global energy use. Especially in production sites with several manufacturing lines working in parallel, the occurrence of failures and anomalies or sudden changes in the production volume may require a re-scheduling of the entire production process. In this regard, a digital twin of each phase of the process would give several indications about the new re-scheduled manufacture in terms of energy consumption and the control strategy to adopt. Therefore, the main goal of this paper is to propose different modeling approaches to a degreasing tank process, which is a preliminary phase at automotive production sites before the application of paint to car bodies. In detail, two different approaches have been developed: the first is a physics-based thermodynamic approach, which relies on the mass and energy balances of the system analyzed, and the second is machine learning-based, with the calibration of several artificial neural networks (ANNs). All the investigated approaches were assessed and compared, and it was determined that, for this application and with the data at our disposal, the thermodynamic approach has better prediction accuracy, with an overall mean absolute error (MAE) of 1.30 °C. Moreover, the model can be used to optimize the heat source policy of the tank, for which it has demonstrated, with historical data, an energy saving potentiality of up to 30%, and to simulate future scenarios in which, due to company constraints, a re-scheduling of the production of more work shifts is required.

Keywords: automotive production process; paint shop; digital twin; model predictive control; artificial neural network; physics-based model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/19/6916/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/19/6916/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:19:p:6916-:d:1252066

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().