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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Francesco Pelella & Luca Viscito & Federico Magnea & Alessandro Zanella & Stanislao Patalano & Alfonso William Mauro & Nicola Bianco, 2023. "Comparison between Physics-Based Approaches and Neural Networks for the Energy Consumption Optimization of an Automotive Production Industrial Process," Energies, MDPI, vol. 16(19), pages 1-22, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6916-:d:1252066
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    References listed on IDEAS

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