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Physical Model for the Simulation of an Air Handling Unit Employed in an Automotive Production Process: Calibration Procedure and Potential Energy Saving

Author

Listed:
  • Luca Viscito

    (Department of Industrial Engineering, Università degli Studi di Napoli—Federico II, P.le Tecchio 80, 80125 Naples, Italy)

  • Francesco Pelella

    (Department of Industrial Engineering, Università degli Studi di Napoli—Federico II, P.le Tecchio 80, 80125 Naples, Italy)

  • Andrea Rega

    (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)

  • Gerardo Maria Mauro

    (Department of Architecture, Università degli Studi di Napoli — Federico II, Via Forno Vecchio 36, 80134 Naples, Italy)

  • Alessandro Zanella

    (Centro Ricerche Fiat, Str. Torino, 50, 10043 Orbassano, 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

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility.

Suggested Citation

  • Luca Viscito & Francesco Pelella & Andrea Rega & Federico Magnea & Gerardo Maria Mauro & Alessandro Zanella & Alfonso William Mauro & Nicola Bianco, 2025. "Physical Model for the Simulation of an Air Handling Unit Employed in an Automotive Production Process: Calibration Procedure and Potential Energy Saving," Energies, MDPI, vol. 18(7), pages 1-25, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1842-:d:1628807
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    References listed on IDEAS

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    1. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    2. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    3. Arpagaus, Cordin & Bless, Frédéric & Uhlmann, Michael & Schiffmann, Jürg & Bertsch, Stefan S., 2018. "High temperature heat pumps: Market overview, state of the art, research status, refrigerants, and application potentials," Energy, Elsevier, vol. 152(C), pages 985-1010.
    4. 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.
    5. Zhai, Xiangyu & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Wang, Peng & Sun, Hongbin, 2025. "Risk-averse energy management for integrated electricity and heat systems considering building heating vertical imbalance: An asynchronous decentralized approach," Applied Energy, Elsevier, vol. 383(C).
    6. Giampieri, A. & Ling-Chin, J. & Ma, Z. & Smallbone, A. & Roskilly, A.P., 2020. "A review of the current automotive manufacturing practice from an energy perspective," Applied Energy, Elsevier, vol. 261(C).
    7. Sindu Daniarta & Piotr Kolasiński & Barbara Rogosz, 2022. "Waste Heat Recovery in Automotive Paint Shop via Organic Rankine Cycle and Thermal Energy Storage System—Selected Thermodynamic Issues," Energies, MDPI, vol. 15(6), pages 1-18, March.
    8. PELELLA, Francesco & ZSEMBINSZKI, Gabriel & VISCITO, Luca & William MAURO, Alfonso & CABEZA, Luisa F., 2023. "Thermo-economic optimization of a multi-source (air/sun/ground) residential heat pump with a water/PCM thermal storage," Applied Energy, Elsevier, vol. 331(C).
    9. Haopeng Zhang & Xiangyu Zhai & Jiahui Zhang & Xiang Bai & Zening Li, 2024. "Mechanism Analysis of the Effect of the Equivalent Proportional Coefficient of Inertia Control for a Doubly Fed Wind Generator on Frequency Stability in Extreme Environments," Sustainability, MDPI, vol. 16(12), pages 1-17, June.
    10. Ogonowski, Zbigniew, 2011. "Drying control system for spray booth with optimization of fuel consumption," Applied Energy, Elsevier, vol. 88(5), pages 1586-1595, May.
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