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Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling

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  • Md Imran H. Khan

    (School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD 4000, Australia
    Research and Development, Agridry Dryers Pty Ltd., 13 Molloy St, Torrington, QLD 4350, Australia)

  • C. P. Batuwatta-Gamage

    (School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD 4000, Australia)

  • M. A. Karim

    (School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD 4000, Australia)

  • YuanTong Gu

    (School of Mechanical, Medical and Process Engineering, Queensland University of Technology (QUT), 2 George St, Brisbane, QLD 4000, Australia)

Abstract

Drying is a complex process of simultaneous heat, mass, and momentum transport phenomena with continuous phase changes. Numerical modelling is one of the most effective tools to mechanistically express the different physics of drying processes for accurately predicting the drying kinetics and understanding the morphological changes during drying. However, the mathematical modelling of drying processes is complex and computationally very expensive due to multiphysics and the multiscale nature of heat and mass transfer during drying. Physics-informed machine learning (PIML)-based modelling has the potential to overcome these drawbacks and could be an exciting new addition to drying research for describing drying processes by embedding fundamental transport laws and constraints in machine learning models. To develop such a novel PIML-based model for drying applications, it is necessary to have a fundamental understanding of heat, mass, and momentum transfer processes and their mathematical formulation of drying processes, in addition to data-driven modelling knowledge. Based on a comprehensive literature review, this paper presents two types of information: fundamental physics-based information about drying processes and data-driven modelling strategies to develop PIML-based models for drying applications. The current status of physics-based models and PIML-based models and their limitations are discussed. A sample PIML-based modelling framework for drying application is presented. Finally, the challenges of addressing simultaneous heat, mass, and momentum transport phenomena in PIML modelling for optimizing the drying process are presented at the end of this paper. It is expected that the information in this manuscript will be beneficial for further advancing the field.

Suggested Citation

  • Md Imran H. Khan & C. P. Batuwatta-Gamage & M. A. Karim & YuanTong Gu, 2022. "Fundamental Understanding of Heat and Mass Transfer Processes for Physics-Informed Machine Learning-Based Drying Modelling," Energies, MDPI, vol. 15(24), pages 1-27, December.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9347-:d:999058
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    References listed on IDEAS

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    1. Wenjuan Zhang & Mohammed Al Kobaisi, 2022. "On the Monotonicity and Positivity of Physics-Informed Neural Networks for Highly Anisotropic Diffusion Equations," Energies, MDPI, vol. 15(18), pages 1-18, September.
    2. El-Sebaii, A.A. & Aboul-Enein, S. & Ramadan, M.R.I. & El-Gohary, H.G., 2002. "Empirical correlations for drying kinetics of some fruits and vegetables," Energy, Elsevier, vol. 27(9), pages 845-859.
    3. Ahmet Beyzade Demirpolat, 2019. "Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System," Energies, MDPI, vol. 12(18), pages 1-14, September.
    4. Zadin, V. & Kasemägi, H. & Valdna, V. & Vigonski, S. & Veske, M. & Aabloo, A., 2015. "Application of multiphysics and multiscale simulations to optimize industrial wood drying kilns," Applied Mathematics and Computation, Elsevier, vol. 267(C), pages 465-475.
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