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

Md Imran H. Khan (), C. P. Batuwatta-Gamage, M. A. Karim and YuanTong Gu
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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
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

Energies, 2022, vol. 15, issue 24, 1-27

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.

Keywords: heat and mass transfer; drying; physics-informed machine learning; porous media; conjugate modelling (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: 2022
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