A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying

C P Batuwatta-Gamage; C M Rathnayaka; H Charminda P Karunasena; W D C C Wijerathne; Hyogu Jeong; Z G Welsh; M A Karim; Y T Gu

doi:10.1016/j.jfoodeng.2022.111137

Back

A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying

Journal article

Peer reviewed

A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying

C P Batuwatta-Gamage, C M Rathnayaka, H Charminda P Karunasena, W D C C Wijerathne, Hyogu Jeong, Z G Welsh, M A Karim and Y T Gu

Journal of Food Engineering, Vol.332, pp.1-19

2022

DOI: https://doi.org/10.1016/j.jfoodeng.2022.111137

Files and links (1)

url

https://doi.org/10.1016/j.jfoodeng.2022.111137View

Published Version

Abstract

deep neural network

food drying

moisture concentration

physics-informed neural network

shrinkage

surrogate framework

This paper presents a Physics-Informed Neural Network-based (PINN-based) surrogate framework, which can couple time-based moisture concentration and moisture-content-based shrinkage of a plant cell during drying. For this, a set of differential equations are coupled to two distinct multilayer feedforward neural networks: (a) PINN-MC to predict Moisture Concentration (MC) with Fick's law of diffusion; and (b) PINN-S to predict Shrinkage (S) with ‘free shrinkage’ hypothesis. Results indicate that compared to a regular deep neural network (DNN), the PINN-MC with fundamental physics guidance produces 53% and 81% accuracy values when unknown data has the lowest five timesteps and the lowest 27 data points, respectively. Moreover, its accuracy is 80% better when predicting any unknown spatiotemporal domain variations. PINN-MC further demonstrates stable and accurate MC predictions irrespective of drying process parameters and microstructural variations. In addition, the PINN-S separately proves that utilising a derived relationship based on the ‘free shrinkage’ hypothesis can improve shrinkage predictions into a realistic behaviour. Also, the PINN-based surrogate framework combines multiple physics for predicting moisture concentration and shrinkage, reassuring its capability as a powerful tool for investigating complicated drying mechanisms. Accordingly, to the best of the authors' knowledge, this surrogate framework is the first of its kind in food engineering applications.

Details

Title: A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying
Authors: C P Batuwatta-Gamage (Author) - Queensland University of Technology
C M Rathnayaka (Author) - University of the Sunshine Coast, Queensland, School of Science, Technology and Engineering
H Charminda P Karunasena (Author) - University of Ruhuna
W D C C Wijerathne (Author) - Queensland University of Technology
Hyogu Jeong (Author) - Queensland University of Technology
Z G Welsh (Author) - Queensland University of Technology
M A Karim (Author)
Y T Gu (Author) - Queensland University of Technology
Publication details: Journal of Food Engineering, Vol.332, pp.1-19
Publisher: Elsevier Ltd
DOI: 10.1016/j.jfoodeng.2022.111137
ISSN: 1873-5770
Organisation Unit: School of Science, Technology and Engineering; University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99640278102621
Output Type: Journal article

Metrics

64 Record Views

17 Times Cited - Web of Science

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Collaboration types: Domestic collaboration; International collaboration
Web Of Science research areas: Engineering, Chemical; Food Science & Technology

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

Source: InCites