dc.contributor.author |
Batuwatta-Gamage, C.P. |
|
dc.contributor.author |
Rathnayaka, C.M. |
|
dc.contributor.author |
Karunasena, H. C. P |
|
dc.contributor.author |
Jeong, H. |
|
dc.contributor.author |
Wijerathne, W.D.C.C. |
|
dc.contributor.author |
Karim, M.A. |
|
dc.contributor.author |
Welsh, Z.G. |
|
dc.contributor.author |
Gu, Y.T. |
|
dc.date.accessioned |
2022-11-15T10:25:05Z |
|
dc.date.available |
2022-11-15T10:25:05Z |
|
dc.date.issued |
2022 |
|
dc.identifier.citation |
Batuwatta-Gamage, C.P., Rathnayaka, C. M., Karunasena, H. C. P., Wijerathne, W. D. C. C., Jeong, H., Welsh, Z.G., Karim, M.A., & Gu, Y. T. (2022). A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying. Journal of Food Engineering, 332 November), 111137. |
en_US |
dc.identifier.issn |
0260-8774 |
|
dc.identifier.uri |
http://ir.lib.ruh.ac.lk/xmlui/handle/iruor/9283 |
|
dc.description.abstract |
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. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
Deep neural network |
en_US |
dc.subject |
Food drying |
en_US |
dc.subject |
Moisture concentration |
en_US |
dc.subject |
Physics-informed neural network |
en_US |
dc.subject |
Shrinkage |
en_US |
dc.subject |
Surrogate framework |
en_US |
dc.title |
A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying |
en_US |
dc.type |
Article |
en_US |