Recent Advances in Food Drying Modeling: Empirical to Multiscale Physics‐Informed Neural Networks DOI

Aluth Durage Hiruni Tharaka Wijerathne,

Mohammad U. H. Joardder, Zachary G. Welsh

et al.

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2025, Volume and Issue: 24(3)

Published: May 1, 2025

ABSTRACT Food insecurity is a major global challenge. preservation, particularly through drying, presents promising solution to enhance food security and minimize waste. Fruits vegetables contain 80%–90% water, much of this removed during drying. However, structural changes across multiple length scales occur compromising stability affecting quality. Understanding these essential, several modeling techniques exist analyze them, including empirical modeling, physics‐based computational methods, purely data‐driven machine learning approaches, physics‐informed neural network (PINN) models. Although methods are straightforward implement, their limited generalizability lack physical insights have led the development methods. These can achieve high spatiotemporal resolution without requiring experimental investigations. complexity costs prompted exploration models for drying processes, which involve comparatively lower more execute. Nonetheless, poor predictive ability with sparse data has restricted application, leading hybrid approach: PINN, merges techniques. This method still holds significant potential advancements in modeling. Therefore, study aims conduct comprehensive literature review state‐of‐the‐art conventional techniques, such as empirical, computational, pure explores PINN approach overcoming limitations associated strategies.

Language: Английский

Reliability and safety of elevators and escalators/ travelators: past, present and future DOI Creative Commons

Ping Kwan Man,

Chak‐Nam Wong, Wai Kit Chan

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 104194 - 104194

Published: Jan. 1, 2025

Language: Английский

Citations

0
Physics informed neural networks for detecting the wear of friction pairs in axial piston pumps DOI
Qun Chao, Yong Hu,

Chengliang Liu

et al.

Reliability Engineering & System Safety, Journal Year: 2025, Volume and Issue: unknown, P. 111144 - 111144

Published: April 1, 2025

Language: Английский

Citations

0
Recent Advances in Food Drying Modeling: Empirical to Multiscale Physics‐Informed Neural Networks DOI

Aluth Durage Hiruni Tharaka Wijerathne,

Mohammad U. H. Joardder, Zachary G. Welsh

et al.

Comprehensive Reviews in Food Science and Food Safety, Journal Year: 2025, Volume and Issue: 24(3)

Published: May 1, 2025

ABSTRACT Food insecurity is a major global challenge. preservation, particularly through drying, presents promising solution to enhance food security and minimize waste. Fruits vegetables contain 80%–90% water, much of this removed during drying. However, structural changes across multiple length scales occur compromising stability affecting quality. Understanding these essential, several modeling techniques exist analyze them, including empirical modeling, physics‐based computational methods, purely data‐driven machine learning approaches, physics‐informed neural network (PINN) models. Although methods are straightforward implement, their limited generalizability lack physical insights have led the development methods. These can achieve high spatiotemporal resolution without requiring experimental investigations. complexity costs prompted exploration models for drying processes, which involve comparatively lower more execute. Nonetheless, poor predictive ability with sparse data has restricted application, leading hybrid approach: PINN, merges techniques. This method still holds significant potential advancements in modeling. Therefore, study aims conduct comprehensive literature review state‐of‐the‐art conventional techniques, such as empirical, computational, pure explores PINN approach overcoming limitations associated strategies.

Language: Английский

Citations

0