AI-based forecasting of dynamic behaviors of Ag and ZnO nanoparticles-enhanced milk in an electromagnetic channel with exponential heating: dairy decontamination.
Journal:
The European physical journal. E, Soft matter
PMID:
40244443
Abstract
Electromagnetic plates can be used to heat milk and other dairy products rapidly and uniformly. The use of electromagnetic fields enables precise thermal control, which is crucial for safe pasteurization while retaining the nutritional and sensory qualities of milk. This study investigates the dynamics of Ag-ZnO/milk under electromagnetic fields generated by Riga plates with exponentially decaying wall temperatures. The model includes radiation heat emission, heat sinks, and Darcy drag forces due to the porous medium. The flow is mathematically depicted through unsteady partial differential equations solved using the Laplace transform approach. Results include tabulated and graphical with an exhaustive analysis of flow entities against model parameters. Findings highlight increased milk velocity with a boosted modified Hartmann number and declined velocity with wider electrodes. An AI-powered computing approach enhances the accuracy in envisaging flow metrics, achieving 100% accuracy in training, testing, and validation phases. This research not only advances dairy processing technologies but also paves the way for innovations in food safety, nano-enhanced dairy production, and sustainable manufacturing practices.