Abstract
Oscillating electromagnetic forces generated from a vibrated Riga plate have broad implications across various scientific and engineering domains. Artificial intelligence (AI) is applied to optimize precision and energy efficiency in pasteurization and sterilization by regulating the thermal and dynamic behavior of nanoparticle-infused milk under electromagnetic heating. The technique ensures accurate temperature control, minimizes the risk of overheating, and preserves the milk's nutritional and sensory qualities. It focuses on predicting the thermal and dynamic behaviors of milk infused with silver and zinc oxide nanoparticles in an electromagnetically vibrated channel experiencing thermal oscillations and rapid pressure changes. The research integrates complex physical phenomena such as radiant heat emission and Darcy drag forces, employing Darcy's model to delve into drag within porous media. Detailed mathematical and physical descriptions of milk flow dynamics are established, with solutions efficiently derived using the Laplace Transform (LT) method. The results, encompassing shear stress (SS) and rate of heat transfer (RHT) analyses, are detailed in tables and graphs. Findings indicate enhanced milk momentum with higher modified Hartmann number and reduced momentum with wider electrode spacing. Elevated oscillation frequencies of the left channel wall stabilize milk flow in both hybrid nano-milk (HNM) and nano-milk (NM). Larger Casson parameter improves SS, while higher radiation parameter reduces RHT. An AI-driven artificial neural network (ANN) is employed for precise estimations, achieving 98.022% accuracy in SS testing, 98.99% in cross-validation, and a flawless 100% accuracy for RHT. The research findings can be implemented to precisely control the mixing of milk and its physical features at a molecular level, enabling more even heat distribution and faster, more efficient pasteurization or homogenization processes.
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