International Journal of Science, Engineering and TechnologyAuthors: A.Harika, B. Naveen
Abstract: The dairy industry faces a critical challenge: optimizing product quality (texture, flavor, shelf-life) while simultaneously minimizing energy consumption, ingredient cost, and environmental footprint. Traditional response surface methodology (RSM) and trial-and-error approaches fail to capture the non-linear, dynamic interactions inherent in dairy matrices. This paper introduces three novel, applicable computational frameworks: (1) a Hybrid Recurrent Neural Network (RNN) – Partial Differential Equation (PDE) solver for dynamic fermentation control, (2) a Generative Adversarial Network (GAN) for novel ingredient substitution with sensory constraint validation, and (3) a Multi-Agent Reinforcement Learning (MARL) system for cold chain and probiotic viability trade-offs. These approaches, validated with real-world process data, demonstrate a 22% reduction in optimization time and a 15% improvement in multi-attribute product scores over conventional methods.
