Hydration Dynamics and Sustainable Bioprocessing: An AI-Enabled Computational Framework for Carbohydrates, Proteins, and Lipids

Water is fundamental to structural integrity, stability, and functional properties of food systems, biomaterials, and biobased industries. The dynamics of hydration, including hydrogen bonding, hydration shell formation, plasticization, and phase transitions, dictate molecular behavior and exert broad influence on energy consumption, shelf life, biodegradability, and resource efficiency. However, the nonlinear and multiscale characteristics of hydration have constrained the predictive capabilities of conventional empirical methods. This study introduces a comprehensive framework that integrates foundational hydration science with advanced computational intelligence to model, predict, and optimize hydration-driven phenomena across diverse biopolymer classes. Leveraging classical insights into carbohydrate stereochemistry, protein hydrophobic hydration, and phospholipid-bound water, we demonstrate how computational approaches can reduce resource use in bioprocessing by 30–50% and optimize drying curves to lower energy consumption by 25%. By establishing hydration as a strategic design parameter, this work charts a pathway toward a resilient and sustainable economy where predictive error rates for hydration dynamics are significantly minimized through data-driven calibration.