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Article Abstract
Oil-water emulsions are prevalent in petroleum, chemical, and materials industries, where their rheological properties significantly impact processing efficiency. This review systematically examines the key factors influencing the apparent viscosity of oil-water emulsions, including oil composition, water characteristics, temperature, shear conditions, and emulsifier properties. It traces the evolution of viscosity prediction methodologies, encompassing conventional, complex, and Pickering emulsions, and assesses modeling approaches ranging from early theoretical frameworks to contemporary machine learning techniques. The reliability and applicability of these models are critically evaluated across various industrial contexts. Furthermore, the review identifies key challenges, research gaps, and prospective directions, emphasizing potential advancements in experimental strategies and modeling methodologies. While focusing on petrochemical emulsions, the insights and analytical approaches discussed are applicable to biological, medical, and other industrial systems, offering guidance for future research and practical implementation.
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