As technology specifically AI, continues to advance at an unprecedented rate, more and more companies are exploring the possibilities of artificial
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The global re-refining industry, particularly in emerging markets like India, faces a trilemma of feedstock heterogeneity, stringent environmental mandates (Extended Producer Responsibility), and volatile margin spreads. By 2026, the industry is projected to pivot from reactive batch processing to autonomous, continuous adaptive manufacturing. This paper investigates the integration of "Soft Sensing" and Physics-Informed Neural Networks (PINNs) in critical unit operations such as Wiped Film Evaporation (WFE) and Hydro-finishing. We argue that AI will evolve from an operational efficiency tool into a strategic asset for real-time feedstock valuation and molecular yield optimization.
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