en inadequate in dealing with nonlinear process dynamics, rapid phase changes, and operational variability inherent in multi-phase crude streams. This proposes a conceptual model for intelligent automation loops that leverage real-time data, adaptive control strategies, and artificial intelligence (AI) to optimize the operation of such units. The proposed model integrates several key components: a distributed sensor network for high-fidelity, multi-phase flow data acquisition; dynamic process modeling using hybrid techniques (first-principles and machine learning); and intelligent control algo
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