Abstract
This work introduces a probabilistic-based integrated process design and self-optimizing control approach grounded in a steady-state model, aiming to strike a balance between solution reliability and optimality. The proposed approach offers a practical and cost-effective solution by considering disturbances as random variables with known probability distribution functions. To propagate these disturbances to process outputs, the unscented transform method is utilized alongside rigorous first-principles process models. Additionally, probability levels are assigned to process constraints based on user-defined specifications, providing flexibility to tailor the design to desired risk confidence levels. The effectiveness and benefits of the approach are demonstrated through an application on an extractive distillation process with pre-concentration.
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