Abstract
Uncertainty is an inherent characteristic in most industrial processes, and a variety of approaches including sensitivity analysis, stochastic programming and robust optimisation have been proposed to deal with such uncertainty. Uncertainty in Real-Time Optimisation (RTO), particularly making robust decisions under uncertainty in real-time has received little attention. This paper discusses various sources of uncertainty within the closed RTO loop. A method, based on stochastic programming, that explicitly incorporates uncertainty into the RTO problem is presented and allows solution using conventional optimisation algorithms. A gasoline blending example is used to demonstrate the proposed robust RTO approach.
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