This paper discusses how to deal explicitly with uncertainty in the optimal management of the hydrogen network of a petroleum refinery. The current system is based on a RTO/MPC system for supervision and on-line optimization that includes a robust data reconciliation to estimate consistent values of the process variables and update the model parameters. It has been extended with a two-stage stochastic optimization to take care of the effect of crude changes in operation of the network. The paper analyses how to formulate the problem in order to obtain implementable solutions and presents results that compare the deterministic and stochastic solutions using real plant data.
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