Distributed stochastic optimization of a process plant start-up

This paper presents a decentralized solution to the stochastic optimization problems that appear when uncertainty is considered explicitly using a set of scenarios in model based control and optimization. In particular, the paper deals with two-stage optimization problems, where the first-stage solution has to fulfil the constraints for all multiple scenarios simultaneously. To deal with the large size of the problem, a reformulation has been performed solving the optimization in parallel for as many deterministic problems as scenarios are, and coordinating their solutions in order to force a common decision for all of them, using a price-driven methodology followed by a sensitivity-based update. The methodology is illustrated with an example involving the optimal start-up of a hydrodesulphurization plant.