PROCESS OPTIMIZATION AND CONTROL UNDER UNCERTAINTY: A CHANCE CONSTRAINED PROGRAMMING APPROACH

A novel chance constrained programming approach for process optimization of large-scale nonlinear dynamic systems and control under uncertainty is proposed. The stochastic property of the uncertainties is explicitly considered in the problem formulation in which some input and state constraints are to be complied with predefined probability levels. This incorporates the issue of feasibility and the contemplation of trade-off between profitability and reliability. The approach considers a nonlinear relation between the uncertain input and the constrained variables. It also involves novel efficient algorithms both to consider time-dependent uncertainties and to compute the probabilities and, simultaneously, their gradients. To demonstrate the performance of the proposed method, a chance constrained NMPC scheme for the online optimization of a batch reactor under safety restrictions, and the optimal operation and control of a coupled two-pressure column system are discussed to show the efficiency and potential for optimization and control under uncertainty.