Approximate robust optimization of nonlinear systems under parametric uncertainty and process noise. (September 2015)

• Record Type: Journal Article
• Authors: Telen, D.; Vallerio, M.; Cabianca, L.; Houska, B.; Van Impe, J.; Logist, F.
• Is Part Of: Journal of process control. Volume 33(2015:Sep.)
• Page Start: 140
• Page End: 154
• Publication Date: 2015-09
• Subjects: Dynamic optimization -- Robust optimization -- Uncertainty propagation -- Optimal control -- Parametric uncertainty -- Process noise; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.06.011
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract : Highlights: Presentation of techniques for propagation of uncertainty. Both parametric uncertainty as process noise are considered. Uncertainty in objective function and constraints can be handled. Iterative procedure with Monte Carlo simulations to reach desired uncertainty level. Optimization using state of the art numerical techniques with two chemical case studies. Abstract: Dynamic optimization techniques for complex nonlinear systems can provide the process industry with sustainable and efficient operating regimes. The problem with these regimes is that they usually lie close to the limits of the process. It is therefore paramount that these operating conditions are robust with respect to the parameter uncertainties and to the process noise such that critical constraints are not violated. Besides the uncertainty in the constraints, also the uncertainty in the objective function needs to be taken into account. However, including robustness in an optimization problem typically leads to semi-infinite optimization problems that are challenging to solve in practice. In the current manuscript several computationally tractable methods are exploited to approximately solve the robust dynamic optimization problem. These methods allow the use of fast deterministic gradient based optimization techniques. The first type of methods are based on a linearization approach while the second method exploits the unscented transformation to construct an estimation of the uncertainty propagation. Both types provide the user with an approximation of the variance–covariance matrix of the critical constraints and of the objective function. This allows the user to easily take them into account in the dynamic optimization routine in a stochastic setting without the need of using computationally expensive Monte Carlo simulations in the optimization procedure. Moreover, an iterative scheme is mentioned to evaluate the approximate results and to improve them if necessary. Two illustrative case studies are discussed, a jacketed tubular reactor and the Williams-Otto reactor.