Optimal process and control design under uncertainty: A methodology with robust feasibility and stability analyses

Chemical Engineering Science, Volume 104, 18 December 2013, Pages 1065–1080
Optimal process and control design under uncertainty: A methodology with robust feasibility and stability analyses
M. Trainor, V. Giannakeas, C. Kiss, L.A. Ricardez-Sandoval
Department of Chemical Engineering, University of Waterloo, Waterloo, Ont., Canada N2L 3G1
Abstract
Provides a novel methodology for the optimal process and control design of dynamic systems under uncertainty. Authors incorporate robust feasibility and stability analyses within the methodology to ensure process dynamic operability and asymptotic stability. They formulate the analyses as convex mathematical problems. As a result, the approach is computationally attractive since it does not require the solution of an MINLP to evaluate dynamic feasibility and stability as it has been proposed by recent dynamic optimization-based methodologies.
Authors employ a norm-bounded metric based on Structured Singular Value (SSV) analysis to estimate the worst-case deviation in the process constraints in the presence of critical realizations in the disturbances. The robust stability test is based on Lyapunov theory and guarantees process asymptotic stability. Accordingly, the optimal process and control design alternative obtained by the method is dynamically feasible and asymptotically stable.
Full Text Source (Subscription or Fee): http://www.sciencedirect.com/science/article/pii/S0009250913007045