Integrated design and control for robust performance: Application to an MSMPR crystallizer

• Published in: Journal of process control Vol. 18; no. 2; pp. 173 - 188
• Main Authors: Grosch, R., Mönnigmann, M., Marquardt, W.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-02-2008
• Subjects: Control and design integration; Crystallization; Nonlinear dynamics; Control and design integration; Nonlinear dynamics; Crystallization
• ISSN: 0959-1524; 1873-2771
• DOI: 10.1016/j.jprocont.2007.07.002

The interplay of different types of performance constraints in an integrated design and control problem is studied by means of a case study. This integrated problem is based on a recent method for robust process design [M. Mönnigmann, W. Marquardt, Normal vectors on manifolds of critical points for parametric robustness of equilibrium solutions of ODE systems, J. Nonlinear Sci. 12 (2002) 85–112; M. Mönnigmann, W. Marquardt, Steady state process optimization with guaranteed robust stability and feasibility, AIChE J. 49 (12) (2003) 3110–3126; M. Mönnigmann, W. Marquardt, Steady state process optimization with guaranteed robust stability and flexibility: application to HDA reaction section, Ind. Eng. Chem. Res. 44 (2005) 2737–2753; W. Marquardt, M. Mönnigmann, Constructive nonlinear dynamics in process systems engineering, Comput. Chem. Eng. 29 (2005) 1265–1275; M. Mönnigmann, Constructive nonlinear dynamics methods for the design of chemical engineering processes, Ph.D. Thesis, RWTH Aachen University, 2003]. The design is found by means of a steady-state optimization problem accounting for process economics and performance requirements. In particular, the latter are represented by constraints which guarantee a user-specified performance of the design in spite of parametric uncertainties. For the first time, two types of dynamic performance constraints are used simultaneously within the adopted framework. These are constraints on time-domain performance indicators as well as on the asymptotic dynamic process behavior. Furthermore, the effect of uncertainty in both, design and model parameters, is accounted for. A key strength of the suggested framework is the direct quantification of the trade-offs between economics and dynamic performance requirements for a selection of uncertainty scenarios. A series of different integrated design and control problems are formulated and solved for a continuous mixed-suspension mixed-product removal (MSMPR) crystallizer. The process exhibits a complex nonlinear behavior and represents a challenging example. The results of the case study allow an in depth understanding of the interactions of design and control for the underlying process.