Currently, chemical process design and control are separate disciplines assisting process development at different stages. Design and control decisions are made independently despite the common aim of ensuring robust plant operations. The lack of confidence in existing methods is underscored by the industrial practice of process overdesign without ascertaining the actual level of robustness. This objective cannot be accomplished by the current practice of intuitive “over-design”, which is susceptible to: (i) unknown safety levels usually determined by experience, or previous over-designs; (ii) impaired dynamic performance like increased process resilience or lag time; (iii) possibility of failure under dynamic operation such as overshoots, point constraint violations. It appears advantageous to consider process design and control decisions simultaneously in order to maximize the overall system performance in face of operational and model uncertainty. This is of particular significance in high-performance processes, whose dynamic operation in the vicinity to constraints requires precise quantification and optimal choice of robustness levels. This presentation aims at addressing critical challenges pertaining to the lack of integration between design and control objectives at the conceptual level.

Scope:

The proposed research aims at determining optimal trade-offs between design and control decisions based on dynamic process models and advanced control. An rigorous approach for detecting and addressing worst-case uncertainty and disturbance scenarios will be addressed. In this presentation, we will emphasize that the simultaneous search for structural decisions and continuous design variables leads to non-polynomial (NP) hard problems. Optimizing control structure and controller tuning alongside process design exceeds the capability of most existing algorithms. Therefore, new problem formulations need to reduce the combinatorial complexity. A major innovation of this presentation is proposed a novel embedded control optimization approach. It suggests a two-stage problem decomposition which leads to a massive reduction of problem size and complexity. The first stage will optimize the reduced space design variables to satisfy the objective function such as minimum cost or maximum profit. In the second stage, the embedded control composed of system identification, state prediction and optimal control moves will automatically guarantee the stability and controllability. A novel stochastic design optimization with embedded control will be demonstrated as a significant advancement towards overcoming the combinatorial complexity of integrated design and control.

Significance:

This presentation advocates the integration of design and control for the consistent attainment of stringent product quality demands. A concise decision-making hierarchy allows designers to arrive at key structural decisions for the process flowsheet and control layout. This was made possible thanks to a formulation that implicitly relates the control with the design. Rigorous mathematical programming approaches are proposed for optimizing parametric design variables as well as structural alternatives. The case study will show that the proposed methodology is implementable to a realistic industrial polymerization reactor design. As a result an optimal design was obtained. This new design can satisfactorily operate under the most adverse input condition.

Reference:

Bansal, V.; Perkins, J. D.; Pistikopoulos, E. N, A Case Study in Simultaneous Design and Control Using Rigorous Mixed-Integer Dynamic Optimization Models. Ind. Eng. Chem. Res., 41, 760-778, 2002.

Mohideen, M. J.; Perkins, J. D. and Pistikopoulos, E. N. Optimal synthesis and design of dynamic systems under uncertainty Comp. Chem. Eng, 20, 2, S895-S900, 1996.

Ostrovsky, Gennadi M.; Achenie, Luke E. K.; Wang, Yiping; Volin, Y. M, A New Algorithm for Computing Process Flexibility, Industrial & Engineering Chemistry Research , 39(7), 2368-2377, 2000.

Pistikopoulos, E. N. and. Grossmann, I. E, Stochastic optimization of flexibility in retrofit design of linear systems, Computers & Chemical Engineering, 12 (12), Pages 1215-1227, 1988

Van Overschee, P. and De Moor, B.; Subspace Identification for Linear Systems, Kluver Academic Publishers, Norwell, MA, 1996

Zhou, K., Doyle, J.C., Glover, K., Robust and Optimal Control, Prentice Hall, NJ, 1995.

Zhu, G.Y., Henson, M.A. and Ogunnaike, B.A. A hybrid model predictive control strategy for nonlinear plant-wide control. J. of Process Control, 10 (5) 449-458, 2000.