A Tabu Search-based algorithm for the integrated process and control system design
Systematic methods and tools for managing the complexity
Tools Integration - CAPE Methods & Tools (T4-10)
Keywords: Integrated Process and Control System Design, Mixed-Integer Nonlinear Programming, Metaheuristic, Tabu Search
During the last decade, the importance of a simultaneous (integrated) process design approach, considering operability together with the economic issues, has been widely recognized (e.g. Pistikopoulos and Ross, 1999). The aim is to obtain profitable and operable process and control structures in a systematic way. Both the process design characteristics, control strategies, control structure and controller’s tuning parameters have to be selected optimally in order to minimize the total cost of the system while satisfying a large number of feasibility constraints in the presence of time-varying disturbances. The arising optimization problem is a challenging mixed-integer dynamic optimization (MIDO) problem. The multimodal (non-convex) nature of this problem has been highlighted in the open literature. As a result global optimization (GO) techniques for nonlinear dynamic systems are used which have received increased attention from engineers, mathematicians and computer scientists.
There are different approaches to solve this MIDO problem, such as dynamic programming, control parameterization and complete discretization. This work focuses on the control parameterization approach, obtaining as a result a finite dimensional mixed-integer nonlinear programming (MINLP) problem. An adaptation of the metaheuristic Tabu Search (TS) which was originally developed by Glover (see Glover and Laguna, 1997) is used to solve the MINLP. The developed algorithm called Mixed-Integer Tabu Search (MITS) is an advancement of the approach proposed by Battiti and Tecchiolli (1996).
The basic idea of the algorithm MITS can be summarized as follows. MITS contains of a combinatorial component and a local solver. The aim of the combinatorial component is to locate areas in which good local minima are expected. To increase the quality of a local solution we use a Mixed-Integer Sequential Quadratic Programming algorithm called MISQP by Exler and Schittkowski (2006).
Numerical results are presented for the optimization of the well-known Tennessee Eastman Process (TEP) by Downs and Vogel (1993), which has been widely used in the literature as a case study due to its challenging properties from a control engineering point of view.
Acknowledgement: We acknowledge the support of the EU and the Marie-Curie Actions program. We thank the project PRISM – project number MRTN-CT-2004-512233 – ‘Towards Knowledge-based Processing Systems’ for the support.
References: Battiti R. and Tecchiolli G. (1996). The continuous tabu search: Blending combinatorial optimization and stochastic search for global optimization. Annals of Operations Research 63, 153-188.
Downs J.J. and Vogel E.F. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering. 17, 245-255.
Exler O. and Schittkowksi K. (2006): A trust region SQP algorithm for mixed-integer nonlinear programming, Optimization Letters, DOI 10.1007/s11590-006-0026-1.
Glover F. and Laguna M. (1997). Tabu search. Boston, Kluwer Academic Publishers.
Pistikopoulos E.N., Ross R. et al. (1999). Towards the Integration of Process Design, Process Control and Process Operability. Current Status and Future Trends. Foundations of Computer-Aided Process Design (FOCAPD). Snowmass, Colorado.
See the full pdf manuscript of the abstract.
Presented Thursday 20, 09:55 to 10:12, in session Tools Integration - CAPE Methods & Tools (T4-10).
