A Tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design

• Published in: Computers & chemical engineering Vol. 32; no. 8; pp. 1877 - 1891
• Main Authors: Exler, Oliver, Antelo, Luis T., Egea, Jose A., Alonso, Antonio A., Banga, Julio R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 22.08.2008
• Subjects: Integrated process and control design; Metaheuristic; Mixed-integer nonlinear programming (MINLP); Tabu search; Tennessee Eastman plant; Mixed-integer nonlinear programming (MINLP); Metaheuristic; Tabu search; Tennessee Eastman plant; Integrated process and control design
• ISSN: 0098-1354; 1873-4375; 1873-4375
• DOI: 10.1016/j.compchemeng.2007.10.008

In this contribution, we consider mixed-integer nonlinear programming problems subject to differential-algebraic constraints. This class of problems arises frequently in process design, and the particular case of integrated process and control system design is considered. Since these problems are frequently non-convex, local optimization techniques usually fail to locate the global solution. Here, we propose a global optimization algorithm, based on extensions of the metaheuristic Tabu Search, in order to solve this challenging class of problems in an efficient and robust way. The ideas of the methodology are explained and, on the basis of two case studies, the performance of the approach is evaluated. The first benchmark problem is a Wastewater Treatment Plant model [Alex, J., Bteau, J. F., Copp, J. B., Hellinga, C., Jeppsson, U., Marsili-Libelli, S., et al. (1999). Benchmark for evaluating control strategies in wastewater treatment plants. In Proceedings of the ECC’99 conference] for nitrogen removal and the second case study is the well-known Tennessee Eastman Process [Downs, J. J., & Vogel, E. F. (1993). A plant-wide industrial process control problem. Computers & Chemical Engineering, 17, 245-255]. Numerical experiments with our new method indicate that we can achieve an improved performance in both cases. Additionally, our method outperforms several other recent competitive solvers for the two challenging case studies considered.