Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 362841
Title Global optimization for integrated design and control of computationally expensive process models
Author(s) Egea, J.A.; Vries, D.; Alonso, A.A.; Banga, J.R.
Source Industrial & Engineering Chemistry Research 46 (2007)26. - ISSN 0888-5885 - p. 9148 - 9157.
Department(s) Systems and Control Group
Publication type Refereed Article in a scientific journal
Publication year 2007
Keyword(s) water treatment plants - control strategies - simulation - denitrification - nitrification
Abstract The problem of integrated design and control optimization of process plants is discussed in this paper. We consider it as a nonlinear programming problem subject to differential-algebraic constraints. This class of problems is frequently multimodal and "costly" (i.e., computationally expensive to evaluate). Thus, on the one hand, local optimization techniques usually fail to locate the global solution, and, on the second hand, most global optimization methods require many simulations of the model, resulting in unaffordable computation times. As an alternative, one may consider global optimization methods which employ surrogate-based approaches to reduce computation times and which require no knowledge of the underlying problem structure. A challenging wastewater treatment plant (WWTP) benchmark model1 is used here to evaluate the performance of these techniques. Numerical experiments with different optimization solvers indicate that the proposed benchmark optimization problem is indeed multimodal, and that via global optimization we can achieve an improvement of the controllers' performance compared to the best tuned controllers' settings available in the literature. Moreover, these results show that surrogate-based methods may reduce computation times while ensuring convergence to the best known solutions. --------------------------------------------------------------------------------
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