| Abstract
| - The problem of integrated design and control optimization of process plants is discussed in this paper. Weconsider it as a nonlinear programming problem subject to differential-algebraic constraints. This class ofproblems is frequently multimodal and “costly” (i.e., computationally expensive to evaluate). Thus, on theone 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 computationtimes. As an alternative, one may consider global optimization methods which employ surrogate-basedapproaches to reduce computation times and which require no knowledge of the underlying problem structure.A challenging wastewater treatment plant (WWTP) benchmark model is used here to evaluate the performanceof these techniques. Numerical experiments with different optimization solvers indicate that the proposedbenchmark optimization problem is indeed multimodal, and that via global optimization we can achieve animprovement of the controllers' performance compared to the best tuned controllers' settings available in theliterature. Moreover, these results show that surrogate-based methods may reduce computation times whileensuring convergence to the best known solutions.
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