Global Optimization for Integrated Design and Control of Computationally Expensive Process Models

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 (i) non-convex and (ii) "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 problem structure. A challenging Wastewater Treatment Plant benchmark model (see [1] and references therein) is used to evaluate the performance of these techniques. Numerical experiments with different optimization solvers indicate that the proposed benchmark optimization problem is indeed non-convex, and that we can achieve an improvement of the controller performance compared to the best tuned controller settings available in the literature. Moreover, these results show that surrogate-based methods may indeed reduce computation times while, at the same time ensuring convergence to the best known solutions.