Decision support in Industrial Applications: Multi-Criteria Optimization at its Best
Process design is a multicriteria optimization problem: Costs as small as possible, quality indicators as high as possible shall be achieved. This requires calculating the Pareto boundary, which makes many model evaluations necessary and thus is a computationally demanding task. Once the Pareto boundary has been determined, it can be used for an interactive decision support, which enables the engineer to balance the objectives e.g. increase quality at competitive total costs.
In this contribution, we review computationally highly efficient methods to resolve the Pareto boundary within a predefined accuracy. Among these are adaptive scalarization (Bortz et al, 2014) and exploration schemes (Heese et al, 2019), where the latter employ machine-learning methods. Furthermore, the application of multicriteria approaches in the presence of parametric model uncertainties is described (Bortz et al, 2017).
The benefit of applying multicriteria optimization in process design is reported for different industrial applications. The transferability of the method to data reconciliation and the detection of faulty measurements is demonstrated as well.
M. Bortz, J. Burger, N. Asprion, S. Blagov, R. Böttcher, U. Nowak, A. Scheithauer, R. Welke, K.-H. Küfer, and H. Hasse, 2014, Comp. Chem. Eng., 60, 354–363
M. Bortz, J. Burger, E. von Harbou, M. Klein, J. Schwientek, N. Asprion, R. Böttcher, K.-H. Küfer, and H. Hasse. Industrial & Engineering Chemistry Research, 56(44):12672-12681, 2017
R. Heese, M. Walczak, T. Seidel, N. Asprion, M. Bortz, 2019, Comp. Chem. Eng. 124, 326-342
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