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Open AccessFeature PaperArticle

Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes

1
Institute of Energy and Process Systems Engineering, Technische Universität Braunschweig, Franz-Liszt-Straße 35, 38106 Braunschweig, Germany
2
Center of Pharmaceutical Engineering (PVZ), Technische Universität Braunschweig, Franz-Liszt-Straße 35a, 38106 Braunschweig, Germany
3
International Max Planck Research School (IMPRS) for Advanced Methods in Process and Systems Engineering, Sandtorstraße 1, 39106 Magdeburg, Germany
*
Author to whom correspondence should be addressed.
Processes 2018, 6(10), 183; https://doi.org/10.3390/pr6100183
Received: 6 September 2018 / Revised: 21 September 2018 / Accepted: 26 September 2018 / Published: 3 October 2018
(This article belongs to the Special Issue Process Modelling and Simulation)
Model-based design principles have received considerable attention in biotechnology and the chemical industry over the last two decades. However, parameter uncertainties of first-principle models are critical in model-based design and have led to the development of robustification concepts. Various strategies have been introduced to solve the robust optimization problem. Most approaches suffer from either unreasonable computational expense or low approximation accuracy. Moreover, they are not rigorous and do not consider robust optimization problems where parameter correlation and equality constraints exist. In this work, we propose a highly efficient framework for solving robust optimization problems with the so-called point estimation method (PEM). The PEM has a fair trade-off between computational expense and approximation accuracy and can be easily extended to problems of parameter correlations. From a statistical point of view, moment-based methods are used to approximate robust inequality and equality constraints for a robust process design. We also apply a global sensitivity analysis to further simplify robust optimization problems with a large number of uncertain parameters. We demonstrate the performance of the proposed framework with two case studies: (1) designing a heating/cooling profile for the essential part of a continuous production process; and (2) optimizing the feeding profile for a fed-batch reactor of the penicillin fermentation process. According to the derived results, the proposed framework of robust process design addresses uncertainties adequately and scales well with the number of uncertain parameters. Thus, the described robustification concept should be an ideal candidate for more complex (bio)chemical problems in model-based design. View Full-Text
Keywords: robust optimization; uncertainty; point estimation method; equality constraints; parameter correlation robust optimization; uncertainty; point estimation method; equality constraints; parameter correlation
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MDPI and ACS Style

Xie, X.; Schenkendorf, R.; Krewer, U. Toward a Comprehensive and Efficient Robust Optimization Framework for (Bio)chemical Processes. Processes 2018, 6, 183.

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