S
Sebastian Engell
Researcher at Technical University of Dortmund
Publications - 627
Citations - 8977
Sebastian Engell is an academic researcher from Technical University of Dortmund. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 41, co-authored 599 publications receiving 7966 citations. Previous affiliations of Sebastian Engell include Control Group & Indian Institute of Technology Bombay.
Papers
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Feedback control for optimal process operation
TL;DR: In this paper, the focus is on direct optimizing control by optimizing an economic cost criterion online over a finite horizon where the usual control specifications in terms of, e.g., product purities enter as constraints and not as set-points.
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Scope for industrial applications of production scheduling models and solution methods
Iiro Harjunkoski,Christos T. Maravelias,Peter Bongers,Pedro M. Castro,Sebastian Engell,Ignacio E. Grossmann,John N. Hooker,Carlos A. Méndez,Guido Sand,John M. Wassick +9 more
TL;DR: The aim of the paper is to focus on the industrial aspects of scheduling and discuss the main characteristics, including strengths and weaknesses of the presented approaches, as well as some lessons learned from industry.
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Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
TL;DR: In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.
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Model predictive control using neural networks
TL;DR: A feedforward neural network is used as the nonlinear prediction model in an extended DMC-algorithm to control the pH-value in a laboratory-scale neutralization reactor and the resulting control algorithm performs much better than the conventional PI-controller.
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Iterative set-point optimization of batch chromatography
Weihua Gao,Sebastian Engell +1 more
TL;DR: The gradients of the plant mapping which are required by the iterative optimization strategy are computed by a technique, which considers the influence of measurement errors and the number of additional set-point perturbations.