J
Jan Van Impe
Researcher at Katholieke Universiteit Leuven
Publications - 423
Citations - 5847
Jan Van Impe is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Optimal control & Multi-objective optimization. The author has an hindex of 35, co-authored 416 publications receiving 4930 citations. Previous affiliations of Jan Van Impe include Catholic University of Leuven.
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Perspectives from CO+RE: How COVID-19 changed our food systems and food security paradigms
Serafim Bakalis,Serafim Bakalis,Vasilis P. Valdramidis,Dimitrios Argyropoulos,Lilia Ahrné,Jianshe Chen,Patrick J. Cullen,Enda Cummins,Ashim K. Datta,Christos Emmanouilidis,Timothy J. Foster,Peter J. Fryer,Ourania Gouseti,Almudena Hospido,Kai Knoerzer,Alain Le-Bail,Alejandro G. Marangoni,Pingfan Rao,Oliver Schlüter,Petros Taoukis,Epameinondas Xanthakis,Jan Van Impe +21 more
TL;DR: Graphical abstract Image 1 : as discussed by the authors ) is an example of the work presented in this paper, which is based on the work of as discussed by the authors... ]]..
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Fast Pareto set generation for nonlinear optimal control problems with multiple objectives
TL;DR: ACADO Multi-Objective as mentioned in this paper is an add-on module of the automatic control and dynamic optimisation toolkit ACADO (both freely available at www.acadotoolkit.org).
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Growth and indole-3-acetic acid biosynthesis of Azospirillum brasilense Sp245 is environmentally controlled.
TL;DR: The obtained profile for IAA synthesis coincides with the expression of the indole-3-pyruvate decarboxylase gene (ipdC), encoding a key enzyme in the IAA biosynthesis of A. brasilense.
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Recombinant protein production and streptomycetes.
TL;DR: This overview of the biopharmaceutical market focuses on Streptomyces lividans, a Gram-positive bacterium with a proven excellence in secretion capacity, as host for heterologous protein production, and discusses its advantages and disadvantages.
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Robust multi-objective optimal control of uncertain (bio)chemical processes
TL;DR: In this paper, the authors exploit advanced deterministic techniques to efficiently and accurately generate robust Pareto sets in the presence of model uncertainty, i.e., sets in which robustness is an additional objective.