Christian Hoffmann
Christian Hoffmann
Process Dynamics and Operations Group, Technische Universität Berlin
Bestätigte E-Mail-Adresse bei tu-berlin.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Towards demand-side management of the chlor-alkali electrolysis: Dynamic, pressure-driven modeling and model validation of the 1, 2-dichloroethane synthesis
C Hoffmann, J Weigert, E Esche, JU Repke
Chemical Engineering Science 214, 115358, 2020
172020
MOSAIC–Enabling Large‐Scale Equation‐Based Flow Sheet Optimization
E Esche, C Hoffmann, M Illner, D Müller, S Fillinger, G Tolksdorf, H Bonart, ...
Chemie Ingenieur Technik 89 (5), 620-635, 2017
142017
Generation of Data-Driven Models for Chance-Constrained Optimization
J Weigert, E Esche, C Hoffmann, JU Repke
Computer Aided Chemical Engineering 47, 311-316, 2019
42019
Towards demand-side management of the chlor-alkali electrolysis: dynamic modeling and model validation
J Weigert, C Hoffmann, E Esche, P Fischer, JU Repke
Computers & Chemical Engineering 149, 107287, 2021
32021
A pressure-driven, dynamic model for distillation columns with smooth reformulations for flexible operation
C Hoffmann, J Weigert, E Esche, JU Repke
Computers & Chemical Engineering 142, 107062, 2020
32020
Impact of the chlorine value chain on the demand response potential of the chloralkali process
F Klaucke, C Hoffmann, M Hofmann, G Tsatsaronis
Applied Energy 276, 115366, 2020
32020
Integration of Design and Control Based on Large-Scale Nlp Formulations and An Optimal Economic Nmpc
C Hoffmann, E Esche, JU Repke
Computer Aided Chemical Engineering 47, 125-130, 2019
32019
Moving-horizon State Estimation with Gross Error Detection for a Hydroformylation Mini-plant
C Hoffmann, M Illner, D Müller, E Esche, G Wozny, LT Biegler, JU Repke
Computer Aided Chemical Engineering 38, 1485-1490, 2016
32016
Optimization Under Uncertainty Based on a Data-driven Model for a Chloralkali Electrolyzer Cell
E Esche, J Weigert, T Budiarto, C Hoffmann, JU Repke
Computer Aided Chemical Engineering 46, 577-582, 2019
22019
Evaluation of Discretization Methods for Modeling the Chloralkali Membrane Process
T Budiarto, J Weigert, C Hoffmann, E Esche, JU Repke
Computer Aided Chemical Engineering 46, 589-594, 2019
12019
Assessing the Realizable Flexibility Potential of Electrochemical Processes
C Hoffmann, J Hübner, F Klaucke, N Milojević, R Müller, M Neumann, ...
Industrial & Engineering Chemistry Research 60 (37), 13389-13748, 2021
2021
Semi-supervised Learning for Data-driven Soft-sensing of Biological and Chemical Processes
E Esche, T Talis, J Weigert, G Brand-Rihm, B You, C Hoffmann, JU Repke
arXiv preprint arXiv:2107.13822, 2021
2021
Enabling Dynamic Real-Time Optimization under Uncertainty using Data-Driven Chance Constraints
J Weigert, C Hoffmann, E Esche, JU Repke
Computer Aided Chemical Engineering 48, 1189-1194, 2020
2020
Integration of Design and Operation Using Dynamic Perturbation and Chance Constraints with Unscented Transform
C Hoffmann, J Weigert, E Esche, JU Repke
Computer Aided Chemical Engineering 48, 751-756, 2020
2020
Parameter Estimation for Thermodynamic Models Using an Identifiability Analysis and Subset Selection
C Hoffmann, J Weigert, E Esche, JU Repke
Computer Aided Chemical Engineering 46, 583-588, 2019
2019
Demand‐Response‐Potenziale elektrochemischer Verfahren am Beispiel der Chlor‐Alkali‐Elektrolyse
C Hoffmann, J Weigert, T Budiarto, E Esche, JU Repke
Chemie Ingenieur Technik 90 (9), 1166-1166, 2018
2018
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