Dr.-Ing. Erik Esche
Dr.-Ing. Erik Esche
Process Dynamics and Operations Group, Technische Universität Berlin
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Machine learning in chemical engineering: A perspective
AM Schweidtmann, E Esche, A Fischer, M Kloft, JU Repke, S Sager, ...
Chemie Ingenieur Technik 93 (12), 2029-2039, 2021
Pilot scale investigations of the removal of carbon dioxide from hydrocarbon gas streams using poly (ethylene oxide)–poly (butylene terephthalate) PolyActive™) thin film …
T Brinkmann, C Naderipour, J Pohlmann, J Wind, T Wolff, E Esche, ...
Journal of Membrane Science 489, 237-247, 2015
Rhodium-catalyzed hydroformylation of long-chain olefins in aqueous multiphase systems in a continuously operated miniplant
T Pogrzeba, D Müller, T Hamerla, E Esche, N Paul, G Wozny, ...
Industrial & Engineering Chemistry Research 54 (48), 11953-11960, 2015
Hydroformylation in microemulsions: proof of concept in a miniplant
M Illner, D Müller, E Esche, T Pogrzeba, M Schmidt, R Schomäcker, ...
Industrial & Engineering Chemistry Research 55 (31), 8616-8626, 2016
Computer‐aided process and plant development. A review of common software tools and methods and comparison against an integrated collaborative approach
VA Merchan, E Esche, S Fillinger, G Tolksdorf, G Wozny
Chemie Ingenieur Technik 88 (1‐2), 50-69, 2016
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
Techno-economic evaluation of a biogas-based oxidative coupling of methane process for ethylene production
AT Penteado, M Kim, HR Godini, E Esche, JU Repke
Frontiers of Chemical Science and Engineering 12, 598-618, 2018
An algorithm for the identification and estimation of relevant parameters for optimization under uncertainty
D Müller, E Esche, G Wozny
Computers & chemical engineering 71, 94-103, 2014
Demand response potentials for the chemical industry
F Klaucke, T Karsten, F Holtrup, E Esche, T Morosuk, G Tsatsaronis, ...
Chemie Ingenieur Technik 89 (9), 1133-1141, 2017
Design and assessment of a membrane and absorption based carbon dioxide removal process for oxidative coupling of methane
A Penteado, E Esche, D Salerno, HR Godini, G Wozny
Industrial & Engineering Chemistry Research 55 (27), 7473-7483, 2016
Dynamic real-time optimization under uncertainty of a hydroformylation mini-plant
D Müller, M Illner, E Esche, T Pogrzeba, M Schmidt, R Schomäcker, ...
Computers & Chemical Engineering 106, 836-848, 2017
Systematic phase separation analysis of surfactant-containing systems for multiphase settler design
D Müller, E Esche, T Pogrzeba, M Illner, F Leube, R Schomäcker, ...
Industrial & Engineering Chemistry Research 54 (12), 3205-3217, 2015
A discrete-time scheduling model for power-intensive processes taking fatigue of equipment into consideration
A Obermeier, C Windmeier, E Esche, JU Repke
Chemical Engineering Science 195, 904-920, 2019
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
Adsorption separation of oxidative coupling of methane effluent gases. Mini-plant scale experiments and modeling
L García, YA Poveda, G Rodríguez, E Esche, HR Godini, G Wozny, ...
Journal of natural gas science and engineering 61, 106-118, 2019
Improving interoperability of engineering tools–Data exchange in plant design
S Fillinger, H Bonart, W Welscher, E Esche, JU Repke
Chemie ingenieur technik 89 (11), 1454-1463, 2017
Biogas as a renewable feedstock for green ethylene production via oxidative coupling of methane: Preliminary feasibility study
AT Penteado, M Kim, HR Godini, E Esche, JU Repke
Chemical Engineering Transactions 61, 589-594, 2017
Synthesis and granulation of a 5A zeolite-based molecular sieve and adsorption equilibrium of the oxidative coupling of methane gases
L Garcı́a, YA Poveda, M Khadivi, G Rodriguez, O Görke, E Esche, ...
Journal of Chemical & Engineering Data 62 (4), 1550-1557, 2017
Architectures for neural networks as surrogates for dynamic systems in chemical engineering
E Esche, J Weigert, GB Rihm, J Göbel, JU Repke
Chemical Engineering Research and Design 177, 184-199, 2022
Dynamic model of chloralkali membrane process
T Budiarto, E Esche, JU Repke, E Leksono
Procedia engineering 170, 473-481, 2017
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