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Johannes Jäschke
Johannes Jäschke
Professor, Department Chemical Engineering, Norwegian University of Science and
Bestätigte E-Mail-Adresse bei ntnu.no - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Combining machine learning and process engineering physics towards enhanced accuracy and explainability of data-driven models
T Bikmukhametov, J Jäschke
Computers & Chemical Engineering 138, 106834, 2020
1822020
First principles and machine learning virtual flow metering: a literature review
T Bikmukhametov, J Jäschke
Journal of Petroleum Science and Engineering 184, 106487, 2020
1762020
NCO tracking and self-optimizing control in the context of real-time optimization
J Jäschke, S Skogestad
Journal of Process Control 21 (10), 1407-1416, 2011
1132011
Fast economic model predictive control based on NLP-sensitivities
J Jäschke, X Yang, LT Biegler
Journal of Process Control 24 (8), 1260-1272, 2014
1102014
Self-optimizing control–A survey
J Jäschke, Y Cao, V Kariwala
Annual Reviews in Control 43, 199-223, 2017
1082017
Oil production monitoring using gradient boosting machine learning algorithm
T Bikmukhametov, J Jäschke
Ifac-Papersonline 52 (1), 514-519, 2019
932019
Design considerations for industrial water electrolyzer plants
M Rizwan, V Alstad, J Jäschke
International Journal of Hydrogen Energy 46 (75), 37120-37136, 2021
672021
Optimal operation of heat exchanger networks with stream split: Only temperature measurements are required
J Jäschke, S Skogestad
Computers & chemical engineering 70, 35-49, 2014
442014
Optimal controlled variables for polynomial systems
J Jäschke, S Skogestad
Journal of Process Control 22 (1), 167-179, 2012
392012
Multiple shooting for training neural differential equations on time series
EM Turan, J Jäschke
IEEE Control Systems Letters 6, 1897-1902, 2021
342021
A Predictor-Corrector Path-Following Algorithm for Dual-Degenerate Parametric Optimization Problems
V Kungurtsev, J Jäschke
SIAM Journal on Optimization 27 (1), 538–564, 2017
302017
Gibbs sampler for noisy transformed gamma process: Inference and remaining useful life estimation
X Liu, J Matias, J Jäschke, J Vatn
Reliability Engineering & System Safety 217, 108084, 2022
292022
Integrating self-optimizing control and real-time optimization using zone control MPC
JEA Graciano, J Jäschke, GAC Le Roux, LT Biegler
Journal of Process Control 34, 35-48, 2015
292015
Dynamic model and control of heat exchanger networks for district heating
L Dobos, J Jäschke, J Abonyi, S Skogestad
Hungarian Journal of Industry and Chemistry, 2009
272009
Optimal scheduling of flexible thermal power plants with lifetime enhancement under uncertainty
J Rúa, A Verheyleweghen, J Jäschke, LO Nord
Applied Thermal Engineering 191, 116794, 2021
262021
Classification of undesirable events in oil well operation
EM Turan, J Jäschke
2021 23rd international conference on process control (PC), 157-162, 2021
252021
Improving scenario decomposition for multistage MPC using a sensitivity-based path-following algorithm
D Krishnamoorthy, E Suwartadi, B Foss, S Skogestad, J Jäschke
IEEE control systems letters 2 (4), 581-586, 2018
232018
Modeling and control of an inline deoiling hydrocyclone
T Das, J Jäschke
IFAC-PapersOnLine 51 (8), 138-143, 2018
222018
Sensitivity-based economic NMPC with a path-following approach
E Suwartadi, V Kungurtsev, J Jäschke
Processes 5 (1), 8, 2017
222017
Sensitivity-assisted multistage nonlinear model predictive control: Robustness, stability and computational efficiency
M Thombre, ZJ Yu, J Jäschke, LT Biegler
Computers & Chemical Engineering 148, 107269, 2021
212021
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