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Johannes Jäschke
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Year
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
1512020
First principles and machine learning virtual flow metering: a literature review
T Bikmukhametov, J Jäschke
Journal of Petroleum Science and Engineering 184, 106487, 2020
1362020
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
1072011
Fast economic model predictive control based on NLP-sensitivities
J Jäschke, X Yang, LT Biegler
Journal of Process Control 24 (8), 1260-1272, 2014
1052014
Self-optimizing control–A survey
J Jäschke, Y Cao, V Kariwala
Annual Reviews in Control 43, 199-223, 2017
872017
Oil production monitoring using gradient boosting machine learning algorithm
T Bikmukhametov, J Jäschke
Ifac-Papersonline 52 (1), 514-519, 2019
692019
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
412014
Design considerations for industrial water electrolyzer plants
M Rizwan, V Alstad, J Jäschke
International Journal of Hydrogen Energy 46 (75), 37120-37136, 2021
332021
Optimal controlled variables for polynomial systems
J Jäschke, S Skogestad
Journal of Process Control 22 (1), 167-179, 2012
302012
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
292017
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
272015
Dynamic model and control of heat exchanger networks for district heating
LC Dobos, J Jäschke, J Abonyi, S Skogestad
Hungarian Journal of Industrial Chemistry 37 (1), 37-49, 2009
242009
Multiple shooting for training neural differential equations on time series
EM Turan, J Jäschke
IEEE Control Systems Letters 6, 1897-1902, 2021
212021
Sensitivity-based economic NMPC with a path-following approach
E Suwartadi, V Kungurtsev, J Jäschke
Processes 5 (1), 8, 2017
202017
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
192022
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
192018
Modeling and control of an inline deoiling hydrocyclone
T Das, J Jäschke
IFAC-PapersOnLine 51 (8), 138-143, 2018
192018
Framework for combined diagnostics, prognostics and optimal operation of a subsea gas compression system
A Verheyleweghen, J Jäschke
IFAC-PapersOnLine 50 (1), 15916-15921, 2017
182017
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
162021
Data-driven robust optimal operation of thermal energy storage in industrial clusters
M Thombre, Z Mdoe, J Jäschke
Processes 8 (2), 194, 2020
162020
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Articles 1–20