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 | 161 | 2020 |
First principles and machine learning virtual flow metering: a literature review T Bikmukhametov, J Jäschke Journal of Petroleum Science and Engineering 184, 106487, 2020 | 144 | 2020 |
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 | 110 | 2011 |
Fast economic model predictive control based on NLP-sensitivities J Jäschke, X Yang, LT Biegler Journal of Process Control 24 (8), 1260-1272, 2014 | 105 | 2014 |
Self-optimizing control–A survey J Jäschke, Y Cao, V Kariwala Annual Reviews in Control 43, 199-223, 2017 | 92 | 2017 |
Oil production monitoring using gradient boosting machine learning algorithm T Bikmukhametov, J Jäschke Ifac-Papersonline 52 (1), 514-519, 2019 | 73 | 2019 |
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 | 41 | 2014 |
Design considerations for industrial water electrolyzer plants M Rizwan, V Alstad, J Jäschke International Journal of Hydrogen Energy 46 (75), 37120-37136, 2021 | 40 | 2021 |
Optimal controlled variables for polynomial systems J Jäschke, S Skogestad Journal of Process Control 22 (1), 167-179, 2012 | 33 | 2012 |
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 | 29 | 2017 |
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 | 26 | 2015 |
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 | 25 | 2009 |
Multiple shooting for training neural differential equations on time series EM Turan, J Jäschke IEEE Control Systems Letters 6, 1897-1902, 2021 | 23 | 2021 |
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 | 21 | 2022 |
Sensitivity-based economic NMPC with a path-following approach E Suwartadi, V Kungurtsev, J Jäschke Processes 5 (1), 8, 2017 | 21 | 2017 |
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 | 20 | 2018 |
Modeling and control of an inline deoiling hydrocyclone T Das, J Jäschke IFAC-PapersOnLine 51 (8), 138-143, 2018 | 20 | 2018 |
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 | 18 | 2017 |
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 | 17 | 2021 |
Classification of undesirable events in oil well operation EM Turan, J Jäschke 2021 23rd international conference on process control (PC), 157-162, 2021 | 17 | 2021 |