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Johannes Jäschke
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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
942011
Fast economic model predictive control based on NLP-sensitivities
J Jäschke, X Yang, LT Biegler
Journal of Process Control 24 (8), 1260-1272, 2014
932014
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
722020
First principles and machine learning virtual flow metering: a literature review
T Bikmukhametov, J Jäschke
Journal of Petroleum Science and Engineering 184, 106487, 2020
612020
Self-optimizing control–A survey
J Jäschke, Y Cao, V Kariwala
Annual Reviews in Control 43, 199-223, 2017
592017
Oil production monitoring using gradient boosting machine learning algorithm
T Bikmukhametov, J Jäschke
Ifac-Papersonline 52 (1), 514-519, 2019
342019
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
322014
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
242017
Optimal controlled variables for polynomial systems
J Jäschke, S Skogestad
Journal of Process Control 22 (1), 167-179, 2012
242012
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
232009
Sensitivity-based economic NMPC with a path-following approach
E Suwartadi, V Kungurtsev, J Jäschke
Processes 5 (1), 8, 2017
192017
Modeling and control of an inline deoiling hydrocyclone
T Das, J Jäschke
IFAC-PapersOnLine 51 (8), 138-143, 2018
182018
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
182015
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
172017
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
152018
Optimal operation of energy storage in buildings: Use of the hot water system
V de Oliveira, J Jäschke, S Skogestad
Journal of energy storage 5, 102-112, 2016
122016
Self-optimizing invariants in dynamic optimization
J Jäschke, M Fikar, S Skogestad
2011 50th IEEE Conference on Decision and Control and European Control …, 2011
122011
Data-driven robust optimal operation of thermal energy storage in industrial clusters
M Thombre, Z Mdoe, J Jäschke
Processes 8 (2), 194, 2020
112020
Using a neural network for estimating plant gradients in real-time optimization with modifier adaptation
J Matias, J Jäschke
IFAC-PapersOnLine 52 (1), 808-813, 2019
112019
An autonomous approach for driving systems towards their limit: an intelligent adaptive anti-slug control system for production maximization
V de Oliveira, J Jäschke, S Skogestad
IFAC-PapersOnLine 48 (6), 104-111, 2015
112015
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Artikler 1–20