Johannes Jäschke

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Sigurd SkogestadProfessor of Chemical Engineering, Norwegian University of Science and Technology (NTNU)Verified email at ntnu.no
José MatiasAssistant Professor, KU LeuvenVerified email at kuleuven.be
Adriaen Verheyleweghenstipendiat i kjemisk prosessteknologi ved Norges teknisk-naturvitenskaplige universitet (NTNU)Verified email at ntnu.no
Dinesh KrishnamoorthyEindhoven University of TechnologyVerified email at seas.harvard.edu
Timur BikmukhametovNorwegian University of Science and TechnologyVerified email at ntnu.no
Tamal DasNorwegian University of Science and TechnologyVerified email at ntnu.no
Eka SuwartadiEmbedded System Engineer at Mettler ToledoVerified email at mt.com
Zawadi MdoeProcess Performance Project Engineer, ABB NorwayVerified email at ntnu.no
Vyacheslav KungurtsevCzech Technical UniversityVerified email at fel.cvut.cz
Galo Antonio Carrillo Le RouxUniversidade de São PauloVerified email at usp.br
Xue YangTuSimple, ex Shell Global Solutions, Carnegie Mellon UniversityVerified email at alumni.cmu.edu
Milan StankoAssociate professor in Production Engineering, NTNUVerified email at ntnu.no
vinay kariwalaSankhyaSutra LabsVerified email at sankhyasutralabs.com
Yi CaoProfessor, Zhejiang UniversityVerified email at zju.edu.cn
José Eduardo Alves GracianoUniversity of São PauloVerified email at usp.br
Janos AbonyiUniversity of PannoniaVerified email at mk.uni-pannon.hu
Bjarne FossProfessor, Dept. Engineering Cybernetics, Norwegian University of Science and TechnologyVerified email at ntnu.no
Miroslav FikarProfessor, Slovak University of Technology in BratislavaVerified email at stuba.sk
Paul I BartonLammot du Pont Professor, MITVerified email at mit.edu
Ramprasad YelchuruTechnical fellow, Carrier

Johannes Jäschke

Professor, Department Chemical Engineering, Norwegian University of Science and

Verified email at ntnu.no - Homepage

Process optimization and control Real-time optimization Nonlinear optimization Model predictive control Subsea processing


Title Sort by citations Sort by year Sort by title	Cited by Cited by	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	167	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	158	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	112	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	107	2014
Self-optimizing control–A survey J Jäschke, Y Cao, V Kariwala Annual Reviews in Control 43, 199-223, 2017	99	2017
Oil production monitoring using gradient boosting machine learning algorithm T Bikmukhametov, J Jäschke Ifac-Papersonline 52 (1), 514-519, 2019	75	2019
Design considerations for industrial water electrolyzer plants M Rizwan, V Alstad, J Jäschke International Journal of Hydrogen Energy 46 (75), 37120-37136, 2021	52	2021
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	42	2014
Optimal controlled variables for polynomial systems J Jäschke, S Skogestad Journal of Process Control 22 (1), 167-179, 2012	36	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	30	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	28	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	26	2009
Multiple shooting for training neural differential equations on time series EM Turan, J Jäschke IEEE Control Systems Letters 6, 1897-1902, 2021	25	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	24	2022
Sensitivity-based economic NMPC with a path-following approach E Suwartadi, V Kungurtsev, J Jäschke Processes 5 (1), 8, 2017	22	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	21	2018
Modeling and control of an inline deoiling hydrocyclone T Das, J Jäschke IFAC-PapersOnLine 51 (8), 138-143, 2018	21	2018
Classification of undesirable events in oil well operation EM Turan, J Jäschke 2021 23rd international conference on process control (PC), 157-162, 2021	20	2021
Data-driven robust optimal operation of thermal energy storage in industrial clusters M Thombre, Z Mdoe, J Jäschke Processes 8 (2), 194, 2020	19	2020
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

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