Johannes Jäschke

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Sigurd SkogestadProfessor of Chemical Engineering, Norwegian University of Science and Technology (NTNU)在 ntnu.no 的電子郵件地址已通過驗證
José MatiasAssistant Professor, KU Leuven在 kuleuven.be 的電子郵件地址已通過驗證
Adriaen Verheyleweghenstipendiat i kjemisk prosessteknologi ved Norges teknisk-naturvitenskaplige universitet (NTNU)在 ntnu.no 的電子郵件地址已通過驗證
Dinesh KrishnamoorthyEindhoven University of Technology在 seas.harvard.edu 的電子郵件地址已通過驗證
Timur BikmukhametovNorwegian University of Science and Technology在 ntnu.no 的電子郵件地址已通過驗證
Tamal DasNorwegian University of Science and Technology在 ntnu.no 的電子郵件地址已通過驗證
Eka SuwartadiEmbedded System Engineer at Mettler Toledo在 mt.com 的電子郵件地址已通過驗證
Zawadi MdoeProcess Performance Project Engineer, ABB Norway在 ntnu.no 的電子郵件地址已通過驗證
Vyacheslav KungurtsevCzech Technical University在 fel.cvut.cz 的電子郵件地址已通過驗證
Galo Antonio Carrillo Le RouxUniversidade de São Paulo在 usp.br 的電子郵件地址已通過驗證
Milan StankoAssociate professor in Production Engineering, NTNU在 ntnu.no 的電子郵件地址已通過驗證
Xue YangTuSimple, ex Shell Global Solutions, Carnegie Mellon University在 alumni.cmu.edu 的電子郵件地址已通過驗證
vinay kariwalaSankhyaSutra Labs在 sankhyasutralabs.com 的電子郵件地址已通過驗證
Yi CaoProfessor, Zhejiang University在 zju.edu.cn 的電子郵件地址已通過驗證
José Eduardo Alves GracianoUniversity of São Paulo在 usp.br 的電子郵件地址已通過驗證
Janos AbonyiUniversity of Pannonia在 mk.uni-pannon.hu 的電子郵件地址已通過驗證
Bjarne FossProfessor, Dept. Engineering Cybernetics, Norwegian University of Science and Technology在 ntnu.no 的電子郵件地址已通過驗證
Miroslav FikarProfessor, Slovak University of Technology in Bratislava在 stuba.sk 的電子郵件地址已通過驗證
Paul I BartonLammot du Pont Professor, MIT在 mit.edu 的電子郵件地址已通過驗證
Ramprasad YelchuruTechnical fellow, Carrier

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Johannes Jäschke

Professor, Department Chemical Engineering, Norwegian University of Science and

在 ntnu.no 的電子郵件地址已通過驗證 - 首頁

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


標題按引用次數排序按年份排序按標題排序	引用次數引用次數	年份
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	174	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	171	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	108	2014
Self-optimizing control–A survey J Jäschke, Y Cao, V Kariwala Annual Reviews in Control 43, 199-223, 2017	101	2017
Oil production monitoring using gradient boosting machine learning algorithm T Bikmukhametov, J Jäschke Ifac-Papersonline 52 (1), 514-519, 2019	83	2019
Design considerations for industrial water electrolyzer plants M Rizwan, V Alstad, J Jäschke International Journal of Hydrogen Energy 46 (75), 37120-37136, 2021	59	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	37	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	29	2015
Multiple shooting for training neural differential equations on time series EM Turan, J Jäschke IEEE Control Systems Letters 6, 1897-1902, 2021	28	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	26	2022
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
Classification of undesirable events in oil well operation EM Turan, J Jäschke 2021 23rd international conference on process control (PC), 157-162, 2021	23	2021
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	23	2018
Modeling and control of an inline deoiling hydrocyclone T Das, J Jäschke IFAC-PapersOnLine 51 (8), 138-143, 2018	22	2018
Sensitivity-based economic NMPC with a path-following approach E Suwartadi, V Kungurtsev, J Jäschke Processes 5 (1), 8, 2017	22	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	21	2021
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	19	2021

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