Ralf Hannemann-Tamas
Ralf Hannemann-Tamas
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Zitiert von
Zitiert von
A two-layer architecture for economically optimal process control and operation
L Würth, R Hannemann, W Marquardt
Journal of Process Control 21 (3), 311-321, 2011
Neighboring-extremal updates for nonlinear model-predictive control and dynamic real-time optimization
L Würth, R Hannemann, W Marquardt
Journal of Process Control 19 (8), 1277-1288, 2009
Discrete first-and second-order adjoints and automatic differentiation for the sensitivity analysis of dynamic models
R Hannemann, W Marquardt, U Naumann, B Gendler
Procedia Computer Science 1 (1), 297-305, 2010
Continuous and discrete composite adjoints for the Hessian of the Lagrangian in shooting algorithms for dynamic optimization
R Hannemann, W Marquardt
SIAM journal on scientific computing 31 (6), 4675-4695, 2010
Dyos-a framework for optimization of large-scale differential algebraic equation systems
A Caspari, AM Bremen, JMM Faust, F Jung, CD Kappatou, S Sass, ...
Computer Aided Chemical Engineering 46, 619-624, 2019
Incremental single shooting—a robust method for the estimation of parameters in dynamical systems
C Michalik, R Hannemann, W Marquardt
Computers & Chemical Engineering 33 (7), 1298-1305, 2009
An iterative partition-based moving horizon estimator with coupled inequality constraints
R Schneider, R Hannemann-Tamás, W Marquardt
Automatica 61, 302-307, 2015
How to verify optimal controls computed by direct shooting methods?–A tutorial
R Hannemann-Tamás, W Marquardt
Journal of Process Control 22 (2), 494-507, 2012
Model complexity reduction of chemical reaction networks using mixed-integer quadratic programming
R Hannemann-Tamás, A Gabor, G Szederkenyi, KM Hangos
Computers & Mathematics with Applications 65 (10), 1575-1595, 2013
Optimized Hollow Fiber Sorbents and Pressure Swing Adsorption Process for H2 Recovery
B Ohs, J Lohaus, D Marten, R Hannemann-Tamás, A Krieger, M Wessling
Industrial & Engineering Chemistry Research 57 (14), 5093-5105, 2018
Robust dynamic optimization of batch processes under parametric uncertainty: Utilizing approaches from semi-infinite programs
J Puschke, H Djelassi, J Kleinekorte, R Hannemann-Tamás, A Mitsos
Computers & Chemical Engineering 116, 253-267, 2018
Higher-order discrete adjoint ODE solver in C++ for dynamic optimization
J Lotz, U Naumann, R Hannemann-Taḿas, T Ploch, A Mitsos
Procedia Computer Science 51, 256-265, 2015
Fast computation of the Hessian of the Lagrangian in shooting algorithms for dynamic optimization
R Hannemann, W Marquardt
Proceedings of the 8th International IFAC Symposium on Dynamics and Control …, 2007
Adjoint Sensitivity Analysis for Optimal Control of Non-Smooth Differential-Algebraic Equations
R Hannemann-Tamás
Shaker, 2013
Multiscale dynamic modeling and simulation of a biorefinery
T Ploch, X Zhao, J Hüser, E von Lieres, R Hannemann‐Tamás, ...
Biotechnology and Bioengineering 116 (10), 2561-2574, 2019
Modeling of dynamic systems with a variable number of phases in liquid–liquid equilibria
T Ploch, M Glass, AM Bremen, R Hannemann‐Tamás, A Mitsos
AIChE Journal 65 (2), 571-581, 2019
Adjoint sensitivity analysis for nonsmooth differential-algebraic equation systems
R Hannemann-Tamas, DA Munoz, W Marquardt
SIAM Journal on Scientific Computing 37 (5), A2380-A2402, 2015
Model-based investment planning model for stepwise capacity expansions of chemical plants
A Wiesner, M Schlegel, J Oldenburg, L Würth, R Hannemann, A Polt
Computer Aided Chemical Engineering 25, 307-312, 2008
Integrated process and control design by the normal vector approach: Application to the Tennessee-Eastman process
DA Muñoz, J Gerhard, R Hannemann, W Marquardt
Computer Aided Chemical Engineering 29, 668-672, 2011
Simulation of differential-algebraic equation systems with optimization criteria embedded in Modelica
T Ploch, E von Lieres, W Wiechert, A Mitsos, R Hannemann-Tamás
Computers & Chemical Engineering 140, 106920, 2020
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