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Alexander Mitsos

Chemieingenieur

Name	Alexander Mitsos
Ort	Griechenland Deutschland
Geschlecht	männlich
Akademischer Grad bzw. Titel	Prof. Dr.
Beruf	Chemieingenieur (charakteristischer Beruf) Hochschullehrer
Variante	A. Mitsos
Beziehung	Aachener Verfahrenstechnik
Weitere Angaben	seit 09/2012 Universitätsprofessor für das Fach Systemsverfahrenstechnik der Fakultät für Maschinenwesen der RWTH Aachen University; 2006, Promotion auf dem Gebiet der Systemsverfahrenstechnik, Department of Chemical Engineering am MIT; 1994-1999: Diplomstudium des Chemieingenieurwesens an der Universität Karlsruhe
Datensatztyp	Person
GND-Nummer	137297297
Datensatz-ID	137297297
Beteiligt an alle anzeigen Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions A branch-and-bound algorithm with growing datasets for large-scale parameter estimation Combustion versus Gasification in Power- and Biomass-to-X Processes: An Exergetic Analysis End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control Global parameter estimation for the efficient optimization of real-world applications Graph neural networks for surfactant multi-property prediction Hierarchical heat transfer modeling of a continuous millireactor Identifying key environmental objectives for integrated process and fuel design In-silico and in-situ optimization for enhanced synthesis of functional microgels Industrial demand response potential of simultaneous day-ahead and intraday electricity market participation Itaconic acid production by co‐feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction Model-based evaluation of ammonia energy storage concepts at high technological readiness level Modeling and optimization of continuous millireactors with computational fluid dynamics Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions Nonlinear manifold learning determines microgel size from Raman spectroscopy Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response Overcoming the Central Planner Approach – Bilevel Optimization of the European Energy Transition Simultaneous design of fermentation and microbe Thermodynamics-consistent graph neural networks Uncertainty Quantification in Integrated Fuel and Process Design Globally optimal scheduling of an electrochemical process via data-driven dynamic modeling and wavelet-based adaptive grid refinement Aachener Verfahrenstechnik Unsicherheit in Stromnetzen : Robuste Optimierung für eine robuste Stromversorgung A digital twin environment for in-situ solar tower plant optimization Co-feeding enhances the yield of methyl ketones Cost-optimal Power-to-Methanol : Flexible operation or intermediate storage? Geometry optimization of a continuous millireactor via CFD and Bayesian optimization Gibbs–Duhem-informed neural networks for binary activity coefficient prediction Globally optimal scheduling of an electrochemical process via data-driven dynamic modeling and wavelet-based adaptive grid refinement Graph machine learning for design of high-octane fuels Graph machine learning for design of high-octane fuels Molecular design of fuels for maximum spark-ignition engine efficiency by combining predictive thermodynamics and machine learning Power-to-X processes based on PEM water electrolyzers: A review of process integration and flexible operation Simultaneous real-time scheduling of multi-energy systems and dynamic production processes Switchable oxygen depolarized cathodes in flexible electrolysis operation Towards global parameter estimation exploiting reduced data sets Virtual patient modeling for heterogeneous intensive care unit data for the support of artificial intelligence Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans? Adaptive discretization-based algorithms for semi-infinite programs with unbounded variables Global dynamic optimization with Hammerstein–Wiener models embedded Bilevel optimization for joint scheduling of production and energy systems Comparison of MINLP formulations for global superstructure optimization Adaptive discretization-based algorithms for semi-infinite programs with unbounded variables Bilevel optimization for joint scheduling of production and energy systems Blend for all or pure for few? Well-to-wheel life cycle assessment of blending electricity-based OME$_{3–5}$ with fossil diesel Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans? Comparison of MINLP formulations for global superstructure optimization Demand response potential of industrial processes considering uncertain short-term electricity prices Design of dynamically operated air separation units considering fatigue of equipment and ambient temperatures Dynamic modeling of aqueous electrolyte systems in Modelica Electrified production of ammonia as chemical product and energy storage Global dynamic optimization with Hammerstein-Wiener models embedded Guaranteed feasibility of optimal control problems with and without uncertainty HybridML : Open source platform for hybrid modeling Identifiability, observability and model-based control of nonlinear systems Inline spectroscopy with model-based spectra evaluation for biorefinery unit operations Markt- und Stromsystem, Managementsysteme und Technologien energieflexibler Fabriken Normalizing flow-based scenario generation for energy system optimization Optimal biorefinery processes using differential-algebraic equation systems with optimization criteria embedded

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Katalog der Deutschen Nationalbibliothek

Alexander Mitsos

Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions

A branch-and-bound algorithm with growing datasets for large-scale parameter estimation

Combustion versus Gasification in Power- and Biomass-to-X Processes: An Exergetic Analysis

End-to-end reinforcement learning of Koopman models for economic nonlinear model predictive control

Global parameter estimation for the efficient optimization of real-world applications

Graph neural networks for surfactant multi-property prediction

Hierarchical heat transfer modeling of a continuous millireactor

Identifying key environmental objectives for integrated process and fuel design

In-silico and in-situ optimization for enhanced synthesis of functional microgels

Industrial demand response potential of simultaneous day-ahead and intraday electricity market participation

Itaconic acid production by co‐feeding of Ustilago maydis: A combined approach of experimental data, design of experiments, and metabolic modeling

ML-SAFT: A machine learning framework for PCP-SAFT parameter prediction

Model-based evaluation of ammonia energy storage concepts at high technological readiness level

Modeling and optimization of continuous millireactors with computational fluid dynamics

Noisecut: a python package for noise-tolerant classification of binary data using prior knowledge integration and max-cut solutions

Nonlinear manifold learning determines microgel size from Raman spectroscopy

Optimal design of a local renewable electricity supply system for power-intensive production processes with demand response

Overcoming the Central Planner Approach – Bilevel Optimization of the European Energy Transition

Simultaneous design of fermentation and microbe

Thermodynamics-consistent graph neural networks

Uncertainty Quantification in Integrated Fuel and Process Design

Globally optimal scheduling of an electrochemical process via data-driven dynamic modeling and wavelet-based adaptive grid refinement

Aachener Verfahrenstechnik

Unsicherheit in Stromnetzen : Robuste Optimierung für eine robuste Stromversorgung

A digital twin environment for in-situ solar tower plant optimization

Co-feeding enhances the yield of methyl ketones

Cost-optimal Power-to-Methanol : Flexible operation or intermediate storage?

Geometry optimization of a continuous millireactor via CFD and Bayesian optimization

Gibbs–Duhem-informed neural networks for binary activity coefficient prediction

Globally optimal scheduling of an electrochemical process via data-driven dynamic modeling and wavelet-based adaptive grid refinement

Graph machine learning for design of high-octane fuels

Graph machine learning for design of high-octane fuels

Molecular design of fuels for maximum spark-ignition engine efficiency by combining predictive thermodynamics and machine learning

Power-to-X processes based on PEM water electrolyzers: A review of process integration and flexible operation

Simultaneous real-time scheduling of multi-energy systems and dynamic production processes

Switchable oxygen depolarized cathodes in flexible electrolysis operation

Towards global parameter estimation exploiting reduced data sets

Virtual patient modeling for heterogeneous intensive care unit data for the support of artificial intelligence

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Adaptive discretization-based algorithms for semi-infinite programs with unbounded variables

Global dynamic optimization with Hammerstein–Wiener models embedded

Bilevel optimization for joint scheduling of production and energy systems

Comparison of MINLP formulations for global superstructure optimization

Adaptive discretization-based algorithms for semi-infinite programs with unbounded variables

Bilevel optimization for joint scheduling of production and energy systems

Blend for all or pure for few? Well-to-wheel life cycle assessment of blending electricity-based OME$_{3–5}$ with fossil diesel

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Comparison of MINLP formulations for global superstructure optimization

Demand response potential of industrial processes considering uncertain short-term electricity prices

Design of dynamically operated air separation units considering fatigue of equipment and ambient temperatures

Dynamic modeling of aqueous electrolyte systems in Modelica

Electrified production of ammonia as chemical product and energy storage

Global dynamic optimization with Hammerstein-Wiener models embedded

Guaranteed feasibility of optimal control problems with and without uncertainty

HybridML : Open source platform for hybrid modeling

Identifiability, observability and model-based control of nonlinear systems

Inline spectroscopy with model-based spectra evaluation for biorefinery unit operations

Markt- und Stromsystem, Managementsysteme und Technologien energieflexibler Fabriken

Normalizing flow-based scenario generation for energy system optimization

Optimal biorefinery processes using differential-algebraic equation systems with optimization criteria embedded