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Validation Methods for Energy Time Series Scenarios From Deep Generative Models

IEEE access, 2022, Vol.10, p.8194-8207

2022

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A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

Energy & fuels, 2015-09, Vol.29 (9), p.5781-5801

2015

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Model-Based Design of Tailor-Made Biofuels

Energy & fuels, 2016-02, Vol.30 (2), p.1109-1134

2016

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Model-Based Formulation of Biofuel Blends by Simultaneous Product and Pathway Design

Energy & fuels, 2017-04, Vol.31 (4), p.4096-4121

2017

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Principal component density estimation for scenario generation using normalizing flows

Data-Centric Engineering (Online), 2022-01, Vol.3, Article e7

2022

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Correction to Model-Based Formulation of Biofuel Blends by Simultaneous Product and Pathway Design

Energy & fuels, 2019-05, Vol.33 (5), p.4681-4681

2019

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Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points

Computers & chemical engineering, 2022-05, Vol.161, p.107745, Article 107745

2022

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Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows

Applied energy, 2023-09, Vol.346, p.121370, Article 121370

2023

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Simultaneous mixed‐integer dynamic scheduling of processes and their energy systems

AIChE journal, 2022-08, Vol.68 (8), p.n/a

2022

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Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Computers & chemical engineering, 2023-03, Vol.171, p.108153, Article 108153

2023

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Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators

Computers & chemical engineering, 2022-10, Vol.166, p.107923, Article 107923

2022

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Graph Neural Networks for Prediction of Fuel Ignition Quality

Energy & fuels, 2020-09, Vol.34 (9), p.11395-11407

2020

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Multivariate scenario generation of day-ahead electricity prices using normalizing flows

Applied energy, 2024-08, Vol.367, p.123241, Article 123241

2024

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Uncertainty Quantification in Integrated Fuel and Process Design

Energy & fuels, 2024-08, Vol.38 (15), p.14743-14756

2024

Volltextzugriff (PDF)

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

AIChE journal, 2022-11, Vol.68 (11), p.n/a

2022

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A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms

SIAM journal on scientific computing, 2024-01, Vol.46 (2), p.A719-A743

2024

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Physical pooling functions in graph neural networks for molecular property prediction

Computers & chemical engineering, 2023-04, Vol.172, p.108202, Article 108202

2023

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Integrated design of processes and products: Optimal renewable fuels

Computers & chemical engineering, 2020-03, Vol.134, p.106712, Article 106712

2020

Volltextzugriff (PDF)

Fuel Ignition Delay Maps for Molecularly Controlled Combustion

Energy & fuels, 2024-07, Vol.38 (14), p.13264-13277

2024

Volltextzugriff (PDF)

Tailor-made fuels for future engine concepts

International journal of engine research, 2016-01, Vol.17 (1), p.16-27

2016

Volltextzugriff (PDF)

The demand response potential in copper production

Journal of cleaner production, 2022-08, Vol.362, p.132221, Article 132221

2022

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

Computers & chemical engineering, 2024-06, Vol.185, p.108656, Article 108656

2024

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Demand response for flat nonlinear MIMO processes using dynamic ramping constraints

Computers & chemical engineering, 2023-04, Vol.172, p.108171, Article 108171

2023

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Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

SIAM journal on scientific computing, 2022-01, Vol.44 (4), p.A1911-A1933

2022

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Dynamic ramping for demand response of processes and energy systems based on exact linearization

Journal of process control, 2022-10, Vol.118, p.218-230

2022

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Validation Methods for Energy Time Series Scenarios From Deep Generative Models

A Novel Group Contribution Method for the Prediction of the Derived Cetane Number of Oxygenated Hydrocarbons

Model-Based Design of Tailor-Made Biofuels

Model-Based Formulation of Biofuel Blends by Simultaneous Product and Pathway Design

Principal component density estimation for scenario generation using normalizing flows

Correction to Model-Based Formulation of Biofuel Blends by Simultaneous Product and Pathway Design

Simultaneous optimization of design and operation of an air-cooled geothermal ORC under consideration of multiple operating points

Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows

Simultaneous mixed‐integer dynamic scheduling of processes and their energy systems

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids

Normalizing flow-based day-ahead wind power scenario generation for profitable and reliable delivery commitments by wind farm operators

Graph Neural Networks for Prediction of Fuel Ignition Quality

Multivariate scenario generation of day-ahead electricity prices using normalizing flows

Uncertainty Quantification in Integrated Fuel and Process Design

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

A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms

Physical pooling functions in graph neural networks for molecular property prediction

Integrated design of processes and products: Optimal renewable fuels

Fuel Ignition Delay Maps for Molecularly Controlled Combustion

Tailor-made fuels for future engine concepts

The demand response potential in copper production

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

Demand response for flat nonlinear MIMO processes using dynamic ramping constraints

Personalized Algorithm Generation: A Case Study in Learning ODE Integrators

Dynamic ramping for demand response of processes and energy systems based on exact linearization

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