Zobrazeno 1 - 10
of 7 104
pro vyhledávání: '"Mitsos, A"'
Autor:
Yingwei Yuan, Kamil A. Khan
Publikováno v:
Digital Chemical Engineering, Vol 7, Iss , Pp 100097- (2023)
With increasing digitalization and vertical integration of chemical process systems, nonconvex optimization problems often emerge in chemical engineering applications, yet require specialized optimization techniques. Typical global optimization metho
Externí odkaz:
https://doaj.org/article/63221708ed6e425f85116300b4176a4f
Motivated by the increasing need to hedge against load and generation uncertainty in the operation of power grids, we propose flexibility maximization during operation. We consider flexibility explicitly as the amount of uncertainty that can be handl
Externí odkaz:
http://arxiv.org/abs/2411.18178
Time-series information needs to be incorporated into energy system optimization to account for the uncertainty of renewable energy sources. Typically, time-series aggregation methods are used to reduce historical data to a few representative scenari
Externí odkaz:
http://arxiv.org/abs/2411.14320
Autor:
Brozos, Christoforos, Rittig, Jan G., Akanny, Elie, Bhattacharya, Sandip, Kohlmann, Christina, Mitsos, Alexander
Surfactants are key ingredients in foaming and cleansing products across various industries such as personal and home care, industrial cleaning, and more, with the critical micelle concentration (CMC) being of major interest. Predictive models for CM
Externí odkaz:
http://arxiv.org/abs/2411.02224
Autor:
Pirnay, Jonathan, Rittig, Jan G., Wolf, Alexander B., Grohe, Martin, Burger, Jakob, Mitsos, Alexander, Grimm, Dominik G.
Generative deep learning has become pivotal in molecular design for drug discovery and materials science. A widely used paradigm is to pretrain neural networks on string representations of molecules and fine-tune them using reinforcement learning on
Externí odkaz:
http://arxiv.org/abs/2411.01667
Autor:
Rittig, Jan G., Mitsos, Alexander
We propose excess Gibbs free energy graph neural networks (GE-GNNs) for predicting composition-dependent activity coefficients of binary mixtures. The GE-GNN architecture ensures thermodynamic consistency by predicting the molar excess Gibbs free ene
Externí odkaz:
http://arxiv.org/abs/2407.18372
Autor:
Velioglu, Mehmet, Zhai, Song, Rupprecht, Sophia, Mitsos, Alexander, Jupke, Andreas, Dahmen, Manuel
In chemical engineering, process data are expensive to acquire, and complex phenomena are difficult to fully model. We explore the use of physics-informed neural networks (PINNs) for modeling dynamic processes with incomplete mechanistic semi-explici
Externí odkaz:
http://arxiv.org/abs/2406.01528
We propose a method to construct representative price profiles of the day-ahead (DA) and the intraday (ID) electricity spot markets and use this method to provide examples of ready-to-use price data sets. In contrast to common scenario generation app
Externí odkaz:
http://arxiv.org/abs/2405.14403
We present a method for end-to-end learning of Koopman surrogate models for optimal performance in a specific control task. In contrast to previous contributions that employ standard reinforcement learning (RL) algorithms, we use a training algorithm
Externí odkaz:
http://arxiv.org/abs/2403.14425