Oberkirsch, Laurin und Grobbel, Johannes und Maldonado Quinto, Daniel und Schwarzbözl, Peter und Hoffschmidt, Bernhard (2022) Controlling a solar receiver with multiple thermochemical reactors for hydrogen production by an LSTM neural network based cascade controller. Solar Energy, Seiten 483-493. Elsevier. doi: 10.1016/j.solener.2022.08.007. ISSN 0038-092X.
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0038092X22005539
Kurzfassung
Solar reactors for batch processes have a varying heat demand over time. The receiver of large-scale solar chemical plants consists of several of these reactors to quasi-continuously use the solar resource. For this, the heliostat field needs to provide a non-uniform time-varying flux density distribution. As this differs from the heliostat field control of a typical CSP plant, it challenges its control. Here, we propose a cascade controller for a coupled system of heliostat field and receiver consisting of 19 thermochemical batch reactors for hydrogen generation. In the receiver controller, a fast long short-term memory (LSTM) neural network model predicts the future temperatures and the ceria reduction extends in the reactors as a function of the flux density. It is trained with data from a thermochemical reactor model. The trained LSTM neural network is embedded in a hybrid automaton to model the continuous batch process states. Discrete state changes are made when certain conditions are reached. In this way, the receiver controller determines flux density setpoints leading to high efficiencies for each reactor. The heliostat field controller applies aim point optimization to provide flux densities close to these setpoints. With this cascade control, reactor array efficiencies of 79% are obtained simulatively. Moreover, the individual reactors operate within their material limits and come close to their optimal efficiency. In addition, it is found that secondary concentrators complicate the control and decrease the reactor array efficiency. However, they still increase the overall efficiency by 51.3% due to a significantly higher optical efficiency
elib-URL des Eintrags: | https://elib.dlr.de/188792/ | ||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||
Titel: | Controlling a solar receiver with multiple thermochemical reactors for hydrogen production by an LSTM neural network based cascade controller | ||||||||||||||||||||||||
Autoren: |
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Datum: | 19 August 2022 | ||||||||||||||||||||||||
Erschienen in: | Solar Energy | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||
DOI: | 10.1016/j.solener.2022.08.007 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 483-493 | ||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||
ISSN: | 0038-092X | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | Concentrating solar power Solar tower Solar thermochemical water splitting Hydrogen generation Aim point optimization LSTM neural network | ||||||||||||||||||||||||
HGF - Forschungsbereich: | Energie | ||||||||||||||||||||||||
HGF - Programm: | Materialien und Technologien für die Energiewende | ||||||||||||||||||||||||
HGF - Programmthema: | Thermische Hochtemperaturtechnologien | ||||||||||||||||||||||||
DLR - Schwerpunkt: | Energie | ||||||||||||||||||||||||
DLR - Forschungsgebiet: | E SW - Solar- und Windenergie | ||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | E - Intelligenter Betrieb, E - Solare Brennstoffe | ||||||||||||||||||||||||
Standort: | Jülich , Köln-Porz | ||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Solarforschung > Solare Kraftwerktechnik Institut für Future Fuels | ||||||||||||||||||||||||
Hinterlegt von: | Oberkirsch, Laurin | ||||||||||||||||||||||||
Hinterlegt am: | 12 Okt 2022 09:53 | ||||||||||||||||||||||||
Letzte Änderung: | 27 Feb 2024 08:29 |
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