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Controlling a solar receiver with multiple thermochemical reactors for hydrogen production by an LSTM neural network based cascade controller

Oberkirsch, Laurin and Grobbel, Johannes and Maldonado Quinto, Daniel and Schwarzbözl, Peter and Hoffschmidt, Bernhard (2022) Controlling a solar receiver with multiple thermochemical reactors for hydrogen production by an LSTM neural network based cascade controller. Solar Energy, pp. 483-493. Elsevier. doi: 10.1016/j.solener.2022.08.007. ISSN 0038-092X.

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Official URL: https://www.sciencedirect.com/science/article/pii/S0038092X22005539

Abstract

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

Item URL in elib:https://elib.dlr.de/188792/
Document Type:Article
Title:Controlling a solar receiver with multiple thermochemical reactors for hydrogen production by an LSTM neural network based cascade controller
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Oberkirsch, LaurinUNSPECIFIEDhttps://orcid.org/0000-0001-7018-3664UNSPECIFIED
Grobbel, JohannesUNSPECIFIEDhttps://orcid.org/0000-0002-9942-5484UNSPECIFIED
Maldonado Quinto, DanielUNSPECIFIEDhttps://orcid.org/0000-0003-2929-8667UNSPECIFIED
Schwarzbözl, PeterUNSPECIFIEDhttps://orcid.org/0000-0001-9339-7884UNSPECIFIED
Hoffschmidt, BernhardUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:19 August 2022
Journal or Publication Title:Solar Energy
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.solener.2022.08.007
Page Range:pp. 483-493
Publisher:Elsevier
ISSN:0038-092X
Status:Published
Keywords:Concentrating solar power Solar tower Solar thermochemical water splitting Hydrogen generation Aim point optimization LSTM neural network
HGF - Research field:Energy
HGF - Program:Materials and Technologies for the Energy Transition
HGF - Program Themes:High-Temperature Thermal Technologies
DLR - Research area:Energy
DLR - Program:E SW - Solar and Wind Energy
DLR - Research theme (Project):E - Smart Operation, E - Solar Fuels
Location: Jülich , Köln-Porz
Institutes and Institutions:Institute of Solar Research > Solar Power Plant Technology
Institute of Future Fuels
Deposited By: Oberkirsch, Laurin
Deposited On:12 Oct 2022 09:53
Last Modified:27 Feb 2024 08:29

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