| Issue |
E3S Web Conf.
Volume 221, 2020
Energy Systems Environmental Impacts (ESEI 2020)
|
|
|---|---|---|
| Article Number | 01003 | |
| Number of page(s) | 6 | |
| Section | Influence of Engines on the Environment | |
| DOI | https://doi.org/10.1051/e3sconf/202022101003 | |
| Published online | 17 December 2020 | |
Optimization of combined production of electricity and hydrogen at nuclear power plants using a neural network
1
Peter the Great St.Petersburg Polytechnic University, 195251 St. Petersburg, Russia
2
National Technology Initiative Center for Advanced Manufacturing Technologies based on the Institute of Advanced Manufacturing Technologies of Peter the Great St. Petersburg Polytechnic University Polytechnicheskaya, 29, St.Petersburg, 195251, Russia
* Corresponding author: novikova-olga1970@yandex.ru
This article discusses a project with a basis on implementation of combined production of electricity and hydrogen based on a HTGR reactor in the Primorsky Krai of Russia. One of the major advantages of the fourth-generation reactors of the HTGR type is, that water vapor reaches 800 degrees Celsius, which allows not only to efficiently transfer thermal energy to external circuits, but also to use it in the production of hydrogen using the steam reforming of methane [1]. The results of the research were composed mainly of two fully-calculated investment projects, which showed an significant increase in the economic efficiency of combined production of electricity and hydrogen when included in the neural network planning system. Moreover, further technological advancement in developing this method of forecasting could prove highly beneficial in implementing a higher percentage of renewable energy sourced power plants into energy industry[2].
Key words: hydrogen / atomic power plant / artificial neural network / sustainable development / energy engineering
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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