Application of Phase Change Material and Artificial Neural Networks for Smoothing of Heat Flux Fluctuations

Tomasz Tietze, Piotr Szulc, Daniel Smykowski, Andrzej Sitka and Romuald Redzicki
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Tomasz Tietze: Department of Energy Conversion Engineering, Mechanical and Power Engineering Faculty, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Piotr Szulc: Department of Energy Conversion Engineering, Mechanical and Power Engineering Faculty, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Daniel Smykowski: Department of Energy Conversion Engineering, Mechanical and Power Engineering Faculty, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Andrzej Sitka: Department of Energy Conversion Engineering, Mechanical and Power Engineering Faculty, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Romuald Redzicki: Department of Energy Conversion Engineering, Mechanical and Power Engineering Faculty, Wrocław University of Science and Technology, 50-370 Wrocław, Poland

Energies, 2021, vol. 14, issue 12, 1-17

Abstract: The paper presents an innovative method for smoothing fluctuations of heat flux, using the thermal energy storage unit (TES Unit) with phase change material and Artificial Neural Networks (ANN) control. The research was carried out on a pilot large-scale installation, of which the main component was the TES Unit with a heat capacity of 500 MJ. The main challenge was to smooth the heat flux fluctuations, resulting from variable heat source operation. For this purpose, a molten salt phase change material was used, for which melting occurs at nearly constant temperature. To enhance the smoothing effect, a classical control system based on PID controllers was supported by ANN. The TES Unit was supplied with steam at a constant temperature and variable mass flow rate, while a discharging side was cooled with water at constant mass flow rate. It was indicated that the operation of the TES Unit in the phase change temperature range allows to smooth the heat flux fluctuations by 56%. The tests have also shown that the application of artificial neural networks increases the smoothing effect by 84%.

Keywords: phase change material (PCM); molten salt; artificial neural networks; pilot installation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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