Thermophysical properties of KCl-NaF reciprocal eutectic by artificial neural network prediction and experimental measurements
文献类型：期刊论文
| 作者 | Wang, Y; Ling, CJ; Yin, HQ; Liu, WH; Tang, ZF; Li, Z |
| 刊名 | SOLAR ENERGY
 |
| 出版日期 | 2020 |
| 卷号 | 204页码:667-672 |
| 关键词 | THERMAL-ENERGY STORAGE PHASE-CHANGE MATERIALS THERMODYNAMIC EVALUATION CARBONATE SALT SYSTEM OPTIMIZATION PERFORMANCE DIAGRAMS CHLORIDE ALLOY |
| ISSN号 | 0038-092X |
| DOI | 10.1016/j.solener.2020.05.029 |
| 文献子类 | 期刊论文 |
| 英文摘要 | Fluoride and chloride reciprocal salts are potential novel media with suitable working temperature and high latent heat for next-generation solar power. A back propagation (BP) artificial neural network (ANN) algorithm was developed based on the known data of salts. The composition and melting point of two unknown binary fluoride and chloride reciprocal salts were predicted by the trained ANN model. The predicted composition and melting point of the reciprocal salts were verified by experimental tests. The predicted results of composition are in good agreement with the experimental values, and the predicted errors of the melting point are less than 1.5%. The melting point and fusion enthalpy of KCl-NaF reciprocal eutectic salt are 648 +/- 2 degrees C and 365 +/- 5 J/g, respectively. The thermal stability of this reciprocal eutectic salt is very good and the weight loss is still less than 3.0% even up to 800 degrees C. The good performance of KCl-NaF reciprocal eutectic salt at high temperatures suggest that it can be a good candidate for thermal energy storage systems with supercritical CO2 cycles. The ANN is an effective method to prediction composition and properties of molten salts, this method is expected to a quick method for design and selection of phase change material for the high temperature latent heat energy storage systems. |
| 语种 | 英语 |
| 源URL | [http://ir.sinap.ac.cn/handle/331007/33211]  |
| 专题 | 上海应用物理研究所_中科院上海应用物理研究所2011-2017年 |
| 作者单位 | 1.Dalian Natl Lab Clean Energy, Dalian 116023, Peoples R China 2.Chinese Acad Sci, Shanghai Inst Appl Phys, Shanghai 201800, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Key Lab Interfacial Phys & Technol, Shanghai 201800, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Y,Ling, CJ,Yin, HQ,et al. Thermophysical properties of KCl-NaF reciprocal eutectic by artificial neural network prediction and experimental measurements[J]. SOLAR ENERGY,2020,204:667-672. |
| APA | Wang, Y,Ling, CJ,Yin, HQ,Liu, WH,Tang, ZF,&Li, Z.(2020).Thermophysical properties of KCl-NaF reciprocal eutectic by artificial neural network prediction and experimental measurements.SOLAR ENERGY,204,667-672. |
| MLA | Wang, Y,et al."Thermophysical properties of KCl-NaF reciprocal eutectic by artificial neural network prediction and experimental measurements".SOLAR ENERGY 204(2020):667-672. |
入库方式： OAI收割
来源：上海应用物理研究所
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