Prediction of hydrate production in compressive cold storage system based on grey relational BP neural network

被引：0

作者：

Yang W. ^{[1
]}

Xie Y. ^{[1
]}

Yan K. ^{[1
]}

Zou J. ^{[1
]}

Shu S. ^{[1
]}

机构：

[1] School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai

来源：

Huagong Jinzhan/Chemical Industry and Engineering Progress | 2021年 / 40卷 / 02期

关键词：

Carbon dioxide; Hydrate; Neural networks; Prediction;

D O I：

10.16085/j.issn.1000-6613.2020-0600

中图分类号：

学科分类号：

摘要：

As a new cold storage medium, CO2 hydrate has a good application prospect. In the cold storage system, the amount of CO2 hydrate generation has a direct impact on the amount of cold storage, but the calculation of the amount of CO2 hydrate generation is complicated, which leads to the calculation of the amount of cold storage in the system is also complicated. Therefore, it is of practical significance to establish a model that can quickly analyze and predict the amount of hydrate production in the system. In this paper, BP neural network model (BP) and grey relational prediction model [GRM(1, n)] which can solve complex problems were introduced, and GRM(1, n)-BP neural network combination model was established by Matlab programming language to predict hydrate production. Three models were selected to predict the data of the experimental system, and the results of the three models were compared with the experimental results. The results showed that the GRM(1, n)-BP neural network combination model has better accuracy and stability. Finally, the accuracy of the GRM(1, n)-BP neural network combination model by investigating the influence of the single variable of charging pressure on hydrate production and comparing the predicted results of the model was further verified. © 2021, Chemical Industry Press Co., Ltd. All right reserved.

引用

页码：664 / 670

页数：6

共 30 条

[21]

HUANG Hongyun, LIU Weixiao, DING Zuohua, Sales forecasting based on multi-dimensional grey model and neural network, Journal of Software, 30, 4, pp. 1031-1044, (2019)

[22]

YIN Kedong, GENG Yan, LI Xuemei, Improved grey prediction model based on exponential grey action quantity, Journal of Systems Engineering and Electronics, 29, 3, pp. 560-570, (2018)

[23]

ZHAO Biao, YU Tianyu, DING Wenfen, Et al., BP neural network based flexural strength prediction of open-porous Cu-Sn-Ti composites, Progress in Natural Science: Materials International, 28, 3, pp. 315-324, (2018)

[24]

ZHAO Guodong, ZHANG Yuewei, SHI Yiqi, Et al., The application of BP neural networks to analysis the national vulnerability, Computers Materials & Continua, 58, 2, pp. 421-436, (2019)

[25]

WANG Shu-tao, CHEN Dong-ying, WANG Xing-long, Et al., A new method for the determination of potassium sorbate combining fluorescence spectra method with PSO-BP neural network, Spectroscopy and Spectral Analysis, 35, 12, pp. 3549-3554, (2015)

[26]

LIU Zhenpeng, HE Yupeng, WANG Wensheng, Et al., DDoS attack detection scheme based on entropy and PSO-BP neural network in SDN, China Communications, 16, 7, pp. 144-155, (2019)

[27]

WANG Zhifang, WANG Shutao, WANG Guichuang, Application of particle swarm optimization BP neural network in methane detection, Acta Photonica Sinica, 48, 4, pp. 147-154, (2019)

[28]

VISCONDI G F, ALVES-SOUZA S N., A Systematic literature review on big data for solar photovoltaic electricity generation forecasting, Sustainable Energy Technologies and Assessments, 31, pp. 54-63, (2019)

[29]

FAN Shenglong, QIU Lingjing, RU Kaili, Et al., Classification method of agricultural land quality based on back-propagation neural network and support vector machine, Journal of China Agricultural University, 23, 12, pp. 138-148, (2018)

[30]

WU Qiang, ZHANG Jiahao, GAO Xia, Et al., Effect of thermodynamics promoters on phase equilibrium of mine gas hydrate, Journal of Heilongjiang University of Science and Technology, 26, 3, pp. 235-239, (2016)

← 1 2 3 →