基于大数据方法的持液率预测模型
Liquid holdup prediction model based on big data approach
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- 引用格式:
-
郑琳,刘云.基于大数据方法的持液率预测模型[J].天然气与石油,2021,39(4):20-25.doi:10.3969/j.issn.1006-5539.2021.04.004
ZHENG Lin, LIU Yun.Liquid holdup prediction model based on big data approach[J].Natural Gas and Oil,2021,39(4):20-25.doi:10.3969/j.issn.1006-5539.2021.04.004
- DOI:
- 10.3969/j.issn.1006-5539.2021.04.004
- 作者:
- 郑琳 刘云
ZHENG Lin, LIU Yun
- 作者单位:
- 长江大学石油工程学院, 湖北 武汉 430100
Petroleum Engineering College, Yangtze University, Wuhan, Hubei, 430100, China
- 关键词:
- 气液两相流;持液率;BP神经网络;灰色关联熵;遗传算法
Gas-liquid two-phase flow; Liquid holdup; BP neural network; Grey Relational Entropy; Genetic Algorithm
- 摘要:
- 为解决气液两相流持液率的问题,通过软件编程建立了一种基于大数据方法的新遗传算法优化经灰色关联熵加权的神经网络模型(Genetic Algorithm Optimizes Neural Network Model Weighted by Grey Relational Entropy,GA-GRE-BP)，并选取基本的BP神经网络模型和传统模型分别对持液率进行了预测,用于新模型的准确性和可行性分析。研究结果表明:GA-GRE-BP神经网络模型相较于基本的BP神经网络模型,不仅收敛速度更快,而且预测精度有显著提升;相较于传统模型,其应用范围更加广泛且应用简便。由此可见，GA-GRE-BP神经网络模型用于预测气液两相流持液率是准确可行的。
In order to solve the issue of liquid holdup of gas-liquid two-phase flow in a pipeline, a new genetic algorithm based on big data approach was established through software programming to optimize the neural network model weighted by Grey Relational Entropy (Genetic Algorithm Optimizes Neural Network Model Weighted by Grey Relational Entropy, GA-GRE-BP). The basic BP neural network model and the traditional model were selected to predict the liquid holdup respectively and used for analysing the accuracy and feasibility of the new model. The results show that compared with the basic BP neural network model, the new one not only converges faster, but also has a significant improvement in prediction accuracy. When compared with the traditional model, the GA-GRE-BP neural network model has a wider range of applications and is simple to apply. This shows that the GA-GRE-BP neural network model is accurate and feasible for predicting the liquid holdup of gas-liquid two-phase flow.
