摘要
提出了一种基于人工神经网络模型预测高含CO_2天然气的含水量的新方法。网络输入变量CO_2摩尔分数、温度、压力,网络的输出为高含CO_2天然气的含水量。该人工神经网络模型能够估算温度在20.0～200.0℃,压力在0.1～70.0 MPa,CO_2摩尔分数高达70%天然气中水蒸汽的含量。对比文中建立的人工神经网络模型和目前常用的3种预测高含CO_2天然气的含水量的经验模型,结果表明,人工神经网络的平均相对误差值最小,为1.275%,3种经验模型在CO_2含量较高时,预测精度较低。这就表明,人工神经网络模型在预测高含CO_2天然气含水量时,比3种常用的经验模型更具有优势。
In this paper, a new method based on artificial neural network （ANN）for prediction of natural gas mixture water content is presented. COg mole fraction, temperature, and pressure have been input variables of the network and water content has been set as network output. The proposed ANN model is able to estimate water content as a function of CO2 composition up to 70%, temperature between 20.0-200.0 ℃ and pressure from 0.1 to 70.0 MPa. Comparisons show average absolute relative error equal to 1.275%between ANN estimations and experimental data, which is smaller than the other three commonly used empirical correlations. Furthermore, there is considerable deviation between experimental data and the other three commonly used empirical correlations for prediction of high CO2 content natural gas water content. But artificial neural network has good prediction results in high CO2 content natural gas. Results show ANN superiority to the common three correlations in literatures.
出处
《西南石油大学学报（自然科学版）》
CAS
CSCD
北大核心
2013年第4期121-125,共5页
Journal of Southwest Petroleum University(Science & Technology Edition)
基金
国家自然科学基金"废弃气藏CO_2地质封存机制及运移规律研究"(51274173)
关键词
神经网络
高含CO_2
天然气
含水量
artificial neural network
high C02 content
natural gas
water content
