摘要
油水混合物的光谱分析方法已经成为当前油水两相流测量的研究热点。然而,传统的油水混合物光谱分析中,一般是通过主成分分析、连续投影算法等降维技术实现其光谱特征提取,所提取光谱数大多在10条以上,这使得油水两相测量光纤式传感器的制造成本很高且工程实现难度很大。为提高基于光谱分析的油水两相测量光纤式传感器的实用性,需要实现油水两相红外光谱的超稀疏表示。为此提出了油水混合物光谱自-互相关联合(self-cross correlation,SCC)的光谱超稀疏表示方法。为了验证方法的有效性,搭建了油水混合物红外光谱含水率测量实验装置,从SCC算法筛选出的6个波段中,根据实际生产工艺选择了1 050 nm和1 650 nm波段进行动态实验,实验结果表明,1 050 nm和1 650 nm波段对油和水的混合流型响应良好,且两者呈现出了显著的互相关性。显然,本文研究有助于提升工业光纤式传感器的使用性能。
The spectral analysis method of oil-water mixture has become a research hotspot of current oil-water two-phase flow measurement.However,in traditional oil-water mixture spectral analysis,the spectral feature extraction is generally achieved with the dimensionality reduction techniques,such as principal component analysis,successive projection algorithm and etc.The number of extracted spectra is mostly more than 10,which makes the oil-water two phase measurement fiber optic sensor expensive to manufacture and difficult to implement.In order to improve the practicability of the oil-water two-phase measurement fiber-optic sensor based on spectral analysis,it is necessary to realize the ultra-sparse representation of the oil-water two-phase infrared spectrum.In order to achieve this goal,a spectral ultra-sparse representation method with oil-water mixture spectral self-cross correlation(SCC)is proposed.In order to verify the effectiveness of the method,an experiment device for measuring the water content of oil-water mixture using infrared spectrum technique was established.From the 6 bands selected with the SCC algorithm,the bands of 1 050 nm and 1 650 nm were selected according to actual production process,and the dynamic experiments were carried out.The experiment results show that bands of1 050 nm and 1 650 nm respond well to the mixed flow pattern of oil and water,and the two bands exhibit significant cross-correlation.Obviously,this study will help improve the service performance of industrial fiber-optic sensors.
作者
韩建
李雨昭
曹志民
刘强
牟海维
Han Jian;Li Yuzhao;Cao Zhimin;Liu Qiang;Mu Haiwei(College of Electronics Science,Northeast Petroleum University,Daqing 163318,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2019年第6期78-85,共8页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51574087)项目资助
关键词
近红外光谱学
原油含水率
油水二相流
稀疏表示
near infrared spectroscopy
crude oil water content
oil-water two-phase flow
sparse representation
