摘要
研究石脑油的烃组成预测问题。针对石脑油组分极其复杂和冗余特点,传统的检测方法求解方法复杂、测定时间过长等难题,为了提高准确度,提出一种基于在线拉曼光谱技术结合主成分粒子群算法的预测方法。首先获得石脑油样品的拉曼谱图,利用主成分分析对数据进行降维处理,消除数据间的冗余信息,然后采用粒子群算法对主成分分析后的数据进行预测,得到各个组成成分在石脑油中的质量分数,并将样品预测值与真实值的相关性进行分析。实验结果表明:相对于其他预测方法,该方法准确性高,现场适应性强,测定时间短,是一种高效、实时性好的石脑油组成预测方法。
Prediction of hydrocarbon composition of naphtha is researched. Aiming at the features of complex and redundant of the composition of naphtha, and the problem of complex and long time of traditional method, a predicting method of principal component analysis （PCA） and particle swarm optimization （ PS0 ） is proposed based on online Raman spectroscopy to improve the accuracy of the prediction. First, the dimension of data is reduced using the PCA to eliminate the redundant information after obtaining the Raman spectrum of naphtha sample. Then using the particle swarm algorithm to predict data after analyzed by PCA, and then get the mass fraction of various components and analyze the correlation of the predictive value and the true value of the sample. Experimental results show that compared to other predicting methods, the proposed method is more accurate, more efficient and more adaptable to the site. It is an efficient prediction method with high real-time performance.
出处
《传感器与微系统》
CSCD
北大核心
2012年第12期69-72,75,共5页
Transducer and Microsystem Technologies
基金
国家"863"计划资助项目(2009AA04Z161)
关键词
在线拉曼光谱仪
主成分分析
粒子群优化算法
石脑油
online Raman spectrometer
principal component analysis （PCA）
particle swarm optimization（PSO） algorithm
naphtha
