喇曼光谱仪测定芳烃装置中各组分含量的研究

为了提高在线喇曼光谱仪在芳烃装置的组分检测中的实时性和精度,采用偏最小二乘法(PLS)结合粒子群算法(PSO)建立了预测模型。对一定的芳烃样品进行试验。先通过光谱仪获得芳烃成分的喇曼光谱,再运用PLS算法对喇曼数据进行主因子提取,从而降低数据间的冗余性,然后应用PSO算法对芳烃组分含量进行快速搜索,找到最优解,最后将样品的真实值与预测值进行相关性分析。结果表明,与传统方法相比,喇曼光谱结合PSO算法和PLS算法的模型具有精确度高、分析速度快的特点。该研究为芳烃装置中组分的检测提供了新方法。

Detection of components of aromatics hydrocarbons unit based on Raman spectrometer

In order to enhance the real-time and improve the accuracy of on-line Raman spectrometer during the testing of composition of aromatic hydrocarbon unit, the prediction model was created based on partial least square (PLS) algorithm and particle swarm optimization (PSO) algorithm. Some samples of aromatic hydrocarbons were tested. Firstly, Raman spectroscopy of aromatic composition was gotten by spectroscopy. Then, the main factors of Raman data were extracted by means of PLS algorithm in order to reduce the redundancy between data. The quick search of composition content of aromatics hydrocarbons were made by PSO algorithm to find the optimal solution. Finally, the correlation of actual values and predictive values of samples was analyzed. The results show that, compared with the old method, the new created model (Raman spectrum with PSO and PLS) has high precision and quick analysis speed. It provides a new method for detection of components of aromatic hydrocarbons unit.