| 光谱学与光谱分析 |
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| Study on Refined Oil Identification and Measurement Based on the Extension Neural Network Pattern Recognition |
| ZHANG Li-guo1,3, CHEN Zhi-kun1, 2, WANG Li1*, CAO Li-fang1, YAN Bing1, WANG Yu-tian1 |
1. Measurement Technology and Instrumentation Key Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China
2. Electrical Engineering College, North China University of Science and Technology, Tangshan 063009, China
3. Hebei Automation Research Institute, Shijiazhuang 050081，China |
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Abstract There are four major problems related to fuel consumption, “large consumption”, “low quality”, “lack of front-end clean” and “lack of end emission control”, which needs to address urgently for our country. More than 60 percent of the air pollution is due to the burning of coal and oil in our country, so the haze problem depends on how much we can deal with energy issues. We should achieve the identification and measurement of gasoline, diesel, kerosene and other refined oil products rapidly and accurately, which is important for the implementation of air pollution monitoring and controlling. in order to characterize the type information of the refined oil accurately and to improve the efficiency of the network model identification, it is effective to use principal component analysis method which could achieve the data dimension reductionwhile reducing the complexity of the problem. With principal component analysis of the most commonly used three-dimensional fluorescence spectra based on excitation-emission matrix (Excitation-Emission Matrix, EEM) data, we could obtain finer, deeper characteristic parameters. During the process of classification, it could avoid the “over-fitting” phenomenon because of the application of the cross-validation method, A neural network capable of both qualitative and quantitative analysis is designed. The neural network pattern recognition result becomes feedback to the input of the concentration network, together with the relative slope, the comprehensive background parameters, and the relative fluorescence intensity, we could achieve the measurement of the concentration of the corresponding types, then use the extension neural network pattern recognition technology to achieve identification and measurement of kerosene, diesel, gasoline and other refined oil products. The results of the study show that the average recognition rate reaches 0.99, the average recovery rate of concentration reaches 0.95, the average time of pattern recognition is 2.5 seconds and this time is 48.5% of the time used by PARAFAC model analysis method. The method significantly improves the operation speed with ideal application effect . It should be pointed out that, in order to ensure the accuracy and precision of the analysis, we should make corresponding calibration samples for specific analytes in terms of the analysis of complex mixtures such as refined oil, pesticides, tea, etc.
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Received: 2015-04-17
Accepted: 2015-08-16
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Corresponding Authors:
WANG Li
E-mail: lydia_smile@163.com
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[1] Lü Gui-cai, ZHAO Wei-hong, WANG Jiang-tao(吕桂才, 赵卫红, 王江涛). Chinese Journal of Analytical Chemistry(分析化学), 2010, 38(8): 1144.
[2] ZHAO You-quan, LU Xue-feng, LIANG Ying, et al(赵友全, 路雪峰, 梁 瑛, 等). Chinese Journal of Scientific Instrument(仪器仪表学报), 2012, 23(6): 1275.
[3] ZHOU Yu, QIAN Xu, ZHANG Jun-cai, et al(周 玉, 钱 旭, 张俊彩, 等). Application Research of Computers(计算机应用研究), 2010, 27(1): 1.
[4] QIAO Jun-fei, HAN Hong-gui(乔俊飞, 韩红桂). Acta Automatica Sinica(自动化学报), 2010, 36(6): 865.
[5] WANG Yan-ni, FAN Yang-yu(王燕妮, 樊养余). Computer Engineering and Applications(计算机工程与应用), 2010, 46(17): 23.
[6] YANG Guo-wei, WANG Shou-jue(杨国为, 王守觉). Journal of Harbin Institute of Technology(哈尔滨工业大学学报), 2006, 38(7): 1129.
[7] Mas S, Anna de Juan, Tauler R. Talanta, 2010, 80(3): 1052.
[8] Wang C, Li W, Luan X. Talanta, 2010, 81(1-2): 684.
[9] LIU Ling-ling, WU Yan-wen, ZHANG Xu, et al(刘玲玲, 武彦文, 张 旭, 等). Acta Chimica Sinica(化学学报), 2012, 70(8): 995.
[10] Valderrama P, Marco P H, Locquet N, et al. Chemometrics and Intelligent Laboratory Systems, 2011, (106): 166.
[11] YANG Li-li, WANG Yu-tian, LU Xin-qiong(杨丽丽, 王玉田, 鲁信琼). Chinese Journal of Lasers(中国激光), 2013, 40(6): 0615002. |
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