中红外光谱对甲醇汽油甲醇含量检测研究

doi:10.3964/j.issn.1000-0593(2019)02-0459-06

Abstract
References
Related Citation (15)

Download: PDF (3001 KB)
Export: BibTeX | EndNote (RIS)

Abstract Methanol gasoline is a new type of environmentally friendly diesel fuel, and the performance and quality of methanol-gasoline are based on the diesel methanol content. In this work, mid-infrared spectroscopy was successfully used to evaluate the methanol contents in methanol-gasoline samples with the aid of chemometric approaches. First, the mid-infrared spectral data obtained were pre-processed by smoothing standard normal, multiple scatter correction (MSC), baseline correction and normalization, and the partial least-square (PLS) quantitative calibration models were established, and the best pre-processed method was found. It was found that the PLS model pre-processed by MSC was much better than others, and the r and RMSEP evaluated were 0.918 and 2.107, respectively. In order to simplify the model and improve the prediction accuracy, uninformative variable elimination (UVE) was used to select the optimal wavelengths, and the experimental results showed that the prediction ability was greatly improved. Different quantitative calibration models by UVE selected wavelength, such as partial least-square (PLS), principal component regression (PCR) and least square-support vector machine (LS-SVM) for measuring methanol content were established and their prediction results were compared. It was found that the UVE-PLS model was much better than others, and the r and RMSEP evaluated were 0.923 and 2.075. It suggested that infrared spectroscopy in the detection of methanol content in the methanol gasoline is feasible and can bring good prediction results. UVE is an effective method for methanol gasoline in the infrared spectrum of band selection method, which is significant for the development of oil chemical industry.

Key words：Mid-infrared spectroscopy; Ethanol gasoline; UVE; LS-SVM

Received: 2016-12-23 Accepted: 2017-05-04

ZTFLH:

O657.6

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)02-0459-06 OR https://www.gpxygpfx.com/EN/Y2019/V39/I02/459

[1] LI Yan-ru(李雁如). Shanxi Chemical Industry(山西化工), 2014, 4: 38.
[2] JIN Pan-pan, ZHANG Teng, WANG Min, et al(金盼盼, 张腾, 王珉，等). Energy and Energy Conservation(能源与节能), 2012, 5: 3.
[3] OUYANG Ai-guo, LIU Jun, WANG Ya-ping(欧阳爱国, 刘军, 王亚平). Laser & Infrared(激光与红外), 2012, 8: 901.
[4] YAO Jie, DAI Lian-kui, LIN Yi-ling(姚捷, 戴连奎, 林艺玲). Chinese Journal of Light Scaltering(光散射学报), 2013, 1: 59.
[5] LI Yan-ru, SUN Rui-qing, WANG Yong-miao(李雁如, 孙瑞卿, 王永苗). Modern Chemical Industry(现代化工), 2013, 2: 113.
[6] DING Hong-yuan, HUANG Rong-hua, WANG Zhao-wen, et al(丁红元, 黄荣华, 王兆文，等). Journal of Huazhong University of Science and Technology·Natural Science Edition(华中科技大学学报·自然科学版), 2012, 6: 113.
[7] JIN Pan-pan, ZHANG Teng, YANG Xiao-ping, et al(金盼盼, 张腾, 杨晓平, 等). Auto Sci-Tech(汽车科技), 2012, 5: 66.
[8] LIU Yan-de, WU Ming-ming, SUN Xu-dong, et al(刘燕德, 吴明明, 孙旭东, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 6: 289.
[9] ZU Qin, ZHANG Shui-fa, CAO Yang, et al(祖琴, 张水发, 曹阳, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2015, 35(2): 479.
[10] HAN Zhong-zhi, WAN Jian-hua, LIU Kang-wei(韩仲志, 万剑华, 刘康炜). Chinese Journal of Analysis Laboratory(分析试验室), 2015, 11: 1268.
[11] OUYANG Ai-guo, TANG Tian-yi, LIU Yan-de, et al(欧阳爱国, 唐天义, 刘燕德，等). Chinese Journal of Luminescence(发光学报), 2016, 10: 1253.
[12] ZHANG Rui-hua(张瑞华). Journal of Science and Technology Information(科技信息), 2014, (15): 298.
[13] MAO Jia-wei, ZOU Yong, ZENG Ling-xi, et al(毛佳伟, 邹勇, 曾令羲, 等). Shandong Chemical Industry(山东化工)，2015, (3)：85.
[14] OUYANG Ai-guo, LIU Jun(欧阳爱国, 刘军). Journal of Southwest China Normal University·Natural Science Edition(西南师范大学学报·自然科学版), 2012, 9: 98.
[15] YAO Jie, DAI Lian-kui, LIN Yi-ling(姚捷, 戴连奎, 林艺玲). Chinese Journal of Light Scattering(光散射学报), 2013, 1: 59.