基于温度修正模型的柴油凝点快速检测方法

doi:10.3964/j.issn.1000-0593(2021)10-3111-06

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Abstract Portable near-infrared (NIR) spectrometer for quick on-site measurement is an important trend in the study field of NIR spectroscopy. However, in order to achieve quick measurement, a portable NIR spectrometer is generally not equipped with temperature-controlled device. Therefore, the change in ambient temperature will bring a relatively large measurement error to the predicted results. Reducing the error caused by the changes in ambient temperature is an important problem that has to be solved before the large-scale application of portable near-infrared spectrometer in the field of quick on-site measurement. The condensation points of diesel is an important parameter to evaluate diesel quality and temperature range for diesel application. Development of the on-site quick measurement of condensation point can effectively reduce the cost of traditional measurement. In the present study, the NIR spectra are collected by a portable spectrometer in the wavelength range of 950~1 650 nm for 50 kinds of diesel samples with different condensation points. The effect of changes in ambient temperature on the quantitative analysis results is studied using one new type of NIR spectrometer. This type of spectrometer is a portable spectrometer designed based on a digital micromirror device(DMD), which is developed for quick on-site measurement without temperature-controlled device sample cell. Firstly, the predicting model is developed for condensation point under the condition of ambient temperature at T₀=25 ℃, using based on the partial least square method. Then, the spectra measured under other ambient temperatures (T_E=-10, 0, 10, 20, 30, 40 and 50 ℃) are introduced into this model to predict the condensation point, and the relationship between prediction error and changes in ambient temperature (T_E-T₀) is studied. The linear function fitted the relationship between prediction error and ambient temperature. It is found that the average value of condensation point prediction error is Δｃ=-0.019 8(T_E-T₀). The compensation factor of environmental temperature is brought into the prediction model developed under 25 ℃, and a temperature compensation model for the change in ambient temperature is established to predict the condensation point of diesel with NIR spectra collected under other conditions ambient temperature. The root means square error (RMSE) of condensation point prediction at 10 ℃ is improved from 14.6 to 8.8, and the coefficient of determination increased from 0.4 to 0.7. The study shows that the temperature compensation model can effectively reduce the error caused by ambient temperature. This method can improve the time cost for developingthe model and extend the temperature range in applying a portable NIR spectrometer.

Key words：Portable near infrared spectrometer; Temperature compensation model; Partial least square method; Condensation point of diesel

Received: 2020-10-09 Accepted: 2021-02-25

ZTFLH:

O433.4

Corresponding Authors: ZHANG Hong-ming E-mail: hmzhang@ipp.ac.cn

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https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)10-3111-06 OR https://www.gpxygpfx.com/EN/Y2021/V41/I10/3111

[1] CHU Xiao-li, CHEN Pu, LI Jing-yan, et al(褚小立, 陈瀑, 李敬岩，等). Journal of Instrumental Analysis(分析测试学报), 2020, 39(10): 1181.
[2] CHU Xiao-li, YUAN Hong-fu, LU Wan-zhen, et al(褚小立, 袁洪福, 陆婉珍，等). Analytical Instrumentation(分析仪器), 2006，(2): 1.
[3] CHU Xiao-li, WANG Yan-bin, LU Wan-zhen, et al(褚小立, 王艳斌, 陆婉珍，等). Analytical Instrumentation(分析仪器), 2007，(4): 1.
[4] SUN Yan-hua, FAN Yong-tao(孙彦华, 范永涛). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2020, 40(6): 1690.
[5] Thamasopinkul, Ritthiruangdej, Kasemsumran, et al. Journal of Near Infrared Spectroscopy, 2017, 25(1): 36.
[6] GENG Rui(耿锐). Chemical Engineering Design Communications(化工设计通讯), 2020, 46(9): 34.
[7] FENG Xin-lu, SHI Yong-gang(冯新泸, 史永刚). Near-Infrared Spectroscopy and Its Application in the Analysis of Petroleum Products(近红外光谱及其在石油产品分析中的应用). Beijing: China Petrochemical Press(北京：中国石化出版社), 2002. 201.
[8] HONG Yong-sheng, YU Lei, ZHU Ya-xing, et al(洪永胜, 于雷, 朱亚星, 等). Scientia Agricultura Sinica(中国农业科学), 2017, 50(19): 3766.
[9] WANG Tao, ZHANG Lu-da, LAO Cai-lian, et al(王韬, 张录达, 劳彩莲，等). Journal of China Agricultural University(中国农业大学学报), 2004, 9(6): 76.
[10] LIANG Hui, LI Li-jie, JIN Shao-hua, et al(梁惠, 李丽洁, 金韶华，等). Chinese Journal of Energetic Materials(含能材料), 2018, 26(5): 441.
[11] NI Li-jun, ZHANG Li-guo(倪力军, 张立国). Basic Chemometrics and Its Applications(基础化学计量学及其应用). Shanghai: East China University of Science and Technology Press(上海：华东理工大学出版社), 2011. 20.
[12] WANG Wei-jing, ZHANG Fu-min, FENG Wei, et al(王惟婧, 张福民, 冯维，等). Acta Optica Sinica(光学学报), 2017, 37(12): 153.
[13] GAO Jia-ming, SHE Chong-chong, CHEN Jun, et al(高嘉明, 佘冲冲, 陈军，等). Acta Armamentarii(兵工学报), 2020, 41(5): 1034.
[14] CHEN Bin, ZOU Xian-yong, ZHU Wen-jing, et al(陈斌, 邹贤勇, 朱文静，等). Journal of Jiangsu University·Natural Science Edition(江苏大学学报·自然科学版), 2008，(4): 277.
[15] DU Yi-ping, PAN Tie-ying, ZHANG Yu-lan, et al(杜一平, 潘铁英, 张玉兰，等). Chemometrics Applications(化学计量学应用). Beijing: Chemical Industry Press(北京：化学工业出版社), 2008. 163.