Petroleum ChemistryPetroleum ChemistryНефтехимия0028-24213034-5626The Russian Academy of Sciences65558610.31857/S0028242123050076RZPYBPArticlesСтатьиResearch ArticleOnline Determination on the Properties of Naphtha as the Ethylene Feedstock Using Near-Infrared SpectroscopyИнтерактивное определение свойств нафты (лигроина) как этиленового сырья с использованием спектроскопии в ближнем инфракрасном диапазонеChenFanpetrochem@ips.ac.ruTianboLiupetrochem@ips.ac.ruGuihuaHupetrochem@ips.ac.ruMingleiYangpetrochem@ips.ac.ruJianLonglongjian@ecust.edu.cnKey Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and TechnologyEast China University of Science and TechnologySinopec Jinan companyQingyuan Innovation Laboratory15092023635NO5 (2023)№5 (2023)68870011022025Copyright ©; 2023, Russian Academy of SciencesCopyright ©; 2023, Российская академия наук2023Russian Academy of SciencesРоссийская академия наукhttps://journals.eco-vector.com/0028-2421/article/view/655586

Providing real-time information on the properties of naphtha as the ethylene feedstock within the minimal time is significant for improvement of the process simulation, control, and real-time optimization. To develop models predicting naphtha properties for different pre-processing methods, an online full transmittance near-infrared (NIR) spectrum measurement system has been used along with the principal component regression and partial least squares (PLS) methods. The results show that the Savitzky-Golay smoothing combined with the first-derivative pre-processing provides the best denoising effect compared to other methods. The predicted relative errors of the NIR models developed by PLS, especially for the cutting temperature points of the test set, basically make 1‒5% indicating it can be used to create good NIR prediction models for the on-line determination of naphtha properties.

Предоставление актуальной информации о свойствах нафты как этиленового сырья в режиме реального времени имеет большое значение для улучшения моделирования, управления и оптимизации процессов. Интерактивная (в режиме онлайн) система измерения спектра полного пропускания в ближнем ИК-диапазоне (NIR), а также метод регрессии основных компонентов и метод частичных наименьших квадратов (PLS) использованы для разработки моделей, прогнозирующих свойства нафты при различных методах предварительной обработки. Прогнозируемые относительные ошибки моделей NIR, разработанных методом PLS, особенно для точек температуры фракционирования тестового набора, составляют в основном 1-5%, т.е. их можно использовать для создания приемлемых моделей прогнозирования в ближнем ИК-диапазоне при интерактивном определении свойств нафты cглаживание Савицкого- Голея в сочетании с предварительной обработкой первой производной обеспечивает наилучший эффект устранения шумов по сравнению с другими методами.

near-infrared spectroscopynaphtha propertiespre-processingpartial least squaresonline determinationспектроскопия в ближнем ИК-диапазонесвойства нафтыпредварительная обработкачастичные наименьшие квадратыинтерактивное определениеZhang S., Wang S., Xu Q. Emission constrained dynamic scheduling for ethylene cracking furnace system // Ind. Eng. Chem. Res. 2017. V. 56. № 5. P. 1327-1340. https://doi.org/10.1021/acs.iecr.6b02822Gu H., Li J., Mu P., Zhu Q. Improving the operational efficiency of ethylene cracking integrated with refining by molecular management // Ind. Eng. Chem. Res. 2020. V. 59. № 29, P. 13160-13174. https://doi.org/10.1021/acs.iecr.0c01705Zhao C., Liu C., Xu Q. Dynamic scheduling for ethylene cracking furnace system // Ind. Eng. Chem. Res. 2011. V. 50. № 21. P. 12026-12040. https://doi.org/10.1021/ie200318pTrichard F., Forquet V., Gilon N., Lienemann C.P., Baco-Antoniali F. Detection and quantification of sulfur in oil products by laser-induced breakdown spectroscopy for on-line analysis // Spectrochim. Acta, Part B. 2016. V. 118. P. 72-80. https://doi.org/10.1016/j.sab.2016.02.001Wang K., Du W., Long J. Near-infrared wavelength-selection method based on joint mutual information and weighted bootstrap sampling // IEEE Trans. Ind. Inf. 2020. V. 16. № 9. P. 5884-5894. https://doi.org/10.1109/TII.2020.2972351Santos F.D., Santos L.P., Cunha P.H.P., Borghi F.T., Romão W., de Castro E.V. R., de Oliveira E.C., Filgueiras P.R. Discrimination of oils and fuels using a portable NIR spectrometer // Fuel. 2021. V. 283. P. 118854. https://doi.org/10.1016/j.fuel.2020.118854Joshi N.B., Mullins O.C., Jamaluddin A., Creek J., McFadden J. Asphaltene precipitation from live crude oil // Energy Fuels. 2001. V. 15. № 4. P. 979-986. https://doi.org/10.1021/ef010047lBarreira F.R., Reis L.G., Nunes R.d.C.P., Filipakis S.D., Lucas E.F. Asphaltenes precipitation onset: Influence of the addition of a second crude oil or its asphaltenes fractions (C3I and C5I) // Energy Fuels. 2018. V. 32. № 10. P. 10391-10397. https://doi.org/10.1021/acs.energyfuels.8b01749Wang K., He K., Du W., Long J. Novel adaptive sample space expansion approach of NIR model for in-situ measurement of gasoline octane number in online gasoline blending processes // Chem. Eng. Sci. 2021. V. 242, p. 116672. https://doi.org/10.1016/j.ces.2021.116672Long J., Jiang S. Y., Liu T. B., Wang K., He R.C., Zhao L. Modified hybrid strategy integrating online adjustable oil property characterization and data-driven robust optimization under uncertainty: Application in gasoline blending // Energy Fuels. 2022. V. 36. № 12. P. 6581-6596. https://doi.org/10.1021/acs.energyfuels.2c00195Long J., Jiang S. Y., He R.C., Zhao L. Diesel blending under property uncertainty: A data-driven robust optimization approach // Fuel. 2021. V. 306. P. 121647. https://doi.org/10.1016/j.fuel.2021.121647Chen M., Khare S., Huang B., Zhang H., Lau E., Feng E. Recursive wavelength-selection strategy to update near-infrared spectroscopy model with an industrial application // Ind. Eng. Chem. Res. 2013. V 52. № 23. P. 7886-7895. https://doi.org/10.1021/ie4008248Yu H., Du W., Lang Z.Q., Wang K., Long J. A Novel integrated approach to characterization of petroleum naphtha properties from near-infrared spectroscopy // IEEE Trans. Instrum. Meas. 2021. V. 70. P. 1-13. Art. № 2510413. https://doi.org/10.1109/TIM.2021.3077659Wang S., Liu S., Zhang J., Che X., Wang Z., Kong D. Feasibility study on prediction of gasoline octane number using NIR spectroscopy combined with manifold learning and neural network // Spectrochim. Acta, Part A. 2020. V. 228. P. 117836. https://doi.org/10.1016/j.saa.2019.117836Wang X., Yang K., Kalivas J.H. Comparison of extreme learning machine models for gasoline octane number forecasting by near-infrared spectra analysis // Optik. 2020. V. 200. P. 163325. https://doi.org/10.1016/j.ijleo.2019.163325Correa Gonzalez S., Kroyan Y., Sarjovaara T., Kiiski U., Karvo A., Toldy A.I., Larmi M., Santasalo-Aarnio A. Prediction of gasoline blend ignition characteristics using machine learning models // Energy Fuels. 2021. V. 35. № 11. P. 9332-9340. https://doi.org/10.1021/acs.energyfuels.1c00749He K., Zhong M., Fang J., Li Y. Biased minimax probability machine-based adaptive regression for online analysis of gasoline property // IEEE Trans. Ind. Inf. 2020. V. 16. № 4. P. 2799-2808. https://doi.org/10.1109/TII.2019.2945001Li J., Chu X. Rapid determination of physical and chemical parameters of reformed gasoline by near-infrared (NIR) spectroscopy combined with the Monte Carlo virtual spectrum identification method // Energy Fuels. 2018. V. 32. № 12. P. 12013-12020. https://doi.org/10.1021/acs.energyfuels.8b00854Купцов А.Х., Арбузова Т.В. Исследование тяжелых фракций нефти методом Фурье-спектроскопии КР ближнего ИК-диапазона // Нефтехимия. 2011. Т. 51. № 3. С. 214-222. EDN NWCMOB [Kuptsov A.K., Arbuzova T.V. A study of heavy oil fractions by Fourier-transform near-infrared Raman spectroscopy // Petrol. Chemistry. 2011. V. 51. № 3. P. 203-211. https://doi.org/10.1134/S0965544111020083].Kashaev R.S. Structural-dynamic phase transitions in petroleum disperse systems // Petrol. Chemistry. 2019. V. 59. № 2. P. 143-150. https://doi.org/10.1134/S0965544119020087Huang Y., Dong W., Chen Y., Wang X., Luo W., Zhan B., Liu X., Zhang H. Online detection of soluble solids content and maturity of tomatoes using Vis/NIR full transmittance spectra // Chemometrics and Intelligent Laboratory Systems. 2021. V. 210. P. 104243. https://doi.org/10.1016/j.chemolab.2021.104243Hosani E.A., Meribout M., Al-Durra A., Al-Wahedi K., Teniou S. A new optical-based device for online black powder detection in gas pipelines // IEEE Trans. Instrum. Meas. 2014. V. 63. № 9. P. 2238-2252. https://doi.org/10.1109/TIM.2014.2308985Haghi R.K., Yang J., Tohidi B. Fourier transform near-infrared (FTNIR) spectroscopy and partial least-squares (PLS) algorithm for monitoring compositional changes in hydrocarbon gases under in situ pressure // Energy Fuels. 2017. V. 31. № 9. P. 10245-10259. https://doi.org/10.1021/acs.energyfuels.7b01677