宋进舟,沈亮,郭祎,等. 成品油管道顺序输送甲醇混油界面在线检测系统及应用[J]. 油气储运,2025,x(x):1−10.
引用本文: 宋进舟,沈亮,郭祎,等. 成品油管道顺序输送甲醇混油界面在线检测系统及应用[J]. 油气储运,2025,x(x):1−10.
SONG Jinzhou, SHEN Liang, GUO Yi, et al. Online detection system and its application for mixed-oil interfaces in batch transportation of methanol via refined oil pipelines[J]. Oil & Gas Storage and Transportation, 2025, x(x): 1−10.
Citation: SONG Jinzhou, SHEN Liang, GUO Yi, et al. Online detection system and its application for mixed-oil interfaces in batch transportation of methanol via refined oil pipelines[J]. Oil & Gas Storage and Transportation, 2025, x(x): 1−10.

成品油管道顺序输送甲醇混油界面在线检测系统及应用

Online detection system and its application for mixed-oil interfaces in batch transportation of methanol via refined oil pipelines

  • 摘要:
    目的 准确检测成品油管道顺序输送多种介质(汽油、柴油、甲醇等)混油界面的质量指标与混油浓度对分辨混油界面、确定混油切割点、实现管道高精度与智能化运行异常重要,且目前行业内还未出现能同时检测管输混油质量指标与混油浓度的在线检测系统。
    方法 基于国产傅里叶变换近红外光谱仪器,设计了可直接采集高压管输成品油、甲醇等介质近红外光谱的在线检测系统。在乌兰成品油管道兰州末站进站端收集了不同炼厂不同批次的0#柴油、92#汽油样品,配制了柴油-汽油混油样品、甲醇-柴油混油样品、甲醇-汽油混油样品,并采集了样品的近红外光谱,测量了0#柴油及柴汽混油、92#汽油及汽柴混油的关键质量指标(柴油闪点、汽油终馏点)作为参考值,再利用化学计量学多元校正方法建立了0#柴油、92#汽油及其混油的关键质量指标近红外光谱定标数据库,汽柴混油浓度定标数据库、甲醇成品油混油浓度定标数据库。在线检测系统在乌兰成品管道兰州末站进站端完成了汽柴及其混油的在线验证。
    结果 该在线检测系统对0#柴油闪点、92#汽油终馏点、柴汽混油闪点的近红外预测值与参考值的均方根误差分别为2.3 ℃、2.4 ℃、2.8 ℃,满足标准方法再现性误差要求。汽柴混油样品中汽油体积分数近红外预测值与参考值的绝对误差不大于0.6%。甲醇分别与柴油、汽油的混油中甲醇体积分数近红外预测值与参考值的绝对误差均不大于0.6%。
    结论 所设计的近红外光谱在线检测系统利用成功地攻克了无法同时检测混油关键质量指标与混油浓度的技术难题,对管道顺序输送成品油、甲醇及其混油界面的精准监测,实现生产过程智能化具有重要意义。

     

    Abstract:
    Objective Accurate detection of quality indicators and mixed oil concentration at the mixed oil interface during the batch transportation of various media (gasoline, diesel, methanol, etc.) in refined oil pipelines is essential for identifying the mixed oil interface, determining the mixed oil cutting point, and facilitating high-precision, intelligent pipeline operations. Currently, no online detection system in the industry can simultaneously detect both quality indicators and mixed oil concentration for pipeline transportation.
    Methods Using a domestic Fourier transform near-infrared spectrometer, an online detection system was designed to directly collect near-infrared spectra of media transported in high-pressure pipelines, such as refined oil and methanol. At the inlet of the Lanzhou Terminal of the Urumqi-Lanzhou Refined Oil Pipeline, samples of 0# diesel and 92# gasoline from different refineries and batches were collected. Mixed-oil samples of diesel-gasoline, methanol-diesel, and methanol-gasoline were then prepared, and their near-infrared spectra were acquired. Key quality indicators (flash point of diesel and final boiling point of gasoline) were measured for 0# diesel, diesel-gasoline mixed oil, 92# gasoline, and gasoline-diesel mixed oil as reference values. Chemometric multivariate calibration methods were then employed to establish near-infrared spectral calibration databases for the key quality indicators of 0# diesel, 92# gasoline, and their mixed oils, as well as for the concentration of gasoline-diesel and methanol-refined oil mixed oils. The online detection system was verified online for gasoline, diesel, and their mixed oils at the inlet of the Lanzhou Terminal of Urumqi-Lanzhou Refined Oil Pipeline.
    Results The root-mean-square errors between the near-infrared predicted values and the reference values for the flash point of 0# diesel, the final boiling point of 92# gasoline, and the flash point of diesel-gasoline mixed oil were 2.3 °C, 2.4 °C, and 2.8 °C, respectively, meeting the error requirements for the reproducibility of standard methods. The absolute error between the near-infrared predicted value and the reference value for the gasoline volume fraction in gasoline-diesel mixed oil samples did not exceed 0.6%, while the absolute errors for the methanol volume fraction in methanol-diesel and methanol-gasoline mixed oils were also within 0.6%.
    Conclusion The online near-infrared spectral detection system effectively addresses the technical challenge of simultaneously detecting key quality indicators and the concentration of mixed oil. This capability is crucial for accurately monitoring the batch transportation of refined oil and methanol, as well as interfaces of their mixed oils, thereby enhancing production process intelligence.

     

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