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Comprehensive and multidimensional tools for crude oil property prediction and petrochemical industry refinery inferences

机译：原油属性预测和石化行业炼油厂推断的综合多维工具

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摘要

The association of comprehensive bidimensional gas chromatography with time-of-flight mass spectrometry (GCxGC-TOFMS) with high-order chemometric, N-way partial least squares (N-PLS), is an analytical innovation for the characterization of complex samples such as crude oil. The N-PLS method was applied to calibrate third-order data for sets of crude oil samples using whole oil comprehensive bidimensional chromatograms. The calibration model for API gravity had a bias equal to -5.8x10(-3) and R-Cal(2) of 0.9808 and WAT model had coefficient of determination for calibration model equal to 0.9436 and a bias of 8.7x10(-3). The results obtained by the decomposition of 11 components for API gravity were 99.79% for the X data and 98.08% for the Y data. The root mean square error for calibration (RMSEC) was equal to 0.81 and 1.01, while the root mean square error for prediction (RMSEP) was equal to 1.96 and 1.97 for the API gravity model and WAT, respectively and the explained variance obtained by decomposition in 9 components for WAT was 99.90% for the X data and 94.36% for the Y data. In the calibration models, all the errors for each sample were below 3.0 and 2.5 for degrees API and WAT, respectively. For the prediction set that was used to validate the model, the errors for each sample were below 3.0 and 3.2 for degrees API and WAT, respectively. The data indicates improvements for the correlation of petroleomic properties, thus allowing for the simultaneous prediction of certain properties instead of traditional analyses for each property when making inferences in the refining process. This application allows automation of the responses generated using crude oil samples without the need for pretreatment or fractionation steps; in addition, only one drop of each sample is required. This analytical application leads to cost reductions compared to the numerous forms of traditional analyses currently used as well as lower sample and time consumption, thus allowing for environmentally cleaner characterization.
机译：全面的二维气相色谱仪与飞行时间质谱仪（GCxGC-TOFMS）和高阶化学计量学N向偏最小二乘（N-PLS）的结合，是表征复杂样品的分析创新，例如原油。 N-PLS方法用于使用全油综合二维色谱图校准原油样品组的三阶数据。 API重力校正模型的偏差等于-5.8x10（-3），R-Cal（2）为0.9808，WAT模型的校正系数为0.9436，偏差为8.7x10（-3） 。通过分解11种成分的API重力得到的结果对于X数据为99.79％，对于Y数据为98.08％。对于API重力模型和WAT，校准的均方根误差（RMSEC）等于0.81和1.01，而预测的均方根误差（RMSEP）等于1.96和1.97，并且通过分解获得的解释方差在WAT的9个组件中，X数据的99.90％和Y数据的94.36％。在校准模型中，API和WAT度的每个样品的所有误差分别低于3.0和2.5。对于用于验证模型的预测集，API和WAT度的每个样本的误差分别低于3.0和3.2。数据表明，改善了石油特性的相关性，因此在精炼过程进行推断时，可以同时预测某些特性，而不是对每种特性进行传统分析。该应用程序可以自动处理使用原油样品产生的响应，而无需预处理或分馏步骤。此外，每个样品只需滴一滴。与当前使用的多种形式的传统分析相比，该分析应用程序可降低成本，并降低样品和时间消耗，从而实现更清洁的环境表征。

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