Abrahamsson Victor, Ristic Nenad, Franz Kristina, Van Geem Kevin
Lund University, Department of Chemistry, Centre for Analysis and Synthesis, Lund, Sweden.
Ghent University, Laboratory for Chemical Technology, Ghent, Belgium.
J Chromatogr A. 2017 Jun 9;1501:89-98. doi: 10.1016/j.chroma.2017.04.021. Epub 2017 Apr 14.
Fouling tendencies of a series of gas condensates were evaluated using comprehensive two-dimensional gas chromatography with flame ionization detection and sulfur chemiluminescence detection. A pixel-based approach was applied in order to identify parts of the chromatograms which were associated with the reactor coil fouling. Particular emphasis is given in this work to evaluate several feature selection methodologies along with various data preprocessing procedures. It was found that both aspects were crucial for studying the fouling tendencies and, as part of the subsequent partial least squares model development, predominantly the feature selection. Based on the flame ionization detector chromatograms and using the RReliefF algorithm for feature selection, a partial least squares regression model with one latent variable resulted in a root mean square error of the cross-validation of 0.65gdeposit/6h (17%). Based on the sulfur chemiluminescence detector chromatograms, the F-statistics feature selection generated a slightly better partial least squares regression model compared to using RReliefF, thus generating a model using one latent variable with a root mean square error of the cross-validation of 0.81gdeposit/6h (21%). Heavy aromatic compounds and heavy sulfur containing compounds were negatively associated with the fouling rate. Both were crucial in developing a partial least squares model with good prediction power, however, worked independently as predictors.
使用配有火焰离子化检测和硫化学发光检测的全二维气相色谱法评估了一系列气体凝析液的结垢倾向。采用基于像素的方法来识别色谱图中与反应器盘管结垢相关的部分。这项工作特别强调评估几种特征选择方法以及各种数据预处理程序。结果发现，这两个方面对于研究结垢倾向都至关重要，并且作为后续偏最小二乘模型开发的一部分，主要是特征选择。基于火焰离子化检测器色谱图并使用RReliefF算法进行特征选择，具有一个潜变量的偏最小二乘回归模型的交叉验证均方根误差为0.65g沉积物/6小时（17%）。基于硫化学发光检测器色谱图，与使用RReliefF相比，F统计量特征选择产生了稍好的偏最小二乘回归模型，从而生成了一个使用一个潜变量的模型，其交叉验证均方根误差为0.81g沉积物/6小时（21%）。重芳烃化合物和含重硫化合物与结垢速率呈负相关。两者在开发具有良好预测能力的偏最小二乘模型中都至关重要，然而，它们作为预测因子是独立起作用的。
