Abstract:

The dry point of gasoline is difficult to be measured in real time. A large number of data samples need to be extracted to test the quality of each section of the oil. In order to solve this problem, predictive control was carried out by establishing a soft sensor model. The least squares support vector machine model is too sensitive to outliers, which is easy to affect the prediction accuracy. By establishing the weighted least squares support vector machine model (WLSSVM), the fitting error is weighted, which weakens the influence of outliers on the model and improves the anti?interference ability of the model. The improved WLSSVM was applied to the prediction of gasoline dry point. The results show that the maximum absolute error of the improved WLSSVM is 11.65% lower than that of the least squares support vector machine model, and its prediction performance and robustness have obvious advantages.

Key words: Gasoline dry point, Least squares support vector machine, Weight distribution

摘要：

汽油干点难以实时测量，需要通过大量的数据样本对各段油品的质量进行检验。为了解决此问题，通过建立软测量模型进行预测控制。最小二乘支持向量机（LSSVM）模型对异常点过于敏感，容易影响预测精度。通过建立加权最小二乘支持向量机（WLSSVM）模型，对拟合误差进行加权处理，削弱了异常点对模型的影响，提高了模型的抗干扰能力。将改进后的加权最小二乘支持向量机（IWLSSVM）模型应用于汽油干点的预测。结果表明，IWLSSVM模型的最大绝对误差比LSSVM模型降低了11.65%，其预测性能和鲁棒性具有明显的优势。

关键词: 汽油干点, 最小二乘支持向量机, 权值分布