基于机器学习的汽油加氢裂化辛烷值损失预测和脱硫优化

辛烷值损失的准确预测有助于汽油炼制过程的优化与控制,以达到更好的脱硫效果.原油的加氢脱硫是一个十分复杂的物化反应过程,对于该过程中的参数控制多依赖于工人的经验,因此基于大数据建立辛烷值损失预测模型可以用于优化脱硫效果,从而提高产品质量,减轻工人的劳动强度,具有十分重大的实际意义.采用单因素分析、方差过滤、随机森林等方法...

Prediction of octane loss and optimization of desulfurization in gasoline hydrocracking based on machine learning

In order to help optimize and control the gasoline refining process to achieve a better desulfurization effect, the octane loss must be accurately predicted. The hydrodesulfurization of crude oil is a very complex physical and chemical reaction process, and the parameter control in the process mostly depends on the experience of the workers. Therefore, the establishment of an octane loss prediction model based on big data can be used to optimize the desulfurization effect, thereby product quality can be improved and the labor intensity of workers can be reduced, which is of great practical significance. Feature selection was carried out using methods such as single factor analysis, variance filtering, random forest and others, and finally the octane loss prediction model was established based on three machine learning algorithms including logistic regression, BP neural network and support vector machine (SVM). Experimental results show that the prediction accuracy of octane loss based on SVM reaches 98.24%, which is better than logistic regression and BP neural network prediction models. The model is applied to desulfurization optimization, and when the sulfur content of the generated gasoline meets the standard, the optimal control variable combination is obtained to achieve the goal of the lowest octane loss.