基于机器学习的乙烯裂解过程模型比较与集成

Comparison and integration of machine learning based ethylene cracking process models

乙烯是石油化工的重要产品,蒸汽裂解生产乙烯的工艺十分复杂。构建精确的石脑油裂解模型,可以实现石脑油裂解制乙烯过程的裂解深度快速、准确预测。该文比较了支持向量回归、k-近邻回归和极限梯度提升3种机器学习模型。通过具有噪声的基于密度的聚类算法(DBSCAN)和局部异常因子检测算法,对工业数据集进行重要变量和样本筛选,训练3个子模型,并构建集成模型以提高预测效果。集成模型结合各子模型的优势,减轻过拟合、对噪声敏感等不足,加强稳定性与泛化能力。实测集成模型的预测值R~2为0.955,平均绝对百分比误差约为0.23%,满足过程研究和工业应用的实际需求。

Ethylene is an essential petrochemical industry product produced in a complex steam cracking process. Fast, accurate predictions of ethylene cracking depths depend on accurate naphtha cracking models. This paper compares three machine learning models based on a support vector regression(SVR), a k-nearest neighbor regression, and an extreme gradient boosting(XGBoost) to predict the ethylene cracking depth. Several industrial datasets are screened to identify the critical variables controlling the process using the density-based spatial clustering of applications with noise(DBSCAN) and a local abnormal factor detection algorithm. These three models are then trained and combined into an ensemble model to provide better predictions. The ensemble model combines the advantages of the three models and reduces the overfitting, the sensitivity to noise and other shortcomings. The ensemble model then has better prediction stability and generalization ability. The ensemble model predictions have R~2=0.955 and an average absolute percentage error of about 0.23%, which is sufficient for process research and industrial applications.