基于梯度提升决策树的三组元精馏流程结构最优化

Optimization of ternary-distillation sequence based on gradient boosting decision tree

针对三组元精馏流程结构最优选择问题,提出一种基于梯度提升决策树(gradient boosting decision tree,GBDT)的机器学习方法,利用7种精馏流程结构分离9种三组元混合物的经济评价结果数据用于模型训练。分别应用传统决策树和GBDT模型对多个实际案例进行验证,结果显示无论是对已知物系还是未知物系,提出的GBDT模型相较传统决策树模型具有更高的预测准确率。

A machine learning method based on gradient boosting decision tree(GBDT)is proposed for the optimal selection of ternary-distillation process structure.Economic evaluation data of 9 ternary mixtures separated by 7 distillation process structures were used for model training.By applying both traditional decision tree and GBDT models to multiple cases,the results show that the proposed GBDT model has higher prediction accuracy than the traditional decision tree model for both known and unknown ternary mixtures.