基于梯度提升决策树的焦炭质量预测模型研究

焦炭是高炉炼铁的重要原料,其质量是影响铁水质量和高炉顺行的重要因素,针对焦炭质量存在检验难、滞后性、预测误差大等问题,提出一种基于梯度提升决策树算法的焦炭预测模型;结合专家经验与相关性分析方法,深入研究配合煤质量对焦炭质量的影响;最后利用配合煤质量指标对焦炭质量指标灰分、硫分、耐磨强度、抗碎强度进行建模预测;根据某焦化厂历史生产数据对模型进行评估,实验结果表明:基于梯度提升决策树的焦炭质量预测模型相较于线性回归模型、随机森林模型,决策树模型误差小、准确率高,可以为焦化厂配煤炼焦提供一定的理论依据。

Research on Coke Quality Prediction Model Based on Gradient Boosting Decision Tree

Coke is an important raw material for blast furnace ironmaking, and its quality is an important factor affecting the quality of molten iron and the smooth operation of blast furnace. In order to solve the problems of difficult inspection, hysteresis, and large prediction errors in coke quality, a coke prediction model based on gradient boosting decision tree algorithm is proposed. Combined with expert experience and correlation analysis, the influence of mixed coal quality on coke quality is studied. Finally, the mixed coal quality parameters are used to predict the ash content, sulfur content, M10 and M40 of coke quality parameters. The model is evaluated based on the historical production data of a coking plant. The experimental results show that the coke quality prediction model based on the gradient boosting decision tree has less error and higher accuracy than the linear regression model, random forest model, and decision tree model. It can provide a certain theoretical basisfor coal blending and coking of the coking plant.