基于支持向量回归模型的焦炭质量智能分析

Intelligent analysis of coke quality based on support vector regression model

炼焦配煤是焦化企业生产过程中的一项关键工作。在钢铁工业中,焦炭在高温下的CSR和CRI质量指标尤其重要。为了改善需依赖经验的传统配煤手段,基于原料煤指标预测焦炭质量的思路被提出。通过分析国内外的一些预测方法,针对目前存在的不足进行改良,以西来峰焦化厂一期和二期为例,采用支持向量回归模型,对焦炭的CSR和CRI这2个质量指标进行了智能分析,并通过系统分析训练样本比例与预测精度的关系,指明进一步提升焦炭质量预测性能的工作方向。

Coal blending plays a key role in the production of a coking plant.In the iron and steel industry,CSR and CRI indexes of coke at high temperature are of particular importance.In order to improve traditional coal blending process that relies on expert experience,the idea of predicting coke quality based on raw coal indexes has been put forward.This article aims to analyze some coke quality prediction methods at home and abroad and improve the existing deficiencies.Taking Phase I and II projects of Xilaifeng coking plant as an example,this article conducts intelligent analysis,using SVR model,on CSR and CRI indexes of coke.By showing the relationship between training sample proportion and prediction accuracy,it further points out research orientation of coke quality prediction work.