摘要
活化焙烧是一种回收利用含钛高炉渣中钛资源的新方法。为通过反应条件快速获得回收渣中成分含量,建立了基于粒子群优化的广义回归神经网络(PSO-GRNN)预测模型。借助斯皮尔曼(Spearman)相关性分析筛选特征变量作为模型输入,利用PSO优化GRNN神经网络的权重与节点阈值,通过与偏最小二乘回归(PLS)、随机森林(RF)以及支持向量回归(SVR)算法的对比,确定了提出模型的优势。研究结果表明,PSO-GRNN具有最小的RMSE和最大的R2,表明在该数据集上所设计的PSO-GRNN有最佳的模型性能,可以为后续实验或工业应用提供一定的指导。
Activation roasting is a new method to recover and utilize titanium resources in titaniumcontaining blast furnace slag.In order to quickly obtain the content of components in the recycled slag through the reaction conditions,a Generalized Regression Neural Network prediction model based on Particle Swarm Optimization(PSO-GRNN)is established.With the help of Spearman’s correlation analysis to screen the characteristic variables as model inputs,PSO is used to optimize the weights and node thresholds of the GRNN neural network,and the advantages of the proposed model are identified by comparing it with the Partial Least Squares Regression(PLS),Random Forest(RF)and Support Vector Regression(SVR)algorithms.The results show that the PSO-GRNN has the smallest RMSE and the largest R2,indicating that the designed PSO-GRNN has the best model performance on this dataset,which can provide some guidance for subsequent experimental or industrial applications.
作者
张宁
何茂琪
方文
ZHANG Ning;HE Maoqi;FANG Wen(BGRIMM Technology Group Co.,Ltd.,Beijing 100160,China;State Key Laboratory of Intelligent Optimized Manufacturing in Mining&Metallurgy Process,Beijing 102628,China)
出处
《中国矿业》
北大核心
2024年第S01期453-459,468,共8页
China Mining Magazine
关键词
广义回归神经网络
粒子群优化
回归模型
含钛高炉渣
活化焙烧
Generalized Regression Neural Network
Particle Swarm Optimization
regression model
titanium-containing blast furnace slag
activation roasting
作者简介
第一作者:张宁(1997—),男,硕士,主要从事矿冶过程智能控制等方面的研究工作,E-mail:zdhzhangning@bgrimm.com。
