Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform
Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform
复制标题
Hadoop云平台下高炉冶炼过程大数据驱动铁水硅含量动态预测研究
DOI：
10.1155/2018/8079697
复制
发表时间：
2018-01-01
期刊：
影响因子：
2.3
通讯作者：
Zhang, Yu-Zhu
中科院分区：
文献类型：
--
作者：
Han, Yang;Li, Jie;Zhang, Yu-Zhu
为了探索具有良好泛化性能的铁水[Si]含量动态预测模型，提出了一种改进的支持向量机算法，以增强其在冶炼过程大数据样本集上的实用性。首先，提出了一种并行化方案，在Hadoop平台下设计基于MapReduce模型的SVM求解算法，以提高SVM在大数据样本集上的求解速度。其次，基于随机次梯度投影的特点，SVM求解器算法的执行时间不依赖于样本集的大小，提出了一种基于近邻传播算法的结构化SVM算法，并在此基础上设计了求解训练集协方差矩阵的并行算法和随机次梯度投影的第t次迭代的并行算法。最后，利用高炉配铁过程中的反应机理、控制机理和灰色关联模型，对唐钢11号高炉2015年12月1日至2016年11月30日期间的历史生产大数据进行了分析，建立了输入为[x（1）（k），x（2）（k - 3），x（3）（k - 3），...，x（18）（k），x（19）（k - 1）]和输出[Si]（k + 1），利用该结构和并行化SVM求解算法，在Hadoop平台上得到铁水[Si]含量动态预测模型和铁水[Si]波动动态预测模型。研究结果表明，结构化和并行SVM算法在铁水[Si]含量值动态预测和提升动态预测中的命中率分别为91.2%和92.2%。基于结构化和并行化的两种动态预测算法分别比传统的串行求解算法快54倍和5倍。
In order to explore a dynamic prediction model with good generalization performance of the content of [Si] in molten iron, an improved SVM algorithm is proposed to enhance its practicability in the big data sample set of the smelting process. Firstly, we propose a parallelization scheme to design an SVM solution algorithm based on the MapReduce model under a Hadoop platform to improve the solution speed of the SVM on big data sample sets. Secondly, based on the characteristics of stochastic subgradient projection, the execution time of the SVM solver algorithm does not depend on the size of the sample set, and a structured SVM algorithm based on the neighbor propagation algorithm is proposed, and on this basis, a parallel algorithm for solving the covariance matrix of the training set and a parallel algorithm of the tth iteration of the random subgradient projection are designed. Finally, the historical production big data of No. 1 blast furnace in Tangshan Iron Works 11 was analyzed during 2015.12.01 similar to 2016.11.30 using the reaction mechanism, control mechanism, and gray correlation model in the process of blast furnace iron-mating, an essential sample set with input [x(1)(k), x(2)(k - 3), x(3)(k - 3), ... ,x(18)(k),x(19)(k - 1)] and output [Si](k + 1) is constructed, and the dynamic prediction model of the content of [Si] in molten iron and the dynamic prediction model of [Si] fluctuation in the molten iron are obtained on the Hadoop platform by means of the structure and parallelized SVM solving algorithm. The results of the research show that the structural and parallel SVM algorithms in the hot metal [Si] content value dynamic prediction hit rate and lifting dynamic prediction hit rate were 91.2% and 92.2%, respectively. Two kinds of dynamic prediction algorithms based on structure and parallelization are 54 times and 5 times faster than traditional serial solving algorithms.