Abstract
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 II was analyzed during 2015.12.01~2016.11.30 using the reaction mechanism, control mechanism, and gray correlation model in the process of blast furnace iron-making, an essential sample set with input x1k,x2k−3,x3k−3,…,x18k,x19k−1 and output Sik+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.
Highlights
Blast furnace anterograde and hot metal quality are the primary goals of iron-making process control
Shalev-Shwartz [17] pointed out that the number of iterations required for the stochastic gradient projection as the support vector machine (SVM) solution method is O 1/ε, where ε is the precision of the solution, and the algorithm execution time does not depend on the size of the sample set and the scale cluster system can achieve the acceleration of the algorithm
The support vector machine is a machine learning algorithm based on the maximum interval theory
Summary
Blast furnace anterograde and hot metal quality are the primary goals of iron-making process control. SVM, as a machine learning algorithm with a solid foundation of mathematical theory and excellent generalization performance, should use the Hadoop cloud computing platform and parallel computing to break through its bottleneck in the processing of big data sets and promote the application scope of the algorithm. Shalev-Shwartz [17] pointed out that the number of iterations required for the stochastic gradient projection as the SVM solution method is O 1/ε , where ε is the precision of the solution, and the algorithm execution time does not depend on the size of the sample set and the scale cluster system can achieve the acceleration of the algorithm. Based on the deep analysis of the SVM solution process and stochastic subgradient projection algorithm, a parallel SVM algorithm using the stochastic subgradient projection algorithm and considering the structure of sample data is designed in this paper, with the help of the MapReduce model on the Hadoop cloud computing platform. The algorithm is applied to deal with the big historical data produced in the process of blast furnace production in order to obtain the efficient dynamic prediction model of [Si] in molten iron
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