Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform

Yang Han, Jie Li, Xiao-Lei Yang, Wei-Xing Liu and Yu-Zhu Zhang

Complexity, 2018, vol. 2018, 1-16

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 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 and output 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:8079697

DOI: 10.1155/2018/8079697

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