A Fuzzy Sequential Pattern Mining Algorithm Based on Independent Pruning Strategy for Parameters Optimization of Ball Mill Pulverizing System

H. Cao, Y. Zhang, L. Jia, G. Si

Abstract


This paper presents a fuzzy sequential pattern mining algorithm based on independent pruning strategy for parameters optimization of ball mill pulverizing system. Based on the Apriori-alike process, the proposed algorithm uses the independent pruning strategy to mine the fuzzy sequential patterns, which could enhance the efficiency of the algorithm. Then, the optimal values of the process variables are determined by a searching method with the mined sequential patterns. The improved fuzzy sequential pattern support and the fuzzy sequential pattern confidence are adopted to ensure the accuracy of the mined sequential patterns. Moreover, the sliding time window technique is used to ensure the completeness of mining results. The experimental results for parameters optimization of ball mill pulverizing system also verify that the proposed algorithm could determine the optimal values correctly and the running time is not long. In addition, the proposed algorithm has been put into practice successfully and the statistic data show that the pulverizing capability of ball mill pulverizing system is increased and the energy consumption would be reduced.

DOI: http://dx.doi.org/10.5755/j01.itc.43.3.5180


Keywords


ball mill pulverizing system; parameters optimization; data mining; fuzzy sequential pattern mining; independent pruning strategy

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Print ISSN: 1392-124X 
Online ISSN: 2335-884X