Data Mining And Its Application In Optimizing The Process Of Copper Matte Converting In PS Converter

Data mining is a new technique of data processing and analyzing, which develops rapidly with the development of the technique to store data, the enlargement of the scale of database, the accretion of people's need to take information from data set. It is considered as the offspring of computer technology, database technology, artificial intelligence, statistics and so on. In this paper, the evolutive history, basic principles and some important research fruits about data mining were introduced, the status quo of the application in optimizing industrial process was introduced too. Considering the importance of data preprocessing for proper data mining, several necessary techniques of data preprocessing were introduced in detail.An approach based on wavelet analysis to detect and amend anomalies in 2-dimension data set was proposed in this paper. Taking full advantage of wavelet analysis' character of multiple scale and its superiority in local analysis, this approach judges whether a sample is an anomaly according to the value of its wavelet transformation coefficient, and amend anomalous samples when it is necessary. Inspired by this approach, it was proposed to detect anomalies in multi-dimension data set using wavelet analysis and attribute reduction, that is, reduce the number of independent variables of multi-dimension data to 2 or 1, then detect the anomaly using 1-dimension or 2-dimension wavelet analysis. According to this idea, an approach based on wavelet analysis and non-linear mapping was proposed to detect the anomalies in multi-dimension data set. The simulate experiments show that the approaches are accurate and practical.The approach based on data mining to optimize the process of copper matte converting in PS converter and its superiority was analyzed, general process to realize it was proposed. To illuminate the process, an approach based on data mining to predict the output of slag was proposed. A model based on regression analysis and neural network was built, the simulative experiment based on real industrial data show that the method and technique to build model are feasible and the model build in this paper has a good performance in predicting the output of slag.