Optimal method of selecting silicon content data in blast furnace hot metal based on k-means++

High-quality data sets are the basis for accurate prediction of silicon content in blast furnace hot metal. There are some difficulties in processing the silicon content in hot metal. One challenge is the uneven recording, especially multiple silicon contents have a large fluctuation in some sample periods. Another is that silicon content is difficult to correlate with input variables. Aiming at these problems, we proposed an optimal method of selecting silicon content data in hot metal based on k-means++ clustering algorithm. Firstly, the fast clustering ability of k-means++ is utilized to divide samples to represent different furnace conditions. Secondly, the frequency histogram of the silicon content of each cluster is counted to determine the high frequency interval. Finally, using the high frequency range as the criterion, we select the best silicon content value associated with the sample. Taking a 2650 m3 blast furnace in a certain steel works as an example, established the deep learning models respectively based on multi-layer perceptron and LSTM for prediction. The results indicated that compared with the traditional averaging method, the mean square error (MSE) can be reduced by 0.003 and the hit rate is increased by more than 10%. Thus, this method has a good guiding significance for preprocessing the silicon content data in hot metal. © 2020, Chemical Industry Press Co., Ltd. All right reserved.