An Operation State Identification System Of Blast Furnace Based On Gaussian Mixture And Na(?)ve Bayes Model

Iron and steel industry plays an important role in China's national industry.Since 2014,China's steel production has accounted for 40%of the world's production.Blast furnace(BF)is an important object of this industry,with a huge body,complex chemical reactions,consuming much energy.Smooth operation state is a pre-condition of a high production and energy utilization efficiency.That why keeping the BF running in a smooth state is of great importance.Currently,there has been many researches about the irregular state diagnosis of the blast furnace,such as expert system,artificial neural network and support vector machine,but those researches mainly focus on the later period the irregular operation state,which means a diagnosis after the occurrence of the problems.The diagnosis of earlier period still depends on human experiences in the plant.Moreover,the existing researches are basically based on supervising learning methods,which means a set of labels for historical data is necessary.However,there is often not any label set or the label set is not quite reliable in reality.Based on the facts above,this paper propose a unsupervised method based on Gaussian Mixture Model(GMM)and Naive Bayes Classifier(NBC),aimed to the earlier period diagnosis of BF's irregular operation state,solving the inaccessible label set problem.Firstly,this paper conducts a brief introduction and analysis of the related datasets and deal with the missing and irregular records in the datasets,concluding that the first principal component(FPC)of BF's parameters is correlated to the stability of the operation state.Expectation,standard deviation and maximum value of FPC are chosen as the features representing the stability.Secondly,this paper constructs a method based on GMM-NBC to diagnose the irregular state,solving the inaccessible label set problem.Decision Tree,Logistic Regression and Support Vector Machine are chosen as the baseline of the proposed method.The proposed method is better all the baselines in terms of the first type error rate,the second type error rate and the comprehensive type error rate.Lastly,this paper builds an irregular state identification system used for historical data analysis,model training and real time surveillance of the BF.