A comprehensive prediction model of sinter quality based on machine learning algorithm was proposed. First of all, the mass historical data of actual sintering production was collected, cleaned and integrated. On this basis, the drum index and screening index of sinter were analysed by cluster analysis, and the quality of sinter was evaluated synthetically by clustering results and iron grade of sinter. Then, the important characteristic variables related to sinter quality index were screened by Recursive feature elimination, stability selection and random forest selection. The comprehensive classification model of sinter quality and the regression model of sinter's total iron content were established by using various machine learning algorithms. The results show that the prediction accuracy of the classification model and regression model established by the extra tree is the best, and the application effect of the model is verified by using the testing set. The F1-score of the comprehensive quality index classification model is 0.92 and the R 2 of the total iron content regression model is 0.882. This model has good learning and generalization ability, and the accurate prediction of sinter quality index is realized.
According to the characteristics of sintering process, a sintering end-point prediction system based on gradient boosting decision tree (GBDT) algorithm and decision rules is proposed in this paper. The on-line parameters of the sintering machine, which can characterize the change of the properties of the sintered raw materials in real time, were selected as the input of the model. The soft measurement results of the burn-through point position and temperature were selected as the output. The problem of establishing a system model based on the data collected in the sintering process to dynamically predict the state of burn through point (BTP) was solved. With the combination of process knowledge and several feature selection methods, the important characteristic variables related to the BTP were screened out. the algorithm of GBDT was used to establish the prediction model of BTP and burn through temperature (BTT). The parameters of the ensemble algorithm were optimized by using the methods of grid search and cross-validation, and the system model based on training data was established. On this basis, the corresponding decision model was added to the output of the prediction model, and the prediction accuracy of the system was improved. The establishment process of system model is introduced in detail. The operation results show that the system has better performance.
La 2 O 3 is a combustion improver suitable for burning pulverized coal in blast furnace. La 2 O 3 forms the active species La 3? (CO -) 3 that weakens the bridge adhesion of carbon structural units and alters the lattice structures, thus reducing the activation energy of the pulverized coal and accelerating the burning process. Research shows that La 2 O 3 can form the active species La 3? (CO -) 3 , which weakens the bridge adhesion of carbon structural units and alters the lattice structures of the fixed carbon, hence decreasing the activation energy of the pulverized coal and accelerating the burning process.
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