Prediction of Si content of blast furnace hot metal based on data-driven approach
Thermal state is a basic system of blast furnace production.For the complex and difficult-to-control blast furnace system with hourly delay,it is of great significance to grasp the trend of Si content of blast furnace hot metal in advance in order to improve the blast furnace heat level and furnace smoothness.In this paper,a data-driven prediction model of Si content of blast furnace hot metal was proposed.In the modeling process,the data of raw fuel,process operation,smelting status,and slag-iron discharge of the entire blast furnace process were comprehensively analyzed,with a total of 152 variables and 9 223 sets of data.And the input features for determining the prediction model of Si content of blast furnace hot metal were identified through the feature filtering and feature selection process,which played an important role in the modeling.Compared with the traditional single machine learning algorithm,the model performance of the integrated learning method using Stacking framework was significantly improved,and the performance of the proposed method was verified by comparing it with other five machine learning algorithms.According to statistics,the hit rate of Si content of the hot metal predicted by this algorithm within the error range of-0.05％to 0.05％is 90.48％.
Si content of the hot metaldata-driven approachfeature selectionensemble learning
NETL
NSTL
万方数据
