Blast furnace is a highly complex production process with the characteristics of nonlinearity

large delay

big noise

parameters distribution

and so on. According to the problem that coke ratio is used as energy consumption evaluation index but can't provide real-time guidance in the blast furnace field

carbon monoxide utilization ratio is studied as the energy consumption index and a prediction method for it based on improved support vector regression(SVR) is proposed. Firstly

relevant operation factors are selected by analyzing the mechanism of blast furnace combined with mutual information method. Next

wavelet transform is used to remove the noise including in the production data. Additionally

relative gray correlation analysis is applied for temporal registration to avoid the time delay of blast furnace operation

and then the prediction model of carbon monoxide utilization ratio is established. Support vector regression method and adaptive particle swarm optimization algorithm(APSO) are united to overcome the randomness of the selection of parameters and improve the accuracy of the prediction model. The simulation results demonstrate that APSO-SVR provides an effective way to predict the carbon monoxide utilization

which serves as a scientific decision support for the following optimization of blast furnace operation as well as energy saving and emission reduction.