An Improved CBR Model Using Time-series Data for Predicting the End-point of a Converter

Abstract

The end-point temperature is one of parameters for the end-point control in the converter. Accurate prediction of the end-point temperature is helpful to improve the hit rate of the end-point. An improved CBR model using time-series data (CBR_TM) was proposed to predict the end-point carbon content and temperature in the converter according to the data types of process parameters. The attributes of the cases in the model not only include the influencing factors of single-value type such as composition and temperature of hot metal, but also include the influencing factors of time-series type such as lance position and oxygen flow, in the case retrieval process, the single-value data similarity and time-series data similarity between the cases were calculated based on the Euclidean distance and the dynamic time warping algorithm, and then weighted to obtain the comprehensive similarity. Then the influence of the weight of the time-series data similarity on the prediction accuracy was studied based on the production data. Finally, the prediction accuracy of the established model was also compared to models based on SVR and BPNN. The results show that: The prediction accuracy of the model increases at first and then decreases with the increase of similarity weight of time series data. The prediction accuracy of the model was the highest when the weight of time-series data similarity was 0.4 and was better than the SVR and BPNN models. The established can meet the requirements of field production.