Intelligent Case-based Hybrid Model for Process and Endpoint Prediction of Converter via Data Mining Technique
Shen-yang Song, Jing Li, Wei Yan
Abstract
An intelligent case-based hybrid converter model has been established to predict the converter endpoint and process operations. The case-based reasoning (CBR) technique combined with the similarity calculation system was used in the intelligent converter model. The actual production data during the converter process in a time span from 2018 to 2020 was collected in the model database. The influencing factors during converter stage were divided into five categories, including steel composition, steel temperature, total processing time, steel cleanliness and total steelmaking cost of converter process in the model. All these factors were used in the similarity degree calculation, and then the historical cases in the model database were retrieved to guide the actual operation by analyzing its similarity degree to new case. The prediction accuracy was improved through self-learning with constant supplement of massive data. The prediction results based on 120-ton industrial converter showed a reasonable operation selection, the mean deviation of endpoint [C] content and [P] was 9.66% and 6.24%, respectively. Moreover, the difference between the predicted endpoint temperature and the measured endpoint temperature is within 10°C. Accordingly, the intelligent converter model was stable and suitable for large-scale prediction in converter endpoint and process operations.