Research Article
Coke Quality Prediction Based on Blast Furnace Smelting Process Data
@INPROCEEDINGS{10.1007/978-3-031-50580-5_10, author={ShengWei Zhang and Xiaoting Li and Kai Yang and Zhaosong Zhu and LiPing Wang}, title={Coke Quality Prediction Based on Blast Furnace Smelting Process Data}, proceedings={Multimedia Technology and Enhanced Learning. 5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part IV}, proceedings_a={ICMTEL PART 4}, year={2024}, month={2}, keywords={Coke quality Artificial intelligence algorithm Prediction model}, doi={10.1007/978-3-031-50580-5_10} }- ShengWei Zhang
Xiaoting Li
Kai Yang
Zhaosong Zhu
LiPing Wang
Year: 2024
Coke Quality Prediction Based on Blast Furnace Smelting Process Data
ICMTEL PART 4
Springer
DOI: 10.1007/978-3-031-50580-5_10
Abstract
Coke is the main material of blast furnace smelting. The quality of coke is directly related to the quality of finished products of blast furnace smelting, and the evaluation of coke quality often depends on the quality of finished products. However, it is impractical to evaluate coke quality based on finished product quality. Therefore, it is of great significance to establish an artificial intelligence model for quality prediction based on the indicators of coke itself. In this paper, starting from the actual production case, taking the indicators of coke as the feature vector and the quality of finished product as the label, different artificial intelligence models are established. These models predict coke quality, and compare and discuss related algorithms, which lays a foundation for further algorithm improvement.
