Optimization and Analysis for Coke Consumption Based on Clustering
In order to reduce the coke consumption of Blast Furnace（BF），a relevance analysis is carried out for operation parameters and fuel rate of BF，and a prediction method that is combining clustering analysis and artificial neural network for coke rate is proposed. The data cluster is divided into several classes by clustering analysis，the data similarity is high，and the neural network model is used to realize the prediction of coke rate. By combining the neural network with clustering analysis，the data in one BF is simulated，and the results are compared with the traditional neural network model. The result shows that the improved neural network has a higher accuracy, the average absolute error can be decreased by 3.13kg/t, and the average relative error can be decreased by 5.19%, it will have a good using foreground.
Jinglong Bu, Pengcheng Wang, Liqun Ai, Xiaoming Sang, Yungang Li
J. H. Zhang "Optimization and Analysis for Coke Consumption Based on Clustering", Advanced Materials Research, Vols. 287-290, pp. 1112-1115, 2011