Ill-posed Echo State Network based on L-curve Method for Prediction of Blast Furnace Gas Flow

Abstract

Blast furnace gas is a significant energy resource in steel industry. Keeping stable of blast furnace gas flow is an important task for the furnace itself and the application of byproduct gas. However, owing to fluctuation and noisy of gas flow, echo state network is usually ill-posed in the prediction and it is very difficult to accurately predict the amount of gas. In this paper, in order to increase the accuracy of prediction in ill-posed echo state network model, L-curve method is used to compute the regularization parameter, which can alleviate the influence of ill-condition for ESN. Finally, to verify the effectiveness of the proposed method, the real data from blast furnace is employed in the experiments. Compared with two parameter regularization methods and four types of prediction methods, the results demonstrate that the proposed method exhibits a higher prediction accuracy for gas prediction.