Prediction of Endpoint Sulfur Content in KR Desulfurization Based on the Hybrid Algorithm Combining Artificial Neural Network With SAPSO

In the present work, the endpoint sulfur content prediction model of Kambara Reactor (KR) desulfurization in the steelmaking process is investigated. For Artificial Neural Network (ANN), the effects of different structure parameters, including the number of hidden layer neurons, activation functions and training functions, on the performance of desulfurization model are studied. The initial weights and biases of the neural network is optimized to further elevated the prediction accuracy of the model. Three models established by using Multiple Linear Regression (MLR), ANN and a hybrid algorithm (artificial neural network optimized by SAPSO, named SAPSO-ANN) are compared by the Correlation Coefficient (R), Root Mean Square Error (RMSE) and Mean Absolute Relative Error (MARE). The results show that in the process of KR desulfurization, the nonlinear model of ANN and SAPSO-ANN has a higher accuracy than the linear model of MLR. Among the three models, the SAPSO-ANN model achieves the highest accuracy with R value of 0.54, RMSE of 2.61×10
^-4
% and MAER of 0.47, which is selected to analyze the effect of process parameters on the desulfurization rate and design the amount of desulfurization flux in the KR desulfurization process. Experimental results show good agreements with the calculation results, indicating the practicability of the model.