Prediction of Blast Furnace Gas Output Based on GA-Elman Neural Network

Blast furnace gas (BFG) output in steel industry fluctuate sharply and there are many influencing factors. It is difficult to establish a suitable mechanism model to predict it effectively, and the prediction accuracy of a single neural network model is low. In order to improve the accuracy, an Elman neural network (ENN) prediction model based on Genetic algorithm(GA) is proposed. By optimizing the initial weights and thresholds of the ENN, the possibility of network falling into local optimum is reduced greatly. Combining the characteristics of BFG output with actual data to simulate and analyze, the simulation results show that compared with other models, the model proposed in this paper has higher accuracy.