Prediction of Blast Furnace Gas Output in a Steel Complex Based on PNN-HP-ENN-LSSVM Model
LI Hong-juan WANG Jian-jun WANG Hua MENG Hua
Kunming University of Science and Technology School of Metallurgy and Resource，Anhui University of Technology Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology
Abstract： Aimed at the difficult problem of accurate prediction on blast furnace gas output in an integrated iron and steel works with mechanism models available, by analyzing the gas output using probabilistic neural network (PNN) for classification according to various conditions and characteristics of probabilistic neural network, HP filter, Elman neural network (ENN) and least squares support vector machine (LSSVM), a PNN-HP-ENN-LSSVM model was established. The simulation results using the practical gas consumption data in an iron and steel complex showed that for 80 sites in 1# blast furnace, 60 sites in 2# blast furnace, the classification accuracies of 95%, and 93% were tested respectively. Then a forecasting model was founded based on the classification results to predict gas output, the average relative errors of 1.0% and 1.1% were obtained, the PNN-HP-ENN-LSSVM model was more suitable for blast furnace gas output prediction than other methods. And the Wilcoxon symbol rank test also proved the validity of the combined classification method.
李红娟 王建军 王华 孟华. 建立PNN-HP-ENN-LSSVM模型预测钢铁企业高炉煤气发生量[J]. , 2013, 13(3): 451-457.
LI Hong-juan WANG Jian-jun WANG Hua MENG Hua. Prediction of Blast Furnace Gas Output in a Steel Complex Based on PNN-HP-ENN-LSSVM Model. , 2013, 13(3): 451-457.