Prediction of Green Properties of Flux Pellets Based on Improved Generalized Regression Neural Network

Abstract

In order to improve the quality of magnesia flux pellets and meet the production needs of the iron and steel industry, a pellet formation experiment was carried out. The effects of alkalinity R, SiO2 mass fraction, MgO mass fraction on the green pellets’ burst temperature, compressive strength, and falling strength were studied. The results showed that with the increase in alkalinity, the bursting temperature of green pellets decreases, but has no obvious effect on the compressive strength or drop strength; with the increase in SiO2 content, the bursting temperature of green pellets decreases gradually, and the green pellets’ strength also decreases slightly; with the increase in MgO content, the compressive strength of green pellets shows an upward trend, while the falling strength gradually decreases, and the burst temperature of green pellets shows a trend of rising first and then decreasing. The change trend is coupled with the software test data amplification method algorithm, based on the search algorithm of longicorn (MBAS), to expand a small amount of experimental data. Through data analysis and algorithm comparison, an improved generalized regression neural network (CFA-GRNN), based on culture firefly, was proposed to establish an optimization model for green pellet performance prediction. CFA uses the weights in the input layer and hidden layer of GRNN, the weights in the hidden layer and output layer, the threshold of the hidden layer and the threshold of the output layer as codes for optimization. The evolutionary goal is to obtain the most appropriate and optimal neural network structure. The results show that the MBAS algorithm, combined with the experimental research, can expand the effective data to 1000 pieces. Secondly, the green pellets’ burst temperature, compressive strength and falling strength predicted by the improved generalized regression neural network are in good agreement with the real values, and the average relative errors were 1.88%, 3.18% and 3.62%, respectively. The error analysis shows that the improved model algorithm has higher accuracy, meets the classification of pellets, and can be used to guide the production of pellets.