GRNN-SA-Based Model for Formula Optimization of Reconstructed Steel Slag

In order to solve the complex operation involved in steel slag reconstruction caused by the fluctuation of chemical compositions of steel slag,the general regression neural network（GRNN） model was constructed,taking the chemical compositions of steel slag,quicklime,fly ash and calcium fluoride（CaF₂） as input variables and activity index as output variables,respectively.And the formula optimization model of reconstructed steel slag based on GRNN-SA was established by using simulated annealing algorithm（SA） to optimize the calculation.Through empirical analysis,it is concluded that the model can realize the intelligent calculation of reconstruction batching process.This model is universal and can guide the intelligent calculation for steel slag from different sources.And it can predict the experimental results of steel slag reconstruction.The relative error between the actual activity index value and the predicted value is less than 5%,having a high simulation accuracy.