熔剂性球团生球性能预测及优化模型研究

Research on prediction and optimization model of fresh pellet performance for fluxed pellets

本文采用造球试验,研究了碱度(R)、w(SiO_(2))、w(MgO)对熔剂性球团成球率、生球爆裂温度、抗压强度、落下强度的影响规律,并基于BP神经网络建立了生球性能预测优化模型。研究结果表明:制备熔剂性球团时,R控制在0.6~1.4之间、w(SiO_(2))控制在3.5%~5.0%之间、w(MgO)控制在1.8%~2.6%之间,造球的成球率均在90%以上;混合料中细粒级部分占比的增多会使生球爆裂温度升高、抗压强度与落下强度均先增大后减小;模型预测造球成球率、生球爆裂温度、抗压强度、落下强度的平均绝对百分比误差(MAPE)分别为2.64%、5.09%、5.75%、11.19%,预测精准度能够满足冶金行业预测误差要求,可实现生球性能的精准预测;试验研究结合模型预测得出,当混合料的碱度为1.0、w(SiO_(2))为5.5%、w(MgO)为1.8%时生球的各项性能最佳。

The influence of alkalinity(R),w(Si O_(2))and w(MgO)on the granulation ratio,fresh pellets decrepitation temperature,compressive strength and falling strength of the fluxed pellets is studied by a pelletizing experiment,and a prediction and optimization model of the fresh pellet performance is established based on the BP neural network.The results show that,when preparing fluxed pellets,R is controlled between 0.6~1.4,w(Si O_(2))is controlled between 3.5%~5.0%,w(MgO)is controlled between 1.8%~2.6%,and the resulting granulation ratio is above 90%.The increase of the proportion of fine-grained parts in the compound will increase the decrepitation temperature of the fresh pellets,increase the compressive strength first and then decrease,and increase and then decrease the falling strength.The average absolute percentage errors(MAPE)of the model predicting the spherical granulation ratio,fresh pellets decrepitation temperature,compressive strength and falling strength are 2.64%,5.09%,5.75%and 11.19%,respectively,and the prediction accuracy could meet the requirements of the prediction error of the metallurgical industry and realize the accurate prediction of the performance of the fresh pellets.Combined with experimental research and model predictions,it is concluded that the performance of the fresh pellets is the best when the alkalinity of the compound is 1.0,w(Si O_(2))is 5.5%,and w(MgO)is 1.8%.