摘要
结合广义回归神经网络(GRNN)在非线性拟合和柔性网络结构等方面的优势,构建熟球抗压强度预报模型,确定球团矿生产过程中的原料成分配比(Ca、Si、Mg等含量)与表征球团矿质量的重要参数(熟球抗压强度)之间的定量关系。基于抗压强度预报模型,借助天牛须搜索(BAS)算法,构建球团原料最佳配比智能推荐模型,在球团矿配料可调控区间上,智能推荐原料最佳配比方案。仿真与实验结果显示:熟球抗压强度预报模型具有超强的插值能力和优良的泛化性能;在球团矿各种配料变化不超过20%的区间内,智能推荐的最佳配料方案平均提升熟球抗压强度达16%以上,且系统运行稳健、仿真结果有效;将BAS智能推荐模型应用于球团制造实际流程中后较前一年同一时期的熟球抗压强度日均值有明显提升,实际应用效果佳。
Combining the advantages of generalized regression neural network(GRNN)in non-linear fitting and flexible network structure,a prediction model for the anti-compression strength of cooked pellets is constructed to determine the proportion of raw materials(Ca,Si,Mg,etc.)and the important parameters characterizing the quality of pellets in the pellet production process.Based on the anti-compression strength prediction model and with beetle antennae search(BAS)algorithm,an intelligent recommendation model for optimum pellet ingredient proportion is constructed.In the adjustable range of pellet ingredient,the intelligent recommendation scheme for optimum pellet ingredient proportion is presented.The simulation and experiment results of the recommended scheme show that the prediction model of cooked ball anti-compression strength has super interpolation ability and excellent generalization performance.In the pellet ingredient change range of no more than 20%,the intelligent recommended optimal pellet ingredient proportion scheme can increase the cooked ball anti-compression strength by more than 16%on average;the system operates steadily and the simulation results are effective.The BAS intelligent recommendation model was applied in the actual pellet manufacturing process,compared with that in the same period of the previous year,the daily average anti-compression strength of cooked pellets is improved significantly and the practical application effect is good.
作者
韩阳
杨爱民
张玉柱
Han Yang;Yang Aimin;Zhang Yuzhu(College of Metallurgy and Energy,North China University of Science and Technology,Tangshan 063210,China;College of Science,North China University of Science and Technology,Tangshan 063210,China;Tangshan Key Laboratory of Engineering Computing,Tangshan 063210,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2019年第9期246-254,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(51674121,51974130)
河北省自然科学基金(E2017209178,E2018209336)项目资助
关键词
BAS
原料配比
柔性网络
智能推荐
BAS
raw material proportion
flexible network
intelligent recommendation
