BP神经网络预测矿物聚合材料强度的影响因素

硅酸盐通报 , 2008, 27(3): 640-644.

BP神经网络预测矿物聚合材料强度的影响因素

张祖华 ^1, , 姚晓 ^2, , 诸华军 ^3, , 华苏东 ^4, , 陈悦 ^5,

1.南京工业大学材料科学与工程学院,南京210009

2.南京工业大学材料科学与工程学院,南京210009;材料化学工程国家重点实验室,南京210009

3.南京工业大学材料科学与工程学院,南京210009

4.南京工业大学材料科学与工程学院,南京210009

5.南京工业大学材料科学与工程学院,南京210009

基金项目: 国家高技术研究发展计划(863计划)(2006AA06Z225)

关键词：矿物聚合材料 , 煅烧高岭土 , BP神经网络 , 强度

Factors Affecting Strength of Geopolymer Predicted by Back Propagation Neural Network

基于矿物聚合材料制备过程中激发剂和配料组成的多样性与复杂性,采用BP神经网络方法考察了影响矿物聚合材料强度的3个因素:激发剂NaOH溶液的浓度、配料中碱硅物质的量比和铝硅物质的量比.结果表明:BP神经网络可以准确地预测矿物聚合材料抗折强度和抗压强度(误差在10-2数量级);高碱激发下(COH-=12 mol/L),M2O/SiO2对抗压及抗折强度影响显著,预测M2O/SiO2=0.332,Al2O3/SiO2=0.441时制品的抗压强度达30.96 MPa,抗折强度高达9.33 MPa;SEM分析和MIP实验证明,提高激发剂NaOH溶液浓度和碱硅物质的量比更有利于形成内部结构完整、强度更高的矿物聚合材料.

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