合成地聚物是循环流化床锅炉粉煤灰高值化利用的有效途径，力学性能是地聚物作为建筑材料使用的重要性能指标，受粉煤灰原料和合成工艺过程诸多因素影响。采用山西朔州王坪电厂循环流化床粉煤灰，利用高分辨率扫描电镜(SEM)、傅里叶变换红外光谱(FT-IR)和固体核磁共振仪(NMR)探究粉煤灰形成地聚物的演化历程和地聚反应机理，在此基础上考察主要工艺参数对地聚物力学性能的影响，并借助多层前馈神经网络对制备条件进行优化。结果表明，粉煤灰中无定形的硅铝化合物在激发剂的作用下形成硅、铝单体，单体再经水解、缩聚及凝胶化反应聚合为地聚物凝胶，凝胶间相互结合形成更大的凝胶体，此后向凝胶失水、硬化阶段转变，最终生成地聚物，构成地聚物的主要化学键为Si—O—Al。增加激发剂模数、减少液固比、提高激发剂中Na2O质量分数、降低养护温度等能够增加地聚物凝胶质量分数，从而提升力学性能;养护时间和搅拌时间过长，不利于产物力学性能的提升。基于脱模后常温养护7 d的地聚物实验数据，构建了结构为6-8-1的力学性能预测模型、进行神经网络优化，模型具有较好的训练精度和泛化能力，训练误差和测试误差分别为0.98%和3.85%。以高力学性能地聚物为目标，得到的优化工艺参数为激发剂模数1.6、液固比0.8、Na2O质量分数9%、养护温度20 ℃、养护时间24 h、搅拌时间20 min。

Geopolymerization is an efficient approach to the utilization of the circulating fluidized bed boiler (CFBB) fly ash.The mechanical performances of synthesized geopolymers are largely dominated by the CFBB fly ash and process parameters,and have further impacts on the application of derived geopolymers used as building materials.In this study,the fly ash,from the CFBB of the Wangping power plant in Shuozhou,Shanxi Province,China was sampled and geopolymerized.The geopolymerization mechanism was studied with the aids of a high-resolution scanning electron microscope,Fourier transform infrared spectroscopy as well as solid-state nuclear magnetic resonance.Besides,the effects of primary process parameters on the mechanical properties of derived geopolymers were systematically investigated and optimized by the model of back-propagation artificial neural network (BPANN) to obtain geopolymer with excellent mechanical properties.The results showed that the amorphous silicon or/and aluminum compounds in the fly ash were activated and formed silicon or/and aluminum monomers in the process of geopolymerization.Then these monomers gradually became geopolymer gels by hy-drolysis,condensation,and gelation.Afterwards,these gels were combined,and the larger gels formed geopoly-mers through dehydration and solidification,and Si—O—Al bonds were the dominating chemical bonding patterns in the generated geopolymer.The mechanical properties of the geopolymer were improved with the piling up of the amount of gel resulted from the increase of the modulus and Na2O content of the activator,and a decrease in the ratio of liquid to solid and curing temperature.However,excessive curing and stirring time were detrimental to the mechanical properties of geopolymers.Based on the experimental data of the geopolymer cured at room tem-perature for 7 days,a BPANN model with the structure of 6-8-1 was established to predict the mechanical properties of the geopolymers.The training error and test error of the established model were 0.98% and 3.85% respectively,indicating that the model had good training accuracy and generalization ability in predicting the mechanical properties of the geopolymers.As a result,the optimized process parameters for synthesizing geopolymer with sufficient mechanical properties were obtained,where activator modulus was 1.6,the liquid-solid ratio was 0.8,Na2O content was 0.09,curing temperature was 20 ℃,curing time was 24 h,and stirring time was 20 min.

1 实验部分

   1.1 循环流化床粉煤灰

   1.2 水玻璃溶液

   1.3 循环流化床地聚物的制备及性能测定

   1.4 多层前馈神经网络

2 结果与讨论

   2.1 循环流化床粉煤灰地聚反应分析

   2.2 原料和制备过程对地聚物强度的影响分析

   2.3 粉煤灰基地聚物抗压强度的神经网络优化

3 结论