基于30m^3聚合釜的纳米CaCO_3原位聚合PVC树脂颗粒特性预测与优化

Prediction and optimization of the particle characteristics of PVC manufactured by in-situ polymerization of nano-CaCO_3 in 30 m^3 polymerizers

通过微乳化分散技术使CaCO3实现良好分散,通过氯乙烯原位悬浮聚合制得了纳米CaCO3微乳化法原位聚合PVC树脂(简称纳米PVC树脂)。为解决纳米PVC树脂的颗粒形态控制难题,提出了基于组合神经网络的软测量方法,建立了纳米PVC树脂颗粒特性的软测量预测模型,应用效果表明该软测量模型能较准确地预测纳米PVC树脂的平均粒径。利用该软测量预测模型在30 m3聚合釜上实现了纳米PVC树脂颗粒特性优化,制得具有较理想颗粒特性的纳米PVC树脂。

Nano-CaCO3 in-situ polymerization PVC resin （ nano-PVC resin） was manufactured by in-situ polymerization of vinyl chloride with nano-CaCO3 which was well dispersed by microemulsion dispersion techniques. To resolve the difficult problems of controlling the particle morphology of nano-PVC resin, a soft-measuring method based on combined neural networks was provided, and thus a soft-measuring prediction model for nano-PVC particle characteristics was developed. The application results showed that the soft-measuring model could accurately predict the mean particle size of nano-PVC resin. Optimization of particle characteristics of nano-PVC resin was successfully implemented based on the soft-measuring model in 30 m^3 polymerizers, and thus nano-PVC resin with desirable particle characteristics was prepared.