采取自动连续补加方式，可以使催化剂保持高性能，从而获得稳定的丙烯腈收率，实现反应器平稳优化操作。
The results showed that the catalyst maintained high characteristics, stationary acrylonitrile yield was obtained and the operation of fluidized bed reactor was optimized.
实验结果表明，该模型的性能优于粒子群神经网络模型，能够准确预测丙烯腈收率，具有较高的精度和良好的应用前景。
Experiment results show that the model based on PSOFNN has higher precision and better performance than the model based on PSONN.
研究了聚丙烯腈基活性碳纤维的制备条件，如活化温度、活化时间对产品收率、比表面积及对正丁硫醇吸附与脱除性能的影响。
The influence of preparation conditions such as activated temperature and time on yield, specific surface area and adsorption behavior towards n-butane thiol was studied.
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