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研究了利用神经网络优化复合纳滤膜制备工艺。以聚砜基膜和聚醚砜酮膜(PPESK)为例,首先,在实验数据的基础上建立并优化了神经网络模型,比较两种不同复合纳滤膜制备工艺的模拟结果与实验结果,充分证明神经网络的适用性和可信性;然后,利用神经网络的预测能力,优化聚砜基膜的制备工艺,确定最佳工艺条件:水相浓度0.6%,有机相浓度0.6%,有机相处理时间6 min。该法不仅可以减少实验成本,且能提供较可信的最优工艺条件,具有一定的实用价值。
The preparation of composite nanofiltration membrane was optimized by using neural network. Taking polysulfone membrane and polyethersulfone membrane (PPESK) as an example, a neural network model was established and optimized based on experimental data. The simulation results and experimental results of two different composite nanofiltration membranes were compared. And then fully proved the applicability and credibility of the neural network. Then, the neural network prediction ability was used to optimize the preparation process of the polysulfone-based membrane to determine the optimum process conditions: aqueous phase concentration 0.6%, organic phase concentration 0.6%, organic phase Processing time 6 min. The method can not only reduce the experimental costs, and can provide more credible optimal process conditions, has a certain practical value.