【摘要】 针对MBR膜污染因子较为复杂且各因子之间相互交叉,提出基于PSO-BP神经网络的膜污染预测方法.首先用主成分分析法实现输入变量的去维和去相关,简化网络的输入,然后应用粒子群算法优化神经网络的权值和阈值.网络训练时使用的数据是在不同操作条件下,采用孔径为0.038μm的聚醚砜超滤膜处理印染废水溶液时得到的膜通量实验数据,最后用训练好的PSO-BP神经网络对膜通量进行预测.结果表明,与传统BP算法相比,PSO-BP神经网络算法能更快实现收敛,提高运算速度以及膜通量预测的准确度.更多还原

【Abstract】 Membranes fouling in MBR process is caused by many complex and interactional factors.A flux prediction model is put forward based on the PSO-BP neural network,which adjusts weights of BP neural network using particle swarm optimization(PSO) rather than the traditional gradient descent method.First,principal component analysis(PCA) is used to reduce the dimensions and correlations of input parameters.Second,the PSO-BP is used to optimize the weights and thresholds of the neural networks.Based on the experimental data(0.038 μm polyethersulfone membrane for printing and dyeing wastewater treatment),the simulation is performed with MATLAB.Results show that the PSO-BP neural network has a faster convergence speed and a better agreement with the real data than traditional BP neural network.更多还原