Flux Prediction of MBR Based on PSO-BP Neural Network

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.