The membrane fouling is an important factor of restricting wide application of MBR (Membrane Bio-Reactor), which causes the fall of membrane flux and reduces the membrane cleaning period. So the Bandelet neural network is proposed through combining Bandelet transform and neural network, which predicts membrane flux and its recovery rate for making proper membrane cleaning decision. Firstly, the main affecting factors of membrane fouling are discussed. Secondly, the architecture of Bandelet neural network is designed with Bandelet function and its scale function as activation functions of hidden and output layers respectively. Thirdly, the improved Bat algorithm is established, which is applied to improve the optimization effect of parameters of Bandelet neural network. Finally, the simulation analysis is carried out, the improved bat algorithm has higher performance than the traditional bat algorithm through analyzing the single objective optimization problem from 2018 CEC competition, the optimal number of nodes in hidden layer is confirmed based on comparison analysis and statistical tests. The proposed BNN-IBA has obvious superiority in prediction accuracy and speed according to prediction simulation results of membrane fouling of MBR, which has better prediction results than other state-of-art prediction models optimized by the novel optimal algorithms. In addition, the proper membrane cleaning period and method are confirmed according to the prediction results of membrane flux and its recovery rate.

膜结垢是限制MBR（膜生物反应器）广泛应用的重要因素，MBR会导致膜通量下降并缩短膜清洁时间。因此，结合Bandelet变换和神经网络，提出了Bandelet神经网络，可以预测膜通量及其恢复率，从而做出正确的膜清洁决策。首先，讨论了膜污染的主要影响因素。其次，利用Bandelet函数及其规模函数分别作为隐藏层和输出层的激活函数，设计了Bandelet神经网络的体系结构。第三，建立改进的Bat算法，以提高Bandelet神经网络参数的优化效果。最后进行仿真分析 通过分析2018年CEC竞赛中的单目标优化问题，改进的bat算法比传统的bat算法具有更高的性能，通过比较分析和统计检验确定了隐层中的最优节点数。根据MBR膜污染的预测模拟结果，提出的BNN-IBA在预测精度和速度上均具有明显的优势，其预测结果优于通过新型最优算法优化的其他最新预测模型。另外，根据膜通量的预测结果及其回收率，可以确定适当的膜清洗时间和方法。根据比较分析和统计检验确定隐层中的最佳节点数。根据MBR膜污染的预测模拟结果，提出的BNN-IBA在预测精度和速度上均具有明显的优势，其预测结果优于通过新型最优算法优化的其他最新预测模型。另外，根据膜通量的预测结果及其回收率，可以确定适当的膜清洗时间和方法。根据比较分析和统计检验确定隐层中的最佳节点数。根据MBR膜污染的预测模拟结果，提出的BNN-IBA在预测精度和速度上均具有明显的优势，其预测结果优于通过新型最优算法优化的其他最新预测模型。另外，根据膜通量的预测结果及其回收率，可以确定适当的膜清洗时间和方法。与通过新型最佳算法优化的其他最新技术的预测模型相比，该算法具有更好的预测结果。另外，根据膜通量的预测结果及其回收率，可以确定适当的膜清洗时间和方法。与通过新型最佳算法优化的其他最新技术的预测模型相比，该算法具有更好的预测结果。另外，根据膜通量的预测结果及其回收率，可以确定适当的膜清洗时间和方法。