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Prediction of microfiltration membrane fouling using artificial neural network models

机译：使用人工神经网络模型预测微滤膜结垢

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In this study, artificial neural network (ANN) models were applied to predict the performance of micro-filtration (MF) system for water treatment. A series of bench scale experiments were conducted at critical flux and supra-critical flux conditions with various permeate fluxes and feed water qualities. The effects of operating parameters on membrane performance were evaluated based on the comparison of transmembrane pressure (TMP) as a function of operating time. The ANN models used five input variables including permeate flux (J_w), feed water turbidity (Tur_f), UV254, time (h), and backwash frequency for predicting corresponding TMP. The modeling results indicated that there was an excellent agreement between the experimental data and predicted values. Nevertheless, selection of database for training is important for the accuracy of ANN prediction. Relative weights of each input variable were calculated to find out key operational factors affecting the performance of MF system.
机译：在这项研究中，人工神经网络（ANN）模型被应用于预测微滤（MF）系统用于水处理的性能。在临界通量和超临界通量条件下，以各种渗透通量和给水水质进行了一系列实验规模的实验。基于跨膜压力（TMP）与操作时间的函数关系比较，评估了操作参数对膜性能的影响。 ANN模型使用五个输入变量（包括渗透通量（J_w），给水浊度（Tur_f），UV254，时间（h）和反冲洗频率）来预测相应的TMP。建模结果表明，实验数据和预测值之间有很好的一致性。然而，用于训练的数据库的选择对于神经网络预测的准确性很重要。计算每个输入变量的相对权重，以找出影响MF系统性能的关键操作因素。

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