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The Application and Research of the GA-BP Neural Network Algorithm in the MBR Membrane Fouling

机译：GA-BP神经网络算法在MBR膜污染中的应用研究

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It is one of the important issues in the field of today's sewage treatment of researching the MBR membrane flux prediction for membrane fouling. Firstly this paper used the principal component analysis method to achieve dimensionality and correlation of input variables and obtained the three major factors affecting membrane fouling most obvious: MLSS, total resistance, and operating pressure. Then it used the BP neural network to establish the system model of the MBR intelligent simulation, the relationship between three parameters, and membrane flux characterization of the degree of membrane fouling, because the BP neural network has slow training speed, is sensitive to the initial weights and the threshold, is easy to fall into local minimum points, and so on. So this paper used genetic algorithm to optimize the initial weights and the threshold of BP neural network and established the membrane fouling prediction model based on GA-BP network. As this research had shown, under the same conditions, the BP network model optimized by GA of MBR membrane fouling is better than that not optimized for prediction effect of membrane flux. It demonstrates that the GA-BP network model of MBR membrane fouling is more suitable for simulation of MBR membrane fouling process, comparing with the BP network.
机译：研究MBR膜通量对膜污染的研究是当今污水处理领域的重要问题之一。首先，本文采用主成分分析方法获得输入变量的维数和相关性，得出影响膜污染的三个主要因素：MLSS，总阻力和工作压力。然后使用BP神经网络建立了MBR智能仿真的系统模型，三个参数之间的关系以及膜污染程度的膜通量表征，因为BP神经网络训练速度较慢，对初始敏感权重和阈值，很容易落入局部最小值，依此类推。为此，本文采用遗传算法对BP神经网络的初始权重和阈值进行了优化，建立了基于GA-BP网络的膜污染预测模型。正如这项研究表明的那样，在相同条件下，用MBR膜污染的遗传算法优化的BP网络模型优于未优化的膜通量预测效果的BP网络模型。结果表明，与BP网络相比，MBR膜污染的GA-BP网络模型更适合于MBR膜污染过程的模拟。

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