Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor
Application of radial basis function artificial neural network to quantify interfacial energies related to membrane fouling in a membrane bioreactor
复制标题
应用径向基函数人工神经网络量化膜生物反应器中与膜污染相关的界面能
DOI：
10.1016/j.biortech.2019.122103
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发表时间：
2019-12-01
影响因子：
11.4
通讯作者：
Lin, Hongjun
中科院分区：
文献类型：
--
作者：
Chen, Yifeng;Yu, Genying;Lin, Hongjun
由于界面能决定了膜污染物层的形成，因此有效地量化与膜污染相关的界面能代表了膜生物反应器（MBA）的主要兴趣。在这项研究中，径向基函数（RBF）人工神经网络（ANN）与五个相关因素作为输入变量，应用于量化界面能与随机粗糙的膜表面。结果表明，径向基神经网络能够很好地捕捉到相关因素与界面能之间复杂的非线性关系。RBF神经网络定量显示出较高的回归系数和准确性，表明其具有较高的定量界面能的能力。与高级扩展Derjaguin-Landau-Verwey-Overbeek（XDLVO）方法至少一周的时间消耗相比，RBF ANN的量化对于同一情况仅需要几秒钟，表明RBF ANN的高效率。此外，RBF神经网络的能力可以进一步提高。鲁棒RBF神经网络的提出为膜生物反应器中膜污染的研究开辟了一条新的途径。
Efficient quantification of interfacial energy related with membrane fouling represents the primary interest in membrane bioreactors (MBAs) as interfacial energy determines foulant layer formation. In this study, radial basis function (RBF) artificial neural networks (ANNs) with five related factors as input variables were applied to quantify interfacial energy with randomly rough membrane surface. It was found that, RBF ANNs could well capture the complex non-linear relationships between the related factors and interfacial energy. RBF ANN quantification showed high regression coefficient and accuracy, suggesting its high capacity to quantify interfacial energy. Compared to at least one-week time consumption of the advanced extensive Derjaguin-Landau-Verwey-Overbeek (XDLVO) approach, quantification by RBF ANNs only took several seconds for a same case, indicating the high efficiency of RBF ANNs. Moreover, the abilities of RBF ANNs can be further improved. The robust RBF ANNs proposed paved a new way to study membrane fouling in MBRs.