Kim M H, Kim Y S, Prabu A A, Yoo C K
College of Environmental and Applied Chemistry, Center for Environmental Studies/Green Energy Center, Kyung Hee University, Seocheon-dong 1, Gyeonggi-Do 446-701, South Korea.
Water Sci Technol. 2009;60(2):363-70. doi: 10.2166/wst.2009.346.
The well-known mathematical modeling and neural networks (NNs) methods have limitations to incorporate the key process characteristics at the wastewater treatment plants (WWTPs) which are complex, non-stationary, temporal correlation, and nonlinear systems. In this study, a systematic methodology of NNs modeling which can be efficiently included in the key modeling information of the WWTPs is performed by selecting the temporal effect of the hydraulics based on multi-way principal components analysis (MPCA). The proposed method is applied for modeling wastewater quality of a full-scale plant, which is a Daewoo nutrient removal (DNR) process. Through the experimental results in a full-scale plant, the efficiency of the proposed method is evaluated and the prediction capability is highly improved by the inclusion of the hydraulics term due to the optimized structure of neural networks.
著名的数学建模和神经网络（NNs）方法在纳入污水处理厂（WWTPs）的关键过程特征方面存在局限性，这些污水处理厂是复杂、非平稳、具有时间相关性的非线性系统。在本研究中，通过基于多向主成分分析（MPCA）选择水力的时间效应，执行了一种可有效纳入污水处理厂关键建模信息的神经网络建模系统方法。所提出的方法应用于对一个全尺寸工厂的废水水质进行建模，该工厂采用大宇营养物去除（DNR）工艺。通过在全尺寸工厂的实验结果，评估了所提出方法的效率，并且由于神经网络的优化结构，通过纳入水力项，预测能力得到了显著提高。
