Abstract:

Accurate and reliable measurement of the effluent quality indicators of wastewater treatment plants is the key to successful control and optimization of wastewater treatment plants. Due to the complexity of the operation and the delay of laboratory analysis, it is difficult to achieve real-time control of effluent quality. In order to improve the accuracy and reliability of the estimation, this paper proposes a method of stochastic configuration network based on partial least squares (PLS-SCN). In order to overcome the forecast risk caused by high dimensionality and multicollinearity of the input data, the partial least squares(PLS) is embedded into the stochastic configuration network(SCN) framework replacing the classic ordinary least squares (OLS). The PLS-SCN method extracts the main latent variables that affect the effluent quality from the output of the hidden layer, and enhances the generalization performance through orthogonal projection operations. The simulation results of the effluent quality index of a municipal sewage treatment plant show that the PLS-SCN network has a good input and output relationship, and its performance is better than traditional SCN and PLS, and it can quickly and reliably estimate the sewage quality.

Key words: estimation of effluent quality, neural networks, stochastic configuration networks, partial least squares method, mathematical modeling, prediction

摘要：

准确、可靠地测量污水处理厂的出水水质指标是成功控制和优化污水处理厂的关键。由于现有的离线化验方法存在操作繁复、测量滞后的问题，难以实现水质的实时控制。为了提高估计的准确性和可靠性，提出了一种偏最小二乘的随机配置网络方法（PLS-SCN）。为了克服输入数据高维度和多重共线性导致的预测风险，将偏最小二乘（PLS）方法嵌入到随机配置网络（SCN）框架中，以代替经典的普通最小二乘（OLS）方法。PLS-SCN方法从隐含层输出中提取影响水质指标的主要潜在变量，通过正交投影运算来增强泛化性能。某城市污水处理厂水质指标仿真结果表明，PLS-SCN网络具有良好的输入输出关系，性能优于传统SCN和PLS方法，能够快速、可靠地估计污水水质的质量。

关键词: 污水水质估计, 神经网络, 随机配置网络, 偏最小二乘法, 数学模拟, 预测