污水处理厂出水水质变量区间预测建模

doi:10.11949/0438-1157.20190215

摘要/Abstract

摘要：

为了实现污水处理厂的有效运行，需要建立能够精确描述水厂行为的模型。根据水厂入水和出水数据，采用径向基函数神经网络建立污水处理过程模型。考虑到建模误差有界，使用参数线性集员辨识算法分别得到隐含层到输出层各神经元连接权值向量的不确定集合描述。与现有的单输出区间预测模型相比，该模型能够根据水厂入水数据同时给出多个出水水质变量的置信区间。这些区间能表征出水变量的存在范围，从而实现水质变量的可靠估计，进而评估出水水质或水厂性能。此外，还将此出水区间预测模型用于污水处理厂的故障检测与隔离，以提高水厂运行的可靠性。实验结果表明文中所提方法的有效性。

关键词: 污水处理, 模型, 参数估值, 预测, 故障检测与隔离, 集员辨识, 径向基函数神经网络

Abstract:

To achieve efficient operation of the wastewater treatment plant(WWTP), it is necessary to establish a model that accurately describes the behavior of the plan. In this paper, the radial basis function neural network (RBFNN) is utilized in the modeling of the WWTP basing on the available influent and effluent data. Considering the bounded modeling error, linear-in-parameters set membership identification algorithm is used to describe an uncertain set of each vector representing the weights of the links between all the hidden neurons and one output neuron. Comparing with the existing methods which are all proposed for a single effluent variable, the method here builds a predictor model which can compute confidence intervals for multiple effluent variables simultaneously according to the values of the influent variables. The confidence intervals can characterize the existence ranges of the effluent variables, such that reliable estimates of them are obtained. By the estimates, the effluent quality or the WWTP performance can be evaluated. Besides, the interval predictor model is also applied to the fault detection and isolation of the WWTP to realize reliable operation. The experiment results show the satisfying performance of the proposed method.

Key words: wastewater treatment, model, parameter estimation, prediction, fault detection and isolation, set membership identification, radial basis function neural network

中图分类号:

TP 183

柴伟, 郭龙航, 池彬彬. 污水处理厂出水水质变量区间预测建模[J]. 化工学报, 2019, 70(9): 3449-3457.

Wei CHAI, Longhang GUO, Binbin CHI. Interval model for predicting effluent quality variables of wastewater treatment plants[J]. CIESC Journal, 2019, 70(9): 3449-3457.

图/表 9

图1 污水处理厂整体布局

Fig.1 Schematic layout of wastewater treatment plant

图2 RBF神经网络模型

Fig.2 RBF neural network

图3 故障检测与隔离整体方案

Fig.3 General scheme of fault detection and isolation

图4 采用集员辨识所建模型给出的出水变量置信区间

Fig.4 Confidence intervals of effluent variables given by model built using set membership identification

图5 采用最小二乘法所建模型给出的出水COD置信区间

Fig.5 Confidence interval of effluent COD given by model built using least squares

表1 置信区间的性能

Table 1 Performance of confidence intervals

水质变量	集员辨识		最小二乘法
水质变量	平均宽度/ (g/m³)	包含实测值概率/ %	平均宽度/ (g/m³)	包含实测值概率/ %
BOD	0.343	100	0.391	99.7
COD	2.298	100	2.338	99.4
TSS	1.817	100	1.689	99.3

图6 场景1发生时出水TSS及其置信区间

Fig.6 Effluent TSS and its confidence interval in Scenario 1

图7 场景2发生时出水TSS及其置信区间

Fig.7 Effluent TSS and its confidence interval in Scenario 2

图8 场景3发生时出水变量及其置信区间

Fig.8 Effluent variables and their confidence intervals in Scenario 3

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