基于互信息和自组织RBF神经网络的出水BOD软测量方法

doi:10.11949/j.issn.0438-1157.20181362

摘要/Abstract

摘要：

针对污水处理过程出水生化需氧量（biochemical oxygen demand，BOD）难以实时准确测量的问题，提出了一种基于互信息和自组织RBF神经网络的软测量方法对出水BOD进行预测。首先，使用基于互信息的方法提取相关特征参量作为软测量模型的输入变量；其次，设计一种基于误差校正-敏感度分析的自组织RBF神经网络，使用改进的Levenberg-Marquardt（LM）算法对网络进行训练以提高训练速度；最后将软测量模型应用于UCI公开数据集及实际的污水处理过程，实验结果表明该软测量模型结构紧凑，训练时间相对较短，预测精度有所提高，能够对出水BOD实现快速准确预测。

关键词: 神经网络, 动态建模, 互信息, RBF, 自组织, 出水BOD, 预测

Abstract:

It is difficult to achieve real-time accurate measurement for effluent biochemical oxygen demand (BOD). To solve this problem, a soft-measurement method based on mutual information and a self-organizing RBF neural network is proposed for BOD prediction in this paper. First, a method based on mutual information is employed to extract feature variables, and these variables are used as inputs to the soft-measurement model. Second, a self-organizing radial basis function (RBF) neural network based on error-correction method and sensitivity analysis is designed, and the improved Levenberg-Marquardt (LM) algorithm is used to train parameters of the neural network to shorten its training time. Finally, the soft-measurement model is applied to UCI public datasets and the real wastewater treatment process. The results show that the soft-measurement model has a more compact structure and relatively short training time, and improves the prediction accuracy, which realizes a fast and accurate prediction for BOD.

Key words: neural networks, dynamic modeling, mutual information, RBF, self-organization, effluent BOD, prediction

中图分类号:

TP 183

李文静, 李萌, 乔俊飞. 基于互信息和自组织RBF神经网络的出水BOD软测量方法[J]. 化工学报, 2019, 70(2): 687-695.

Wenjing LI, Meng LI, Junfei QIAO. Effluent BOD soft measurement based on mutual information and self-organizing RBF neural network[J]. CIESC Journal, 2019, 70(2): 687-695.

图/表 9

参考文献 29

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数据集	训练样本数	测试样本数	特征数目	选择特征维数
Abalone	2000	2177	8	4
Cal Housing	8000	12640	8	5

数据集	训练样本数	测试样本数	特征数目	选择特征维数
Abalone	2000	2177	8	4
Cal Housing	8000	12640	8	5

数据集	方法	测试MSE	隐含层神经元数	训练时间/s
Abalone	MRAN	0.0070^φ	—	255.80^①
	GAP-RBF	0.0093^φ	—	14.28^①
	ErrCor-RBF	0.0059^φ	4	4.81 ^①
	ESRBFNN-All	0.0057	3	11.82
	ESRBFNN-MI	0.0056	3	7.98
Cal Housing	MRAN	0.0255^②	64^②	2891.5^②
	GGAP-RBF	0.0193^②	24^②	191.23^②
	ErrCor-RBF	0.0178	5	77.00
	ESRBFNN-All	0.0175	4	133.21
	ESRBFNN-MI	0.0173	4	83.87

数据集	方法	测试MSE	隐含层神经元数	训练时间/s
Abalone	MRAN	0.0070^φ	—	255.80^①
	GAP-RBF	0.0093^φ	—	14.28^①
	ErrCor-RBF	0.0059^φ	4	4.81 ^①
	ESRBFNN-All	0.0057	3	11.82
	ESRBFNN-MI	0.0056	3	7.98
Cal Housing	MRAN	0.0255^②	64^②	2891.5^②
	GGAP-RBF	0.0193^②	24^②	191.23^②
	ErrCor-RBF	0.0178	5	77.00
	ESRBFNN-All	0.0175	4	133.21
	ESRBFNN-MI	0.0173	4	83.87

序号	变量	归一化互信息
1	出水总氮浓度	0.8494
2	出水氨氮浓度	0.8468
3	进水总氮浓度	0.8450
4	进水BOD浓度	0.8338
5	进水氨氮浓度	0.8302
6	出水磷酸盐浓度	0.8285
7	生化MLSS浓度	0.8228
8	生化池DO浓度	0.8159
9	进水磷酸盐浓度	0.8073
10	进水COD浓度	0.8006