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。
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 selforganizing 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.
作者
李文静
李萌
乔俊飞
LI Wenjing;LI Meng;QIAO Junfei(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China)
出处
《化工学报》
EI
CAS
CSCD
北大核心
2019年第2期687-695,共9页
CIESC Journal
基金
国家自然科学基金项目(61603009
61533002)
北京市自然科学基金项目(4182007)
北京市教委科技一般项目(KM201910005023)
北京工业大学日新人才计划项目(2017-RX(1)-04)
关键词
神经网络
动态建模
互信息
RBF
自组织
出水BOD
预测
neural networks
dynamic modeling
mutual information
RBF
self-organization
effluent BOD
prediction
