Research On The COD Soft-measuring
Mechanism Based On The Clustering Approach
Author(s)
Y. Z. Feng, G. C. Chen, D. Feng & M. Zhuo
Abstract
By analyzing laws of growth and reproduction of microorganisms in the
activated sludge (AS) sewage treatment system, this paper proposes a multineural
network (NN) COD (chemical oxygen demand) soft-measuring method
based on clustering approach for the sewage treatment project. Various reasons
which might affect the accuracy of the model are analyzed. Experiments show
that the same radiuses of multiple neural network diffusion constant are quite
close which means the prediction accuracy is high and the soft-measuring
method based on clustering approach is suitable for COD measuring.
Keywords: sewage treatment, clustering approach, neural network,
microorganisms.
1 Introduction
Cybenko [1] has proved theoretically that if there are plenty of training data and
without restriction of the size of the network, modelling based on NN can always
get a satisfactory model structure. But, in the actual industrial process, people
often need to face the limited effective process data and due to the real-time
requirements, the network structure also cannot be expanded unlimited.
Modeling effect normally relies on the good generalization ability of the
network. Paper [2] proposed a robust classification method by combining
different models which are based on neural networks with fuzzy combination
approach. Paper [3] presented a method to improve model prediction accuracy
and robustness by adding different models together. Paper [4] discussed Stacked
Neural Network Approach for process modeling. The basic idea of these
Keywords
sewage treatment, clustering approach, neural network,
microorganisms
Related Book
Other papers in this volume
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