Research on a soft measurement model of sewage treatment based on a case-based reasoning approach | Zendy

Jiayan Zhang | Zendy; Cuicui Du | Zendy; Xugang Feng | Zendy

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Research on a soft measurement model of sewage treatment based on a case-based reasoning approach

Author(s) -

Jiayan Zhang,

Cuicui Du,

Xugang Feng

Publication year - 2017

Publication title -

water science and technology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.406

H-Index - 137

eISSN - 1996-9732

pISSN - 0273-1223

DOI - 10.2166/wst.2017.417

Subject(s) - artificial neural network , case based reasoning , process (computing) , dimension (graph theory) , computer science , sewage treatment , artificial intelligence , backpropagation , feed forward , genetic algorithm , data mining , machine learning , feedforward neural network , engineering , control engineering , environmental engineering , mathematics , pure mathematics , operating system

In this paper, the measurement of biochemical oxygen demand (BOD) in a wastewater treatment process is analyzed and an intelligent integrated prediction method based on case-based reasoning (CBR) is proposed in order to overcome difficulties. Due to the fact that there are many factors that influence the accuracy of the prediction model, the radial basis function, which is a neural network with a 3 layer feedforward network, is employed to reduce the dimension of input values. Under these circumstances, a back propagation neural network combining with a nearest neighbor retrieval strategy is adopted to match case. Then, the measurement of BOD in wastewater treatment process is analyzed. Finally, the validity of the improved CBR in sewage treatment is demonstrated by using numerical results.

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