NDLI: Comparison of Chemical-Biological Flocculation Process Model Based on Artificial Neural Network

Content Provider	IEEE Xplore Digital Library
Author	Huang Tian-yin Xia Si-qing Li Ning Huang Yong
Copyright Year	2008
Description	Author affiliation: Sch. of Environ. Sci. & Eng., Suzhou Univ. of Sci. & Technol., Suzhou (Huang Tian-yin)
Abstract	Based on the experimental research on a pilot units of the chemical-biological flocculation process, the multi-input multi-output (MIMO) model and the multi-input single-output (MISO) model have been built followed by the back-propagation (BP) artificial networks. Trained by the data (water temperatures, flocculant dosages, recycle ratio, CODCr, TP, SS, etc.) from the six different operating modes of the processes, all of the two models achieved convergence well. The data of another two operating modes was used for the model prediction. The relative errors of the MISO model prediction were lower than those of the MIMO model prediction; and all of relative errors from the MISO model prediction were less than 9.0 %. As a result, the MISO model is an easy-to-use modelling tool to obtain a quick preliminary assessment for the effluent quality prediction of the chemical-biological flocculation process.
Starting Page	824
Ending Page	827
File Size	181064
Page Count	4
File Format	PDF
ISBN	9781424417476
DOI	10.1109/ICBBE.2008.202
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-05-16
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Chemical engineering Humans Artificial neural networks Effluents Predictive models Signal processing MIMO Chemical processes Chemical technology
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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