Neural network prediction and control model for ammonia oxidizing process under low DO concentration - Details

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Abstract：

Under　low　dissolved　oxygen　(DO)　concentration,　the　neural　network　prediction　method　was　applied　in　SBR　for　treating　domestic　wastewater.　The　neural　network　control　model　was　built　to　predict　and　control　the　ammonia　oxidizing　process.　The　model　was　divided　into　two　parts.　In　the　first　part　with　the　correlation　coefficient　(R　value)　of　0.9985,　the　end　of　ammonia　oxidization　was　predicted　based　on　the　on-line　pH　variations.　In　the　second　part　with　R　value　of　0.9083,　the　ammonia　concentration　was　real-time　predicted　based　on　the　on-line　pH　variations.　The　results　showed　that　the　model　with　high　prediction　accuracy,　good　controllability,　better　adaptability　and　stability,　can　not　only　benefit　for　achieving　and　stabilizing　short-cut,　but　also　promote　the　application　of　anaerobic　ammonium　oxidation　for　treating　domestic　wastewater.　©　2017,　Editorial　Board　of　China　Environmental　Science.　All　right　reserved.

Keyword：

Wastewater treatment Forecasting Neural networks Nitrogen removal Models Ammonia Dissolved oxygen pH Process control Predictive analytics

Author Community：

[ 1 ] [Feng, Hong-Li]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China
[ 2 ] [Liu, Xiu-Hong]School of Environment & Natural Resources, Renmin University of China, Beijing; 100872, China
[ 3 ] [Yang, Qing]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China
[ 4 ] [Huang, Si-Ting]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China
[ 5 ] [Cui, Bin]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China
[ 6 ] [Zhou, Tong]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China
[ 7 ] [Yang, Yu-Bing]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China
[ 8 ] [Zhou, Xue-Yang]Key Laboratory of Beijing Water Quality Science and Water Environment Recovery Engineering, Beijing University of Technology, Beijing; 100022, China

Reprint Author's Address：

杨庆
[yang, qing]key laboratory of beijing water quality science and water environment recovery engineering, beijing university of technology, beijing; 100022, china

Email：

yangqing@bjut.edu.cn

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