Soft-sensor method for effluent ammonia nitrogen based on interval type-2 fuzzy neural networks - Details

Author：

Han, Honggui (Han, Honggui.) (Scholars：韩红桂) | Chen, Zhiyuan (Chen, Zhiyuan.) | Qiao, Junfei (Qiao, Junfei.) (Scholars：乔俊飞) | Zhang, Huiqing (Zhang, Huiqing.)

Indexed by：

EI PKU CSCD

Abstract：

A　soft-sensor　method　for　online　detection　of　effluent　ammonia　nitrogen　(NH4-N)　in　waste　water　treatment　process　was　proposed　on　the　basis　of　interval　type-2　fuzzy　neural　networks　(IT2FNN).　First,　actual　operation　data　related　to　pre-treatment　process　variables　was　collected　and　process　variables　having　strong　correlation　to　effluent　NH4-N　were　selected　by　principal　component　analysis　(PCA)　technique.　Second,　a　self-sensor　model　between　principal　component　variables　and　effluent　NH4-N　was　established　via　IT2FNN　and　model　parameters　were　adjusted　by　gradient　algorithm.　Finally,　the　proposed　soft-sensor　method　was　used　in　a　real　waste　water　treatment　process　(WWTP).　The　experimental　results　show　that　the　new　method　can　predict　effluent　NH4-N　online　with　better　accuracy　than　traditional　methods.　©　All　Right　Reserved.

Keyword：

Ammonia Effluent treatment Fuzzy inference Dynamic models Nitrogen Fuzzy neural networks Fuzzy logic Wastewater Effluents Wastewater treatment Water treatment

Author Community：

[ 1 ] [Han, Honggui]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Han, Honggui]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 3 ] [Chen, Zhiyuan]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Chen, Zhiyuan]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 5 ] [Qiao, Junfei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 6 ] [Qiao, Junfei]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China
[ 7 ] [Zhang, Huiqing]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
[ 8 ] [Zhang, Huiqing]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing; 100124, China

Reprint Author's Address：

韩红桂
[han, honggui]beijing key laboratory of computational intelligence and intelligent system, beijing; 100124, china;;[han, honggui]faculty of information technology, beijing university of technology, beijing; 100124, china

Email：

1307441474@emails.bjut.edu.cn

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