Optimal Control of Effluent Ammonia Nitrogen for Municipal Wastewater Treatment Process - Details

Author：

Han, Honggui (Han, Honggui.) (Scholars：韩红桂) | Yang, Shiheng (Yang, Shiheng.) | Zhang, Lu (Zhang, Lu.) | Qiao, Junfei (Qiao, Junfei.) (Scholars：乔俊飞)

Indexed by：

EI Scopus CSCD

Abstract：

To　improve　the　treatment　effect　of　effluent　ammonia　nitrogen　in　municipal　wastewater　treatment　process,　an　optimal　control　method　was　proposed　in　this　paper.　First,　the　performance　index　of　effluent　ammonia　nitrogen　concentration　was　analyzed　by　using　the　mechanism　characteristics.　Then,　a　relationship　model　with　the　adaptive　kernel　function　between　the　performance　index　and　the　control　variables　was　established.　Next,　a　particle　swarm　optimization　algorithm　was　used　to　obtain　the　optimal　solutions　of　dissolved　oxygen　concentration.　After　that,　an　adaptive　fuzzy　neural　network　controller　was　designed　to　complete　the　tracking　control　of　dissolved　oxygen　concentration.　Finally,　the　proposed　optimal　control　method　was　applied　to　the　benchmark　simulation　model　No.1　(BSM1).　The　results　demonstrated　that　the　proposed　optimal　control　method　can　not　only　improve　the　treatment　effect　of　effluent　ammonia　nitrogen,　but　also　effectively　reduce　the　energy　consumption.　©　2020,　Shanghai　Jiao　Tong　University　Press.　All　right　reserved.

Keyword：

Dissolved oxygen Particle swarm optimization (PSO) Nitrogen Wastewater treatment Ammonia Energy utilization Fuzzy neural networks Effluent treatment Effluents Adaptive control systems Process control

Author Community：

[ 1 ] [Han, Honggui]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 2 ] [Yang, Shiheng]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 3 ] [Zhang, Lu]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China
[ 4 ] [Qiao, Junfei]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, Beijing; 100124, China