Prediction of activated sludge bulking based on recurrent fuzzy neural network - Details

Author：

Xu, Shaopeng (Xu, Shaopeng.) | Han, Honggui (Han, Honggui.) (Scholars：韩红桂) | Qiao, Junfei (Qiao, Junfei.) (Scholars：乔俊飞)

Indexed by：

EI Scopus PKU CSCD

Abstract：

Sludge　volume　index(SVI),　a　key　sludge　sedimentation　performance　evaluation　index,　is　difficult　to　be　obtained　accurately　online　and　the　conventional　approaches　are　time-consuming,　tedious　and　complicated.　A　new　recurrent　fuzzy　neural　network(HRFNN)　method　is　proposed　in　this　paper　to　predict　the　evolution　of　the　sludge　volume　index(SVI).　HRFNN　is　constructed　by　adding　feedback　connections　with　the　internal　variable　in　the　third　layer　of　the　fuzzy　neural　network,　so　it　achieves　output　information　feedback.　Finally,　the　results　of　simulation　indicate　the　efficiency　of　the　modeling　method.　And　compared　with　other　fuzzy　neural　networks,　the　scale　of　network　can　be　simplified　and　its　capability　of　dealing　with　dynamic　information　can　be　strengthened,　it　also　has　better　accuracy.　©　All　Rights　Reserved.

Keyword：

Recurrent neural networks Sewage sludge Fuzzy inference Wastewater treatment Fuzzy neural networks Multilayer neural networks Activated sludge process Fuzzy logic

Author Community：

[ 1 ] [Xu, Shaopeng]College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 2 ] [Han, Honggui]College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China
[ 3 ] [Qiao, Junfei]College of Electronic and Control Engineering, Beijing University of Technology, Beijing 100124, China