Multi‐Variable Direct Self‐Organizing Fuzzy Neural Network Control for Wastewater Treatment Process

2018 ◽  
Vol 22 (2) ◽  
pp. 716-728 ◽  
Author(s):  
Wei Zhang ◽  
Jun‐fei Qiao
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Network Control ◽  
Neural Network Control ◽  
Wastewater Treatment Process ◽  
Fuzzy Neural ◽  
Self Organizing

Decoupling control for wastewater treatment process based on recurrent fuzzy neural network

2018 ◽  
Vol 21 (3) ◽  
pp. 1270-1280 ◽  
Author(s):  
Jun‐Fei Qiao ◽  
Gai‐Tang Han ◽  
Hong‐Gui Han ◽  
Cui‐Li Yang ◽  
Wei Li
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Decoupling Control ◽  
Wastewater Treatment Process ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

Study on the Fuzzy Neural Network Control Used in Wastewater Treatment

2011 ◽  
Vol 71-78 ◽  
pp. 3127-3132 ◽  
Author(s):  
Zhong Qi Wang ◽  
Cheng Zhao
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Dissolved Oxygen ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Neural Network Control ◽  
Neural Network Controller ◽  
Network Controller ◽  
Strong Robustness ◽  
Fuzzy Neural

In this paper, we introduce the study on fuzzy neural network control used in wastewater treatment. An effective fuzzy neural network controller is proposed. The simulation result shows that the system gives strong robustness and good dynamic characteristics. It is used to control dissolved oxygen and forecast water quality. The result indicates that the concentration of dissolved oxygen can reach expectation fleetly and effectively. The model has better precision of forecasting and faster speed of convergence.

Modeling of a paper-making wastewater treatment process using a fuzzy neural network

2012 ◽  
Vol 29 (5) ◽  
pp. 636-643 ◽  
Author(s):  
Mingzhi Huang ◽  
Jinquan Wan ◽  
Yan Wang ◽  
Yongwen Ma ◽  
Huiping Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Wastewater Treatment Process ◽  
Paper Making ◽  
Fuzzy Neural

scholarly journals Prediction of sludge bulking using the knowledge-leverage-based fuzzy neural network

2017 ◽  
Vol 77 (3) ◽  
pp. 617-627 ◽  
Author(s):  
Honggui Han ◽  
Zheng Liu ◽  
Luming Ge ◽  
Junfei Qiao
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Treatment Plant ◽  
Prediction Method ◽  
Wastewater Treatment Process ◽  
Sludge Bulking ◽  
Fuzzy Neural ◽  
Real Wastewater

Abstract One of the most important steps and the main bottleneck of the activated sludge wastewater treatment process (WWTP) is the secondary clarification, where sludge bulking is still a widespread problem. In this paper, an intelligent method, based on a knowledge-leverage-based fuzzy neural network (KL-FNN), is developed to predict sludge bulking online. This proposed KL-FNN can make full use of the data and the existing knowledge from the operation of WWTP. Meanwhile, a transfer learning mechanism is applied to adjust the parameters of the proposed method to improve the predicting accuracy. Finally, this proposed method is applied to a real wastewater treatment plant for predicting the sludge bulking risk, and then for predicting the sludge bulking. The experimental results indicate that the proposed prediction method can be used as a tool to achieve better performance and adaptability than the existing methods in terms of predicting accuracy for sludge bulking.

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