Multiobjective design of fuzzy neural network controller for wastewater treatment process

2018 ◽  
Vol 67 ◽  
pp. 467-478 ◽  
Author(s):  
Hong-Gui Han ◽  
Lu Zhang ◽  
Hong-Xu Liu ◽  
Jun-Fei Qiao
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Treatment Process ◽  
Wastewater Treatment Process ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Multiobjective Design

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.

Varying Parameters of the Object Model on Fuzzy Neural Network Controller for Activated Sludge Method of the Sewage Treatment System

2011 ◽  
Vol 230-232 ◽  
pp. 339-345
Author(s):  
Zhao Hui Shi ◽  
Cheng Zhi Wang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Fuzzy Neural Network ◽  
Sewage Treatment ◽  
Network Control ◽  
Neural Network Control ◽  
Dissolved Oxygen Concentration ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural

In this paper, we take characteristics of wastewater treatment and process technology, drawing on the effectiveness of thetraditional PID control and on the basis of its lack, with the key steps in the sewage treatment process - Aeration control of part of the process parameters, Fuzzy neural network control of dissolved oxygen concentration (DO) to achieve negative feedback control loop,design a model-based closed-loop cascade control system. Fuzzy systems, membership function, the structure of the network topology and algorithms are based on the actual issues identified in the fuzzy variables. Aiming at the four parts of the fuzzy control, adopting four fuzzy neural network based on the standard model - the input layer, Fuzzy layer,Inference layer,Clear layer are corresponding with it. Standing on two points: the dissolved oxygen concentration control and the rate of change from the error ,then design the Fuzzy neural network controller. Then the fuzzy neural network control technology could be used in wastewater treatment on the specific application of process control.

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