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.

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

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 Research on a soft measurement model of sewage treatment based on a case-based reasoning approach

2017 ◽  
Vol 76 (12) ◽  
pp. 3181-3189 ◽  
Author(s):  
Jiayan Zhang ◽  
Cuicui Du ◽  
Xugang Feng
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Oxygen Demand ◽  
Prediction Method ◽  
Measurement Model ◽  
Case Based Reasoning ◽  
Wastewater Treatment Process ◽  
Case Based

Abstract In this paper, the measurement of biochemical oxygen demand (BOD) in a wastewater treatment process is analyzed and an intelligent integrated prediction method based on case-based reasoning (CBR) is proposed in order to overcome difficulties. Due to the fact that there are many factors that influence the accuracy of the prediction model, the radial basis function, which is a neural network with a 3 layer feedforward network, is employed to reduce the dimension of input values. Under these circumstances, a back propagation neural network combining with a nearest neighbor retrieval strategy is adopted to match case. Then, the measurement of BOD in wastewater treatment process is analyzed. Finally, the validity of the improved CBR in sewage treatment is demonstrated by using numerical results.

Possible impact of treated wastewater discharge on incidence of antibiotic resistant bacteria in river water

2001 ◽  
Vol 43 (2) ◽  
pp. 91-99 ◽  
Author(s):  
T. Iwane ◽  
T. Urase ◽  
K. Yamamoto
Keyword(s):  
Wastewater Treatment ◽  
River Water ◽  
Wastewater Treatment Plant ◽  
Treatment Process ◽  
Treatment Plant ◽  
Treated Wastewater ◽  
Resistant Bacteria ◽  
Wastewater Discharge ◽  
Wastewater Treatment Process ◽  
Antibiotic Resistant

Escherichia coli and coliform group bacteria resistant to seven antibiotics were investigated in the Tama River, a typical urbanized river in Tokyo, Japan, and at a wastewater treatment plant located on the river. The percentages of antibiotic resistance in the wastewater effluent were, in most cases, higher than the percentages in the river water, which were observed increasing downstream. Since the possible increase in the percentages in the river was associated with treated wastewater discharges, it was concluded that the river, which is contaminated by treated wastewater with many kinds of pollutants, is also contaminated with antibiotic resistant coliform group bacteria and E.coli. The percentages of resistant bacteria in the wastewater treatment plant were mostly observed decreasing during the treatment process. It was also demonstrated that the percentages of resistance in raw sewage are significantly higher than those in the river water and that the wastewater treatment process investigated in this study works against most of resistant bacteria in sewage.

scholarly journals Sensitivity analysis of a municipal wastewater treatment plant model

2019 ◽  
Vol 252 ◽  
pp. 05010
Author(s):  
Paweł Król ◽  
Alberto Gallina ◽  
Michał Lubieniecki ◽  
Tadeusz Uhl ◽  
Tadeusz Żaba
Keyword(s):  
Wastewater Treatment ◽  
Municipal Wastewater ◽  
Treatment Process ◽  
Biological Process ◽  
Treatment Plant ◽  
Numerical Procedure ◽  
Municipal Wastewater Treatment ◽  
Wastewater Treatment Process ◽  
Plant Model ◽  
The Relationship

Waste management is a crucial process to keep the environment in wholesome conditions. The environmental impact of solid waste and wastewater is reduced through construction of appropriate disposal installations. The objective of wastewater treatment in biological reactors is to control the process of biomaterial growth by aerating the sewage content. The process is complex, as depending on a plenty of parameters. In the last decades an effective numerical model, called the Activated Sludge Model (ASM), has been proposed for describing the biological process. The ASM is implemented in the Benchmark Simulation Model (BSM) that simulates the whole wastewater treatment process. The most important parameters in ASM are the kinetic and stoichiometric coefficients. The former describes rate-concentration dependence. The latter characterises the relationship between the components of chemical reactions taking place in the cleaning process. Above parameters are determined by on-site calibration and their importance is relevant during the development of numeric models. This paper aims to examine the influence of kinetic and stoichiometric parameters on the wastewater treatment process of a plant in Płaszów, Kraków. The analysis is carried out by a sample-based numerical procedure. It highlights the ASM parameters playing a major role in the treatment process. Results obtained from the analysis are important for future validation and optimisation processes.

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