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.

Effect of the rotifer Lecane inermis, a potential sludge bulking control agent, on process parameters in a laboratory-scale SBR system

2013 ◽  
Vol 68 (9) ◽  
pp. 2012-2018 ◽  
Author(s):  
Wioleta Kocerba-Soroka ◽  
Edyta Fiałkowska ◽  
Agnieszka Pajdak-Stós ◽  
Mateusz Sobczyk ◽  
Małgorzata Pławecka ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Control Agent ◽  
Batch Reactors ◽  
Activated Sludge Process ◽  
Filamentous Bacteria ◽  
Wastewater Treatment Process

The influence of a high density of rotifers, which is known to be able to control filamentous bacteria, on the parameters of an activated sludge process was examined in four professional laboratory batch reactors. These reactors allow the imitation of the work of a wastewater treatment plant with enhanced nutrient removal. The parameters, including oxygen concentration, pH and temperature, were constantly controlled. The experiment showed that Lecane rotifers are able to proliferate in cyclically anaerobic/anoxic and aerobic conditions and at dissolved oxygen concentrations as low as 1 mg/L. In 1 week, rotifer density increased fivefold, exceeding the value of 2,200 ind./mL. The grazing activity led to an improvement in settling properties. Extremely high numbers of rotifers did not affect the main parameters, chemical oxygen demand (COD), N-NH4, N-NO3, P-PO4 and pH, during sewage treatment. Therefore, the use of rotifers as a tool to limit the growth of filamentous bacteria appears to be safe for the entire wastewater treatment process.

scholarly journals Prediction of BOD Concentration in Wastewater Treatment Process Using a Modular Neural Network in Combination with the Weather Condition

2020 ◽  
Vol 10 (21) ◽  
pp. 7477
Author(s):  
Wenjing Li ◽  
Junkai Zhang
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Prediction Accuracy ◽  
Clustering Algorithm ◽  
Treatment Process ◽  
Weather Condition ◽  
Oxygen Demand ◽  
Wastewater Treatment Process ◽  
Training Time ◽  
Modular Neural Network

Since weather has a huge impact on the wastewater treatment process (WWTP), the prediction accuracy for the Biochemical Oxygen Demand (BOD) concentration in WWTP would degenerate if using only one single artificial neural network as the model for soft measurement method. Aiming to solve this problem, the present study proposes a novel hybrid scheme using a modular neural network (MNN) combining with the factor of weather condition. First, discriminative features among different weather groups are selected to ensure a high accuracy for sample clustering based on weather conditions. Second, the samples are clustered based on a density-based clustering algorithm using the discriminative features. Third, the clustered samples are input to each module in MNN, with the auxiliary variables correlated with BOD prediction input to the corresponding model. Finally, a constructive radial basis function neural network with the error-correction algorithm is used as the model for each subnetwork to predict BOD concentration. The proposed scheme is evaluated on a standard wastewater treatment platform—Benchmark Simulation Model 1 (BSM1). Experimental results demonstrate the performance improvement of the proposed scheme on the prediction accuracy for BOD concentration in WWTP. Besides, the training time is shortened and the network structure is compact.

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.

scholarly journals Unravelling diversity and metabolic potential of microbial consortia at each stage of leather sewage treatment

RSC Advances ◽  
2017 ◽  
Vol 7 (66) ◽  
pp. 41727-41737 ◽  
Author(s):  
Hebin Liang ◽  
Dongdong Ye ◽  
Lixin Luo
Keyword(s):  
Wastewater Treatment ◽  
Activated Sludge ◽  
Treatment Process ◽  
Sewage Treatment ◽  
Microbial Consortia ◽  
Biological Wastewater Treatment ◽  
Metabolic Potential ◽  
Ecological Functions ◽  
Wastewater Treatment Process ◽  
System P

Activated sludge is essential for the biological wastewater treatment process and the identification of active microbes enlarges awareness of their ecological functions in this system.

scholarly journals A Novel Model with GA Evolving FWNN for Effluent Quality and Biogas Production Forecast in a Full-Scale Anaerobic Wastewater Treatment Process

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Zehua Huang ◽  
Renren Wu ◽  
XiaoHui Yi ◽  
Hongbin Liu ◽  
Jiannan Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Wastewater Treatment ◽  
Treatment Process ◽  
Biogas Production ◽  
Anaerobic Treatment ◽  
Production Rates ◽  
Wastewater Treatment Process ◽  
Effluent Quality ◽  
Anaerobic Wastewater Treatment ◽  
Novel Model

The anaerobic treatment process is a complicated multivariable system that is nonlinear and time varying. Moreover, biogas production rates are an important indicator for reflecting operational performance of the anaerobic treatment system. In this work, a novel model fuzzy wavelet neural network based on the genetic algorithm (GA-FWNN) that combines the advantages of the genetic algorithm, fuzzy logic, neural network, and wavelet transform was established for prediction of effluent quality and biogas production rates in a full-scale anaerobic wastewater treatment process. Moreover, the dataset was preprocessed via a self-adapted fuzzy c-means clustering before training the network and a hybrid algorithm for acquiring the optimal parameters of the multiscale GA-FWNN for improving the network precision. The analysis results indicate that the FWNN with the optimal algorithm had a high speed of convergence and good quality of prediction, and the FWNN model was more advantageous than the traditional intelligent coupling models (NN, WNN, and FNN) in prediction accuracy and robustness. The determination coefficients R2 of the FWNN models for predicting both the effluent quality and biogas production rates were over 0.95. The proposed model can be used for analyzing both biogas (methane) production rates and effluent quality over the operational time period, which plays an important role in saving energy and eliminating pollutant discharge in the wastewater treatment system.

scholarly journals Development of a cost-effective wastewater treatment process: combination of different process

2021 ◽  
Vol 233 ◽  
pp. 01106
Author(s):  
Song Du ◽  
Wenbiao Jin
Keyword(s):  
Wastewater Treatment ◽  
Catalytic Oxidation ◽  
Treatment Process ◽  
Batch Reactor ◽  
Oxygen Demand ◽  
Cost Effective ◽  
Fenton Oxidation ◽  
Wastewater Treatment Process ◽  
Combination Process ◽  
Electro Catalytic Oxidation

Caprolactam wastewater produced by the production process of caprolactam is characterized by a very high toxicity and chemical oxygen demand (COD) values, having potential harm to the environment if treated improperly. However, these characteristics make caprolactam wastewaters difficult to treat using traditional methods. So the aim of this work was to develop a cost-effective caprolactam wastewater treatment process. Fenton oxidation, sequencing batch reactor activated sludge process (SBR) and electro-catalytic oxidation were proposed to treat caprolactam wastewater in the laboratory scale, and the treatment effects were investigated. Compared with Fenton oxidation, SBR and electro-catalytic oxidation can treat caprolactam wastewater at a lower cost and more efficiently. The pilot test results indicate that the COD can be decreased to less than 1000 mg/L by the combination process, and when the COD removal rates maintain 90%, the cost of caprolactam wastewater treatment is below 6 yuan/m3. The combination process showed better economic benefit.

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

A MATHEMATICAL MODEL FOR WASTEWATER TREATMENT PROCESS OF AN OXIDATION POND

2016 ◽  
Vol 78 (3-2) ◽  
Author(s):  
Amir S. A. Hamzah ◽  
Akbar Banitalebi ◽  
Ali H. M. Murid ◽  
Zainal A. Aziz ◽  
Hasniza Ramli ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Wastewater Treatment ◽  
Dissolved Oxygen ◽  
Treatment Process ◽  
Oxygen Demand ◽  
Wastewater Treatment Process ◽  
Oxidation Pond ◽  
Coupled Equations ◽  
Proposed Model ◽  
The Mathematical Model

This study presents a mathematical model for wastewater treatment process (WWTP) of an oxidation pond. The model permits investigating the effects of a biological-based product called mPHO on the degradation of contaminants as well as increase the amount of dissolved oxygen (DO) in the pond. At this aim, an ordinary differential equation with coupled equations has been developed to study the correlation between the amount of bacteria (phototrophic and Coliform), chemical oxygen demand (COD), and dissolved oxygen (DO) existing in the pond. The mathematical model is employed to simulate the behaviour of the system where the numerical results demonstrate that the proposed model gives a good approximation of the interaction processes that occur naturally between biological and chemical substances involved in the pond

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