scholarly journals Qualitative evaluation of wastewater treatment plant performance by a neural network model optimized by genetic algorithm

2020 ◽  
Author(s):  
Bojan Đurin ◽  
Sara Dadar ◽  
Atena Pezeschi ◽  
Dragana Dogančić
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Network Model ◽  
Neural Network Model ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Qualitative Evaluation ◽  
Plant Performance

The Application of Genetic Algorithm Backpropagation Neural Network Model on the Prediction and Optimization of Wastewater Treatment System

2013 ◽  
Vol 838-841 ◽  
pp. 2525-2531
Author(s):  
Fu Quan Jia ◽  
Zhang Wei He ◽  
Zhu Jun Tian ◽  
Zhao Bo Chen ◽  
Hong Cheng Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Wastewater Treatment ◽  
Network Model ◽  
Neural Network Model ◽  
Correlation Coefficients ◽  
Treatment System ◽  
Backpropagation Neural Network ◽  
Predictive Values ◽  
Wastewater Treatment System

Prediction and optimization on water quality parameters (WQPs) have become more and more important to the wastewater treatment system (WWTs). In this study, the genetic algorithm backpropagation neural network model (GA-BPNN) had been used to predict and optimize WQPs of a low-strengthen complex wastewater treatment system (LSCWWTs). Results showed that the correlation coefficients between the predicted values and measured values were R2 =0.946 for COD, R2=0.962 for BOD, R2=0.933 for TN, R2=0.985 for NH3-N, R2=0.969 for TP, and R2=0.968 for SS, indicating the predictive values by the GA-BPNN model well fitted the mesured values of effluent WQPs. The optimal effluent WQPs were COD=27.6mg/L, BOD=7.1mg/L, TN=5.4mg/L, NH3-N=0.9mg/L, TP=0.11mg/L and SS=9.25mg/L, respectively. And the corresponding operating parameters were MLSS=3045.4mg/L, MLVSS=2405.9mg/L, T=23.2 °C, R=1.4, SRT=12.5d, HRT=17.3h, CODin =643.3mg/L, BODin=342.2mg/L, TNin=54.2mg/L, NH3-Nin=45.3mg/L, TPin=4.9mg/L, SSin=452.6mg/L, which could be beneficial to the operation optimization of LSCWWTs.

scholarly journals ANN-Based Short-Term Wastewater Flow Prediction for Better WWTP Control

2010 ◽  
Vol 4 (2) ◽  
pp. 159-162
Author(s):  
Leslaw Plonka ◽  
◽  
Korneliusz Miksch ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Control Systems ◽  
Neural Network Model ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Inflow Rate ◽  
Short Term ◽  
Flow Prediction

This paper presents an approach to predict the amount of the wastewater which enters wastewater treatment plant, using artificial neural network. The method presented can be used to give short-term predictions of wastewater inflow-rate. The described neural network model uses a very tiny set of data commonly collected by WWTP control systems.

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