Sequential modelling of a full-scale wastewater treatment plant using an artificial neural network

2011 ◽  
Vol 34 (8) ◽  
pp. 963-973 ◽  
Author(s):  
Joong-Won Lee ◽  
Changwon Suh ◽  
Yoon-Seok Timothy Hong ◽  
Hang-Sik Shin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Full Scale ◽  
Artificial Neural

scholarly journals Artificial Neural Network-Cuckoo Optimization Algorithm (ANN-COA) for Optimal Control of Khorramabad Wastewater Treatment Plant, Iran

2016 ◽  
Vol 2 (11) ◽  
pp. 555-567 ◽  
Author(s):  
Samaneh Khademikia ◽  
Ali Haghizadeh ◽  
Hatam Godini ◽  
Ghodratollah Shams Khorramabadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Mean Square Error ◽  
Optimization Algorithm ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Mean Square ◽  
Cuckoo Optimization Algorithm ◽  
Artificial Neural

In this study a hybrid estimation model ANN-COA developed to provide an accurate prediction of a Wastewater Treatment Plant (WWTP). An effective strategy for detection of some output parameters tested on a hardware setup in WWTP. This model is designed utilizing Artificial Neural Network (ANN) and Cuckoo Optimization Algorithm (COA) to improve model performances; which is trained by a historical set of data collected during a 6 months operation. ANN-COA based on the difference between the measured and simulated values, allowed a quick revealing of the faults. The method could obtain the fault detection and used in solving continuous and discrete optimization problems, successfully. After constructing and modelling the method, selected performance indices including coefficient of Regression, Mean-Square Error, Root-Mean-Square Error and Aggregated Measure used to compare the obtained results. This analysis revealed that the hybrid ANN-COA model offers a higher degree of accuracy for predicting and control the WWTP.

scholarly journals Artificial neural network (ANN) approach for modeling of selected biogenic compounds in a mixture of treated municipal and dairy wastewater

2019 ◽  
Vol 100 ◽  
pp. 00077
Author(s):  
Joanna Struk-Sokołowska ◽  
Piotr Ofman ◽  
Sevgi Demirel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Wastewater Treatment Plant ◽  
Extreme Values ◽  
Treatment Plant ◽  
Dairy Wastewater ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This paper presents artificial neural network (ANN) model of wastewater treatment plant, which was used for average monthly concentrations of N-NH4+, N-NO3-, N-NO2-, total Kiejdahl nitrogen (TKN), PO43- and SO42- approximation. ANN model was developed for wastewater treatment plant located in Bystre, Poland which treats municipal wastewater with a share of dairy wastewater. The object was chosen because of the unique location, in the Great Mazury Lakes area and the need for its special environmental protection. Input layer of developed ANN model consisted of BOD, COD, concentrations of total nitrogen and total phosphorus, total organic carbon, sulphates, wastewater temperature and pH., The developed model reflected extreme values observed during study period. Average error percentage with which output variables were approximated equalled to 35.35%; 8.99%; 21.23%; 5.08%; 10.99%; 3.02% respectively for N-NH4+, N-NO3-, N-NO2-, TKN, PO43- and SO42-.

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