Application of Artificial Neural Network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT

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

Civil Engineering Journal ◽

10.28991/cej-2016-00000058 ◽

2016 ◽

Vol 2 (11) ◽

pp. 555-567 ◽

Cited By ~ 2

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.

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Forecasting Wastewater Temperature Based on Artificial Neural Network (ANN) Technique and Monte Carlo Sensitivity Analysis

Sustainability ◽

10.3390/su12166386 ◽

2020 ◽

Vol 12 (16) ◽

pp. 6386 ◽

Cited By ~ 3

Author(s):

Farzin Golzar ◽

David Nilsson ◽

Viktoria Martin

Keyword(s):

Neural Network ◽

Sensitivity Analysis ◽

Artificial Neural Network ◽

Monte Carlo ◽

Wastewater Treatment ◽

Wastewater Treatment Plant ◽

Heat Recovery ◽

Historical Data ◽

Treatment Plant ◽

Artificial Neural Network Ann

Wastewater contains considerable amounts of thermal energy. Heat recovery from wastewater in buildings could supply cities with an additional source of renewable energy. However, variations in wastewater temperature influence the performance of the wastewater treatment plant. Thus, the treatment is negatively affected by heat recovery upstream of the plant. Therefore, it is necessary to develop more accurate models of the wastewater temperature variations. In this work, a computational model based on artificial neural network (ANN) is proposed to calculate wastewater treatment plant influent temperature concerning ambient temperature, building effluent temperature and flowrate, stormwater flowrate, infiltration flowrate, the hour of day, and the day of year. Historical data related to the Stockholm wastewater system are implemented in MATLAB software to drive the model. The comparison of calculated and observed data indicated a negligible error. The main advantage of this ANN model is that it only uses historical data commonly recorded, without any requirements of field measurements for intricate heat transfer models. Moreover, Monte Carlo sensitivity analysis determined the most influential parameters during different seasons of the year. Finally, it was shown that installing heat exchangers in 40% of buildings would reduce 203 GWh year−1 heat loss in the sewage network. However, heat demand in WWTP would be increased by 0.71 GWh year−1, and the district heating company would recover 176 GWh year−1 less heat from treated water.

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