Virtual water quality monitoring at inactive monitoring sites using Monte Carlo optimized artificial neural networks: A case study of Danube River (Serbia)

2019 ◽  
Vol 654 ◽  
pp. 1000-1009 ◽  
Author(s):  
Tatjana Mitrović ◽  
Davor Antanasijević ◽  
Saša Lazović ◽  
Aleksandra Perić-Grujić ◽  
Mirjana Ristić
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Virtual Water ◽  
Water Quality Monitoring ◽  
Danube River ◽  
Quality Monitoring ◽  
Artificial Neural

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study

2013 ◽  
Vol 20 (12) ◽  
pp. 9006-9013 ◽  
Author(s):  
Davor Antanasijević ◽  
Viktor Pocajt ◽  
Dragan Povrenović ◽  
Aleksandra Perić-Grujić ◽  
Mirjana Ristić
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Dissolved Oxygen ◽  
Oxygen Content ◽  
Danube River ◽  
Artificial Neural

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Sign in / Sign up

Export Citation Format

Share Document