scholarly journals Effluent prediction of chemical oxygen demand from the astewater treatment plant using artificial neural network application

2017 ◽  
Vol 120 ◽  
pp. 156-163 ◽  
Author(s):  
Sani Isa Abba ◽  
Gozen Elkiran
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Oxygen Demand ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Network Application ◽  
Artificial Neural

scholarly journals Using Multilayer Perceptron Artificial Neural Network for Predicting and Modeling the Chemical Oxygen Demand of the Gamasiab River

2018 ◽  
Vol 5 (1) ◽  
pp. 15-20
Author(s):  
Mohamad Parsimehr ◽  
Kamran Shayesteh ◽  
Kazem Godini ◽  
Maryam Bayat Varkeshi
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Chemical Oxygen Demand ◽  
Correlation Coefficient ◽  
Multilayer Perceptron ◽  
Oxygen Demand ◽  
Input Parameters ◽  
Artificial Neural ◽  
The Cost

Concerns about water quality have widely increased in the last three decades; thus, water quality is now as important as its quantity. To study and model the quality of the Gamasiab River, its data, including chemical oxygen demand (COD), biological oxygen demand (BOD), dissolved oxygen (DO), total dissolved solids (TDS), total suspended solids in water, acidity, temperature, turbidity, and cations and anions were measured at four stations. Then, the correlations between these parameters and COD were measured using Pearson’s correlation coefficient and modeled by multilayer perceptron artificial neural network. In order to minimize the cost of the experiments performed and to provide the input parameters to the artificial neural network based on the correlations between the data and COD, the number of input parameters was reduced and finally, model No.3, with the Momentum training function and the TanhAxon activation function with the validation correlation coefficient of 0.97, mean absolute error of 2.88, and normalized root mean square error of 0.11 was identified as the most accurate model with the lowest cost. The results of the present study showed that the multilayer perceptron neural network has high ability in modeling the COD of the river, and those data correlated with each other have the greatest effect on the model. Moreover, the number of input parameters can be reduced in order to lower the cost of experiments while the performance of the model is not undermined.

Using artificial neural network for predicting and controlling the effluent chemical oxygen demand in wastewater treatment plant

2019 ◽  
Vol 30 (3) ◽  
pp. 593-608 ◽  
Author(s):  
Naceureddine Bekkari ◽  
Aziez Zeddouri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Chemical Oxygen Demand ◽  
Wastewater Treatment Plant ◽  
Treatment Plant ◽  
Oxygen Demand ◽  
Activation Functions ◽  
Content Type ◽  
Hidden Layer

Purpose Modeling Wastewater Treatment Plant (WWTP) constitutes an important tool for controlling the operation of the process and for predicting its performance with substantial influent fluctuations. The purpose of this paper is to apply an artificial neural network (ANN) approach with a feed-forward back-propagation in order to predict the ten-month performance of Touggourt WWTP in terms of effluent Chemical Oxygen Demand (CODeff). Design/methodology/approach The influent variables such as (pHinf), temperature (TEinf), suspended solid (SSinf), Kjeldahl Nitrogen (KNinf), biochemical oxygen demand (BODinf) and chemical oxygen demand (CODinf) were used as input variables of neural networks. To determine the appropriate architecture of the neural network models, several steps of training were conducted, namely the validation and testing of the models by varying the number of neurons and activation functions in the hidden layer, the activation function in output layer as well as the learning algorithms. Findings The better results were achieved with an architecture network [6-50-1], hyperbolic tangent sigmoid activation functions at hidden layer, linear activation functions at output layer and a Levenberg – Marquardt method as learning algorithm. The results showed that the ANN model could predict the experimental results with high correlation coefficient 0.89, 0.96 and 0.87 during learning, validation and testing phases, respectively. The overall results indicated that the ANN modeling approach can provide an effective tool for simulating, controlling and predicting the performance of WWTP. Originality/value This work is the first of its kind in this region due to the remarkable development in terms of population and agricultural activity in the region, which drove to the increase of water pollutants, so it is necessary to use the modern technologies to modeling and controlling of WWTP.

scholarly journals Применение искусственной нейронной сети в экспериментах по ядерному спиновому эху

2019 ◽  
Vol 45 (12) ◽  
pp. 16
Author(s):  
С.Н. Полулях ◽  
А.И. Горбованов
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nuclear Spin ◽  
Spin Echo ◽  
Gaussian Form ◽  
The Neural Network ◽  
Network Application ◽  
Artificial Neural

The possibility of artificial neural network application to detect nuclear spin echo signals under conditions when the echo amplitude is comparable to the amplitude of the noise is demonstrated. Data obtained by superimposing the model echo signals of a Gaussian form on experimentally recorded noise signals is proposed to use for training the neural network.

Sign in / Sign up

Export Citation Format

Share Document