scholarly journals Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Pradyut Kundu ◽  
Anupam Debsarkar ◽  
Somnath Mukherjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Experimental Results ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Slaughterhouse Wastewater ◽  
Ann Models ◽  
Artificial Neural

The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.

scholarly journals Artificial neural network modeling of jatropha oil fueled diesel engine for emission predictions

2009 ◽  
Vol 13 (3) ◽  
pp. 91-102 ◽  
Author(s):  
Thirunavukkarasu Ganapathy ◽  
Parkash Gakkhar ◽  
Krishnan Murugesan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Models ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Nox Emissions ◽  
Jatropha Oil ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Artificial Neural Network Models

This paper deals with artificial neural network modeling of diesel engine fueled with jatropha oil to predict the unburned hydrocarbons, smoke, and NOx emissions. The experimental data from the literature have been used as the data base for the proposed neural network model development. For training the networks, the injection timing, injector opening pressure, plunger diameter, and engine load are used as the input layer. The outputs are hydrocarbons, smoke, and NOx emissions. The feed forward back propagation learning algorithms with two hidden layers are used in the networks. For each output a different network is developed with required topology. The artificial neural network models for hydrocarbons, smoke, and NOx emissions gave R2 values of 0.9976, 0.9976, and 0.9984 and mean percent errors of smaller than 2.7603, 4.9524, and 3.1136, respectively, for training data sets, while the R2 values of 0.9904, 0.9904, and 0.9942, and mean percent errors of smaller than 6.5557, 6.1072, and 4.4682, respectively, for testing data sets. The best linear fit of regression to the artificial neural network models of hydrocarbons, smoke, and NOx emissions gave the correlation coefficient values of 0.98, 0.995, and 0.997, respectively.

scholarly journals Artificial Neural Network (ANN) Approach to Modelling of Selected Nitrogen Forms Removal from Oily Wastewater in Anaerobic and Aerobic GSBR Process Phases

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1594 ◽  
Author(s):  
Piotr Ofman ◽  
Joanna Struk-Sokołowska
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Batch Reactor ◽  
Nitrogen Concentration ◽  
Nitrogen Form ◽  
Ann Model ◽  
Nitrogen Forms ◽  
Neural Network Models ◽  
Ann Models ◽  
Artificial Neural

Paper presents artificial neural network models (ANN) approximating concentration of selected nitrogen forms in wastewater after sequence batch reactor operating with aerobic granular activated sludge (GSBR) in the anaerobic and aerobic phases. Aim of the study was to determine parameters conditioning effectiveness of selected nitrogen forms removal in GSBR reactor process phases. Models of artificial neural networks were developed separately for N-NH4, N-NO3 and total nitrogen concentration in particular process phases of GSBR reactor. In total, 6 ANN models were presented in this paper. ANN models were made as multilayer perceptron (MLP), which were learned using the Broyden-Fletcher-Goldfarb-Shanno algorithm. Developed ANN models indicated variables the most influencing of particular nitrogen forms in aerobic and anaerobic phase of GSBR reactor. Concentration of estimated nitrogen form at the beginning of anaerobic or aerobic phase, depending on ANN model, in all ANN models influenced approximated value. Obtained determination coefficients varied from 0.996 to 0.999 and were depending on estimated nitrogen form and GSBR process phase. Hence, developed ANN models can be used in further studies on modeling of nitrogen forms in anaerobic and aerobic phase of GSBR reactors.

scholarly journals Experimental study and artificial neural network modeling of tartrazine removal by photocatalytic process under solar light

2017 ◽  
Vol 76 (2) ◽  
pp. 311-322 ◽  
Author(s):  
Aicha Sebti ◽  
Fatiha Souahi ◽  
Faroudja Mohellebi ◽  
Sadek Igoud
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Removal Efficiency ◽  
Irradiation Time ◽  
Experimental Results ◽  
Neural Network Modeling ◽  
Photocatalytic Process ◽  
Back Propagation Algorithm ◽  
Solar Radiation Intensity ◽  
Artificial Neural

This research focuses on the application of an artificial neural network (ANN) to predict the removal efficiency of tartrazine from simulated wastewater using a photocatalytic process under solar illumination. A program is developed in Matlab software to optimize the neural network architecture and select the suitable combination of training algorithm, activation function and hidden neurons number. The experimental results of a batch reactor operated under different conditions of pH, TiO2 concentration, initial organic pollutant concentration and solar radiation intensity are used to train, validate and test the networks. While negligible mineralization is demonstrated, the experimental results show that under sunlight irradiation, 85% of tartrazine is removed after 300 min using only 0.3 g/L of TiO2 powder. Therefore, irradiation time is prolonged and almost 66% of total organic carbon is reduced after 15 hours. ANN 5-8-1 with Bayesian regulation back-propagation algorithm and hyperbolic tangent sigmoid transfer function is found to be able to predict the response with high accuracy. In addition, the connection weights approach is used to assess the importance contribution of each input variable on the ANN model response. Among the five experimental parameters, the irradiation time has the greatest effect on the removal efficiency of tartrazine.

scholarly journals Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhonghui Thong ◽  
Jolena Ying Ying Tan ◽  
Eileen Shuzhen Loo ◽  
Yu Wei Phua ◽  
Xavier Liang Shun Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Regression Model ◽  
Sensitivity Threshold ◽  
Ann Model ◽  
Blood Samples ◽  
Ann Models ◽  
Artificial Neural ◽  
The Impact ◽  
Age Prediction

AbstractRegression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

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