scholarly journals Prediction of phycoremediation of As(III) and As(V) from synthetic wastewater by Chlorella pyrenoidosa using artificial neural network

2017 ◽  
Vol 7 (7) ◽  
pp. 3949-3971 ◽  
Author(s):  
M. S. Podder ◽  
C. B. Majumder
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chlorella Pyrenoidosa ◽  
Synthetic Wastewater ◽  
Artificial Neural

scholarly journals Biological hydrogen production from synthetic wastewater by an anaerobic migrating blanket reactor: Artificial neural network (ANN) modeling

2019 ◽  
Vol 6 (4) ◽  
pp. 269-276
Author(s):  
Mohammad Ghasemian ◽  
Ensiyeh Taheri ◽  
Ali Fatehizadeh ◽  
Mohammad Mehdi Amin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrogen Production ◽  
Volatile Fatty Acids ◽  
Synthetic Wastewater ◽  
Anaerobic Sludge ◽  
Hydrogen Yield ◽  
Biological Hydrogen Production ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Background: This study aimed to evaluate an anaerobic migrating blanket reactor (AMBR) for biological hydrogen production, and also to investigate its capability to treat synthetic wastewater. Methods: A five-compartment AMBR (9 L effective volume) was made by Plexiglas and seeded with thermal pretreated anaerobic sludge at 100°C for 30 minutes. The AMBR was operated at mesophilic temperature (37 ± 1°C) with continuous fed of synthetic wastewater at five organic loading rates (OLRs) of 0.5 to 8 g COD/L.d. Results: It was revealed that as the OLR increased from 0.5 to 8 g COD/L.d, the hydrogen production and also volumetric hydrogen production rate (VHPR) improved. Increasing the OLR over this range, led to a decrease in the average hydrogen yield from 1.58 ± 0.34 to 0.97 ± 0.45 mol H2 /mol glucose. The concentration of both volatile fatty acids (VFAs) and solvents kept increasing with OLR. During the AMBR operation, the dominant soluble end products (SEPs) were acetic and butyric acids in all of the OLRs studied. Conclusion: Based on the results, the hydrogen yield was related to the acetate/butyrate fermentation. The artificial neural network (ANN) model was well-fitted to the experimental obtained data from the AMBR, and was able to simulate the chemical oxygen demand (COD) removal and hydrogen production

scholarly journals Genetic algorithm and artificial neural network model for prediction of discoloration dye from an electro-oxidation process in a press-type reactor

2018 ◽  
Vol 78 (4) ◽  
pp. 925-935 ◽  
Author(s):  
Alain Picos ◽  
Juan M. Peralta-Hernández
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Synthetic Wastewater ◽  
Operational Conditions ◽  
Type Reactor ◽  
Time Flow ◽  
Electro Oxidation ◽  
Artificial Neural ◽  
Predicted Values

Abstract This study evaluates the effectiveness of an artificial neural network-genetic algorithm (ANN-GA) artificial intelligence (AI) model in the prediction of behavior and optimization of an electro-oxidation pilot press-type reactor, which treats a synthetic wastewater prepared with a dye. The ANN was built from real experimental data using as input the following variables: time, flow, j, dye concentration, and as output discoloration. The performance of the ANN was measured with MAPE (8.3868%), calculated from real and predicted values. The coupled AI model was used to find the best operational conditions: discoloration efficiency (above 90%) at j = 27 mA/cm2 and dye concentration of 230 mg/L.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

Forecasting travel demand: a comparison of logit and artificial neural network methods

1998 ◽  
Vol 49 (7) ◽  
pp. 717-722 ◽  
Author(s):  
M C M de Carvalho ◽  
M S Dougherty ◽  
A S Fowkes ◽  
M R Wardman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Travel Demand ◽  
Network Methods ◽  
Artificial Neural
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