Prediction of Biological Hydrogen Production in a Packed-Bed Bioreactor Using a Genetically Evolved Artificial Neural Network

2011 ◽  
Vol 6 (3) ◽  
pp. 253-257 ◽  
Author(s):  
Ji Hye Jo ◽  
Min Woo Lee ◽  
Seung Han Woo ◽  
Dae Sung Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrogen Production ◽  
Packed Bed ◽  
Packed Bed Bioreactor ◽  
Biological Hydrogen Production ◽  
Artificial Neural

scholarly journals 4-chlorophenol removal by air lift packed bed bioreactor and its modeling by kinetics and numerical model (artificial neural network)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elahe Azizi ◽  
Fariba Abbasi ◽  
Mohammad Ali Baghapour ◽  
Mohammad Reza Shirdareh ◽  
Mohammad Reza Shooshtarian
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Removal Efficiency ◽  
Packed Bed ◽  
Packed Bed Bioreactor ◽  
First Order ◽  
Artificial Neural ◽  
High Concentrations ◽  
Artificial Neural Network Ann ◽  
Air Lift

Abstract4-chlorophenol (4-CP) is a hazardous contaminant that is hardly removed by some technologies. This study investigated the biodegradation, and physical 4-CP removal by a mixed microbial consortium in the Airlift packed bed bioreactor (ALPBB) and modeling by an artificial neural network (ANN) for first the time. The removal efficiency of ALPBB was investigated at 4-CP(1-1000 mg/L) and hydraulic retention time (HRT)(6-96 hr) by HPLC. The results showed that removal efficiency decreased from 85 at 1 to 0.03% at 1000 mg/L, with increasing 4-CP concentration and HRT decreasing. BOD5/COD increased with increasing exposure time and concentration decreasing, from 0.05 at 1000 to 0.96 at 1 mg/L. With time increasing, the correlation between COD and 4-CP removal increased (R2 = 0.5, HRT = 96 h). There was a positive correlation between the removal of 4-CP and SCOD by curve fitting was R2 = 0.93 and 0.96, respectively. Moreover, the kinetics of 4-CP removal follows the first-order and pseudo-first-order equation at 1 mg/L and other concentrations, respectively. 4-CP removal modeling has shown that the 2:3:1 and 2:4:1 were the best structures (MSE: physical = 0.126 and biological = 0.9)(R2allphysical = 0.999 and R2testphysical = 0.999) and (R2allbiological = 0.71, and R2testbiological = 0.997) for 4-CP removal. Also, the output obtained by the ANN prediction of 4-CP was correlated to the actual data (R2physical = 0.9997 and R2biological = 0.59). Based on the results, ALPBB with up-flow submerged aeration is a suitable option for the lower concentration of 4-CP, but it had less efficiency at high concentrations. So, physical removal of 4-CP was predominant in biological treatment. Therefore, the modification of this reactor for 4-CP removal is suggested at high concentrations.

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 Modeling Dark Fermentation of Coffee Mucilage Wastes for Hydrogen Production: Artificial Neural Network Model vs. Fuzzy Logic Model

Energies ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 1663
Author(s):  
Edilson León Moreno Cárdenas ◽  
Arley David Zapata-Zapata ◽  
Daehwan Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fuzzy Logic ◽  
Hydrogen Production ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Logic Model ◽  
Fuzzy Logic Model ◽  
Artificial Neural

This study presents the analysis and estimation of the hydrogen production from coffee mucilage mixed with organic wastes by dark anaerobic fermentation in a co-digestion system using an artificial neural network and fuzzy logic model. Different ratios of organic wastes (vegetal and fruit garbage) were added and combined with coffee mucilage, which led to an increase of the total hydrogen yield by providing proper sources of carbon, nitrogen, mineral, and other nutrients. A two-level factorial experiment was designed and conducted with independent variables of mucilage/organic wastes ratio, chemical oxygen demand (COD), acidification time, pH, and temperature in a 20-L bioreactor in order to demonstrate the predictive capability of two analytical modeling approaches. An artificial neural network configuration of three layers with 5-10-1 neurons was developed. The trapezoidal fuzzy functions and an inference system in the IF-THEN format were applied for the fuzzy logic model. The quality fit between experimental hydrogen productions and analytical predictions exhibited a predictive performance on the accumulative hydrogen yield with the correlation coefficient (R2) for the artificial neural network (> 0.7866) and fuzzy logic model (> 0.8485), respectively. Further tests of anaerobic dark fermentation with predefined factors at given experimental conditions showed that fuzzy logic model predictions had a higher quality of fit (R2 > 0.9508) than those from the artificial neural network model (R2 > 0.8369). The findings of this study confirm that coffee mucilage is a potential resource as the renewable energy carrier, and the fuzzy-logic-based model is able to predict hydrogen production with a satisfactory correlation coefficient, which is more sensitive than the predictive capacity of the artificial neural network model.

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