Enhanced Hydrogen Production from Waste Tires via CO 2-Assisted Gasification by Using Artificial Neural Network and Thermogravimetric Analyses: Modelling and Product Analysis

2021 ◽  
Author(s):  
Humair Ahmed Baloch ◽  
Imtiaz Jamro ◽  
Adnan Raheel Shah ◽  
Muhammad Saffar Korai ◽  
Kashif Anwar ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrogen Production ◽  
Product Analysis ◽  
Thermogravimetric Analyses ◽  
Waste Tires ◽  
Artificial Neural

Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network

2021 ◽  
Vol 302 ◽  
pp. 117567
Author(s):  
Hannah O. Kargbo ◽  
Jie Zhang ◽  
Anh N. Phan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrogen Production ◽  
Biomass Gasification ◽  
Two Stage ◽  
Artificial Neural

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.

Reliability Analysis of Waste Tires Semi-Submersible Breakwater

2017 ◽  
Vol 864 ◽  
pp. 363-368 ◽  
Author(s):  
Long Qi ◽  
Zi Chang Shangguan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
Complex Structure ◽  
Working Environment ◽  
Waste Tires ◽  
The Monte Carlo Method ◽  
Wide Range ◽  
Artificial Neural

With the continuous development of social economy, China's port construction scale has become saturated. As an important part of the harbor, the breakwater plays a crucial role in the safety of the working environment in the harbor, and the reliability of the breakwater is an assessment of its safety. Build on the previous studies, this paper puts forward a semi-submersible breakwater using waste tires. The reliability of this breakwater is analyzed by Monte Carlo method of artificial neural network based on Matlab, and the results are compared to those of Direct sampling Monte Carlo method and Important sampling Monte Carlo method. The results show that the Monte Carlo method is able to analyze the reliability of overturning failure of semi-submersible breakwater of waste tire. Compared with the other two consequences, the result is more accurate. The Monte Carlo method of artificial neural network based on Matlab has the obvious advantage in being devoted to the problem of complex structure and variable. Safety of the breakwater meets the relevant requirements and can be applied to the actual engineering. It can be seen that waste tires has a high degree of reliability, daptability, and a wide range of applications.

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

Estimation of hydrogen production in genetically modified E. coli fermentations using an artificial neural network

2010 ◽  
Vol 35 (24) ◽  
pp. 13186-13192 ◽  
Author(s):  
Luis Manuel Rosales-Colunga ◽  
Raúl González García ◽  
Antonio De León Rodríguez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydrogen Production ◽  
Genetically Modified ◽  
E Coli ◽  
Artificial Neural
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