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.

Artificial Neural Network Model for Prediction of Latent Heat Flux over Bay of Bengal

2019 ◽  
Vol 7 (5) ◽  
pp. 901-905
Author(s):  
Kanchan. V. Shende ◽  
Vishal S. Shirsat ◽  
M. R. Ramesh Kumar ◽  
K.V. Kale
Keyword(s):  
Neural Network ◽  
Heat Flux ◽  
Artificial Neural Network ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
Network Model ◽  
Neural Network Model ◽  
Bay Of Bengal ◽  
Artificial Neural Network Model ◽  
Artificial Neural

scholarly journals A feedforward artificial neural network model for classification and detection of type 2 diabetes

2021 ◽  
Vol 1734 ◽  
pp. 012026
Author(s):  
Enoch A. Frimpong ◽  
Ariyo Oluwasanmi ◽  
Edward Y. Baagyere ◽  
Qin Zhiguang
Keyword(s):  
Neural Network ◽  
Type 2 Diabetes ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural

scholarly journals Using Artificial Neural Networks to Predict Influences of Heterogeneity on Rock Strength at Different Strain Rates

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3042
Author(s):  
Sheng Jiang ◽  
Mansour Sharafisafa ◽  
Luming Shen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Strain Rate ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rock Strength ◽  
Strain Rates ◽  
Filling Material ◽  
Artificial Neural

Pre-existing cracks and associated filling materials cause the significant heterogeneity of natural rocks and rock masses. The induced heterogeneity changes the rock properties. This paper targets the gap in the existing literature regarding the adopting of artificial neural network approaches to efficiently and accurately predict the influences of heterogeneity on the strength of 3D-printed rocks at different strain rates. Herein, rock heterogeneity is reflected by different pre-existing crack and filling material configurations, quantitatively defined by the crack number, initial crack orientation with loading axis, crack tip distance, and crack offset distance. The artificial neural network model can be trained, validated, and tested by finite 42 quasi-static and 42 dynamic Brazilian disc experimental tests to establish the relationship between the rock strength and heterogeneous parameters at different strain rates. The artificial neural network architecture, including the hidden layer number and transfer functions, is optimized by the corresponding parametric study. Once trained, the proposed artificial neural network model generates an excellent prediction accuracy for influences of high dimensional heterogeneous parameters and strain rate on rock strength. The sensitivity analysis indicates that strain rate is the most important physical quantity affecting the strength of heterogeneous rock.

Artificial neural network model for photosynthetic pigments identification using multi wavelength chromatographic data

2016 ◽  
Author(s):  
K. R. Prilianti ◽  
S. Hariyanto ◽  
F. D. D. Natali ◽  
Indriatmoko ◽  
M. A. S. Adhiwibawa ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Photosynthetic Pigments ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Chromatographic Data ◽  
Multi Wavelength ◽  
Artificial Neural
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