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

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  • Edilson León Moreno Cárdenas

    (Laboratorio de Mecanización Agrícola, Departamento de Ingeniería Agrícola y de Alimentos, Universidad Nacional de Colombia-Sede Medellín, Carrera 64c # 63-120, Código Postal 050034, Colombia
    Authors contributed equally to the study.)

  • Arley David Zapata-Zapata

    (Universidad Nacional de Colombia-Sede Medellín-Escuela de Química-Laboratorio de Procesos Biológicos-Carrera 65 # 59A-110, Medellín, Código Postal 050034, Colombia
    Authors contributed equally to the study.)

  • Daehwan Kim

    (Department of Biology, Hood College, 401 Rosemont Avenue, Frederick, MD 21701, USA)

Abstract

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 (R 2 ) 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 (R 2 > 0.9508) than those from the artificial neural network model (R 2 > 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.

Suggested Citation

  • Edilson León Moreno Cárdenas & Arley David Zapata-Zapata & Daehwan Kim, 2020. "Modeling Dark Fermentation of Coffee Mucilage Wastes for Hydrogen Production: Artificial Neural Network Model vs. Fuzzy Logic Model," Energies, MDPI, vol. 13(7), pages 1-13, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1663-:d:340748
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    References listed on IDEAS

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    1. David Orrego & Arley David Zapata-Zapata & Daehwan Kim, 2018. "Optimization and Scale-Up of Coffee Mucilage Fermentation for Ethanol Production," Energies, MDPI, vol. 11(4), pages 1-12, March.
    2. Sözen, Adnan & Kurt, Mustafa & Akçayol, M.Ali & Özalp, Mehmet, 2004. "Performance prediction of a solar driven ejector-absorption cycle using fuzzy logic," Renewable Energy, Elsevier, vol. 29(1), pages 53-71.
    3. Mussatto, Solange I. & Machado, Ercília M.S. & Carneiro, Lívia M. & Teixeira, José A., 2012. "Sugars metabolism and ethanol production by different yeast strains from coffee industry wastes hydrolysates," Applied Energy, Elsevier, vol. 92(C), pages 763-768.
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    Cited by:

    1. Joanna Kazimierowicz & Marcin Dębowski & Marcin Zieliński, 2022. "Progress and Challenges in Biohydrogen Production," Energies, MDPI, vol. 15(15), pages 1-3, July.
    2. Ahmed Tawfik & Shou-Qing Ni & Hanem. M. Awad & Sherif Ismail & Vinay Kumar Tyagi & Mohd Shariq Khan & Muhammad Abdul Qyyum & Moonyong Lee, 2021. "Recent Approaches for the Production of High Value-Added Biofuels from Gelatinous Wastewater," Energies, MDPI, vol. 14(16), pages 1-18, August.
    3. Naveed, Muhammad Hamza & Khan, Muhammad Nouman Aslam & Mukarram, Muhammad & Naqvi, Salman Raza & Abdullah, Abdullah & Haq, Zeeshan Ul & Ullah, Hafeez & Mohamadi, Hamad Al, 2024. "Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).

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