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

Edilson León Moreno Cárdenas, Arley David Zapata-Zapata and Daehwan Kim
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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
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
Daehwan Kim: Department of Biology, Hood College, 401 Rosemont Avenue, Frederick, MD 21701, USA

Energies, 2020, vol. 13, issue 7, 1-13

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.

Keywords: biohydrogen; coffee mucilage; organic waste; dark fermentation; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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