The Energy Potential of Residual Biomass Gasification Integrated with Internal Combustion Engine in Córdoba, Colombia using Artificial Neural Network Techniques

Jesús D. Rhenals-Julio (), Cristina Cogollo Torres (), Héctor Martínez Aguilar (), Jorge Rhenals Hoyos (), Daniel Otero Martínez () and Jorge M. Mendoza Fandiño ()
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Jesús D. Rhenals-Julio: Department of Mechanical Engineering, Universidad de Córdoba, Cra 6 No. 77-305 Montería, Colombia
Cristina Cogollo Torres: School of Industrial Engineering, Universidad Santo Tomás, Carrera 22-Calle 1a Villavicencio, Colombia
Héctor Martínez Aguilar: Department of Mechanical Engineering, Universidad de Córdoba, Cra 6 No. 77-305 Montería, Colombia
Jorge Rhenals Hoyos: Department of Mechanical Engineering, Universidad de Córdoba, Cra 6 No. 77-305 Montería, Colombia
Daniel Otero Martínez: Department of Mechanical Engineering, Universidad de Córdoba, Cra 6 No. 77-305 Montería, Colombia
Jorge M. Mendoza Fandiño: Department of Mechanical Engineering, Universidad de Córdoba, Cra 6 No. 77-305 Montería, Colombia

International Journal of Energy Economics and Policy, 2024, vol. 15, issue 1, 274-280

Abstract: In this study, the energy potential of waste biomass gasification integrated to internal combustion engine in Cordoba, Colombia was investigated using artificial neural network techniques. A model was trained with proximate and elemental analysis data of different biomasses and this model was used to estimate the gasification potential of the four most abundant biomasses in Cordoba. The model developed achieved an adjusted determination coefficient (R2) of 0.9293 for validation and 0.9048 for training, demonstrating high predictive accuracy. The results indicate that temperature positively influences energy generation potential, while moisture content and air-to-fuel ratio have a negative impact. Among the biomass types analyzed, cassava stands out with the highest energy potential, exceeding 9 GWh/year, followed by plantain at approximately 3 GWh/year, maize cobs below 2 GWh/ year, and rice husk with <0.5 GWh/year. These findings provide critical insights for optimizing biomass gasification processes and harnessing regional biomass resources for energy generation.

Keywords: Renewable Energy Sources; Energy Potential; Gasification; Residual Biomass; Artificial Neural Networks (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eco:journ2:v:15:y:2024:i:1:id:17364

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DOI: 10.32479/ijeep.17364

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