Determination of the Calorific Power Value of the Balsa Wood through Artificial Neural Networks Modeling in a Downdraft Gasification Facility
DOI:
https://doi.org/10.46842/ipn.cien.v24n2a10Keywords:
gasification, biomass, mathematical modeling, artificial neural networks, caloric powerAbstract
This work presents the main research results obtained by the authors in the model based on the prediction the calorific power of the synthesis gas obtained through thermochemical downdraft gasification facilities of the boat wood with the of techniques of artificial neural networks. An analysis of the state-of-the-art study of previous research work linked to the mathematical modeling of these facilities was made out by the different techniques reflected in the specialized literature. The modeling is carried out using experimental factor 3n , which is applied in the adquisition of experimental data; with the help of Matlab the prediction tecniques applied on neural networks are performed over the data with satisfactory results.The selection of variables for experimentation takes into account the geographical location from which the forest waste is obtained from the raft, as it is produced in a warm- humid tropical climate. Literature is known that one of the factors that significantly influences calorific power is moisture. Obviously, the amount of oxygen contained in the air in the process is regulated by an intake valve, furthermore is a predominant factor in the mass added to the process. Considering this, the artificial neural network obtained allows the prediction calorific power resulting from the gasification of the raft with an error of 2.6 MJ/g and an adjustment of 86 %, which allows for an appropriate prediction.
References
International Energy Agency (IEA), Good practice guidelines: bioenergy project development and biomass supply, OECD/IEA, 2007, Francia.
P. Basu, Biomass gasification and pyrolysis: practical design and theory, MA, U.S., Elsevier, 2010.
P. Basu, Combustion and gasification in fluidized beds, London: Taylor & Francis Group/CRC Press, 2006.
K. Sharma, "Experimental investigations on a 20 kWe, solid biomass gasification system," Biomass Bioenergy, 2011, vol. 35, pp. 421-8.
S. Shabbar, "Thermodynamic equilibrium analysis of coal gasification using Gibbs energy minimization method," Energy Convers Manage, 2013, vol. 65, pp. 755-63.
N.S. Barman, "Gasification of biomass in a fixed bed downdraft gasifier: a realistic model including tar," Bioresour Technol., 2012, vol. 107, pp. 505-11.
E. Azzone, "Development of an equilibrium model for the simulation of thermochemical gasification and application to agricultural residues," Renewable Energy, 2012, vol. 46, pp. 248-54.
J. Xie, "Simulation on gasification of forestry residues in fluidized beds by Eulerian Lagrangian approach," Bioresour Technol., 2012, vol. 121, pp. 36-46.
Janajreh, M. Al Shrah, "Numerical and experimental investigation of downdraft gasification of woodchips," Energy Convers Manage, 2013, vol. 65, pp. 783-92.
M. P. Arnavat, "Artificial neural network models for biomass gasification in fluidized bed gasifiers," Biomass Bioenergy, 2013, vol. 49, pp. 279-89.
J. Han, "Modeling downdraft biomass gasification process by restricting chemical reaction equilibrium with Aspen Plus," Energy Conversion and Management, 2017, vol. 153, pp. 641-648.
M. Puig-Arnavat, "Review and analysis of biomass gasification models," Renewable and Sustainable Energy Reviews, 2010, vol. 14, pp. 2841-2851.
D. Baruah, "Renewable and Sustainable Energy Reviews,"Renewable and Sustainable Energy Reviews, 2014, vol. 39, pp. 806-815.
M. Puig-Arnavat, "Review and analysis of biomass gasification models," Renewable and Sustainable Energy Reviews, 2010, vol. 14, pp. 2841-2851.
M. Fani, M. H. Niri, F. Joda, "A simplified dynamic thermokinetic-based model of wood gasification process," Process Integration and Optimization for Sustainability, 2018, vol. 2, núm. 3, pp. 269-279.
Y. Li, L. Yan, B. Yang, W. Gao, M.R. Farahani, "Simulation of biomass gasification in a fluidized bed by artificial neural network (ANN)," Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, vol. 40, núm. 5, pp. 544-548, 2018.
T. Y. Ahmed, "Renewable and Sustainable Energy Reviews", Renewable and Sustainable Energy Reviews, 2012, vol. 16, pp. 2304-2315.
C. Pérez, Redes neuronales a través de ejemplos: aplicaciones con MATLAB, España, 2017.
P. Ponce, Inteligencia artificial con aplicaciones a la ingeniería, España, 2011.
F. Berzal, Redes Neuronales & Deep Learning, Granada, España, 2018.
C. A. Carvajal-Jara, P. M. Tafur-Escanta, A.H. Villavicencio-Poveda, E. R. Gutiérrez-Gualotuña, "Caracterización del poder calorífico de la biomasa residual de cacao CCN51 mediante procesos de gasificación anaeróbico y termoquímico," Científica, vol. 22, núm. 2, 2018, pp. 113-123.
E. R. Gutiérrez-Gualotuña, J. A. Soria-Amancha, P. M. Tafur-Escanta, N. Rodríguez-Trujillo, A. H. Villavicencio-Poveda, J. Arzola-Ruiz, "Modelos matemáticos de los parámetros energéticos de desempeño de gasificadores tipo downdraft mediante técnicas de regresión," Científica, vol. 23, núm. 1, 2019, pp. 61-81.
M. S. Arroyo-López, F. M. Guerrero-Espinosa, E. R. Gutiérrez-Gualotuña, "Análisis comparativo de la densidad y velocidad de ignición óptimas para la combustión completa del olote perteneciente al Zea Mays L.," Científica, vol. 23, núm. 1, 2019, pp. 43-50.
E. R. Gutiérrez Gualotuña, J. Almeida Mera, J. C., J. Arzola Ruiz, "Modelado de indicadores de operación de un gasificador downdraft por redes neuronales para biomasa Eichhornia Crassipes," Ingeniería Energética, vol. 40, núm. 3, 2019, pp. 212-222.
Downloads
Published
Issue
Section
License
Copyright (c) 2020 Instituto Politecnico Nacional
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
