Gasifier System Identification for Biomass Power Plants using Neural Network
The use of renewable energy sources becomes more
necessary and interesting. As wider applications of renewable energy
devices at domestic, commercial and industrial levels has not only
resulted in greater awareness, but also significantly installed
capacities. In addition, biomass principally is in the form of woods,
which is a form of energy by humans for a long time. Gasification is
a process of conversion of solid carbonaceous fuel into combustible
gas by partial combustion. Many gasifier models have various
operating conditions; the parameters kept in each model are different.
This study applied experimental data, which has three inputs, which
are; biomass consumption, temperature at combustion zone and ash
discharge rate. One output is gas flow rate. For this paper, neural
network was used to identify the gasifier system suitable for the
experimental data. In the result,neural networkis usable to attain the
answer.
[1] C. Sagues, P. Hirano, Garcia-Bacaicoa, and S. Serrano, "Automatic
control of biomass gasifiers using fuzzy inference systems," Bioresource
Technology., vol. 98, 1987, pp. 845-855.
[2] P. N. Sheth, and B. V. Babu, "Effect of Moisture Content on
Composition Profiles of Producer Gas in Downdraft Biomass Gasifier,"
Proceedings of International Congress Chemistry and Environment
(ICCE), 2005, pp. 356-360.
[3] G. Schuster, G. Loffler, K. Weigl, and H. Hofbauer , "Biomass Steam
Gasification-an Extensive Parametric Modeling Study," Bioresource
Technology., 2001, pp. 71-79.
[4] S. Ashok, and P. Balamurugan ,"Biomass Gasifier Based Hybrid Energy
System for Rural Areas,". IEEE Canada Electrical Power
Conference, 2007, pp. 371-375.
[5] W. Panote, C. F. Chun, and C. N. Chem, "A Study on The Potential of
Corn Cob Engine-Generator for Electricity Generation in Thailand,"
Proceeding of IEEE TENCON-02, 2002, pp. 1958-1961.
[6] K. R. Anil, "Biomass Gasification," Alternative Energy in Agriculture,
vol. II, 1986, pp. 83-102.
[7] A. Oonsivilai, J. Satonsaowapak, T. Ratniyomchai, T.
Kulworawanichpong,P. Pao-La-Or, and B. Marungsri, "Response
Surface Method Application in Gasifier System Identification for
Biomass Power Plants," WSEAS Transactions on Systems, vol. 9, 2010,
pp. 629-638.
[8] S. Chai, B. Veenendaal, G. West, and J. Walker, "Backpropagation
Neural Network for Soil Moisture Retrieval using Nafe-05 Data : a
Comparison of Different Training Algorithms,"The International
Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences., vol.XXXVII, 2008,pp.1345-1349.
[9] C. Tsai, and T. Lee, "Back-Propagation Neural Network in Tidal-Level
Forcasting,"Journal of Waterway, Port, coastal and Ocean
Engineering., vol.125, 1999, pp. 195-202.
[1] C. Sagues, P. Hirano, Garcia-Bacaicoa, and S. Serrano, "Automatic
control of biomass gasifiers using fuzzy inference systems," Bioresource
Technology., vol. 98, 1987, pp. 845-855.
[2] P. N. Sheth, and B. V. Babu, "Effect of Moisture Content on
Composition Profiles of Producer Gas in Downdraft Biomass Gasifier,"
Proceedings of International Congress Chemistry and Environment
(ICCE), 2005, pp. 356-360.
[3] G. Schuster, G. Loffler, K. Weigl, and H. Hofbauer , "Biomass Steam
Gasification-an Extensive Parametric Modeling Study," Bioresource
Technology., 2001, pp. 71-79.
[4] S. Ashok, and P. Balamurugan ,"Biomass Gasifier Based Hybrid Energy
System for Rural Areas,". IEEE Canada Electrical Power
Conference, 2007, pp. 371-375.
[5] W. Panote, C. F. Chun, and C. N. Chem, "A Study on The Potential of
Corn Cob Engine-Generator for Electricity Generation in Thailand,"
Proceeding of IEEE TENCON-02, 2002, pp. 1958-1961.
[6] K. R. Anil, "Biomass Gasification," Alternative Energy in Agriculture,
vol. II, 1986, pp. 83-102.
[7] A. Oonsivilai, J. Satonsaowapak, T. Ratniyomchai, T.
Kulworawanichpong,P. Pao-La-Or, and B. Marungsri, "Response
Surface Method Application in Gasifier System Identification for
Biomass Power Plants," WSEAS Transactions on Systems, vol. 9, 2010,
pp. 629-638.
[8] S. Chai, B. Veenendaal, G. West, and J. Walker, "Backpropagation
Neural Network for Soil Moisture Retrieval using Nafe-05 Data : a
Comparison of Different Training Algorithms,"The International
Archives of the Photogrammetry, Remote Sensing and Spatial
Information Sciences., vol.XXXVII, 2008,pp.1345-1349.
[9] C. Tsai, and T. Lee, "Back-Propagation Neural Network in Tidal-Level
Forcasting,"Journal of Waterway, Port, coastal and Ocean
Engineering., vol.125, 1999, pp. 195-202.
@article{"International Journal of Chemical, Materials and Biomolecular Sciences:49670", author = "Jittarat Satonsaowapak and Thanatchai. Kulworawanichpong. and Ratchadaporn Oonsivilai and Anant Oonsivilai", title = "Gasifier System Identification for Biomass Power Plants using Neural Network", abstract = "The use of renewable energy sources becomes more
necessary and interesting. As wider applications of renewable energy
devices at domestic, commercial and industrial levels has not only
resulted in greater awareness, but also significantly installed
capacities. In addition, biomass principally is in the form of woods,
which is a form of energy by humans for a long time. Gasification is
a process of conversion of solid carbonaceous fuel into combustible
gas by partial combustion. Many gasifier models have various
operating conditions; the parameters kept in each model are different.
This study applied experimental data, which has three inputs, which
are; biomass consumption, temperature at combustion zone and ash
discharge rate. One output is gas flow rate. For this paper, neural
network was used to identify the gasifier system suitable for the
experimental data. In the result,neural networkis usable to attain the
answer.", keywords = "Gasifier System, Identification, Neural Network", volume = "5", number = "12", pages = "1079-6", }