Browse > Article
http://dx.doi.org/10.11627/jkise.2013.36.4.59

Quantitative Analysis for Biomass Energy Problem Using a Radial Basis Function Neural Network  

Baek, Seung Hyun (Division of Business Administration, Hanyang University ERICA)
Hwang, Seung-June (Division of Business Administration, Hanyang University ERICA)
Publication Information
Journal of Korean Society of Industrial and Systems Engineering / v.36, no.4, 2013 , pp. 59-63 More about this Journal
Abstract
In biomass gasification, efficiency of energy quantification is a difficult part without finishing the process. In this article, a radial basis function neural network (RBFN) is proposed to predict biomass efficiency before gasification. RBFN will be compared with a principal component regression (PCR) and a multilayer perceptron neural network (MLPN). Due to the high dimensionality of data, principal component transform is first used in PCR and afterwards, ordinary regression is applied to selected principal components for modeling. Multilayer perceptron neural network (MLPN) is also used without any preprocessing. For this research, 3 wood samples and 3 other feedstock are used and they are near infrared (NIR) spectrum data with high-dimensionality. Ash and char are used as response variables. The comparison results of two responses will be shown.
Keywords
Biomass; Gasification; Radial Basis Function Neural Network; Principal Component Regression; Multilayer Perceptron Neural Network;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Smeenk, J., Biomass processes and technologies : biomass as a renewable energy resource for natural gas displacement, Wood-to-Energy : Iowa Wood By-Products Workshop, June 7, Ainsworth, 2006.
2 Araujo, P., Astray, G., Ferrerio-Lage, J.A., Mejuto, J.C., Rodriguez-Suarez, J.A. and Soto, B., Multilayer perceptron neural network for flow prediction. Journal of Environmental Monitoring, 2011, Vol. 13, No. 1, p 35-41.   DOI   ScienceOn
3 Benoudjit, N., Cools, E., Meurens, M., and Verleysen, M., Chemometric calibration of infrared spectrometers : selection and validation of variables by non-linear models. Chemometrics and intelligent laboratory systems, 2004, Vol. 70, p 47-53.   DOI   ScienceOn
4 Blanco, M. and Villarroya, I., NIR spectroscopy : a rapidresponse analytical too. Trends in analytical chemistry, 2002, Vol. 21, No. 4, p 240-250.   DOI   ScienceOn
5 Bracmort, B.B., Comparison of definitions in legislation through the 112th congress. CRS Report for Congress R40529, Congressional Research Service, 2012.
6 Despagne, F. and Massart, D.L., Neural networks in multivariate calibration, Analyst 1998, Vol. 123, p 157- 178   DOI   ScienceOn
7 Dunia, R., Qin, S.J., Edgar, T.F., and Mcavoy, T.J., Sensor fault identification and reconstruction using principal component analysis, Proceedings of the IFAC World Congress 96, San Francisco, June 30-July 5, 1996, p 259-264.
8 Ghosh, J. and Arindam, N., An overview of radial basis function networks, in Radial Basis Function Networks 2. Physica-Verlag GmbH, 2001.
9 Jolliffe, I.T., Principal component analysis, M. Lovric, eds. International Encyclopedia of Statistical Science, Springer, 2011, p 1094-1096.