산업경영시스템학회지 (Journal of Korean Society of Industrial and Systems Engineering)

  • 제36권4호
  • /
  • Pages.59-63
  • /
  • 2013
  • /
  • 2005-0461(pISSN)
  • /
  • 2287-7975(eISSN)
한국산업경영시스템학회 (Society of Korea Industrial and System Engineering)
권호 표지
DOI QR코드

DOI QR Code

RBF 뉴럴네트워크를 사용한 바이오매스 에너지문제의 계량적 분석

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

  • 백승현 (한양대학교 에리카 경영학부) ;
  • 황승준 (한양대학교 에리카 경영학부)
  • 투고 : 2013.11.04
  • 심사 : 2013.12.05
  • 발행 : 2013.12.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  1. 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. https://doi.org/10.1039/c0em00478b
  2. 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. https://doi.org/10.1016/j.chemolab.2003.10.008
  3. Blanco, M. and Villarroya, I., NIR spectroscopy : a rapidresponse analytical too. Trends in analytical chemistry, 2002, Vol. 21, No. 4, p 240-250. https://doi.org/10.1016/S0165-9936(02)00404-1
  4. Bracmort, B.B., Comparison of definitions in legislation through the 112th congress. CRS Report for Congress R40529, Congressional Research Service, 2012.
  5. Despagne, F. and Massart, D.L., Neural networks in multivariate calibration, Analyst 1998, Vol. 123, p 157- 178 https://doi.org/10.1039/a805562i
  6. 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.
  7. Ghosh, J. and Arindam, N., An overview of radial basis function networks, in Radial Basis Function Networks 2. Physica-Verlag GmbH, 2001.
  8. Jolliffe, I.T., Principal component analysis, M. Lovric, eds. International Encyclopedia of Statistical Science, Springer, 2011, p 1094-1096.
  9. 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.