Rapid Characterization and Prediction of Biomass Properties via Statistical Techniques

Hyun-Woo Cho

Clean Technology, Vol.18, No.3, 265-271, September, 2012

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Rapid Characterization and Prediction of Biomass Properties via Statistical Techniques

Hyun-Woo Cho^†

Department of Industrial and Management Engineering, Daegu University, 201 Daegudae-ro, Jinlyang-eup, Gyeongsan-si, Gyeongbuk 712-714, Korea

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초록

화석연료에 대한 의존성을 줄이기 위한 대안으로서 재생에너지 사용이 요구되고 있다. 청정에너지원의 하나인 바이오매스는 그 물성치의 실시간 파악이 중요하기 때문에 다양한 종류의 바이오매스에 대해 널리 연구되어 왔으며, 방법론적인 측면에서는 비침투성이며 많은 정보를 가진 특징으로 인하여 근적외선 분광법이 성공적으로 적용되었다. 본 논문에서는 여러 바이오매스 종류에 대한 물성치의 빠른 예측을 위해 근적외선 데이터에 기반한 비선형 방법론의 적용성을 평가하였다. 다양한 방법론에 기반한 예측 모델들을 근적외선 데이터의 전처리방법과 조합하여 예측 성능을 평가하였다. 바이오매스 물성예측 모델의 성능에서는 선형 모델보다는 비선형 모델에서 예측오차가 최소화되었으며 전처리 방법과 결합되었을 때 최적의 예측결과를 얻을 수 있었다.

The use of renewable energies has been required to diminish the dependency on fossil fuels. As one of clean energy sources biomass has been extensively studied because various biomass resources necessitated rapid characterization of their chemical and physical properties in an on-line or real-time basis. For such an analysis near-infrared (NIR) spectroscopy has been successfully applied because of its non-invasive and informative characteristics. In this work, the applicability of nonlinear chemometric techniques based on biomass near infrared (NIR) data is evaluated for the rapid prediction of ash/char contents in different types of biomass. The prediction results of various prediction models and the effect of using preprocessing methods for NIR data are compared using six types of biomass NIR data. The results showed that nonlinear prediction models yielded better prediction performance than linear ones. It also turned out that by adopting the use of proper preprocessing methods the performance of prediction of biomass properties improved.

Keywords:Renewable energy;Biomass;Nonlinear chemometric approaches;Near infrared (NIR)-based prediction with preprocessing

[References]

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[1] Saxena RC, Adhikari DK, Goyal HB, Renew. Sust. Energy Rev., 13(1), 167 (2009)

[2] Agelet LE, Hurburgh CR, Crit. Rev. in Anal.Chem., 40(4), 246 (2010)

[3] Qin SJ, J. Chem., 17, 480 (2003)

[4] Scholkopf B, Smola AJ, Muller K, Neural Comp., 10, 1299 (1998)

[5] Ballabio C, Geoderma., 151, 338 (2009)

[6] Kourti T, MacGregor JF, Chem. Intel. Lab. Sys., 28(1), 3 (1995)

[7] Jade AM, Srikanth B, Jayaraman VK, Kulkarni BD, Jog JP, Priya L, Chem. Eng. Sci., 58(19), 4441 (2003)

[8] Wold S, Antti H, Lindgren F, Ohman J, Chem. and Intel.Lab. Sys., 44, 175 (1998)

[9] Li D, Image and Vision Comp., 27(4), 623 (2009)

[10] Cho HW, Liu JJ, Clean Technol., 18(1), 31 (2012)

[11] Thennadil SN, Martin EB, J. Chem., 19(2), 77 (2005)

[12] Kourti T, Int. J. of Adap. Cont. and Signal Proc., 19, 213 (2005)