Lestander Torbjörn A, Rhén Christofer
Unit of Biomass Technology and Chemistry, Swedish University of Agricultural Sciences, P.O. Box 4097, SE 904 03 Umeå, Sweden.
Analyst. 2005 Aug;130(8):1182-9. doi: 10.1039/b500103j. Epub 2005 Jun 29.
The multitude of biofuels in use and their widely different characteristics stress the need for improved characterisation of their chemical and physical properties. Industrial use of biofuels further demands rapid characterisation methods suitable for on-line measurements. The single most important property in biofuels is the calorific value. This is influenced by moisture and ash content as well as the chemical composition of the dry biomass. Near infrared (NIR) spectroscopy and bi-orthogonal partial least squares (BPLS) regression were used to model moisture and ash content as well as gross calorific value in ground samples of stem and branches wood. Samples from 16 individual trees of Norway spruce were artificially moistened into five classes (10, 20, 30, 40 and 50%). Three different models for decomposition of the spectral variation into structure and noise were applied. In total 16 BPLS models were used, all of which showed high accuracy in prediction for a test set and they explained 95.4-99.8% of the reference variable variation. The models for moisture content were spanned by the O-H and C-H overtones, i.e. between water and organic matter. The models for ash content appeared to be based on interactions in carbon chains. For calorific value the models was spanned by C-H stretching, by O-H stretching and bending and by combinations of O-H and C-O stretching. Also -C=C- bonds contributed in the prediction of calorific value. This study illustrates the possibility of using the NIR technique in combination with multivariate calibration to predict economically important properties of biofuels and to interpret models. This concept may also be applied for on-line prediction in processes to standardize biofuels or in biofuelled plants for process monitoring.
目前使用的生物燃料种类繁多，其特性差异很大，这凸显了改进其化学和物理性质表征的必要性。生物燃料的工业应用进一步要求有适用于在线测量的快速表征方法。生物燃料中最重要的单一属性是热值。这受到水分、灰分含量以及干燥生物质化学成分的影响。利用近红外（NIR）光谱和双正交偏最小二乘法（BPLS）回归对树干和树枝木材研磨样品中的水分、灰分含量以及总热值进行建模。从16棵挪威云杉单株采集的样本被人工加湿分为五类（10%、20%、30%、40%和50%）。应用了三种将光谱变化分解为结构和噪声的不同模型。总共使用了16个BPLS模型，所有这些模型在对测试集的预测中都显示出高精度，并且它们解释了参考变量变化的95.4 - 99.8%。水分含量模型由O - H和C - H泛音构成，即介于水和有机物之间。灰分含量模型似乎基于碳链中的相互作用。对于热值，模型由C - H伸缩、O - H伸缩和弯曲以及O - H与C - O伸缩的组合构成。此外，-C = C-键在热值预测中也有贡献。本研究说明了将近红外技术与多变量校准相结合来预测生物燃料经济上重要的属性并解释模型的可能性。这一概念也可应用于生物燃料标准化过程中的在线预测或生物燃料工厂的过程监测。
