Lestander Torbjörn A, Johnsson Bo, Grothage Morgan
Unit of Biomass Technology and Chemistry, Swedish University of Agricultural Sciences, P.O. Box 4097, SE-903 04 Umeå, Sweden.
Bioresour Technol. 2009 Feb;100(4):1589-94. doi: 10.1016/j.biortech.2008.08.001. Epub 2008 Oct 25.
A 2(3)-factorial experiment was carried out in an industrial plant producing biofuel pellets with sawdust as feedstock. The aim was to use on-line near infrared (NIR) spectra from sawdust for real time predictions of moisture content, blends of sawdust and energy consumption of the pellet press. The factors varied were: drying temperature and wood powder dryness in binary blends of sawdust from Norway spruce and Scots pine. The main results were excellent NIR calibration models for on-line prediction of moisture content and binary blends of sawdust from the two species, but also for the novel finding that the consumption of electrical energy per unit pelletized biomass can be predicted by NIR reflectance spectra from sawdust entering the pellet press. This power consumption model, explaining 91.0% of the variation, indicated that NIR data contained information of the compression and friction properties of the biomass feedstock. The moisture content model was validated using a running NIR calibration model in the pellet plant. It is shown that the adjusted prediction error was 0.41% moisture content for grinded sawdust dried to ca. 6-12% moisture content. Further, although used drying temperatures influenced NIR spectra the models for drying temperature resulted in low prediction accuracy. The results show that on-line NIR can be used as an important tool in the monitoring and control of the pelletizing process and that the use of NIR technique in fuel pellet production has possibilities to better meet customer specifications, and therefore create added production values.
在一家以锯末为原料生产生物燃料颗粒的工厂进行了一项2（3）析因实验。目的是利用锯末的在线近红外（NIR）光谱实时预测水分含量、锯末混合物以及颗粒压制机的能耗。变化的因素有：挪威云杉和苏格兰松锯末二元混合物中的干燥温度和木粉干燥度。主要结果是建立了用于在线预测水分含量和两种锯末二元混合物的优秀近红外校准模型，同时还得出了一个新发现，即进入颗粒压制机的锯末的近红外反射光谱可以预测单位颗粒化生物质的电能消耗。这个能耗模型解释了91.0%的变化，表明近红外数据包含了生物质原料压缩和摩擦特性的信息。水分含量模型在颗粒厂使用运行中的近红外校准模型进行了验证。结果表明，对于研磨后干燥至约6 - 12%水分含量的锯末，调整后的预测误差为0.41%的水分含量。此外，尽管使用的干燥温度会影响近红外光谱，但干燥温度模型的预测精度较低。结果表明，在线近红外可作为颗粒压制过程监测和控制的重要工具，并且在燃料颗粒生产中使用近红外技术有可能更好地满足客户规格，从而创造附加值。
