快速检测生物质原料特性对生产高品质压缩成型燃料具有重要意义。 利用光谱技术建立松木、 杉木和棉杆三类农林生物质组分(水分、 灰分、 挥发分和固定碳)和热值预测模型。 相比原始光谱, 基于一阶导数光谱的偏最小二乘回归(PLS)模型预测精度较高。 灰分、 挥发分和水分PLS模型交叉校验决定系数(R2)分别为0.97, 0.94和0.90, 预测偏差比率(RPD)分别为6.57, 4.00和3.01。 固定碳和热值PLS模型精度一般, R2分别为0.85和0.87, RPD分别为2.55和2.73。 实验结果表明, 利用可见-近红外光谱技术完全可以替代传统工业分析方法, 从而实现农林生物质原料组分和热值的快速测定。

Rapid determination of biomass feedstock properties is of value for the production of biomass densification briquetting fuel with high quality. In the present study, visible and near-infrared (Vis-NIR) spectroscopy was employed to build prediction models of componential contents, i.e. moisture, ash, volatile matter and fixed-carbon, and calorific value of three selected species of agricultural biomass feedstock, i.e. pine wood, cedar wood, and cotton stalk. The partial least squares (PLS) cross validation results showed that compared with original reflection spectra, PLS regression models developed for first derivative spectra produced higher prediction accuracy with coefficients of determination (R2) of 0.97, 0.94 and 0.90, and residual prediction deviation (RPD) of 6.57, 4.00 and 3.01 for ash, volatile matter and moisture, respectively. Good prediction accuracy was achieved with R2 of 0.85 and RPD of 2.55 for fixed carbon, and R2 of 0.87 and RPD of 2.73 for calorific value. It is concluded that the Vis-NIR spectroscopy is promising as an alternative of traditional proximate analysis for rapid determination of componential contents and calorific value of agricultural biomass feedstock.