越来越多的农作物秸秆用于生产生物质成型燃料(生物质颗粒), 作为民用和工业锅炉的生物质燃料。 高位热值是衡量生物质燃料燃烧性能的主要参数之一, 反映了生物质可用能含量, 但利用传统的氧弹分析法测试高位热值费时费力, 急需一种快速准确的方法评估农作物秸秆的高位热值, 以制备高质量的生物质颗粒燃料。 基于工业分析/元素分析和可见-近红外光谱分析, 对比分析了五种农作物秸秆(稻秸、 麦秸、 棉秆、 油菜秆和玉米秆)的高位热值预测模型。 首先, 利用多元线性回归(MLR)、 逐步回归(SWR)和反向传播人工神经网络(BPNN)模型, 在基于五种农作物秸秆工业分析和元素分析基础上, 提出了高位热值预测模型并进行验证。 MLR模型具有较好的相关系数(R2), 预测均方根误差(RMSEP)和预测标准差与参比标准差比值(RPD), 分别为0.921 1, 0.135 1和3.49。 此外, 利用可见-近红外光谱分析了农作物秸秆, 发现对光谱数据作最小二乘法回归(PLR), 可建立高位热值预测模型, 预测R2和RMSEP分别为0.881 2和0.412 9。 研究结果表明MLR模型和PLR模型分别适用于基于工业分析/元素分析和可见-近红外光谱建模, 对农作物秸秆的高位热值快速检测设备研发能提供基础模型支持。

Utilization of agricultural residues as compacted fuels (in pellet or briquette form) for both domestic furnace and industrial boiler is more and more promising. Gross calorific value (GCV) is an important performance for biomass as a solid fuel, indicating the useful energy content of biomass. Measurement of gross calorific value using oxygen bomb calorimeter is time consuming. Thus, it is necessary to develop a fast and accurate method to evaluate the GCV of raw biomass residues. This is conducive to control the quality of feedstock for biomass pellets production. In this study, different GCV predicting models for multiple agricultural residues were proposed and analyzed, and optimal modeling and statistical methods for the GCV prediction of 5 crop residues viz. rice straw, wheat straw, corn stalk, rape stalk and cotton stalk were developed. Multiple linear regression (MLR), stepwise regression analysis (SWR), back propagation artificial neural networks (BPNN) models were proposed to predict the GCV from proximate and/or ultimate analysis of 5 crop feedstock. The best coefficients of determination of R2, root mean square error of predict (RMSEP), and the ratio of standard error of prediction to standard deviation of the reference data (RPD) of 0.921 1, 0.135 1 and 3.49 were obtained, respectively, when corresponding variables were introduced to MLR models. Additionally, GCV models developed based on visible-near infrared spectroscopy (Vis-NIR) also showed the highest R2 and RMSEP values of 0.881 2 and 0.412 9, respectively, when partial least squares regression (PLR) was used. This study demonstrates that MLR model and PLR model can be used to estimate the GCV of agricultural feedstock from proximate analysis, ultimate analysis and Vis-NIR technology.