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Ultimate analysis and heating value prediction of straw by near infrared spectroscopy

机译：近红外光谱法对秸秆的终极分析和热值预测

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摘要

Ultimate analysis and heating value determination are two of the most important routine analyses for exploiting agricultural wastes for energy conversion. The use of near infrared spectroscopy (NIRS) was investigated as an alternative method to predict the carbon, hydrogen, and nitrogen content and the heating value of straw. A total of 222 straw samples, collected from 24 provinces of China, were used for NIRS calibration and validation in this study. The R_v~2 and standard error of predictions in independent validation were, respectively, 0.97 and 0.37% for C. 0.77 and 0.17% for H, 0.87 and 0.10% for N and 0.96 and 181 J/g for heating value. A multiple linear regression (MLR) model was also built to predict the heating value from the contents of C, H and N. The MLR equation gave good prediction (standard error of prediction = 224 J/g) when evaluated using the same validation set as the NIRS. Therefore, rapid analysis of straw can be achieved through the constructed equations, saving analytical time and cost.
机译：最终分析和热值确定是利用农业废物进行能量转换的两个最重要的常规分析。研究了使用近红外光谱（NIRS）作为预测碳，氢和氮含量以及秸秆热值的替代方法。本研究共使用来自中国24个省的222个秸秆样品进行NIRS校准和验证。在独立验证中，R_v〜2和预测的标准误差分别为C的0.97和0.37％，H的0.77和0.17％，N的0.87和0.10％，热值的0.96和181 J / g。还建立了多元线性回归（MLR）模型以根据C，H和N的含量预测发热量。当使用相同的验证集进行评估时，MLR方程可提供良好的预测（预测的标准误为224 J / g）。作为NIRS。因此，通过构建的方程可以快速分析稻草，从而节省了分析时间和成本。

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  • 来源
    《Waste Management》 |2009年第6期|1793-1797|共5页
  • 作者

    C. Huang; L. Han; Z. Yang; X. Liu;

  • 作者单位

    College of Engineering, China Agricultural University, P.O. Box 232, No. 17, Qinghua Donglu, Haidian District, Beijing 100083, China;

    College of Engineering, China Agricultural University, P.O. Box 232, No. 17, Qinghua Donglu, Haidian District, Beijing 100083, China;

    College of Engineering, China Agricultural University, P.O. Box 232, No. 17, Qinghua Donglu, Haidian District, Beijing 100083, China;

    College of Engineering, China Agricultural University, P.O. Box 232, No. 17, Qinghua Donglu, Haidian District, Beijing 100083, China;

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