Prediction of heating value of straw by proximate data, and near infrared spectroscopy
Prediction of heating value of straw by proximate data, and near infrared spectroscopy
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DOI：
10.1016/j.enconman.2008.08.020
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发表时间：
2008-12
影响因子：
10.4
通讯作者：
Cai-jin Huang;Lujia Han;Zengling Yang;Xian Liu
中科院分区：
文献类型：
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作者：
Cai-jin Huang;Lujia Han;Zengling Yang;Xian Liu
几十年来，探索秸秆资源用于能源生产一直吸引着农业科学家和工程师。而秸秆的热值一直是开展秸秆生物质能源项目时关注的焦点。然而，测定秸秆热值需要精密且昂贵的热量计，并且耗时。开发快速简便的模型来预测秸秆的热值是非常理想的。在本研究中，我们提出了三种适用的模型，前两个是水分、灰分和挥发物含量的多元线性回归（MLR）方程，另一个是基于近红外光谱（NIRS）技术。所有模型都对秸秆样品的热值进行了令人满意的估计。 MLR模型的调整决定系数为0.9049和0.9039，NIRS模型的校准决定系数为0.9604；当进行独立验证评估时，决定系数分别为 0.8595、0.8524 和 0.8946。结果表明，MLR模型和NIRS模型都具有预测秸秆热值的潜力，其中NIRS模型具有更好的准确性。
Exploration of straw resources for energy production has been attracting agricultural scientists and engineers for decades. And the heating value of straw has always been the focus when initiating a straw-based biomass energy project. Nevertheless determination of heating values of straw needs delicate and expensive calorimeter, and is time-consuming. It’s quite desirable to develop quick and easy model predicting heating values of straw. In this study, we proposed three applicable models, first two are multiple linear regression (MLR) equations by contents of moisture, ash, and volatile matter, the other one is based on the near infrared spectroscopy (NIRS) technology. All the models provide satisfactory estimations of heating values of straw samples. The adjusted determination coefficients for MLR models were 0.9049 and 0.9039, and determination coefficients of calibration for NIRS model was 0.9604; When evaluated on independent validation, the determination coefficients were 0.8595, 0.8524 and 0.8946, respectively. The results indicated that both MLR models and NIRS model have the potential to predict the heating values of straw, while the NIRS model presented better accuracy.