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Feasibility study on quantitative analysis of coal content in co-firing biomass-coal blends by near infrared spectroscopy

机译:近红外光谱法定量分析生物燃料混合煤中煤含量的可行性研究

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Rapid quantitative analysis of coal content in co-firing biomass-coal blends is greatly important for drawing up reasonable precept about biomass co-firing power generation subsidy. The use of near infrared reflectance spectroscopy (NIRS) predicting coal content in straw-coal blends was investigated in this study. A total of 81 straw-coal blends samples with the coal content from 1% to 9% were prepared and separated into a calibration set and a validation set according to 7/9 and 2/9 ratio. Spectra were scanned by a FT-NIR spectrometer. The regression coefficient method (RC) and the genetic algorithm (GA) were used for spectral region selection. Quantitative analysis models for coal content were established by partial least squares (PLS). The results showed that variables for models decreased from 3, 001 to 141, relative coefficient (R), standard error of prediction (SEP) and the ratio of performance to standard deviation (RPD) in validation were 0.95, 0.80% and 3.31 by using RC-PLS method, respectivelly. When using GA-PLS, the variables for models decreased from 3, 001 to 11, R, SEP and RPD in validation were 0.90, 1.17% and 2.59, respectivelly. It is concluded that NIRS with RC-PLS or GA-PLS is feasible for fast quantitative analysis of the coal content in co-firing biomass-coal blends.
机译:快速定量分析生物燃料共混煤中的煤炭含量,对于拟定合理的生物质燃料联合发电补贴规则具有重要意义。在这项研究中,研究了使用近红外反射光谱法(NIRS)预测秸秆-煤共混物中的煤炭含量。总共准备了81个煤含量为1%至9%的草煤混合物样品,并根据7/9和2/9的比例将其分为校准组和验证组。用FT-NIR光谱仪扫描光谱。回归系数法(RC)和遗传算法(GA)用于光谱区域选择。通过偏最小二乘(PLS)建立了煤炭含量的定量分析模型。结果表明,通过验证,模型的变量从3、001减少到141,相对系数(R),预测标准误差(SEP)和性能与标准偏差(RPD)的比率分别为0.95、0.80%和3.31。分别采用RC-PLS方法。当使用GA-PLS时,模型的变量分别从3、001下降到11,验证中的R,SEP和RPD分别为0.90、1.17%和2.59。结论是,采用RC-PLS或GA-PLS的NIRS可用于快速定量分析共燃生物质-煤共混物中的煤含量。

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