光谱学与光谱分析, 2016, 36 (12): 4058, 网络出版: 2016-12-30  

不同来源煤灰激光诱导击穿光谱测量特性对比研究

The Study of Spectral Characteristic of Coal Ash from Different Sources with Laser-Induced Breakdown Spectroscopy
作者单位
1 广东电网公司电力科学研究院, 广东 广州 510080
2 华南理工大学电力学院, 广东 广州 510640
摘要
飞灰含碳量的定量分析需要不同含碳量梯度的飞灰样品作为定标之用。 通常做法是用煤粉按照快速灰化法的要求灼烧得到不同含碳量的灰样, 用于定标分析。 但是这与实际锅炉飞灰的成分存在一定差异, 需要对光谱特性的差异进行研究来指导实际定标工作。 因此, 对比了快速灰化法制备的不同含碳量的煤灰样品与锅炉飞灰在特征谱线强度、 等离子温度等等离子光谱特征方面的差异。 实验证明快速灰化法制备的煤灰样品的Fe, Mg和Al谱线强度强于锅炉飞灰样品, 飞灰等离子温度低于所制备的煤灰样品的等离子体温度, 这可能是不同处理过程的物理化学特性差异造成的。 用主成分分析法考察了导致光谱差异的主要原因, 认为Fe, Mg, Al和Si等元素是导致二者在光谱特性差异的主要因素, 这可能因为实验室内按照快速灰化法进行制样的飞灰相应矿物质组成不同所导致的。 在用灼烧后的煤灰定标未燃碳时, 应注意由于成灰过程不同所造成的Fe, Mg, Al和Si等元素含量和形态不同所带来的影响。
Abstract
The samples with different carbon content are collected for quantitative analysis. One of the normal methods is the ignition of different coals according to the notice of fast ashing method instead of collecting coal ash in boiler. But there are some differences between fast ashing method in laboratory and actual boiler. It is necessary that the spectral deviation of coal ash from these two sources is studied as a guidance of quantitative analysis in carbon content. In present work, the intensity of the characteristic lines and plasma temperature were compared with different carbon content from these two processes. As a result, Fe, Mg, Al line strength of ash with fast ashing method is stronger and plasma temperature is lower than coal ash in boiler. Principal component analysis was processed, the results show that the difference of Fe, Mg, Al and Si content is the primary factor, and minerals in coal ash with fast ashing method may influence the spectral characteristic. The influence of mineral elements and mineral content on spectra for quantitative analysis with fast ashing method should be noticed.

沈跃良, 陆继东, 张博. 不同来源煤灰激光诱导击穿光谱测量特性对比研究[J]. 光谱学与光谱分析, 2016, 36(12): 4058. SHEN Yue-liang, LU Ji-dong, ZHANG Bo. The Study of Spectral Characteristic of Coal Ash from Different Sources with Laser-Induced Breakdown Spectroscopy[J]. Spectroscopy and Spectral Analysis, 2016, 36(12): 4058.

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