脉冲中子煤质快速分析仪的解谱方法研究

Because all the elements will influence the neutron flux and the diffusion of Gamma-ray in the sample of coal, in the spectrum which is obtained by the pulse neutron coal fast analyzer, the characteristic gamma-ray's count of each element is related to the contents of all the elements in the coal and the relationship is nonlinear. The pulse neutron coal fast analyzer is based on the pulse fast thermal neutron analysis technology.The linear regression, the method of least squares and neural network are often used to analyze the spectrum. In the linear regression and the method of least squares, the relationship between the count of each element and the contents of all the elements is looked as linear relationship, which is incorrect and will introduce much error. In neural network, a huge database needs to be built. If the data of the testing sample isn't in the database, the error will be much large. In order to increase the accuracy and precision of the measurements in neural network, it needs 10,000 hours to build the database which is only the same with this D-T neutron generator. Because the lifetime of the D-T neutron generator is not more than 10,000 hours, the neural network cannot be used in the pulse neutron coal fast analyzer. In this project, the sixty normal coal samples will be obtained by adulterating graphite, polypropylene, melamine, sulfur, silicon dioxide, alumina, ferric oxide and calcium carbonate into the coal. It only needs 60 hours to build the database which is the same with the D-T neutron generator. The nonlinear relationship between the count of each element and the contents of all the elements will be obtained by multiple linear regression, which is a thrice equation with nine unknown. It is difficult to solve the thrice equation group with nine unknown. As a result, the enumeration method will be used to obtain the contents of the main elements in the coal. The accuracy and time of measurement can meet the requirements of the coal industry. The pulse neutron coal fast analyzer can be promoted and applied in the coal industry by this method of spectral analysis in three years.

由于样品区域的中子通量和伽马射线传播受样品内所有元素的影响，在脉冲中子煤质快速分析仪测得的特征伽马射线谱中，任何一种元素的伽马射线峰面积都与样品内所有元素有关，且是非线性的。常见的解谱方法是线性回归、最小二乘法和神经网络。前两者认为元素含量与峰面积是线性关系，不适合解此谱。为使测量精度达到要求，神经网络需要花费一万小时建立仅适用此中子管的伽马射线谱库，此时间长于中子管寿命，无应用价值。 本课题在元素含量已知的煤炭中加入化学性质稳定的物料，配制60个元素含量已知且有梯度的标准样品。更换中子管后，测量标准样品只需60小时。用多元线性回归寻找峰面积和含量及含量高次方间的线性关系，即峰面积和元素含量间的非线性关系。在用此关系计算待测样品的元素含量时，用枚举法代替解9元3次方程组，用线性回归的结果缩小枚举范围，使测量精度和计算时间都达到要求。此方法可以使脉冲中子煤质快速分析仪在三年内得到推广应用。

为获得解谱得需的伽马射线能谱，配制了60个煤炭样品。考虑化学性质及配制成本，配制煤炭样品采用的物料主要有石墨、聚丙烯、三聚氰胺、硫、氧化硅、氧化铝、氧化铁、碳酸钙等。在用脉冲中子煤质快速分析仪测量伽马射线能谱时，中子源用脉冲D-T中子发生器，其产额为 。伽马射线探测系统主要包括BGO晶体（直径和高都是100mm）、主放大器、4096道数字多道分析器和计算机。根据元素特征伽马射线的能量以及实验测得的伽马射线能谱，计算峰面积时碳、氢、氧、氮、硫、硅、铝、铁、钙的特征伽马射线能量（单位：KeV）范围分别为：4430±50，2222±50，6130±50，5269±50，5420±50，4933±50，3465±50，5920±50，4418±50。. 在伽马射线能谱中，元素的伽马射线峰面积是各元素含量的函数。利用Maclaurin级数，当n=3时（研究结果表明此时测量精度已达到煤炭工业应用的要求）元素的伽马射线峰面积是各元素含量及其2次方，3次方的函数。把同一元素含量的不同次方看作不同的变量，利用多元线性回归程序和60个煤炭样品的伽马射线能谱可以回归出上式中的所有系数。在待测煤炭样品的伽马射线能谱中，峰面积可测，所以能够得到9个关于这9种元素的含量的9元3次方程组。由于在实际应用中很难精确解出此方程组，本课题利用枚举法计算各元素含量。. 由于配制的60个煤炭样品的元素变化范围基本覆盖了国内所有煤炭元素的变化范围，保证了本课题研究成果具有通用性。线性回归计算出的元素含量虽然未达到应用要求，但缩短了枚举法的运算时间。枚举法得到的元素含量值使碳、氢、氧、氮、硫、硅、铝、铁、钙含量的误差分别小于1.00%，0.15%，1.00%，0.15%，0.15%，0.30%，0.60%，0.45%，0.30%，运算时间小于120s。这些指标都达到了煤炭工业应用的要求，由于更换D-T中子发生器后测量标准样品所需时间仅为60小时，所以本课题的研究成果可以使脉冲中子煤质快速分析仪在煤炭工业中得到应用。