煤粉气力输送的细度检测对磨煤机工作状态的最优控制具有重要的意义。 传统的检测方法多采用抽检取样法, 通过分样筛等设备检测样品的细度, 耗时长且操作复杂。 国内外对细度地快速检测也有部分研究成果, 但所测粉体浓度须较低, 且设备稳定性还有待提高。 太赫兹时域光谱技术（THz-TDS）是一种新型的无损检测技术, 其低能性、 选择透过性、 相干性等特点使它具备其他光谱测量方法没有的优势。 国内外对太赫兹辐射与颗粒介质相互作用的研究表明, 太赫兹波对颗粒介质的细度具有显著敏感性, 因此通过太赫兹波检测煤粉细度具有可行性。 太赫兹波在高浓度颗粒介质的传播可以被看成是一个非线性动力过程, 这个过程包含了复杂的非线性动力学效应, 导致光谱测量结果具有混沌特征。 将非线性动力系统的概念应用到太赫兹时域光谱信号的分析中, 将太赫兹时域光谱信号视为由复杂非线性动力系统产生的时间序列进行特征分析。 实验中将煤样研磨并筛分为<38.5, 55~74, 74~88, 88~105和105~200 μm六种细度, 并将煤粉与HDPE混合后压制成样品片。 分别提取了的煤粉样品太赫兹时域光谱信号的功率谱熵、 小波能量熵、 盒维数、 关联维数、 偏度和峭度作为太赫兹时域光谱的混沌特征, 通过比较发现这些混沌特征与细度变化具有一定的相关性, 从视觉上可以大致区分出细度范围, 但无法进行定量分析。 支持向量机常用来解决小样本和非线性的分类问题, 但是需要选择合适的参数才能建立较为准确的预测模型。 文中引入粒子群算法来优化支持向量机建模参数选择。 将上述提取的混沌特征向量作为粒子群算法优化的支持向量机的输入变量, 以分样筛筛孔作为回归目标, 对所测量煤粉细度建立回归模型。 实验结果表明利用混沌特征建立的回归模型对<38.5和38.5~55 μm样品的预测结果要逊色于消光谱建模的回归结果, 认为这是因为煤粉细度小, 太赫兹波在样品中传播时与煤粉颗粒相互作用也比较弱, 时域信号的混沌特征表现不明显所导致。 对55~74, 74~88, 88~105和105~200 μm煤粉样品细度的预测结果要明显优于频域消光谱建立的模型, 特别是74~88和105~200 μm样品, 校正集均方根误差相对于消光谱分别下降了29.48%和26.14%, 预测集误差分别下降了88.62%和56.86%。 从预测结果整体上来看, 采用混沌特征建模的预测结果与目标细度的相关系数为0.9618, 消光谱建模的预测结果相关系数仅为0.78。 混沌特征建模的均方根预测误差仅为9.52, 消光谱建模的均方根预测误差为24.48。 同时采用混沌特征的建模时间相对于消光谱的建模时间下降了43.19%。 研究结果为太赫兹时域光谱技术在高浓度煤粉气力输送细度检测上的应用提供了科学依据和参考。

The granularity detection of pulverized coal in pneumatic conveying system is of great significance to the optimum control of coal mill. The traditional approach to detect the granularity of the coal granule is sampling the pulverized coal in the pipeline and applying sieving process. These steps are time-consuming and complex. Some rapid detection methods for the granularity of coal have already been released home and abroad, however, some drawbacks still exist, such as limiting the concentration of the pulverized coal to a low level during measurement and the instability of the testing equipment. Terahertz time-domain spectroscopy system (THz-TDS) is a newly developed non-destructive analytical technique. Compared with other spectrometry, THz-TDS has the superiority of low-energy photon, perm-selectivity and coherency. The previous research on the interaction between THz wave and granular medium indicated that the particle in granular medium has a strong influence on THz wave, which provides the technical feasibility of granularity detection of coal particle adopting THz-TDS. The propagation of the THz wave in granular medium could be regarded as a non-linear dynamic process involving complicated dynamic effect, leading to a THz signal combined with some certain chaos features. In this paper, the concept of non-linear chaos dynamic system was applied to the terahertz spectral analysis for the first time. Following this point of view, the detected THz signal was considered as a time series generated by a complex non-linear dynamic system and the interaction between THz wave and granular medium could be described by some chaos features. In the experiment, the coal was grounded and sieved into <38.5, 55~74, 74~88, 88~105 and 105~200 μm firstly. Then these pulverized coal samples were mixed with HDPE powder and compressed into tablets. The power spectral entropy, wavelet energy entropy, Box dimension, correlation dimension, skewness and kurtosis were extracted from the THz time domain signals of the six coal-HDPE tablets. Visually, the extracted chaos feature vectors showed a dependency with the granularity of the measured coal samples, so that the range of granularity could be roughly distinguished. However, the exact diameters of the measured coal samples remain unknown. Support vector machine (SVM) is a powerful tool for solving the small sample and non-linear classification problem. Appropriate parameters should be selected firstly, so that an accurate prediction model can be established by SVM. Particle swarm optimization (PSO) was used to optimize the parameters selection of SVM. The extracted chaos features were selected as inputs of the PSO-SVM to establish a regression model for predicting the grain size of the investigated granulated coal samples. The experimental result showed that the regression model trained by the chaos feature vectors had a worse performance of predicting the grain size of samples containing <38.5 and 38.5~55 μm coal granule than the model trained by the frequency depended extinction spectrum. This might be because of a relatively weak interaction between the THz wave and small grains that the chaos features of these samples are not significant. For the rest samples containing 55~74, 74~88, 88~105 and 105~200 μm coal grains, a better performance was achieved. Specifically, compared with the model trained by the extinction spectrum, for samples containing 74~88 and 105~200 μm coal grains, the prediction model trained by the chaos features obtained a lower RMSEC that declined by 29.48% and 26.14%, respectively and the RMSEP of this two samples declined by 88.62% and 56.86%, respectively. Overall, for the prediction model trained by the chaos features, the correlation coefficient between the predicted and actual particle diameter is 0.961 8, however, for the prediction model trained by the extinction spectrum, the correlation coefficient between the predicted and actual particle diameter is only 0.780 7. The RMSEP obtained by the model trained by the chaos features is 9.52, while the RMSEP obtained by the model trained by the extinction spectrum is up to 24.48. Furthermore, the elapsed time for modeling when adopting the chaos features declined by 43.19%. The research provides scientific basis and references for the application of granularitydetection of pulverized coal in pneumatic conveying system.