浓相煤粉浓度是煤粉锅炉运行的关键指标之一。将太赫兹时域光谱技术应用到煤粉浓度的定量分析中,为提高对具有复杂化学成分的煤粉混合物太赫兹光谱定量分析的稳定性和准确性,将遗传算法(GA)和偏最小二乘回归(PLS-R)引入浓相煤粉-高密度聚乙烯材料(HDPE)混合物太赫兹时域光谱定量分析中。通过GA构建最优光谱变量集合,利用偏最小二乘建立煤粉浓度的定量分析模型。实验结果表明,GA和PLS-R得到的样本预测集相关系数和均方根误差分别为0.9568和1.0345,与传统的区间偏最小二乘法(iPLS-R和biPLS-R)建立的定量分析模型相比具有更高的准确度和稳定性,为太赫兹时域光谱在浓相煤粉浓度定量分析中的应用提供了参考依据。

Dense phase pulverized coal concentration is a key performance indicator for the operation of pulverized coal boiler. In this paper, terahertz time-domain spectroscopy is applied to the quantitative analysis of pulverized coal concentration. To improve the stability and accuracy of terahertz spectrum quantitative analysis of pulverized coal mixture with complex chemical components, genetic algorithm (GA) and partial least squares regression (PLS-R) are introduced into terahertz time-domain spectrum quantitative analysis of dense phase pulverized coal-high density polyethylene mixture. The optimal set of spectral variables is constructed by GA, and the quantitative analysis model of pulverized coal concentration is established by partial least squares. Experimental results show that the correlation coefficient and root mean square error of sample prediction set obtained by GA and PLS-R are 0.9568 and 1.0345, respectively. Compared with the quantitative analysis model established by traditional interval partial least squares methods (iPLS-R and biPLS-R), the model has higher accuracy and stability, which provides the basis for the application of terahertz time-domain spectrum in the quantitative analysis of dense phase pulverized coal concentration.