通过建立校正集样本不同的模型Ⅰ与模型Ⅱ来研究温度对水泥生料近红外光谱模型的影响,其中模型Ⅰ中的建模样本为温度一致的样本,模型Ⅱ中的建模样本为温度变化的样本,建模方法采用偏最小二乘法。对比两组模型的预测结果可知,模型Ⅱ中SiO2、Al2O3、Fe2O3、CaCO3的预测方均根误差与模型Ⅰ相比分别减小了78.3%、26.4%、42.9%、60.4%。实验结果表明:温度变化会导致预测结果产生一定的误差。在校正集中加入温度不同的样本进行建模,可以降低预测误差,使近红外光谱模型更好地应用于生产现场。

In this paper, the effect of temperature on the near-infrared spectroscopy model of cement raw meal was investigated by developing two models (model I and model II) with different calibration sets. Model I and model II were developed by applying the partial least squares method using same-temperature and different-temperature samples, respectively. In comparison with model I, the root mean square error of prediction (RMSEP) of SiO₂, AlO₃, Fe₂O₃, and CaCO₃ in model II was reduced by 78.3%, 26.4%, 42.9%, and 60.4%, respectively. Experimental results show that the temperature of the cement raw meal sample has a certain influence on the prediction results of the near-infrared spectroscopy model. The influence of temperature on the prediction results can be effectively reduced by modeling with temperature gradient samples in the calibration set, and thus the infrared spectroscopy model can better be applied to production site.