流化床制粒过程颗粒水分预测研究

Prediction of particle moisture in fluidized bed granulation process

对流化床进行工程化改造,安装近红外光谱仪,对制粒过程中的颗粒水分含量进行光谱采集。采用偏最小二乘法(PLS)、粒子群-核岭回归(PSO-KRR)和随机森林-偏最小二乘法(RF-PLS)建立回归模型,对不同的算法的预测精度进行了研究。结果表明,PLS模型预测集均方根误差(RMSE)为0.2180,相关系数R为0.9717;PSO-KRR模型预测集RMSE为0.2154,R为0.9738;RF-PLS模型预测集RMSE为0.2059,R为0.9733。其中RF-PLS模型的预测效果最好。

The fluidized bed was engineered and a near-infrared probe was installed to collect the spectrum of particles in the granulation process.The particle swarm-kernel ridge regression(PSO-KRR)and random forest-partial least squares(RF-PLS)were used to establish regression models with spectral data,and studied the prediction accuracy of different algorithms.The results show that the RMSE of PLS model prediction set is 0.2180 and the correlation coefficient R is 0.9717;the RMSE of PSO-KRR model prediction set is 0.2154 and the correlation coefficient is 0.9738;and the RMSE of RF-PLS model prediction set is 0.2059 and the correlation coefficient is 0.9733.The RF-PLS model has the best prediction effect.