基于宽度学习的浓密机底流浓度软测量

由于浓密脱水过程中浓密机的底流浓度难以在线检测，本文提出了一种基于宽度学习的软测量建模方法，用以解决底流浓度的在线检测问题.该方法精度高，泛化能力强.首先，在浓密机内部安装压力传感器，建立正常工况下的历史数据集;然后，利用宽度学习系统对软测量模型进行训练，从而实现浓密机底流浓度的在线预测;最后，通过仿真实验验证了该方法的有效性.与传统的机器学习方法相比，宽度学习方法具有更高的预测精度.

Soft Sensor of Underflow Concentration for Thickener Based on Broad Learning System

Since it is difficult to online measure the underflow concentration of the thickener in the thickening-dehydration process， a broad learning system(BLS) based soft sensor modeling method is proposed in this paper. The method has high precision and strong generalization capability. First， several pressure sensors are installed inside the thickener， and the historical dataset under normal operating conditions is established. Then， the soft sensor model is trained by employing the BLS method to online predict the underflow concentration of the thickener. Finally， the efficiency of the proposed method is verified by simulation experiments. Compared with other traditional machine learning methods， the BLS method has higher prediction accuracy.