烘丝过程出口水分控制数据分析方法应用研究

The Research of Applying Data Analysis Methods to Control Export Moisture During Cut Tobacco Drying

烟丝含水率是卷烟生产过程中最重要的质量参数,如何尽可能地提高烘丝工序的出口水分控制的平稳性与精准度一直是各卷烟生产厂家研究的重要课题。文章首先对模型预测控制方法(MPC)进行应用研究,基于红河卷烟厂历史烘丝过程控制数据分析,运用多种辨识烘丝过程动态系统方法,对比发现ARX模型较优,在此基础上提出一种基于多级粒度数据建模的多步预测思路,取得一定的预测效果;接着通过多次仿真实验,发现基于数据驱动的控制方法(DDC)相比MPC方法具有更好的控制性能。

Tobacco moisture content is one of the most significant quality parameters during producing cigarettes. And how to improve the stationarity and precision of controlling export moisture during cut tobacco drying has been the important study topic in each cigarette manufacturers. Firstly, this paper carried out the research of applications on the methods of model predictive control （MPC）. Based on the analysis of history data for drying process control in the Red River cigarette factory, a variety of identifying the dynamic system methods in the cut tobacco drying process were applied, founding that the ARX model was better. On the basis of the result, a multi-step prediction idea based on modeling multi-level granularity data was proposed, obtaining some effective prediction. Lastly compared with the MPC methods, many times simulation experiments showed that, the control method based on data driven （DDC） had better control performance.