基于出口水分预测模型的松散回潮加水量控制算法

Control Algorithm Based on Outlet Moisture Content Prediction Model for Loosening-conditioning Process

为解决松散回潮出口物料水分波动大、难以控制等问题,提出一种基于出口水分预测模型的加水量控制算法。预测模型可以较好地描述加水量及入口物料水分变化对于出口水分的影响,而基于模型的预测控制可以明显改善纯反馈控制下的滞后现象。基于中试线构建自动控制程序并进行试验,结果表明:在采用该算法编写的自动控制程序的控制下的批次,含水率标准偏差下降到0.2以下,相比过去得到了明显的提高。该算法有较大的推广潜力,可为解决烟草制丝过程中的各种延时、滞后问题提供可靠方案。

To better control the severely oscillating outlet moisture content of the loosening-conditioning process,an algorithm for water addition based on the outlet moisture content prediction model is proposed in this study.The model shows satisfactory accuracy in predicting how the outlet moisture content is affected by the inlet moisture content and water addition,and the proposed model-based control algorithm can significantly improve the lagging phenomenon that happened in the traditional pure feedback-controlled batches.An automatic control program is built for a pilot line based on the proposed algorithm,and several tests using the raw material of mainstream brands are conducted in the line.The results show that the average standard deviation of the outlet moisture content is reduced to lower than 0.2 for batches controlled by the proposed algorithm,which is a considerable advancement compared with the past.The proposed algorithm can be easily generalized to other scenarios and can become potential solutions to various problems with delay and lagging in primary tobacco processing.