Abstract: Aiming at the time-variant and nonlinear characteristics of polypropylene production process,a discount moving window recursive PLS based on data blocks (DMW-RPLS) is proposed in the paper. Based on PLS,the amount of data blocks is controlled through bringing in the moving window and data blocks are discounted through bringing in the forgetting factor,and then updates the soft model in order to predict the measuring variables in real time.Through the study for the polypropylene melt index,the result shows the discount moving window recursive PLS can reduce the calculation and overcome the data saturation,and then use the process information effectively to reflect the process characteristic quickly and accurately.

Key words: recursive PLS, forgetting factor, discount moving window recursive PLS, polypropylene melt index, soft sensing

摘要： 针对聚丙烯生产过程的时变特性和非线性，提出一种基于数据块的折息移动窗递推PLS算法（discount moving window RPLS，DMW-RPLS）。该算法以经典递推PLS为基础，通过引入移动窗来控制数据块的数据量，并对数据块引入遗忘因子来进行折息，然后更新模型，实现对待测变量的实时预测估计。以聚丙烯熔融指数为对象进行了仿真研究，表明提出的折息移动窗递推PLS方法能减少计算量和解决数据饱和问题，有效利用过程信息，快速准确地反映过程特性。

关键词: 递推PLS, 遗忘因子, 折息移动窗递推PLS, 聚丙烯熔融指数, 软测量