中文摘要：

传统MKPLS方法在进行核技巧计算时需要对全部的测量变量进行两两运算,造成了运算量过大、存储空间需求高的问题。针对间歇过程在在线质量预测方面的非线性问题以及计算量的问题,提出一种新的基于特征空间（FS）的核偏最小二乘算法,用于对间歇过程的质量数据进行在线软测量。首先,算法将间歇过程采集到的数据进行三维展开及标准化处理;之后,将这些数据进行特征向量提取以降低投影到核空间中的数据量以及计算量;最后,针对传统特征向量提取算法在向量选择顺序方面的盲目性,将质量数据纳入考量,构建一种新的特征向量提取方式,以解决在线软测量方面的非线性问题,进一步提高在线软测量精度。最后将提出的算法用于青霉素发酵仿真及实际过程的在线监测,验证了方法的有效性。

英文摘要：

Conventional multiway kernel partial least squares（ MKPLS） method needs to calculate all the measured variables in every pair of two variables when using the kernel trick,which causes great amount of calculation and memory requirement. Aiming at the nonlinear and calculation burden problems in online quality prediction of batch process,a new feature space（ FS） based kernel partial least squares algorithm is proposed to carry out the on-line quality prediction of batch processes. First,the proposed algorithm expands the 3-D collected data into 2-D ones and performs the normalization processing. Then,a feature vector selection method is applied to reduce the data calculation burden during PLS kernel trick implementation. Last,aiming at the blindness of traditional feature vector selection algorithm in feature vector selection sequence,the quality data are taken into account and a new feature vector selection method is suggested to solve the nonlinear problem in online soft sensing and further improve the accuracy of online soft sensing. Finally,the proposed method was applied in the penicillin fermentation process simulation and the actual process online monitoring,which verify the validity of the proposed method.