Abstract To cope with the difficulty of on-line quality prediction for multi-grade processes widely operated in process industries, a just-in-time latent variable modeling method is proposed based on extracting the common and special features of multi-grade processes. Considering the complicated nonlinear characteristics of multi-grade processes encountered in engineering applications, a just-in-time learning (JITL) strategy is developed to choose the relevant samples from different grades with respect to the query sample. A novel common feature extraction algorithm is proposed to determine the common directions shared by different grades of processes. After extracting the common features, a partial least-squares modeling algorithm is used to extract the special directions for each grade, respectively. Hence, product quality prediction can be simply conducted by integrating the common and special parts of each grade for model building in terms of a JITL strategy. A numerical case and an industrial polyethylene process are used to demonstrate the effectiveness and advantage of the proposed method.

中文翻译：

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通过实时潜在变量建模与通用和特殊功能的集成对多级过程的质量预测

摘要 针对过程工业中广泛运行的多级过程质量在线预测困难的问题，提出了一种基于提取多级过程共性和特殊性的实时潜变量建模方法。考虑到工程应用中遇到的多级过程的复杂非线性特征，开发了一种即时学习（JITL）策略，以针对查询样本从不同等级中选择相关样本。提出了一种新的公共特征提取算法来确定不同等级进程共享的公共方向。提取共同特征后，采用偏最小二乘建模算法分别提取各年级的特殊方向。因此，产品质量预测可以简单地通过JITL策略整合每个等级的公共和特殊部分进行模型构建。数值案例和工业聚乙烯过程用于证明所提出方法的有效性和优势。