现代过程工业正逐渐倚重于生产小批量、多品种、高附加值产品的间歇过程。基于多元统计模型的过程监测是保障生产安全和产品质量的重要工具，从间歇过程独特的数据特性出发，将现有的多元统计建模方法进行合理的分类，简要回顾了各类方法的起源、发展及延伸的历程，除了阐述每种方法的基本原理，还详细讨论了各种方法的适用背景，相互关联及优缺点等内容，并对这一领域中依然存在的问题以及研究前景给出中肯的评述。

A process analysis and quality prediction scheme is proposed based on a stage-based PLS modeling for batch processes. Without any requirement of prior process knowledge, the scheme first divides a batch process into stages of different process characteristics. Subsequently, a strategy is developed to identify stages that have critical influences on concerned qualities, defined as critical-to-quality stages. Within these critical-to-quality stages, an algorithm is then further developed to identify the variables that have significant contributions to the quality variations. Finally, based on the identified nature of quality and stage relationships, a set of algorithms is developed for online quality prediction. The applications of the proposed scheme to injection molding show that the proposed analysis and quality prediction are not only effective but are also able to enhance process understanding and identify specific variables and periods for quality improvement.

A stage-based quality control scheme, which combines an online adjustment strategy with the authors’ previous works on stage partial least squares(PLS) modeling and quality prediction, is developed for within-batch control of end-product quality for batch processes. Considering the inherent time-specific nature of process trajectories to the end-product quality, a critical-to-quality-control stage is introduced for quality control and stability improvement, together with guidelines on the manipulating variable selection and no-control region. The effectiveness and feasibility of the proposed scheme are illustrated on an injection molding process.