Data-driven quality prediction methods are widely used in industrial chemical plants. However, it is often difficult to develop prediction models for multi-grade batch processes. Two major issues need to be considered when developing high-accuracy models. The first is the unavailability of sufficient data to create models for each grade of these processes. The other is that each batch cycle typically has an excessive number of explanatory variables. This paper proposes two methods to predict the quality of products manufactured in these multi-batch processes in chemical plants. These methods combine the features of two techniques: the first is a flexible clustered multi-task learning method, which utilizes data from other grades effectively to create high-performance quality prediction models with a small amount of data. This is useful when more data are available for the other grades. The other is a sparsity technique to overcome the high-dimensionality problem of input features. The effectiveness of the proposed methods is demonstrated on a numerical dataset, and finally applied to data generated during an actual industrial blow molding process.

数据驱动的质量预测方法已广泛应用于工业化工厂。但是，通常很难为多级批处理过程开发预测模型。开发高精度模型时，需要考虑两个主要问题。首先是没有足够的数据来为这些过程的每个等级创建模型。另一个是每个批处理周期通常都有过多的解释变量。本文提出了两种方法来预测化工厂在这些多批次过程中生产的产品的质量。这些方法结合了两种技术的特征：第一种是灵活的集群多任务学习方法，该方法有效地利用了其他等级的数据来创建具有少量数据的高性能质量预测模型。当更多数据可用于其他年级时，此功能很有用。另一种是稀疏技术，可以克服输入要素的高维问题。数值数据集证明了所提出方法的有效性，并最终将其应用于实际工业吹塑过程中生成的数据。