Quality prediction for multi-grade batch process using sparse flexible clustered multi-task learning
Quality prediction for multi-grade batch process using sparse flexible clustered multi-task learning
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DOI：
10.1016/j.compchemeng.2021.107320
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发表时间：
2021-04
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影响因子：
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通讯作者：
Takafumi Yamaguchi;Y. Yamashita
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文献类型：
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作者：
Takafumi Yamaguchi;Y. Yamashita
数据驱动的质量预测方法在工业化工厂中得到了广泛的应用。然而，建立多级间歇过程的预测模型往往是困难的。在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有过多的解释变量。本文提出了两种方法来预测化工厂多批工艺生产的产品质量。这些方法结合了两种技术的特点：第一种是灵活的聚类多任务学习方法，它有效地利用其他年级的数据，用少量的数据创建高性能的质量预测模型。当其他等级有更多的数据可用时，这是有用的。另一种是克服输入特征高维问题的稀疏化技术。在数值数据集上验证了所提出方法的有效性，并最终应用于实际工业吹塑过程中产生的数据。
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.