A two-step multivariate statistical learning approach for batch process soft sensing
Aaron Hicks, Matthew L. Johnston, Max Mowbray, Maxwell Barton, Amanda Lane, César Mendoza, Philip A. Martin, Dongda Zhang
Abstract
Statistical machine learning algorithms have been widely used to analyse industrial data for batch process monitoring and control. In this study, we aimed to take a two-step approach to systematically reduce data dimensionality and to design soft-sensors for product quality prediction. The approach first employs partial least squares to screen the entire dataset and identify critical time regions and operational variables, then adopts multiway partial least squares to construct soft-sensors within the reduced space to estimate final product quality. Innovations of this approach include the ease of data visualisation and ability to identify major operational activities within the factory. To highlight efficiency and practical benefits, an industrial personal care product manufacturing process was presented as an example and two soft-sensors were successfully developed for product end viscosity estimation. Furthermore, the accuracy, reliability, and data efficiency of the soft-sensors were thoroughly discussed. This paper, therefore, demonstrates the industrial potential of the proposed approach.