Virtual metrology in long batch processes using machine learning

ABSTRACT

A long batch process typically runs for several hours to produce different process outcomes. During the entire duration of the process, several sensor data are recorded involving complicated non-linear dynamics among process constituents, which are difficult to model. The users are often interested in predicting the eventual process outcomes well before the completion of the process so that the process can be terminated in case the predicted outcome is not as desired. Virtual Metrology (VM), a virtual property estimation procedure, has gained importance over the years as a supporting tool to address this problem. In this paper, we have proposed a generalized VM pipeline including a deep-learning model that can be scaled to support high-dimensional input sensors and outputs. The developed model is able to predict the end-results for an industrial problem with less than 10% error after about one-fifth of the total process-time.

KEYWORDS:

• metrology
• virtual
• simulation
• prediction
• DNN

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/10426914.2023.2220487.

Notes

1. In this paper, we have referred to the final product of the process as outputs, response, process outputs interchangeably.

2. For data secrecy issue, we have normalized all sensor and response data within [0,1].