High-Precision Quality Prediction Based on Two-Dimensional Extended Windows

Luping Zhao () and Jiayang Yang
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Luping Zhao: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Jiayang Yang: College of Information Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2024, vol. 12, issue 9, 1-17

Abstract: A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the directions of sampling time and batch, a newly defined region of support (ROS), called the k - i -back-extended region of support (KIBROS), is proposed; it establishes an extended window by adding two regions of data to the traditional ROS to include all possible important data for quality prediction. Based on the new ROS, extended windows are established, and different models are proposed using the extended windows for batch process quality prediction. Furthermore, using the typical injection molding batch process as an example, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and that the prediction stability is also improved.

Keywords: batch process; partial least squares; extended window; quality prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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