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High-precision Quality Prediction Based on Two-dimensional Extended Windows
Version 1
: Received: 29 March 2024 / Approved: 29 March 2024 / Online: 29 March 2024 (10:02:09 CET)
A peer-reviewed article of this Preprint also exists.
Zhao, L.; Yang, J. High-Precision Quality Prediction Based on Two-Dimensional Extended Windows. Mathematics 2024, 12, 1396. Zhao, L.; Yang, J. High-Precision Quality Prediction Based on Two-Dimensional Extended Windows. Mathematics 2024, 12, 1396.
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 direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.
Keywords
batch process; partial least squares; extended window; quality prediction
Subject
Engineering, Control and Systems Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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