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Language: English

Conflict of Interest: In relation to this article, we declare that there is no conflict of interest.

Publication history: Received July 28, 2022
Revised October 31, 2022
Accepted November 6, 2022

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/bync/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Soft sensor development based on just-in-time learning and dynamic time warping for multi-grade processes

Min Jun Song Sung Hyun Ju Jong Min Lee^†

School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Gwanak-ro 1, Gwanak-gu, Seoul 08826, Korea

jongmin@snu.ac.kr

Korean Journal of Chemical Engineering, May 2023, 40(5), 1023-1036(14), 10.1007/s11814-022-1335-5

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Abstract

This study presents the development of soft sensors based on just-in-time learning (JITL) and dynamic time warping (DTW) for online quality prediction in multi-grade processes. Most industrial chemical processes are multi-grade processes that produce multiple products with distinct properties. Multi-grade processes, however, are difficult to monitor and control due to frequent process transitions and abrupt changes in operating conditions. The DTWbased JITL soft sensor modeling approach is proposed as a solution to the complexity of multi-grade process modeling. In the JITL modeling approach, a local model is trained online using historical samples that are similar to the query sample, allowing the model to account for multi-grade characteristics and process drifts. To account for process dynamics and temporal correlations, the suggested approach utilizes a data sequence as an input rather than a single data point. DTW calculates the similarity of data sequences by stretching the sequences to determine an optimal warping path. Additionally, sensitivity analyses of model hyperparameters are performed and a cross-correlation-based hyperparameter optimization approach is proposed. The advantages of the proposed approach are verified via multi-grade simulation studies. As a result, the proposed model outperforms a conventional JITL model based on the Euclidean distance.

Keywords

Just-in-Time Learning (JITL) Dynamic Time Warping (DTW) Soft Sensor Multi-Grade Process, Machine Learning

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