Slow-Varying Dynamics Supervised Data Decomposition for Batch Process Quality Prediction

This is the authors' abstract. We don't add key points for this paper.

Ensuring high prediction accuracy is essential for maintaining high quality of products in batch processes, given their inherent multi-phase characteristics and dynamic variations in real-world applications. Identifying quality-relevant process variations is crucial to address these challenges and produce interpretable and accurate predictions. This work aims to uncover critical quality-relevant process variables from raw measurements collected from batch processes. The proposed method consists of two key components. First, a dynamic subspace is designed for batch processes to extract the slow-varying features that are relevant to the quality index. Second, the quality-relevant features have been employed to achieve the reliable prediction of the performance index. Through the simulated experiment on a concentration batch production process, the proposed method is illustrated.