Abstract
In this paper, focusing on the slow time-varying characteristics, a series of works have been conducted to implement an accurate quality prediction for batch processes. To deal with the time-varying characteristics along the batch direction, sliding windows can be constructed. Then, the start-up process is identified and the whole process is divided into two modes according to the steady-state identification. In the most important mode, the process data matrix, used to establish the regression model of the current batch, is expanded to involve the process data of previous batches, which is called batch augmentation. Thus, the process data of previous batches, which have an important influence on the quality of the current batch, will be identified and form a new batch augmentation matrix for modeling using the partial least squares (PLS) method. Moreover, considering the multiphase characteristic, batch augmentation analysis and modeling is conducted within each phase. Finally, the proposed method is applied to a typical batch process, the injection molding process. The quality prediction results are compared with those of the traditional quality prediction method based on PLS and the ridge regression method under the proposed batch augmentation analysis framework. The conclusion is obtained that the proposed method based on the batch augmentation analysis is superior.
Highlights
As an important mode of industrial production, batch processing is closely related to modern people’s life, and is widely used in many fields
Principal component analysis (PCA) [1,2,3,4], independent component analysis (ICA) [5,6,7,8] and partial least squares regression (PLS) [9,10,11,12], which are the core of multivariate statistical analysis technology, have been more and more favored by researchers and field engineers because they only need process data and quality data to build models, and they have unique advantages in processing high-dimensional and highly coupled data
In this paper, the regression method based on the batch augmentation analysis is proposed to predict the final quality of slow time-varying batch processes by extracting more process data information corresponding to the process quality
Summary
As an important mode of industrial production, batch processing is closely related to modern people’s life, and is widely used in many fields. In this paper, the regression method based on the batch augmentation analysis is proposed to predict the final quality of slow time-varying batch processes by extracting more process data information corresponding to the process quality. To deal with the time-varying characteristics along the batch direction, the sliding windows are constructed to analyze several batches in the direction of the current batch, where different batches are covered by different sliding windows, and multiple continuous models are established to capture the relationships between the different process variables and quality, respectively, in order to predict the final quality. The rest of this paper consists of the following parts: First, Section 2 mainly introduces the proposed method: the PLS method, slow time-varying batch process quality prediction based on batch augmentation analysis, including the establishment of the sliding window model, start-up process identification, critical-to-quality phase and batch identification, and batch augmentation modeling.
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