Improvements to multivariate data analysis and monitoring of batch processes by multilevel methods

There has been a lot of research activity in the area of batch process analysis and monitoring for abnormal situation detection since the pioneer work of Nomikos and MacGregor []. However, some of the key ideas and the thought process that led to those first papers have been forgotten. Batch process data are dynamic data. The whole philosophy of looking at batch process data with latent variables was developed because batch process variables are both autocorrelated and cross‐correlated. Statistical process control by definition checks deviations from a nominal behavior (a target). Therefore for statistical process control of batch processes we should look at deviations of process variable trajectories from their nominal trajectories and from their nominal auto/cross‐correlations. An added advantage to modeling the deviations from the target trajectory is that a non‐linear problem is converted to a linear one that it is easy to tackle with linear latent variable methods such as principal component analysis (PCA) and partial least squares (PLS). This paper first takes a critical look at the true nature of batch process data. The general case where variables are not present during the entire duration of the batch is addressed. It is then illustrated how proper centering (by taking the deviations from the target trajectory) can retain valuable information on auto‐ and cross‐correlation of the process variables. This auto‐ and cross‐correlation is only modeled with a certain types of models. Topics such as scaling and trajectory alignment are revisited and issues arising when using the indicator variable approach are addressed. The development of control charts for multiblock, multiway PCA/PLS is discussed. Practical issues related to applications in industry are addressed. Then some of the methods that have appeared in the literature are examined as to their assumptions, their advantages and disadvantages and their range of applicability. Finally the nature of transition data (start‐ups, grade transitions) is discussed and issues related to aligning, centering and scaling such types of data are presented. Copyright © 2003 John Wiley & Sons, Ltd.

Batch process data can be arranged in a three-way matrix (batch × variable × time). This paper provides a critical discussion of various aspects of the treatment of these multiway data. First, several methods that have been proposed for decomposing three-way data matrices are discussed in the context of batch process data analysis and monitoring. These methods are multiway principal component analysis (MPCA)—also called Tucker1—parallel factor analysis (PARAFAC) and Tucker3. Secondly, different ways of unfolding, mean centering and scaling the three-way matrix are compared and discussed with respect to their effects on the analysis of batch data. Finally, the role of the time variable in batch process data is considered and methods suggested to predict the per cent completion of batch runs with unequal duration are discussed. Copyright © 1999 John Wiley & Sons, Ltd.

Batch process monitoring is of great importance to ensure the stable operation during the process running. However, traditional deep learning methods have certain limitations when dealing with complex data structures and dynamic features that are prominent in industrial batch processes. This paper proposes a GraphSAGE-LSTM-based deep canonical correlation analysis (GSA-LSTM-CCA) to enhance the performance of feature learning for batch process monitoring. First, the k-nearest neighbor method is used to construct a graph for the variable-unfolding batch process data as a supplementary structural information for the original spatiotemporal correlations. Then, GraphSAGE and LSTM are used to extract the structural features and time-varying features of data, respectively. Finally, the learned features are weighted and fused to construct a residual generator, and CCA is utilized to carry out process monitoring. The effectiveness and superiority of the proposed method are verified in an actual injection molding process.

Batch processes are very important to the chemical and manufacturing industries. Techniques for monitoring these batch processes to ensure their safe operation and that they produce consistent high quality products are needed. Nomikos and MacGregor (1994) present a multi-way principal component analysis (MPCA) approach for monitoring batch processes, and test results show that the method is simple, powerful, and effective. However MPCA is a linear method, and most batch processes are nonlinear. In this paper a nonlinear principal component analysis (NLPCA) method (Dong-McAvoy, 1993) is used for batch process monitoring. The main focus of this paper is on multi-stage batch process monitoring. The proposed approach and special problems for multi-stage batch processes are illustrated through a detailed simulation study.

Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process

Improved monitoring of batch processes by incorporating external information

Real time phase detection based online monitoring of batch fermentation processes

Batch process monitoring in the original measurement's space

On-line batch process monitoring using a consecutively updated multiway principal component analysis model

A Step-wise sequential phase partition algorithm with limited batches for statistical modeling and online monitoring of multiphase batch processes

Cell culture process scale-ups are common in biologics manufacturing as the process moves from the early stages of process development to commercial manufacturing. Statistical batch process monitoring (BPM) is frequently deployed for efficient real-time monitoring and control of cell culture processes. A BPM platform relies on historical process data to train in-control model for real-time monitoring of future batches. Deploying a BPM platform during or immediately after process scale-up is nontrivial due to limited batch campaigns at the commercial scale. In the absence of available at-scale commercial data during scale-ups, we propose an approach to reuse data sets generated at bench or pilot-scale for training BPM models at the commercial scale. The proposed method uses a scaling model to learn complex data scaling relations between bench/pilot-commercial scales. The model is then used to synthesise data sets at a commercial scale using existing in-control bench/pilot data. Using synthesised at-scale commercial scale data, a BPM model can be trained and deployed soon after the scale-up, which would not have been possible otherwise. The efficacy of the proposed approach is illustrated in an industrial cell culture process.

This paper describes the concepts of statistical batch process monitoring and the associated problems. It starts with an introduction to process monitoring in general which is then extended to batch process monitoring. The performance of control charts for batch process monitoring is discussed by means of two performance indices: the overall type I error and the action signal time. Problems associated with the existing approach are discussed and highlighted. Improvements are suggested and checked with the performance indices. To evaluate the effect of the proposed improvements as well as to assess the performance of the existing approach, an industrial batch production process is used.

Intelligent scheduling and monitoring for multi-product networked batch processes

On-line multivariate statistical monitoring of batch processes using Gaussian mixture model

Batch or semi-batch processes have been utilized to produce high-value-added products in the biological, food, semi-conductor industries. Batch process, such as fermentation, polymerization, and pharmacy, is highly sensitive to the abnormal changes in operating condition. Monitoring of such processes is extremely important in order to get higher productivity. However, it is more difficult to develop an exact monitoring model of batch processes than that of continuous processes, due to the common natures of batch process: non-steady, time-varying, finite duration, and nonlinear behaviors. The lack of exact monitoring model in most batch processes leads that an operator cannot identify the faults when they occurred. Therefore, effective techniques for monitoring batch process exactly are necessary in order to remind the operator to take some corrective actions before the situation becomes more dangerous.