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

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.

Biological processes exhibit different behavior depending on the influent loads, temperature, microorganism activity, and so on. It has been shown that a combination of several models can provide a suitable approach to model such processes. In the present study, we developed a multiple statistical model approach for the monitoring of biological batch processes. The proposed method consists of four main components: (1) multiway principal component analysis (MPCA) to reduce the dimensionality of data and to remove collinearity; (2) multiple models with a posterior probability for modeling different operating regions; (3) local batch monitoring by the T(2)- and Q-statistics of the specific local model; and (4) a new discrimination measure (DM) to identify when the system has shifted to a new operating condition. Under this approach, local monitoring by multiple models divides the entire historical data set into separate regions, which are then modeled separately. Then, these local regions can be supervised separately, leading to more effective batch monitoring. The proposed method is applied to a pilot-scale 80-L sequencing batch reactor (SBR) for biological wastewater treatment. This SBR is characterized by nonstationary, batchwise, and multiple operation modes. The results obtained for the pilot-scale SBR indicate that the proposed method has the ability to model multiple operating conditions, to identify various operating regions, and also to determine whether the biosystem has shifted to a new operating condition. Our findings show that the local monitoring approach can give more reliable and higher resolution monitoring results than the global model.

Dynamic model-based fault diagnosis for (bio)chemical batch processes

Process Monitoring Using Principal Component Analysis in Different Operating Time Processes

Data Driven Modelling and Control of Batch Processes in the Pharmaceutical Industry

A 2D-FM model-based robust iterative learning model predictive control for batch processes

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.

Improved monitoring of batch processes by incorporating external information

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.

Traditional single model based soft sensors may have poor performance on quality prediction for batch processes because of the strong nonlinearity, multiple-phase, and time-varying characteristics. Therefore, a phase partition based ensemble learning framework upon least squares support vector regression (LSSVR) is proposed for soft sensor modeling. Firstly, multiway principal component analysis (MPCA) is employed to handle high-dimensional datasets and extract essential correlation information. Then, different operation phases of the process can be identified by the phase partition strategy based on Gaussian mixture model (GMM) method. Meanwhile, the optimal Gaussian component number is determined by Bayesian information criterion (BIC) technique. Further, multiple localized LSSVR models are constructed to characterize the various dynamic relationships between quality and process variables for local regions, while the grid search (GS) and ten-fold cross-validation methods are introduced to parameter optimization for each local model. Finally, the posterior probability for each test sample with respect to different phases can be estimated by Bayesian inference strategy, and local outputs are integrated to produce the final quality prediction results. Feasibility and superiority of the proposed soft sensor are validated through a case study for penicillin fermentation process. It can achieve satisfactory prediction accuracy and effectively tackle nonlinear and multi-phase modeling problems in chemical and biological processes.

This paper investigates fault diagnosis in batch processes and presents a comparative study of feature extraction and classification techniques applied to a specific biotechnological case study: the fermentation process model by Birol et al. (Comput Chem Eng 26:1553-1565, 2002), which is a benchmark for advanced batch processes monitoring, diagnosis and control. Fault diagnosis is achieved using four approaches on four different process scenarios based on the different levels of noise so as to evaluate their effects on the performance. Each approach combines a feature extraction method, either multi-way principal component analysis (MPCA) or multi-way independent component analysis (MICA), with a classification method, either artificial neural network (ANN) or support vector machines (SVM). The performance obtained by the different approaches is assessed and discussed for a set of simulated faults under different scenarios. One of the faults (a loss in mixing power) could not be detected due to the minimal effect of mixing on the simulated data. The remaining faults could be easily diagnosed and the subsequent discussion provides practical insight into the selection and use of the available techniques to specific applications. Irrespective of the classification algorithm, MPCA renders better results than MICA, hence the diagnosis performance proves to be more sensitive to the selection of the feature extraction technique.

The problem of using time-varying trajectory data measured on many process variables over the finite duration of a batch process is considered. Multiway principal-component analysis is used to compress the information contained in the data trajectories into low-dimensional spaces that describe the operation of past batches. This approach facilitates the analysis of operational and quality-control problems in past batches and allows for the development of multivariate statistical process control charts for on-line monitoring of the progress of new batches. Control limits for the proposed charts are developed using information from the historical reference distribution of past successful batches. The method is applied to data collected from an industrial batch polymerization reactor.

This paper summarizes recent progress in the area of estimation and control of batch processes. The task of designing effective strategies for the estimation of unmeasured variables and for the control of the important outputs of the process is linked to our need to optimize the process and its success is depended upon the availability of a process model. For this reason we will provide a substantial focus on the modeling issues that relate to batch processes. In particular we will focus attention on the approach developed in our group and referred to as “tendency modeling” that can be used for the estimation, optimization and control of batch processes. Several batch reactor example processes will be detailed to illustrate the applicability of the general approach. These relate to organic synthesis reactors and bioreactors. The point that distinguishes tendency modeling from other modeling approaches is that the developed Tendency Models are multivariable, nonlinear, and aim to incorporate all the available fundamental information about the process through the use of material and energy balances. These models are not frozen in time as they are allowed to evolve. Because they are not perfectly accurate they are used in the optimization, estimation and control of the process on a tentative basis as they are updated either between batches or more frequently. This iterative or adaptive modeling strategy also influences the controller design. The controller performance requirements and thus the need of a more accurate model increase as successive optimization steps guide the process operation near its constraints.

Temperature Control of Reactors Using Neural Networks

Adsorption thermal energy storage for cogeneration in industrial batch processes: Experiment, dynamic modeling and system analysis