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Abstract 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.
2Abnormal Situation Detection, Three Way Data and Projection Methods; Robust Modeling for Industrial Applications
Abnormal Situation Detection, Three Way Data and Projection Methods; Robust Modeling for Industrial Applications
5Improvements to multivariate data analysis and monitoring of batch processes by multilevel methods
Batch process data contain between‐run and within‐run sources of variation, which complicates data analysis and process monitoring. Multilevel methods, such as multilevel simultaneous component analysis, greatly enhance the interpretation of large sets of batch process data by separating the between‐run and within‐run variation. Using a multilevel approach in batch process monitoring greatly reduces the occurrence of false alarms that are caused by trivial variations and process changes that were made on purpose. This also reduces the need for model updating, which often is a large effort and causes downtime of the monitoring system. Furthermore, relevant phenomena that are masked by larger but uninteresting sources of variation when standard batch process monitoring methods are applied can often be detected if the multilevel approach is used. Chemical batch process data are used to illustrate the methods. Copyright © 2012 John Wiley & Sons, Ltd.
Read more5Batch processes monitoring based on statistical and engineering process control integration: case of alkyd polymerisation reactor
The aim of this research is to expand a framework that integrates two important concepts: statistical process control (SPC) and engineering process control (EPC). Most of the literature researches on integrated SPC/EPC systems are focused into continuous process mainly with algorithmic SPC. The integrated SPC/EPC systems in batch process control have not received the same degree of attention. This paper is a first of its kind in integrated SPC/EPC systems that applied in batch process. The proposed SPC/EPC integration is performed continually in active SPC for the batch making advance and in run-to-run (RTR) control action between batches. As a validation step, the proposed approach is applied to an industrial batch alkyd polymerisation reactor. Through this case study application, process engineers at the company are now able to use a valuable decision making tool when the production process is affected by certain disruptions, with obvious consequences on product quality, productivity and competitiveness.
Read more18Batch process monitoring in the original measurement's space
Batch process monitoring in the original measurement's space
17Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process
Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process
33Comparing alternative approaches for multivariate statistical analysis of batch process data
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.
Read more2Process Monitoring Using Principal Component Analysis in Different Operating Time Processes
Process Monitoring Using Principal Component Analysis in Different Operating Time Processes
Control Charts for Batch Processes
Applying control charts successfully to batch processes requires the recognition of some distinguishing features of batch processing versus single‐unit processing. Traditional Shewhart‐type charts can be modified for use in batch‐processing practitioners of statistical process control (SPC); however, entirely different control chart methods and practices will often be required. This article reviews the common features of batch processing and the data available to control chart practitioners from typical batch processes and suggests alternatives to standard Shewhart charts for effective process monitoring and control.
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Batch-to-Batch Iterative Learning Control of a Fed-Batch Fermentation Process
In this work, Iterative Learning Control on a fed-batch fermentation process using linearised models has been studied. The repetitive nature of batch processes enables ILC to obtain information from a previous batch in order to improve the performance of the current batch such that the product quality converges asymptotically to the desired trajectory The basic batch to batch ILC law presents the control action of a current batch as a summation of the control action from the previous batch and the deviation of the output trajectory from the desired reference trajectory incorporation with a learning rate. In a bid to address the issue of the process non-linearity, the control policy and the output trajectory were linearised around their respective nominal trajectories. The linearised models were then identified using Multi Linear Regression (MLR), Principal Component Analysis (PCR) and Partial Least Squares (PLS). In order to curb the effects of plant-model mismatches and process variations, the linearised models were reidentified after each batch operation. This was done by selecting the immediate previous batch as the nominal batch and then adding the recently obtained process data into the historical data batch on completion of the current batch run. The weighting matrices in the objective function were carefully selected taking into consideration that they have a major influence on the robust performance of the process. In using PLS and PCR models the issue of process collinearity was effectively addressed. The proposed batch to batch ILC strategy was applied to a simulated fed-batch fermentation process for the production of secreted protein. The results of the optimal control policy were comparable to that obtained in using full mechanistic model. ILC, a simple but yet an effective optimal control strategy has demonstrated to be a viable option in complex processes such as batch processes where mechanistic models are difficult to develop. Keywords: Iterative Learning Control, batch process, fed-batch fermentation, batch to batch ILC, control policy. DOI: 10.7176/CMR/14-3-02 Publication date: August 31 st 2022
Read more13Multivariate monitoring of batch process startup
A batch process is finite in duration and can be separated into two stages: startup and production. We develop a methodology to monitor a batch process during the startup stage to reduce the length of the startup stage. We focus on processes that are characterized by multiple process parameters and product characteristics. Because of the complex interdependencies characterizing the process parameters and product characteristics, it is more effective to evaluate them simultaneously. To address the multivariate nature of the process we use a multivariate statistical model: PLS (Projection to Latent Structures). PLS has been applied to several applications in statistical process monitoring. We present a new application of PLS to the startup stage of a batch process. Iterative adjustments made during startup in search of an acceptable production zone consume considerable amounts of material, labor and equipment time. We develop a monitoring procedure to reduce the time as well as the number of iterations and adjustments needed for startup. A PLS model is constructed, using baseline data, to characterize the relationship among process parameters during good production. The startup stage is monitored using the PLS characterization to determine if the process is consistent with good production. We illustrate the improved startup operations with an example from a batch process in filament extrusion, the application that motivates this work. Copyright © 2001 John Wiley & Sons, Ltd.
Read more57Batch process monitoring for consistent production
Batch process monitoring for consistent production
21Fault detection and classification for a two‐stage batch process
Multiway principal component analysis (MPCA) has been extensively applied to batch process monitoring. In the case of monitoring a two‐stage batch process, the inter‐stage variation is neglected if MPCA models for each individual stage are used. On the other hand, if two stages of reference data are combined into a large dataset that MPCA is applied to, the dimensions of the unfolded matrix will increase dramatically. In addition, when an abnormal event is detected, it is difficult to identify which stage's operation induced this alarm. In this paper, partial least squares (PLS) is applied to monitor the inter‐stage relation of a two‐stage batch process. In post‐analysis of abnormalities, PLS can clarify whether root causes are from previous stage operations or due to the changes of the inter‐stage correlations. This approach was successfully applied to a semiconductor manufacturing process. Copyright © 2008 John Wiley & Sons, Ltd.
Read more65On-line batch process monitoring using a consecutively updated multiway principal component analysis model
On-line batch process monitoring using a consecutively updated multiway principal component analysis model
14SPC in an automated manufacturing environment
Implementation of statistical process control (SPC) in an automated environment requires a number of issues to be addressed. Changes in sample data distribution and statistical properties such as independence will affect the use and interpretation of traditional SPC procedures; changed monitoring and adjustment techniques will influence the subsequent decision-making; at the same time, automation could facilitate implementation of SPC with other control techniques. In this paper, the potential of combining SPC with engineering process control methods is discussed. Comparative analysis between traditional SPC methods and SPC combined with feedback control is presented. Actual and simulated data are used to illustrate the procedure which, because of its self-tuning ability, could greatly reduce system readjustment while most of the advantages of traditional SPC are preserved at the same time.
Read more28Resistance to barley scald (Rhynchosporium secalis) in the Ethiopian donor lines ‘Steudelli’ and ‘Jet’, analyzed by partial least squares regression and interval mapping
The resistance of barley (Hordeum vulgare L.) to Rhynchosporium secalis (scald) has been investigated in two crosses between the susceptible cv. 'Ingrid' and two resistant Ethiopian landraces, 'Steudelli' and 'Jet'. Doubled haploids were inoculated in replicated tests using two isolates of R. secalis, '4004' and 'WRS1872'. Expression of resistance differed widely between replicated tests. AFLP, SSR and RFLP markers were used to develop chromosome maps. Results have been analysed using partial least squares regression (PLSR) and interval mapping. In PLSR the major covariance structures or 'latent variables' between X (markers) and Y (isolates, tests) are modelled as principal components and their optimal number determined by cross-validation. In 'Steudelli' two QTL were detected, one on each of chromosomes 3H and 7H, in 4 out of 5 tests, while in 'Jet' only one (different) allele at the 3H locus was found. The validated R(2) varied between 11.0% and 64.9% in the replicated tests with '4004'.With isolate 'WRS1872' the 7H locus and another 3H locus were detected. By interval mapping the QTL detected were less stable and generally gave lower R(2) values than PLSR. PLSR does not depend on maps, but interval mapping based on values predicted by PLSR had R(2) around 90%. It is suggested that PLSR may be a useful tool in QTL analysis.
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