A Technique for Integrated Quality Control, Profile Control, and Constraint Handling for Batch Processes

Chin, In Sik; Lee, Kwang-Sik; Lee, Jay H.

doi:10.1021/ie990305q

Cited by 57 publications

(35 citation statements)

References 14 publications

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“…This similarity was noticed by Chin, Lee, and Lee (2000), who combined ILC and run-to-run control concepts into an integrated technique called QBMPC intended for simultaneous trajectory tracking and end quality control. An added feature of QBMPC was that the end quality variables for an on-going batch run could be inferred from the on-line process measurements, thereby giving the controller the capability to make more immediate adjustment with respect to disturbances.…”

Section: Article In Pressmentioning

confidence: 83%

Iterative learning control applied to batch processes: An overview

Lee

2007

Control Engineering Practice

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257

106

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With the recent emphasis on batch processing by emerging industries like the microelectronics and biotechnology, the interest in batch process control has been renewed. This paper gives an overview of the iterative learning control (ILC) technique, which can be used to improve tracking control performance in batch processes. The fundamental concepts and review of the various ILC algorithms are presented, with a particular focus on a model-based algorithm called Q-ILC and an application involving a rapid thermal processing (RTP) system. The study indicates that one can solve a seemingly very difficult multivariable nonlinear tracking problem with relative ease by intelligently combining the ILC technique with basic process insights and standard system identification techniques. Some related techniques in the literature are brought forth with the hope of unifying them. We aslo suggest some remaining challenges. r

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Section: Article In Pressmentioning

confidence: 83%

Iterative learning control applied to batch processes: An overview

Lee

2007

Control Engineering Practice

Self Cite

257

106

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“…Control is accomplished by manipulating the slope or the level at the decision point. Examples of such approaches can be found in References [75,76].…”

Section: Control Using Latent Variable Methodsmentioning

confidence: 99%

Application of latent variable methods to process control and multivariate statistical process control in industry

Kourti

2005

Adaptive Control & Signal

303

224

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Multivariate monitoring and control schemes based on latent variable methods have been receiving increasing attention by industrial practitioners in the last 15 years. Several companies have enthusiastically adopted the methods and have reported many success stories. Applications have been reported where multivariate statistical process control, fault detection and diagnosis is achieved by utilizing the latent variable space, for continuous and batch processes, as well as, for process transitions as for example start ups and re-starts. This paper gives an overview of the latest developments in multivariate statistical process control (MSPC) and its application for fault detection and isolation (FDI) in industrial processes. It provides a critical review of the methodology and describes how it is transferred to the industrial environment. Recent applications of latent variable methods to process control as well as to image analysis for monitoring and feedback control are discussed. Finally it is emphasized that the multivariate nature of the data should be preserved when data compression and data preprocessing is applied. It is shown that univariate data compression and reconstruction may hinder the validity of multivariate analysis by introducing spurious correlations.

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“…Changes in the disturbance correlation structure, although usually not important for product quality can be important, and should be monitored, if the disturbance correlation structure is being used somehow in a batch-to-batch control algorithm (Chin et al, 2000). In that case the manipulated variables set points are being changed for new batches based on assuming a previously identified disturbance autocorrelation structure.…”

Section: Case Study 1: Detection Of Changes In Correlation From Batchmentioning

confidence: 99%

Multivariate monitoring of batch processes using batch‐to‐batch information

Flores‐Cerrillo

MacGregor

2004

AIChE Journal

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Multiway principal component analysis (MPCA) and multiway partial-least squares (MPLS) are well-established methods for the analysis of historical data from batch processes, and for monitoring the progress of new batches. Direct measurements made on prior batches can also be incorporated into the analysis by monitoring with multiblock methods. An extension of the multiblock MPCA/MPLS approach is introduced to explicitly incorporate batch-to-batch trajectory information summarized by the scores of previous batches, while keeping all the advantages and monitoring statistics of the traditional IntroductionBatch and semibatch processes are the main production devices in pharmaceuticals, paint-coating, adhesive, and in most of the specialty-chemical industries. However, such processes suffer from variations in initial charge, operating conditions, and impurity concentrations in raw materials. To allow for more consistent operation and final product quality, control and monitoring schemes have been implemented around such processes. The use of multivariate statistical process control methods based on multiway principal component analysis (MPCA), and multiway partial-least squares (MPLS), and their associated monitoring statistics have been shown to be successful with industrial data for both the analysis of completed batches, and for the on-line monitoring of new batches (MacGregor and Nomikos, 1992; MacGregor, 1994, 1995a,b;MacGregor and Kourti, 1995;. The success of such methods is largely because of the fact that the type of normal operating data necessary for building the required model is always readily available, and that the statistical control charts used for analysis and monitoring are easily developed from these data. One of the main characteristics of such methods is that the evolving within-batch measurement trajectories are projected into low dimensional latent variable spaces that summarize all the relevant information, and allow monitoring charts to be built in the reduced spaces where visually inspection and interpretation are easier.Multiway PCA and PLS for the analysis, monitoring, and prediction of final product quality in batch processes were first introduced by Nomikos and MacGregor (1992, 1995a. They illustrated the detection of abnormal batches with several criteria, such as the Q statistic (also known as square prediction error (SPE) or distance to the model in the X space (DMODX)), the instantaneous SPE (for on-line monitoring) and the PCA or PLS score plots or equivalently Hotelling's T 2 statistic. modes of operation, and prior processing conditions into the analysis and monitoring of batch processes. These multiblock methods allow one to incorporate any measured variable from prior batches into the analysis and monitoring schemes. In this article, we propose a variation of these multiblock methods to incorporate the PCA or PLS scores from prior batches.Recently, Dorsey and Lee (2001) proposed a monitoring framework based on state-space models that purported to consider batch-to-batch vari...

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A Technique for Integrated Quality Control, Profile Control, and Constraint Handling for Batch Processes

Cited by 57 publications

References 14 publications

Iterative learning control applied to batch processes: An overview

Iterative learning control applied to batch processes: An overview

Application of latent variable methods to process control and multivariate statistical process control in industry

Multivariate monitoring of batch processes using batch‐to‐batch information

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