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PARTICLE FILTERS FOR DYNAMIC DATA RECTIFICATION AND process CHANGE DETECTION

IFAC Proceedings Volumes 2006年第13期39卷 204-209页

作者： Tao Chen Julian Morris Elaine Martin Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU U.K.

The objectives of dynamic data rectification are wide-ranging and include the estimation of the process states, process signal de-noising, and outlier detection and removal. One approach reported in the literature for dynamic data rectification is the conjunction of the extended Kalman filter (EKF) and the expectation-maximization algorithm. However, this approach is limited in terms of its applicability due to the EKF being less appropriate where the state and measurement functions are highly non-linear or where the posterior distribution of the states is non-Gaussian. This paper proposes an alternative approach whereby particle filters are utilized for dynamic data rectification. By formulating the rectification problem within a probabilistic framework, the particle filters generate Monte Carlo samples from the posterior distribution of the system states, and thus provide the basis for rectifying the process measurements. Furthermore, the proposed technique is capable of detecting changes in process operation and thus complements the task of process fault diagnosis. The appropriateness of particle filters for dynamic data rectification is demonstrated through its application to a benchmark pH neutralization process.

关键词： Dynamic data rectification particle filters process change detection sequential Monte Carlo state estimation

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Sensor fault diagnosis in dynamic processes 8th

Sensor fault diagnosis in dynamic processes

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8th IFAC Symposium on Dynamics and control of process Systems, 2007

作者： Alawi, Ahmed Morris, Julian Genzyme Ltd. Hollands Road Haverhill SuffolkCB9 8PU United Kingdom School of Chemical Engineering and Advanced Materials Centre for Process Analytics and Control Technology University of Newcastle Newcastle Upon TyneNE1 7RU United Kingdom

Fault detection and diagnosis are important technologies for the safe and efficient operation of a chemical plant. This paper describes a sensor fault identification approach using variable reconstruction for dynamic systems. The proposed methodology extends sensor fault reconstruction for Canonical Variate Analysis based process performance monitoring which admits process dynamic behaviour in a natural way, and evaluates its capabilities compared to a dynamic PCA approach using a mathematical benchmark problem and a simulation of a closed loop controlled CSTR previously used for studying both simple and complex faults. © Copyright 2007 IFAC.

关键词： process monitoring

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Multiscale fault detection and diagnosis in fed-batch fermentation 10th

Multiscale fault detection and diagnosis in fed-batch fermen...

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10th IFAC Symposium on Computer Applications in Biotechnology, 2007

作者： Alawi, Ahmed Morris, Julian Genzyme Ltd. Hollands Road Haverhill SuffolkCB9 8PU United Kingdom Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle-upon-TyneNE1 7RU United Kingdom

ISBN: (纸本)9783902661616

The importance of the FDA PAT guidelines in pharmaceutical process design space can be influenced by the introduction of robust process malfunction and senor fault detection and diagnosis tools. The paper compares a multi-scale multi-block modelling approach with conventional multiway PCA approaches for batch process monitoring. A benchmark penicillin fermentation simulation is used to evaluate the two methodologies. Contributions plots with confidence bounds enhance the fault diagnosis potential of the approaches studied. The methodology is in the process of being evaluated in fermentation and batch cooling crystallisation. Copyright © 2007 IFAC.

关键词： Fault detection

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Multi-Site Performance Monitoring in Batch Pharmaceutical Production

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IFAC Proceedings Volumes 2004年第1期37卷 683-689页

作者： C.W.L. Wong A.J. Morris E.B. Martin R.E.A. Escott Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials University of Newcastle Newcastle upon Tyne NE1 7RU UK GlaxoSmithKline Chemical Development

A challenge facing the pharmaceutical and chemical industries is how to understand and identify differences in process behaviour where a product is manufactured at two different sites. Three approaches based on multi-group principal component analysis are investigated and benchmarked against single site models. The multi-group approach is shown to remove differences between sites such as operational scale thereby enabling the analysis to focus on identifying differences in variation between the two sites that are not a consequence of process configurations. From the analysis it is observed that the multi-group approach can assist in the understanding of manufacturing performance.

关键词： Biotechnology Manufacturing processes Performance Monitoring Statistical Analysis

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process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control

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Biotechnology Journal 2009年第5期4卷 610-619页

作者： Chen, Zengping Morris, Julian Centre for Process Analytics and Control Technology School of Chemical Engineering and Advanced Materials Newcastle University Newcastle upon Tyne NE1 7RU United Kingdom

Robust fit-for-purpose multivariate calibration models are of critical importance to on-line/in-line quantitative monitoring of bio-chemicals and pharmaceuticals using spectroscopic instruments. Unlike in off-line assays, the spectroscopic measurements in on-line/in-line real-time applications are almost inevitably subjected to variations in measurement conditions (e.g. temperature) and samples' physical properties (e.g. cell density, particle size, sample compactness), which can invalidate the assumption of a linear relationship between the spectroscopic measurements and the concentrations of the target chemical components. This Biotech Highlight discusses the effects of such variations on spectroscopic measurements, and presents an overview of recent work on modelling and correcting of the detrimental effects of variations in measurement conditions and samples' physical properties.A number of application studies to complex datasets and an industrial plant demonstrate the methodologies and algorithms discussed.

关键词： Multiplicative light scattering Multivariate linear calibration Near infrared spectroscopy Optical path length estimation and correction Temperature‐induced spectral variations

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Quality by digital design to accelerate sustainable medicines development

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International Journal of Pharmaceutics 2025年

作者： Chantal L. Mustoe Alice J. Turner Stephanie J. Urwin Ian Houson Helen Feilden Daniel Markl Mohammed M. Al Qaraghuli Magdalene W.S. Chong Murray Robertson Alison Nordon Blair F. Johnston Cameron J. Brown John Robertson Claire Adjiman Hannah Batchelor Brahim Benyahia Massimo Bresciani Christopher L. Burcham Javier Cardona Ciro Cottini Alastair J. Florence CMAC University of Strathclyde Glasgow G1 1RD United Kingdom Strathclyde Institute of Pharmacy & Biomedical Science University of Strathclyde Glasgow G4 0RE United Kingdom WestCHEM Department of Pure and Applied Chemistry and Centre for Process Analytics and Control Technology (CPACT) University of Strathclyde Glasgow G1 1XL United Kingdom Department of Chemical Engineering Sargent Centre for Process Systems Engineering Imperial College London London SW5 2AZ United Kingdom Department of Chemical Engineering Loughborough University Loughborough LE11 3TU United Kingdom Eli Lilly and Company Lilly Corporate Center Indianapolis IN 46285 USA Department of Chemical & Process Engineering University of Strathclyde Glasgow G1 1XJ United Kingdom Chiesi Farmaceutici S.p.A. Parma Italy

We present a shared industry-academic perspective on the principles and opportunities for Quality by Digital Design (QbDD) as a framework to accelerate medicines development and enable regulatory innovation for new medicines approvals. This approach exploits emerging capabilities in industrial digital technologies to achieve robust control strategies assuring product quality and patient safety whilst reducing development time/costs, improving research and development efficiency, embedding sustainability into new products and processes, and promoting supply chain resilience. Key QbDD drivers include the opportunity for new scientific understanding and advanced simulation and model-driven, automated experimental approaches. QbDD accelerates the identification and exploration of more robust design spaces. Opportunities to optimise multiple objectives emerge in route selection, manufacturability and sustainability whilst assuring product quality. Challenges to QbDD adoption include siloed data and information sources across development stages, gaps in predictive capabilities, and the current extensive reliance on empirical knowledge and judgement. These challenges can be addressed via QbDD workflows; model-driven experimental design to collect and structure findable, accessible, interoperable and reusable (FAIR) data; and chemistry, manufacturing and control ontologies for shareable and reusable knowledge. Additionally, improved product, process, and performance predictive tools must be developed and exploited to provide a holistic end-to-end development approach.

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Analysis of Nonlinear Partial Least Squares Algorithms

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IFAC Proceedings Volumes 2004年第9期37卷 739-744页

作者： S. Kumar U. Kruger E.B. Martin A.J. Morris Centre of Process Analytics and Process Technology University of Newcastle NE1 7RU U.K. Intelligent Systems and Control Group Queen's University Belfast BT9 5AH U.K.

This paper presents an analysis of nonlinear extensions to Partial Least Squares (PLS) using error-based minimization techniques. The analysis revealed that such algorithms are maximizing the accuracy with which the response variables are predicted. Therefore, such algorithms are nonlinear reduced rank regression algorithms rather than nonlinear PLS algorithms

关键词： Nonlinear Systems Identification Algorithms Models Gradient Methods Prediction

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Canonical Correlation Analysis in process Fault Detection

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IFAC Proceedings Volumes 2000年第11期33卷 1011-1016页

作者： A. Simoglou E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne. NE1 7RU. UK Tel: +44 191 222 5789 Fax: +44 191 222 5748

Principal Component Analysis and Partial Least Squares have been used extensively for Multivariate Statistical process control (MSPC) with increasing numbers of applications in the chemical manufacturing industries. In contrast the multivariate statistical technique of Canonical Correlation Analysis (CCA), despite its conceptual similarities with Principal Component Analysis and Partial Least Squares, has not found analogous applications. In this paper a library of metrics based on Hotelling’s T and the Squared Prediction Error ( SPE ) are developed for CCA. The various monitoring tools and techniques are validated and compared through application to a simulated spray-drying process.

关键词： Statistical process control Monitoring Fault Detection Sensitivity

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control of Semi-Continuous processes by Mass Resampling

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IFAC Proceedings Volumes 2000年第11期33卷 599-604页

作者： A. Bettoni E.B. Martin A.J. Morris Centre for Process Analytics and Control Technology University of Newcastle Newcastle upon Tyne. NE1 7RU. UK Tel: +44 191 222 5789 Fax: +44 191 222 5748

An industrial ethylene propylene rubber compounding process is used to illustrate some of the issues that arise in the monitoring of the manufacturing performance of a process comprising both batch and continuous unit operations. The key issues relate primarily to the different formats of the data that are routinely collected. For example on batch type processes, measurements are collected for a fixed quantity of produced mass whilst for the continuous units, measurements are recorded for each variable at fixed time points. Thus for the development of a process performance monitoring scheme, it was necessary to provide a common sampling base.

关键词： Statistical process control Multivariate Quality control Batch/Continuous process

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NONLINEAR MULTISCALE FAULT DETECTION AND IDENTIFICATION

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IFAC Proceedings Volumes 2006年第13期39卷 120-125页

作者： Sang Wook Choi Elaine B. Martin Julian Morris In-Beum Lee Centre for Process Analytics and Control Technology School of chemical Engineering and Advanced materials University of Newcastle Newcastle Upon Tyne NE1 7RU UK Department of Chemical Engineering Pohang University of Science and Technology Pohang South Korea

A nonlinear multiscale multivariate statistical process control method is proposed to address fault detection and diagnosis issues at different scales in nonlinear processes. A kernel principal component analysis (KPCA) model is built with the reconstructed data obtained by performing wavelet transform and inverse wavelet transform sequentially on measured data. New variable contributions to monitoring statistics are also derived. A CSTR simulation study compares the proposed approach with several existing methods in terms of false alarm rate, missed alarm rate and detection delay.

关键词： process monitoring Fault diagnosis Fault detection

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