Model-based performance monitoring of batch processes

The use of batch processes is widespread across the manufacturing industries, dominating sectors such as pharmaceuticals, speciality chemicals and biochemicals. The main goal in batch production is to manufacture consistent, high quality batches with minimum rework or spoilage and also to achieve the optimum energy and feedstock usage. A common approach to monitoring a batch process to achieve this goal is to use a recipe-driven approach coupled with off-line laboratory analysis of the product. However, the large amount of data generated during batch manufacture mean that it is possible to monitor batch processes using a statistical model. Traditional multivariate statistical techniques such as principal component analysis and partial least squares were originally developed for use on continuous processes, which means they are less able to cope with the non-linear and dynamic behaviours inherent within a batch process without being adapted. Several approaches to dealing with batch behaviour in a multivariate framework have been proposed including multi-way principal component analysis. A more advanced approach designed to handle the typical characteristics of batch data is that of model-based principal component. It comprises of a mechanistic model combined with a multivariate statistical technique. More specifically, the technique uses a mechanistic model of the process to generate a set of residuals from the measured process variables. The theory being that the non-linear behaviour and the serial correlation in the process will be captured by the model, leaving a set of unstructured residuals to which principal component analysis (PCA) can be applied. This approach is benchmarked against the more standard approaches including multiway principal components analysis, batch observation level analysis. One limitation identified of the model-based approach is that if the mechanistic model of the process is of reduced complexity then the monitoring and fault detection abilities of the technique will be compromised. To address this issue, the model-based PCA technique has been extended to incorporate an additional error model which captures the differences between the mechanistic model and the process. This approach has been termed super model-based PCA (SMBPCA). A number of different error models are considered including partial least squares (linear, non-linear and dynamic), autoregressive with exogenous (ARX) variables model and dynamic canonical correlation analysis. Through the use of an exothermic batch reactor simulation, the SMBPCA approach has been investigated with respect to fault detection and capturing the non-linear and dynamic behaviour in the batch process. The robustness of the technique for application in an industrial situation is also discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Robust detection of incipient faults in VSI-fed induction motors using quality control charts.

A considerable amount of papers have been published in recent years proposing supervised classifiers to diagnose the health of a machine. The usual procedure with these classifiers is to train them using data acquired through controlled experiments, expecting them to perform well on new data, classifying correctly the condition of a motor. But, obviously, the new motor to be diagnosed cannot be the same that has been used during the training process; it may be a motor with different characteristics and fed from a completely different source. These different conditions between the training process and the testing one can deeply influence the diagnosis. To avoid these drawbacks, in this paper a new method is proposed which is based on robust statistical techniques applied in Quality Control applications. The proposed method is based on the online diagnosis of the operating motor and can detect deviations from the normal operational conditions. A robust approach has been implemented using high-breakdown statistical techniques which can reliably detect anomalous data that often cause an unexpected overestimation of the data variability, reducing the ability of standard procedures to detect faulty conditions in earlier stages. A case study is presented to prove the validity of the proposed approach. Motors of different characteristics, fed from the power line and several different inverters, are tested. Three different fault conditions are provoked, broken bar, a faulty bearing and mixed eccentricity. Experimental results prove that the proposed approach can detect incipient faults

Eye Tracking: A Perceptual Interface for Content Based Image Retrieval

In this thesis visual search experiments are devised to explore the feasibility of an eye gaze driven search mechanism. The thesis first explores gaze behaviour on images possessing different levels of saliency. Eye behaviour was predominantly attracted by salient locations, but appears to also require frequent reference to non-salient background regions which indicated that information from scan paths might prove useful for image search. The thesis then specifically investigates the benefits of eye tracking as an image retrieval interface in terms of speed relative to selection by mouse, and in terms of the efficiency of eye tracking mechanisms in the task of retrieving target images. Results are analysed using ANOVA and significant findings are discussed. Results show that eye selection was faster than a computer mouse and experience gained during visual tasks carried out using a mouse would benefit users if they were subsequently transferred to an eye tracking system. Results on the image retrieval experiments show that users are able to navigate to a target image within a database confirming the feasibility of an eye gaze driven search mechanism. Additional histogram analysis of the fixations, saccades and pupil diameters in the human eye movement data revealed a new method of extracting intentions from gaze behaviour for image search, of which the user was not aware and promises even quicker search performances. The research has two implications for Content Based Image Retrieval: (i) improvements in query formulation for visual search and (ii) new methods for visual search using attentional weighting. Futhermore it was demonstrated that users are able to find target images at sufficient speeds indicating that pre-attentive activity is playing a role in visual search. A current review of eye tracking technology, current applications, visual perception research, and models of visual attention is discussed. A review of the potential of the technology for commercial exploitation is also presented

Data integration for the monitoring of batch processes in the pharmeceutical industry

PhD ThesisAdvances in sensor technology has resulted in large amounts of data being available electronically. However, to utilise the potential of the data, there is a need to transform the data into knowledge to realise an enhanced understanding of the process. This thesis investigates a number of multivariate statistical projection techniques for the monitoring of batch fermentation and pharmaceutical processes. In the first part of the thesis, the traditional performance monitoring tools based on the approaches of Nomikos and MacGregor (1994) and Wold et al. (1998) are introduced. Additionally, the application of data scaling as a data pre-treatment step for batch processes is examined and it is observed that it has a significant impact on monitoring performance. Based on the advantages and limitations of these techniques, an alternative methodology is proposed and applied to a simulated penicillin fermentation process. The approach is compared with existing techniques using two metrics, false alarm rate and out-ofcontrol average run length. A further manufacturing challenge facing the pharmaceutical industry is to understand the differences in the performance of a product which is manufactured at two or more sites. A retrospective multi-site monitoring model is developed utilising a pooled sample variancecovariance methodology of the two sites. The results of this approach are compared with a number of techniques that have been previously reported in the literature for the integration of data from two or more sources. The latter part of the thesis focuses on data integration using multi-block analysis. Several blocks of data can be analysed simultaneously to allow the inter- and intra- block relationships to be extracted. The methodology of multi-block Principal Component Analysis (MBPCA) is initially reviewed. To enhance the sensitivity of the algorithm, wavelet analysis is incorporated within the MBPCA framework. The fundamental advantage of wavelet analysis is its ability to process a signal at different scales so that both the global features and the localised details of a signal can be studied simultaneously. Both existing and the modified approach are applied to data generated from an experiment conducted in a batch mini-plant and that was monitored by both physical sensors and on-line UV-Visible spectrometer. The performance of the integrated approaches is benchmarked against the individual process and spectral monitoring models as well as examining their fault detection ability on two additional batches with pre-designed process deviations.Engineering and Physical Sciences Research Council (EPSRC: The Overseas Research Students Award Scheme (ORSAS): The Centre for Process Analytics and Control Technology (CPACT)

Data integration for the monitoring of batch processes in the pharmaceutical industry

Advances in sensor technology has resulted in large amounts of data being available electronically. However, to utilise the potential of the data, there is a need to transform the data into knowledge to realise an enhanced understanding of the process. This thesis investigates a number of multivariate statistical projection techniques for the monitoring of batch fermentation and pharmaceutical processes. In the first part of the thesis, the traditional performance monitoring tools based on the approaches of Nomikos and MacGregor (1994) and Wold et al. (1998) are introduced. Additionally, the application of data scaling as a data pre-treatment step for batch processes is examined and it is observed that it has a significant impact on monitoring performance. Based on the advantages and limitations of these techniques, an alternative methodology is proposed and applied to a simulated penicillin fermentation process. The approach is compared with existing techniques using two metrics, false alarm rate and out-ofcontrol average run length. A further manufacturing challenge facing the pharmaceutical industry is to understand the differences in the performance of a product which is manufactured at two or more sites. A retrospective multi-site monitoring model is developed utilising a pooled sample variancecovariance methodology of the two sites. The results of this approach are compared with a number of techniques that have been previously reported in the literature for the integration of data from two or more sources. The latter part of the thesis focuses on data integration using multi-block analysis. Several blocks of data can be analysed simultaneously to allow the inter- and intra- block relationships to be extracted. The methodology of multi-block Principal Component Analysis (MBPCA) is initially reviewed. To enhance the sensitivity of the algorithm, wavelet analysis is incorporated within the MBPCA framework. The fundamental advantage of wavelet analysis is its ability to process a signal at different scales so that both the global features and the localised details of a signal can be studied simultaneously. Both existing and the modified approach are applied to data generated from an experiment conducted in a batch mini-plant and that was monitored by both physical sensors and on-line UV-Visible spectrometer. The performance of the integrated approaches is benchmarked against the individual process and spectral monitoring models as well as examining their fault detection ability on two additional batches with pre-designed process deviations.EThOS - Electronic Theses Online ServiceEngineering and Physical Sciences Research Council (EPSRC) : Overseas Research Students Award Scheme (ORSAS) : Centre for Process Analytics and Control Technology (CPACT)GBUnited Kingdo

Multivariate statistical process control of chemical processes

PhD ThesisThe thesis describes the application of Multivariate Statistical Process Control (MSPC) to chemical processes for the task of process performance monitoring and fault detection and diagnosis. The applications considered are based upon polymerisation systems. The first part of the work establishes the appropriateness of MSPC methodologies for application to modern industrial chemical processes. The statistical projection techniques of Principal Component Analysis and Projection to Latent Structures are considered to be suitable for analysing the multivariate data sets obtained from chemical processes and are coupled with methods and techniques for implementing MSPC. A comprehensive derivation of these techniques are presented. The second part introduces the procedures that require to be followed for the appropriate implementation of MSPC-based schemes for process monitoring, fault detection and diagnosis. Extensions of the available projection techniques that can handle specific types of chemical processes, such as those that exhibit non-linear characteristics or comprise many distinct units are also presented. Moreover, the novel technique of Inverse Projection to Latent Structures that extends the application of MSPC-based schemes to processes where minimal process data is available is introduced. Finally, the proposed techniques and methodologies are illustrated by applications to a batch and a continuous polymerisation process.BR1TE EURAM CT 93 0523 (INTELPOL: ESPRTT PROJECT 22281 (PROGNOSIS): Centre of Process Analysis, Chemometrics and Control, University of Newcastle: Chemical Process Engineering Research Institute, Thessaloniki, Greece

주성분분석법과 그레인저 인과관계를 이용한 실시간 공정 모니터링 및 이상 전파 경로 계산

학위논문 (박사)-- 서울대학교 대학원 공과대학 화학생물공학부, 2017. 8. 한종훈.Modern industrial process is a complex device industry consisting of a combination of numerous unit processes. Numerous process parameters such as flow rate, temperature, pressure, concentration and composition have strong linear or nonlinear correlation. Since improvement of computing power and process control systems in industrial processes, several board operator and field operator can manage huge amounts of data and whole process information from industrial plant. However, the number of processes and devices to be handled by a single operator will increase, and operators meets a limitation of cognitive ability due to flood of information, causing problems such as process malfunction or instrumental failure. To solve this problem, we propose a PCA modeling procedures that aims to improve monitoring performance by variable selection, removing noise, operation mode classification and mode change detection. Fault diagnosis and causal analysis is also introduced. We calculated the causal relationship matrix between the process variables and find out the root cause of the unexpected process changes. The proposed approach was applied and validated to LNG plant located in Incheon and plasma condition monitoring in plasma etcher. Chapter 2 discusses the application methodologies of signal processing to eliminate noises from OES signal and multivariate statistical techniques to improve monitoring sensitivity. Among the plasma sensors, optical emission spectroscopy (OES) has been widely utilized and its high dimensionality has required multivariate analysis (MVA) techniques such as principal component analysis (PCA). PCA, however, might devaluate physical meaning of target process during its statistical calculation. In addition, inherent noise from charge coupled devices (CCD) array in OES might deteriorate PCA model performance. Therefore, it is desirable to pre-select physically important variables and to filter out noisy signals before modeling OES based plasma data. For these purposes, this chapter introduces a peak wavelength selection algorithm for selecting physically meaningful wavelength in plasma and discrete wavelet transform (DWT) for filtering out noisy signals from a CCD array. The effectiveness of the PCA model introduced in this paper is verified by comparing fault detection capabilities of conventional PCA model under the various source power or pressure faulty situations in a capacitively coupled plasma etcher. The PCA model introduced in this chapter successively detect even extremely small variation such as 0.67% of source power change even though the conventional PCA model fails to detect all of the faulty situations under the tests. Chapter 3 discusses the application methodology of operation mode identification and multimode PCA to improve the performance of LNG mixed refrigeration (MR) process and prevent process shutdown. LNG MR process is usually used for liquefying natural gas. The compressors for refrigerant compression are operated with the high-speed rotating parts to create a high-pressure. However, any malfunction in the compressors can lead to significant process downtime, catastrophic damage to equipment and potential safety consequences. The existing methodology assumes that the process has a single mode of operation, which makes it difficult to distinguish between a malfunction of the process and a change in mode of operation. Therefore, k-nearest neighbor algorithm (k-NN) is employed to classify the operation modes, which is integrated into multi-mode principal component analysis (MPCA) for process monitoring and fault detection. When the fault detection performance is evaluated with real LNG MR operation data, the proposed methodology shows more accurate and early detection capability than conventional PCA. Chapter 4 discusses PCA based fault amplification algorithm to detect both the root cause of fault and the fault propagation path in the system. The developed algorithm project the samples on the residual subspace (RS) to determine the disturbance propagation path. Usually, the RS of the fault data is superimposed with the normal process variations which should be minimized to amplify the fault magnitude. The RS containing amplified fault is then converted into the co-variance matrix followed by singular value decomposition (SVD) analysis which in turn generates the fault direction matrix corresponding to the largest eigenvalue. The fault variables are then re-arranged according to their magnitude of contribution towards a fault which in turn represents the fault propagation path using an absolute descending order functions. Moreover, the multivariate granger causality (MVGC) algorithm is used to analyze the causal relationship among the variables obtained from the developed algorithm. Both the methodologies are tested on the LNG fractionation process train and distillation column operation where some fault case scenarios are assumed to estimate the fault directions. It is observed that the hierarchy of variables obtained from fault propagation path algorithm are in good agreement with the MVGC algorithm. Therefore, fault amplification methodology can be used in industrial systems for identifying the root cause of fault as well as the fault propagation path. The application results show that the proposed multivariate statistical method can improve productivity and safety by providing useful information for process monitoring and fault diagnosis in various processes with distributed control system.CHAPTER 1 Introduction 1 1.1 Research motivation 1 1.2 Research objectives 4 1.3 Outline of the thesis 5 CHAPTER 2 : Multivariate monitoring, variable selection and OES signal filter design of plasma process 6 2.1 Introduction 6 2.2 Issues in PCA Modeling of OES based Plasma Data 8 2.3 Theoretical Background 11 2.3.1 Peak Wavelength Selection Algorithm 11 2.3.2 Discrete Wavelet Transform 14 2.4 Experimental Set-up 19 2.5 Results and Discussion 21 2.5.1 Pre-selected variables in OES data 21 2.5.2 Decomposition of OES signal by DWT 23 2.5.3 Comparison of Fault Detection Performance in OES based PCA Models 25 2.6 Conclusion 35 CHAPTER 3 : Multimode PCA and k-nearest neighbor algorithm for LNG mixed refrigeration process monitoring 36 3.1 Introduction 36 3.2 Target process and data description 38 3.3 Theoretical Background 45 3.3.1 Principal component analysis based fault detection 45 3.3.2 k-Nearest Neighbor classifier 48 3.4 Mode identification and fault detection 49 3.4.1 Operation mode identification and fault detection 49 3.5 Results and Conclusion 55 3.5.1 Consideration in LNG MR process monitoring 55 3.5.2 Global and local PCA modeling 59 3.5.3 Detection of operation mode 61 3.5.4 Comparison of fault detection performance 66 3.6 Conclusion 70 CHAPTER 4 : Estimation of disturbance propagation path using PCA and multivariate Granger Causality 71 4.1 Introduction 71 4.2 Theoretical Background 77 4.2.1 Fault propagation path detection 77 4.2.2 Causal analysis based on Granger Causality (GC) 82 4.3 Application to the Liquefied Natural Gas (LNG) Process 87 4.3.1 Process Description 87 4.3.2 Development of fault case scenarios 90 4.4 Conclusion 116 CHAPTER 5 Concluding Remarks 118 Nomenclature and Abbreviations 121 Literature cited 122 Abstract in Korean (요 약) 133Docto

Development of new methodologies for dating in the forensic field, combining analytical techniques with multivariate regression treatments

318 p.Whenever a crime is committed, there is always a unity of time, place and action that forensic experts will aim to demonstrate throughout the investigation. Determining the succession, simultaneity, frequency or duration of criminal activities as well as the age of objects, persons and traces is therefore one of the most important goals of forensics in reconstructing the crime scene or in finding and understanding the connections between the evidence and the suspects involved therein. However, time has been largely unexplored due to the complexity of the overall challenge at hand, not yet successfully overcome by single cutting-edge techniques. The coupling of such techniques with chemometrics, more specifically with multivariate regression methods, could turn this situation upside down thanks to the development of age quantitation methodologies based on the modelling of the modifications experienced by the evidence in its properties with respect to time. The potential applicability of these chemometric tools, however, remains poorly understood and underexploited due to their recent introduction into forensic dating research and the statistical background required for their optimal application. That is why this thesis focuses on highlighting the usefulness of multivariate regression methods in several forensicfields, such as questioned documents, art forgery and medico-legal death investigation, through the development and validation of dating methodologies in which non-destructive and micro-destructive techniques are applied together with the (orthogonal) partial least squares regression ((O)PLSR) method