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Improving the robustness of particle size analysis by multivariate statistical process control

PARTICLE & PARTICLE SYSTEMS CHARACTERIZATION 2007年第3期24卷 173-183页

作者： Mattila, Marko Saloheimo, Kari Koskinen, Kari Aalto Univ Lab Informat & Comp Syst Automat Helsinki 02015 TKK Finland Outotec Oyj Espoo 02200 Finland

The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC). The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.

关键词： laser diffraction multivariate statistical process control particle size distribution principal component analysis robustness

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Data fusion strategies to combine sensor and multivariate model outputs for multivariate statistical process control

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ANALYTICAL AND BIOANALYTICAL CHEMISTRY 2020年第9期412卷 2151-2163页

作者： de Oliveira, Rodrigo R. Avila, Claudio Bourne, Richard Muller, Frans de Juan, Anna Univ Barcelona Dept Analyt Chem Chemometr Grp Diagonal 645 E-08028 Barcelona Spain Univ Leeds Sch Chem & Proc Engn Leeds LS2 9JT W Yorkshire England

process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs.

关键词： Data fusion multivariate statistical process control Near-infrared Spectroscopic sensors Chemometrics

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

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INTERNATIONAL JOURNAL OF ADAPTIVE control AND SIGNAL processING 2005年第4期19卷 213-246页

作者： Kourti, T McMaster Univ Dept Chem Engn McMaster Adv Control Consortium Hamilton ON L8S 4L7 Canada

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. Copyright (c) 2005 John Wiley & Sons, Ltd.

关键词： multivariate statistical process control PLS PCA batch process monitoring adaptive PLS soft sensors fault detection fault isolation data archiving data compression image analysis

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Integration of multivariate statistical process control and engineering process control: a novel framework

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2015年第1-4期78卷 259-268页

作者： Siddiqui, Yasir A. Saif, Abdul-Wahid A. Cheded, Lahouari Elshafei, Moustafa Rahim, Abdur King Fahd Univ Petr & Minerals Syst Engn Dept Dhahran 31261 Saudi Arabia Univ New Brunswick Facil Business Adm Fredericton NB Canada

statistical process control is being used along with classical feedback control systems (which are also termed as Engineering process control, EPC) for the purposes of detecting faults and avoiding over adjustment of the processes. This paper evaluates the effectiveness of integrating SPC with EPC for both fault detection and control. A novel framework for fault detection using multivariate statistical process control (MSPC) is proposed here and illustrated with a case study. The simultaneous application of MSPC control charts to process inputs and outputs or in other words "joint monitoring" of process inputs and outputs is shown here to provide efficient fault detection capabilities. An example of Heating Ventilation and Air Conditioning (HVAC) systems is simulated here and used as a case study to demonstrate the detection capabilities of the proposed framework. Moreover, the capabilities of the proposed framework were enhanced by inclusion of a corrective action scheme, thus leading to a complete control system with fault detection and correction.

关键词： multivariate statistical process control Integration of engineering process control and statistical process control Fault detection Heating ventilation Air conditioning system

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On reducing false alarms in multivariate statistical process control

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2010年第4A期88卷 430-436页

作者： Chen, Tao Nanyang Technol Univ Sch Chem & Biomed Engn Singapore 637459 Singapore

The primary objective of this note is to reduce the false alarms in multivariate statistical process control (MSPC). The issue of false alarms is inherent within MSPC as a result of the definition of control limits. It has been observed that under normal operating conditions, the occurrence of "out-of-control" data, i.e. false alarms, conforms to a Bernoulli distribution. Therefore, this issue can be formally addressed by developing a Binomial distribution for the number of "out-of-control" data points within a given time window, and a second-level control limit can be established to reduce the false alarms. This statistical approach is further extended to consider the combination of multiple control charts. The proposed methodology is demonstrated through its application to the monitoring of a benchmark simulated chemical process, and it is observed to effectively reduce the false alarms whilst retaining the capability of detecting process faults. (C) 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Bernoulli distribution Binomial distribution False alarm multivariate statistical process control Principal component analysis

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Making Use of process Tomography Data for multivariate statistical process control

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AICHE JOURNAL 2011年第9期57卷 2360-2368页

作者： Boonkhao, Bundit Li, Rui F. Wang, Xue Z. Tweedie, Richard J. Primrose, Ken Univ Leeds Inst Particle Sci & Engn Sch Proc Environm & Mat Engn Leeds LS2 9JT W Yorkshire England Ind Tomog Syst Ltd Manchester M3 2BA Lancs England

A novel strategy for making effective use of on-line process tomography measurements for process monitoring is described. The electrical resistance tomography (ERT) sensing system equipped with sixteen electrodes provides 104 conductivity measurements every 25 ms. The data has traditionally been used for construction of images for display purpose. In this study, ERT data was used for multivariate statistical process control. Data at predefined normal operational conditions was processed using principal component analysis. The compressed data was used to derive two statistics, T-2 and squared prediction error (SPE). T-2 and SPE charts predict the probability that the process being monitored has undergone statistically significant changes from previous state or the so-called normal operational state, in terms of mixing quality. The methodology is illustrated by reference to a case study of a sunflower oil/water emulsion process. (C) 2010 American Institute of Chemical Engineers AIChE J, 57: 2360-2368, 2011

关键词： electrical resistance tomography multivariate statistical process control principal component analysis emulsion

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Integrating multivariate engineering process control and multivariate statistical process control

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2006年第1-2期29卷 129-136页

作者： Yang, Ling Shen, Shey-Huei Natl Taiwan Univ Sci & Technol Dept Ind Management Taipei Taiwan St Johns Univ Dept Ind Engn & Management Taipei Taiwan

multivariate engineering process control (MEPC) and multivariate statistical process control (MSPC) are two strategies for quality improvement that have developed independently. MEPC aims to minimize variability by adjusting process variables to keep the process output on target. On the other hand, MSPC aims to reduce variability by monitoring and eliminating assignable causes of variation. In this paper, the use of MEPC alone is compared to using the MEPC coupled with MSPC. We use simulations to evaluate the average run lengths (ARL) and the averages of the performance measure. The simulation results show that the use of both MEPC and MSPC can always outperform the use of either alone. To detect small sustained shifts of the mean vector, combing MEPC with a multivariate generally weighted moving average (MGWMA) chart (MEPC/MGWMA) is more sensitive than the MEPC/multivariate exponentially weighted moving average (MEWMA) chart and MEPC/Hotelling's chi(2) chart. An example of the application, based on the proposed method, is also given.

关键词： automatic process control control chart EWMA GWMA multivariate engineering process control multivariate statistical process control

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A graphical user interface for PCA-based MSPC: A benchmark software for multivariate statistical process control in MATLAB

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2019年 185卷 135-152页

作者： Villalba, Pedro Sanchis, Javier Ferrer, Alberto Univ Politecn Valencia Dept Estadist & Invest Operat Aplicadas & Calidad Camino Vera S-N E-46022 Valencia Spain Univ Politecn Valencia Inst Univ Automat & Informat Ind Camino Vera S-N E-46022 Valencia Spain

A Graphical User Interface (GUI) is developed in MATLAB as a tutorial for understanding the PCA-based MSPC strategy. The software allows users to analyze both simulated and external data sets. Simulated data are obtained from a nonlinear model of a binary distillation column implemented in Simulink. The nonlinear model has four manipulated variables, four controlled variables and three input measured disturbances, plus 41 M fractions corresponding to every column stage. The methodology for PCA-based MSPC is implemented in two phases. During Phase I, the user can simulate the distillation column under normal operating conditions at three different operating points. When the simulation is finished, the GUI obtains the corresponding PCA model automatically. In Phase II, the user can simulate several scenarios with different combinations of disturbances and failures and monitor them using Squared Prediction Error (SPE) and T-2 control charts. Contribution plots are used to diagnose the original variables responsible of such abnormal situations. The software also incorporates the possibility to analyze external multivariate process datasets.

关键词： multivariate statistical process control Principal component analysis Latent variable multivariate control charts Contribution plots Benchmark Nonlinear distillation column Tutorial GUI

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Designing a risk-based surveillance program for Mycobacterium avium ssp. paratuberculosis in Norwegian dairy herds using multivariate statistical process control analysis

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JOURNAL OF DAIRY SCIENCE 2014年第11期97卷 6835-6849页

作者： Whist, A. C. Liland, K. H. Jonsson, M. E. Saebo, S. Sviland, S. Osteras, O. Norstrom, M. Hopp, P. Norwegian Univ Life Sci Fac Vet Med & Biosci Dept Prod Anim Clin Sci NO-1432 As Norway Norwegian Univ Life Sci Dept Chem Biotechnol & Food Sci NO-1432 As Norway Norwegian Vet Inst NO-0106 As Norway Tine SA Norwegian Cattle Hlth Serv Norwegian Dairies NO-1432 As Norway

Surveillance programs for animal diseases are critical to early disease detection and risk estimation and to documenting a population's disease status at a given time. The aim of this study was to describe a risk-based surveillance program for detecting Mycobacterium avium ssp. paratuberculosis (MAP) infection in Norwegian dairy cattle. The included risk factors for detecting MAP were purchase of cattle, combined cattle and goat farming, and location of the cattle farm in counties containing goats with MAP. The risk indicators included production data [culling of animals >3 yr of age, carcass conformation of animals >3 yr of age, milk production decrease in older lactating cows (lactations 3, 4, and 5)], and clinical data (diarrhea, enteritis, or both, in animals >3 yr of age). Except for combined cattle and goat farming and cattle farm location, all data were collected at the cow level and summarized at the herd level. Predefined risk factors and risk indicators were extracted from different national databases and combined in a multivariate statistical process control to obtain a risk assessment for each herd. The ordinary Hotelling's T-2 statistic was applied as a multivariate, standardized measure of difference between the current observed state and the average state of the risk factors for a given herd. To make the analysis more robust and adapt it to the slowly developing nature of MAP, monthly risk calculations were based on data accumulated during a 24-mo period. Monitoring of these variables was performed to identify outliers that may indicate deviance in one or more of the underlying processes. The highest-ranked herds were scattered all over Norway and clustered in high-density dairy cattle farm areas. The resulting rankings of herds are being used in the national surveillance program for MAP in 2014 to increase the sensitivity of the ongoing surveillance program in which 5 fecal samples for bacteriological examination are collected from 25 dairy herds. Th

关键词： risk-based surveillance Mycobacterium avium ssp paratuberculosis multivariate statistical process control cattle

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The interprocess NIR sampling as an alternative approach to multivariate statistical process control for identifying sources of product-quality variability

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DRUG DEVELOPMENT AND INDUSTRIAL PHARMACY 2017年第3期43卷 379-389页

作者： Markovic, Snezana Kerc, Janez Horvat, Matej Lek Pharmaceut Dd Sandoz Dev Ctr Slovenia Ljubljana Slovenia Univ Ljubljana Fac Pharm Ljubljana Slovenia Sandoz Biopharmaceut Lek Pharmaceut Dd Menges Slovenia

We are presenting a new approach of identifying sources of variability within a manufacturing process by NIR measurements of samples of intermediate material after each consecutive unit operation (interprocess NIR sampling technique). In addition, we summarize the development of a multivariate statistical process control (MSPC) model for the production of enteric-coated pellet product of the proton-pump inhibitor class. By developing provisional NIR calibration models, the identification of critical process points yields comparable results to the established MSPC modeling procedure. Both approaches are shown to lead to the same conclusion, identifying parameters of extrusion/spheronization and characteristics of lactose that have the greatest influence on the end-product's enteric coating performance. The proposed approach enables quicker and easier identification of variability sources during manufacturing process, especially in cases when historical process data is not straightforwardly available. In the presented case the changes of lactose characteristics are influencing the performance of the extrusion/spheronization process step. The pellet cores produced by using one (considered as less suitable) lactose source were on average larger and more fragile, leading to consequent breakage of the cores during subsequent fluid bed operations. These results were confirmed by additional experimental analyses illuminating the underlying mechanism of fracture of oblong pellets during the pellet coating process leading to compromised film coating.

关键词： Near-infrared spectroscopy chemometrics multivariate statistical process control enteric-coated pellets manufacturing

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