DEVELOPMENT OF MULTIVARIATE STATISTICAL PROCESS

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  • DEVELOPMENT OF MULTIVARIATE STATISTICAL PROCESS MONITORING

    USING COMBINATION MLR-PCA METHOD

    NORLIZA BINTI ISHAK

    Thesis submitted in fulfillment of the requirement

    for the award of the degree of Master of Engineering in Chemical

    Faculty of Chemical and Natural Resources Engineering

    UNIVERSITI MALAYSIA PAHANG

    OCTOBER 2015

  • vi

    ABSTRACT

    The most popular types of process monitoring systems is Multivariate Statistical Process

    Monitoring (MSPM) which has the most practical method in handling the complicated

    large scale processes. This is due to the ability of the system in maximizing the usage of

    abundant historial process data, in such a way that the original data dimensions are

    compressed and data variations preserved to certain extent in a set of transformed variables

    by way of linear combinations. Thus, the composite model is generally flexible regardless

    of the amount of variables that utilized. In this regard, conventional Principal Component

    Analysis (PCA) has been widely applied to conduct such compression function particularly

    for MSPM. However, the conventional PCA is a linear technique which results sometimes

    inappropriately employed especially in modeling processes that exhibit severe non-linear

    correlations. Therefore, a new solution is demanded, whereby the number of original

    variables can be reduced to certain extent (in terms of scales), while it still can maintain the

    variation as maximally as possible corresponding to the original, which are then

    transferable into monitoring statistics. One of the potential techniques available in

    addressing the issue is known as Multiple Linear Regression (MLR). The main objective of

    the technique is to predict a set of output values (criterion) based from a specified of linear

    function, which consists of a set of predictor. Therefore, the main multivariate data will be

    divided into two groups, which are the criterion and predictor categories. The study adopts,

    Tennessee Eastman Process (TEP) and Multiple Output and Multiple Input (MIMO) Pilot

    Plant System for demonstration. The general finding is that MLR-PCA normally employs

    less number of PCs compared to PCA, and thus, this will perhaps reduce complication

    during diagnosis. By adopting such approach, the monitoring task can be made simpler and

    perhaps more effective, in the sense that only those selected criterion variables (predicted

    values) will be taken for monitoring, while preserving the rest of the predictor value trends

    in the form of linear functions in association with the criterion variables. This study also

    shows that MLR-PCA works relatively better in terms of fault detection and identification

    against the conventional system.

  • vii

    ABSTRAK

    Sistem pemantauan proses yang paling popular ialahPembolehubah Pemantauan Proses

    Statistik (MSPM) yang dianggap sebagai kaedah yang paling praktikal dalam menanggani

    sistem kompleks serta berskala besar. Hal ini adalah kerana keupayaan sistem dalam

    memaksimumkan penggunaan data proses dalam apa-apa cara bahawa dimensi data asal

    dimampatkan dan variasi data dikekalkan pada tahap tertentu dalam satu set pembolehubah

    yang diubah melalui kombinasi linear. Oleh itu, model komposit biasanya fleksibel tanpa

    mengira jumlah pembolehubah yang digunakan. Dalam hal ini, Analisis Komponen Utama

    konvensional (PCA) telah digunakan secara meluas untuk menjalankan fungsi mampatan

    itu terutamanya untuk MSPM. Walau bagaimanapun, PCA konvensional adalah teknik

    linear yang mana kaedah ini kadang-kadang tidak sesuai digunakan terutama dalam proses

    model kerana mempamerkan korelasi bukan linear yang tidak normal. Oleh itu, satu

    penyelesaian baru diperlukan, di mana bilangan pembolehubah asal boleh dikurangkan

    kepada tahap tertentu (dari segi skala), sementara ia masih boleh mengekalkan perubahan

    maksima sebagaimana ia mungkin sama dengan data yang asal, yang kemudiannya

    dipindahkan ke dalam statistik pemantauan. Salah satu teknik yang berpotensi untuk

    menangani isu ini dikenali sebagai Regresi Linear Berganda (MLR). Objektif utama teknik

    ini untuk meramalkan satu set nilai hasil (kriteria) berdasarkan dari yang dinyatakan fungsi

    linear, yang terdiri daripada satu set peramal. Oleh itu, data multivariat utama akan

    dibahagikan kepada dua kumpulan, iaitu kriteria dan kategori peramal. Kajian ini

    mengadaptasi Proses Tennessee Eastman (TEP) dan Multiple Output and Multiple Input

    (MIMO) Loji Pandu sebagai demostrasi. Kajian mendapati MLR-PCA biasanya

    menggunakan PC nombor yang minimum berbanding PCA dan sekaligus, hal ini

    berkemungkinan dapat mengurangkan kerumitan semasa diagnosis.Dengan mengguna

    pakai pendekatan ini, tugas pemantauan boleh dibuat lebih mudah dan mungkin lebih

    berkesan, dalam erti kata lain bahawa hanya pembolehubah kriteria tertentu sahaja (nilai

    meramalkan) akan diambil untuk dianalisis, di samping memelihara seluruh trend nilai

    peramal dalam bentuk fungsi linear bersama dengan pembolehubah kriteria. Kajian ini juga

    menunjukkan MLR-PCA berfungsi lebih baik dari segi pengesanan ralat dan mengenal

    pasti ralat berbanding kaedah konvensional.

  • viii

    TABLE OF CONTENT

    Page

    TITLE PAGE i

    SUPERVISORS DECLARATION ii

    STUDENTS DECLARATION iii

    DEDICATION vi

    ACKNOWLEDGE v

    ABSTRACT vi

    ABSTRAK vii

    TABLE OF CONTENTS viii

    LIST OF TABLES xi

    LIST OF FIGURES xii

    LIST OF ABBREVIATIONS xiv

    LIST OF SYMBOLS

    xvi

    CHAPTER 1 INTRODUCTION

    1.1 Introduction

    1.2 Research Background

    1.3 Problem Statement

    1.4 Research Objectives

    1.5 Research Scope

    1.6 Rationale and Significant

    1.7 Report Organization

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  • ix

    CHAPTER 2 LITERATURE REVIEW

    2.1 Introduction

    2.2 Fundamental of MSPM

    2.2.1 Principal Component Analysis

    2.3 Industrial Application of MSPM

    2.3.1 Applications of MSPM Based on PCA Extension

    2.3.2 Applications of MSPM Based on Multivariate Compression

    Techniques

    2.3.2.1 Partial Least Square

    2.3.2.2 Independent Component Analysis

    2.3.2.3 Factor Analysis

    2.3.2.4 Principal Component Regression

    2.3.2.5 Multidimensional Scaling

    2.4 Multiple Linear Regression

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    CHAPTER 3 METHODOLOGY

    3.1 Introduction

    3.2 Framework I : PCA-MSPM System Procedures

    3.2.1 Phase I Procedures

    3.2.2 Phase II Procedures

    3.3 Framework II : PCA-MLR MSPM Framework

    3.3.1 Phase I Procedures

    3.3.2 Phase II Procedures

    3.4 Case Study 1: Tennessee Eastman Process

    3.5 Case Study 2: MIMO Training System Pilot Plant

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  • x

    CHAPTER 4 RESULTS AND DISCUSSION

    4.1 Introduction

    4.2 Case Study 1: Tennessee Eastman Process

    4.2.1 Phase I Results

    4.2.1.1 PCA Models

    4.2.2.2 False Alarm Rate Analysis

    4.2.2 Phase II Results

    4.2.2.1 Fault Detection Time

    4.2.2.2 Fault Identification

    4.2.2.3 Case Study on Fault 1

    4.2.2.4 Case Study on Fault 4

    4.2.2.5 Case Study on Fault 11

    4.3 Case Study 2: MIMO Training System Plant

    4.3.1 Phase I Results

    4.3.1.1 PCA Models

    4.3.2 Phase II Results

    4.3.2.1 Fault Detection Time

    4.3.2.2 Fault Identification

    4.3.2.3 Case Study on Fault 1(TE (BURNT OUT))

    4.4 Summary

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    CHAPTER 5 CONCLUSION AND RECOMMENDATION

    5.1 Conclusion

    5.2 Recommendation for Future Work

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    CHAPTER 7 REFERENCES 96

    APPENDICES

    APPENDIX A

    APPENDIX B

    APPENDIX C

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  • xi

    LIST OF TABLES

    Table No. Title Page

    2.1 Applications of MSPM in industrial processes 13

    2.2 Description of Extensions of PCA 16

    2.3 Recent Applications of MLR for Data Prediction 29

    3.1 Process Faults for Tennessee Eastman 45

    3.2 Process List of Variables of TEP for monitoring 46

    3.3 PID Trial Values for FIC, FcIC and TIC, FhIC 50

    3.4 List of Variables for MIMO Plant 51

    4.1 Results of FAR of TEP NOC Data 56

    4.2 Fault Detection Times of Monitoring System 59

    4.3 Analysis of Fastest Detection Between Conventional PCA

    and MLR PCA Systems based on TEP Cases

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    4.4 Fault Identif