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DEVELOPMENT OF
A RULE-BASED FAULT DIAGNOSTIC ADVISORY SYSTEM
FOR PRECUT FRACTIONATION COLUMN
HENG HAN YANN
UNIVERSITI TEKNOLOGI MALAYSIA
DEVELOPMENT OF
A RULE-BASED FAULT DIAGNOSTIC ADVISORY SYSTEM
FOR PRECUT FRACTIONATION COLUMN
HENG HAN YANN
A thesis submitted in fulfilment of the
requirements for the award of the degree of
Master of Engineering (Chemical)
Faculty of Chemical and Natural Resources Engineering
Universiti Teknologi Malaysia
JUNE 2005
iii
Specially dedicated to my beloved family :
Daddy, Mommy, Uncles, Aunties, Sisters and Brothers
iv
ACKNOWLEDGEMENTS
Undertaking and successfully completing this research project has been a lengthy
arduous task that at times seemed insurmountable. There are many people who should
be thanked for helping in the successful completion of this project and for shaping my
experiences here at UTM.
First and foremost, I would like to thank my research supervisor, Associate
Professor Dr. Mohamad Wijayanuddin Ali and my co-supervisor, Associate Professor
Dr. Mohd. Zaki Kamsah for their immense support and advice during the course of this
research. This research would not have been possible without their constant
encouragement, enthusiasm, and their extremely useful insight. Their useful suggestions
and timely review of the thesis are deeply appreciated.
My sincerest gratitude is extended to the Ministry of Science, Technology and
the Innovation (MOSTI) through the IRPA Grant Vot 74015 and PTP Postgraduate
Fellowship Scheme Universiti Teknologi Malaysia who provided early financial support
for this research.
Besides that, I would like also to thank Mr. Jason Chong, Mr. Dana Ariya and
Mr. Mohd Rizza Othman for their kindness in giving some advice and helping me to
solve some computer programming problems.
While this research has been defined by those around me, it has also defined the
lives of many of those close to me. My family has given constant encouragement and
support. To my lab-mates as well, thank you to all. I hope to use what I have learned to
make industrial systems better and safer for future generations.
v
ABSTRACT
This research presents a Fault Diagnostic Advisory (FDA) System which can be
used to detect and diagnose unexpected process deviation in the operation of fatty acid
precut fractionation column. The developed algorithm is expected to enhance the safety
of operation in oleochemical industry. Early detection and diagnosis is useful to avoid
abnormal condition that might lead to the loss of both human live and economic values.
The advisory system algorithm used process history based method and presented by
rule-based approach. It was developed using Borland C++ Builder 6.0 and had a user
friendly interface. Plant model was simulated by using commercial simulator,
HYSYS.PlantT M and verified with real plant data. Univariate Statistical Process Control
technique (Individual and Moving Range (x-MR) chart) and Hazard and Operability
(HAZOP) Study were used for the diagnostic task. The system detected and diagnosed
process deviations using the saved data and the set limit. Fault occurred if the data value
was out of limits. The interface of FDA System then displayed the results in the form of
charts. Finally, the causes and consequences of fault were displayed. Although the
scheme was developed based on data of fatty acid precut fractionation column, the
algorithm of fault detection and diagnosis can be extended to other chemical process by
changing the x-MR chart and HAZOP for each selected monitoring variables.
vi
ABSTRAK
Penyelidikan ini menghasilkan Sistem Penasihat Diagnostik Kesilapan (FDA)
untuk mengesan dan mengenal pasti sisihan proses yang tidak didugai dalam operasi
turus penyulingan asid lelemak. Algoritma yang dibangunkan ini dijangka
meningkatkan operasi keselamatan dalam industri oleo kimia. Pengesanan dan
pengenalan pasti awal dapat membantu mengelakkan keadaan abnormal yang akan
membawa kepada kemusnahan nyawa manusia dan kemerosotan nilai ekonomi.
Algoritma sistem penasihat ini direka berdasarkan kaedah berasaskan sejarah proses dan
disampaikan oleh sistem pakar berasaskan peraturan. Ia dibangunkan menggunakan
Borland C++ Builder 6.0 dan mempunyai perantara muka pengguna yang mesra. Model
loji disimulasi dengan menggunakan penyimulasi komersil HYSYS.PlantT M dan
disahkan dengan data logi. Univariat Kawalan Proses Statistik (Carta Tersendiri dan
Had Pergerakan (x-MR)) dan Kajian Bahaya dan Kemampuan Operasi (HAZOP) telah
digunakan untuk tugas mengenalpastian. Sistem ini telah berjaya mengesan dan
mengenalpasti sisihan proses menggunakan data yang telah disimpan dan had yang telah
ditentukan. Kesilapan berlaku jika nilai data tidak berada dalam had. Seterusnya antara
muka Sistem FDA mempamerkan keputusan dalam bentuk carta. Akhirnya, sebab dan
akibat berlakunya kesilapan dipamerkan. Walaupun, disebabkan rancangan kajian ini
bergantung kepada data dari turus penyulingan asid lelemak, ia boleh diaplikasik an
dalam industri kimia lain dengan mengubah carta x-MR dan HAZOP kepada setiap
pembolehubah yang diawasi.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENTS iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xii
LIST OF FIGURES xiii
LIST OF ABBREVIATIONS xvi
LIST OF SYMBOLS xviii
LIST OF APPENDICES xix
1 INTRODUCTION
1.1 Introduction 1
1.2 Motivation 2
1.3 Research Objective 4
1.4 Scopes of Research 4
1.5 Contribution of Research 5
1.6 Layout of the Thesis 6
viii
2 FUNDAMENTAL THEORY AND LITERATURE
REVIEW
2.1 Introduction 7
2.2 Types of Faults in Process Chemical Industries 7
2.3 Common Task of Fault Detection and Diagnosis 9
2.3.1 Classification of Fault Detection and
Diagnosis Algorithm
10
2.3.2 Fault Diagnostic Advisory System 13
2.4 Rule-based Feature Extraction 15
2.4.1 Expert System Approach in Fault
Detection and Diagnosis
15
2.4.2 Expert Systems 20
2.4.3 Expert System Development Life Cycle
(ESDLC)
24
2.5 Application of Statistical Technique - SPC for
Fault Detection
26
2.5.1 Quality and Statistical Process Control 28
2.5.2 Variables Charts For Limited Data
Group Data
29
2.6 Knowledge Based Hazard Identification 30
2.6.1 HAZOP Study 32
2.6.2 The HAZOP Procedure 35
2.7 Summary 37
3 RESEARCH METHODOLOGY
3.1 Introduction 38
3.2 The Diagnostic Advisory Module Environment 38
3.3 Design of Detection Workspace 42
3.3.1 Plant Simulation 43
ix
3.3.2 Monitoring Variables 44
3.3.3 Individual and Moving-Range Chart
(x-MR Chart)
45
3.3.4 Assigned Discrete State 46
3.4 Design of Fault Diagnosis Workspace 46
3.4.1 Structural HAZOP Study 47
3.5 Inference Logic 47
3.6 Structure of Rule-based FDA System 48
3.6.1 Development of User Interface 48
3.6.2 Development of Detection Knowledge
Base (Phase I)
49
3.6.3 Development of Diagnosis Knowledge
Base (Phase II)
50
3.6.4 Development of Production Rule
(Phase III)
50
3.6.4.1 Development of Rules for
Phase I
50
3.6.4.2 Development of Rules for
Phase II
51
3.6.5 Development of Inference Engine
(Phase IV)
51
3.7 Summary 52
4 RESULTS AND DISCUSSION
4.1 Introduction 53
4.2 Assumptions for Fault Diagnostic Advisory
System
53
4.3 Actual Description of Plant Model 55
4.3.1 Modeling and Simulation Assumption 57
x
4.3.2 Simulation Results 58
4.3.3 Validation of Simulation Results 60
4.4 Result of Developed Algorithm 61
4.4.1 Development of Fault Detection
Knowledge Base for Phase I
61
4.4.1.1 Upper and Lower Limits 66
4.4.1.2 Specification Limits of
Controller
67
4.4.1.3 The Capability Analysis 67
4.4.2 Development of Diagnosis Knowledge
Base for Phase II
68
4.4.2.1 Sensitivity Analysis 69
4.4.2.2 Results of HAZOP Study 69
4.4.3 Development of Rule Base for Phase I 77
4.4.3.1 Process Deviation 77
4.4.4 Development of Production Rules for
Phase II
78
4.4.4.1 Diagnosis Condition of
Monitoring Variables
78
4.4.5 Development of Inference Engine 79
4.4.5.1 Main Specific Procedures of
FDA System
79
4.4.5.2 Database 80
4.4.5.3 Program Interface and Code
Programming for FDA
System
83
4.5 Results from the FDA System 89
4.5.1 Testing of FDA System 90
4.5.2 Experiments and Discussions for Offline
FDA System
100
xi
4.5.2.1 Fault Case 1: Faulty
Condition at Sensor for
LIC 102
102
4.5.2.2 Fault Case 2: Faulty
Condition at Sensor for
LIC 103
102
4.5.2.3 Fault Case 3: Faulty
Condition at Sensor for
FIC 101
103
4.5.2.4 Fault Case 4: Faulty
Condition at Sensor for
TIC 100
4.5.2.5 Fault Case 5: Faulty
Condition at Sensor for
TIC 101
4.6 Summary 108
5 CONCLUSIONS AND RECOMMENDATIONS FOR
FUTURE WORKS
5.1 Introduction 109
5.2 Summary 109
5.3 Conclusions 110
5.4 Future Work 112
REFERENCES 113
Appendices A - C 121 - 145
xii
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Hybrid six-step ESDLC (Awad, 1996) 25
2.2 Typical guide words 33
4.1 Sensor and monitoring variables 59
4.2 Nodes for HAZOP study 60
4.3 Comparison of plant data and simulation for
precut column
61
4.4 Percentage of noise 64
4.5 Results of skewness and kurtosis 65
4.6 Upper and lower limits 66
4.7 The limits function of controller 67
4.8 Results of capability analysis 68
4.9 Sensitivity analysis for controller TIC 100 (º C) 69
4.10 HAZOP worksheet for node 1 70
4.11 HAZOP worksheet for node 2 71
4.12 HAZOP worksheet for node 3 72
4.13 HAZOP worksheet for node 4 73
4.14 HAZOP worksheet for node 5 74
4.15 HAZOP worksheet for node 6 75
4.16 Summary of experiment for FDA system 101
xiii
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Classification of diagnostic algorithms
(Venkatasubramanian et.al., 2003)
12
2.2 The architecture of Expert System
(Darlington, 2000)
21
2.3 The HAZOP study procedure (Wells, 1996) 36
3.1 Fault diagnostic advisory system development
flowchart
39
3.2 Development environment of Borland C++
Builder (BCB) 6.0
40
3.3 Architecture of fault diagnostic advisory
system
41
3.4 Tools used in development of FDA system 42
3.5 Development environment of HYSYS.Plant
simulator
43
3.6 A typical packed column 44
3.7 Flow diagram of structural HAZOP study 47
3.8 Flow chart of forward chaining 52
4.1 Transformation in FDA system 54
4.2 a Main flowsheet for precut fractionation
process using HYSYS.Plant
56
xiv
4.2 b Column flowsheet for precut fractionation
process using HYSYS.Plant
56
4.3 Case study – precut column 59
4.4a Maximum time constant 62
4.4b Minimum time constant 63
4.5 Graph of autocorrelation plot 63
4.6 Histograms for each monitoring variables 65
4.7 Boxplot for each monitoring variables 66
4.8 Flowchart of fault diagnostic advisory system 80
4.9 Development environment of Database
Desktop
81
4.10 Development environment of SQL explorer 81
4.11 Interface of Microsoft Access in developing
table
82
4.12 Example of Macro in Microsoft Access
interface
83
4.13 Main user interface of FDA system 84
4.14a The main window of fault detection and
diagnosis
84
4.14b The interface of fault diagnosis 84
4.15 Interface of editing limits 85
4.16 String list editor Phase I 87
4.17 Editing data source of the chart 88
4.18 String list editor for Phase II 88
4.19 Description of Fault Detection and Diagnosis
interface
90
4.20 FDD workspace (open file window) 91
4.21a Fault diagnosis results 91
4.21b Fault diagnosis advisory results and control chart
93
4.21c FDA results of sensor 1 94
xv
4.21d FDA results of sensor 3 94
4.21e FDA results of sensor 4 94
4.21f FDA results of sensor 5 94
4.21g FDA results of sensor 6 95
4.21h FDA results of sensor 7 95
4.22a Forward chaining procedure for FDA system 96
4.22b Forward chaining procedure for FDA system 97
4.23a Plotting x-MR chart for each sensor 98
4.23b Plotting x-MR chart for each sensor 96
4.24 Fault detected and diagnosed results for sensor of LIC 102
102
4.25 Fault detected and diagnosed results for
sensor of LIC 103
103
4.26a Fault detected and diagnosed results for
sensor of FIC 101 – sensor 2
104
4.26b Fault detected and diagnosed results for
sensor of FIC 101 – sensor 5
104
4.27a Fault detected and diagnosed results for
sensor of TIC 100 – sensor 1
105
4.27b Fault detected and diagnosed results for
sensor of TIC 100 – sensor 4
105
4.27c Fault detected and diagnosed results for
sensor of TIC 100 – sensor 7
106
4.28a Fault detected and diagnosed results for
sensor of TIC 101- sensor 3
107
4.28b Fault detected and diagnosed results for
sensor of TIC 101 – sensor 4
107
4.28c Fault detected and diagnosed results for
sensor of TIC 101 – sensor 6
107
xvi
LIST OF ABBREVIATIONS
x-MR - Individual and Moving Range
AEM - Abnormal Event Management
AI - Artificial Intelligence
API - Application Programming Interface
ASM - Abnormal Situation Management
BCB - Borland C++ Builder
BDE - Borland Database Engine
CL - Center Line
CVS - Comma Separated Values
DMP - Dump
ESDLC - Expert System Development Life Cycle
ESs - Expert Systems
FDD - Fault Detection and Diagnosis
FDA - Fault Diagnostic Advisory
FIC - Flow Indicator Controller Process and
HAZOP - Hazard and Operability Study
HETP - Height-Equivalent Theoretical Plate
LCL - Lower Control Limit
LIC - Level Indicator Controller
ODBC - Open Database Connectivity
PFDD - Process Fault Detection and Diagnosis
PI - Pressure Indicator
PV - Process Variable
xvii
RAD - Rapid Application Development
S1 - Node 1 – From Input Reboiler to Column
S2 - Node 2 – From Sidestream back to Column
SPC - Statistical Process Control
SQL - Structured Query Language
TIC - Temperature Indicator Controller
UCL - Upper Control Limit
VLV - Valve
xviii
LIST OF SYMBOLS
x - Individual
x - Mean
D - Constant for Moving-rang Chart
E - Constant for Individual Chart
MR - Moving Range
xix
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Sensitivity Analysis 121
B FDA System’s Source Code (Matlab and BCB) 123
C Results for FDA System
138
CHAPTER 1
INTRODUCTION
1.1 Introduction
Errors by operators and maintenance workers as well as equipment failures are
recognized as major causes of industrial accidents (Kletz, 1990). Major chemical
accidents can be attributed to explosive, i.e., catastrophic vessel failures as well as non-
explosive, i.e., discharge through relief systems, pipe breaks, release of toxic and
flammable materials. Concern in control and management of chemical major hazards
has increased since the occurrence of major accidents: Flixborough - 1974, Seveso -
1976, Piper Alpha oil platform fire - 1988, Zeebrugge ferry disaster - 1987, Phillips
petroleum fire and explosion - 1989, Challenger disaster - 1986, Esso Australia
Longford explosion - 1998, Kuwait Petrochemical’s refinery - 2000 and Toulouse - 2001.
In the wake of major well-publicized chemical accidents that have occurred in
various parts of the world over the past two decades, many private and national
companies operating in the developing countries have recognized their own vulnerability
to such disastrous accidents. Nowadays, most of the companies in the chemical industry
have their own safety policies. It was their responsibility to prevent and control any
potential accident occurs in processing plant. Much thought have also been given to
ways of reducing and minimizing consequences of the accidents, one of the methods
such as building an advisory system.
2
An effective control is required in the process plant due to the complexity of
process operation. However, effective control could not guarantee unless all information
feed to control system are correct. Hence, diagnostic system is needed to detect any
abnormally such as bias in measurement, sensor failure and equipments failure.
Computer-based solution or a knowledge-based system for diagnostic system is needed
to help ordinary field operators (Venkatasubramanian, 2003). They need information to
make rapid decisions on vital questions, advising emergency services, recommending
avoidance and control measures during the accidental release.
1.2 Motivation
In the past, control community has succeeded using alarm and interlock systems
in removing regulatory control from hands of human operators. These systems have
performed in an automated manner aided by computer due to the complicated plant
operation and led to great progress in the quest for higher productivity, process safety,
process efficiency and profitability.
Poor control or process disrup tion might lead to plant shutdown and such
situations are expected to be solved by human operators with the assistance of an alarm
system (Ahmad, 2004). If correcting abnormal events is fully reliance on human
operators, they might tend to make erroneous decisions and take actions which make
matters even worse. Literature has shown that most industrial accidents are caused by
human errors and these abnormal events have significant economics, safety and
environmental impacts (Lees, 1996). Hence, fault detection and diagnosis is one of the
means for process safety management to aid the operator in improving the process
operation.
Abnormal events could occur due to several factors such as the broad scope of
the diagnostic activity that include a variety of malfunctions - process unit failures,
3
process unit degradation, and parameter drifts and so on. Prolonged situations such as in
the case of instrument failure or any rupture of pipeline could result in disasters, forced
shutdowns, or at least higher operating costs from sub-optimal plant operation and it
could not be avoided unless the failure is promptly detected and accomplished in time
with corrective actions. Thus, it is the purpose of diagnosis to detect any fault and give
advice to personnel in taking action which caused by failure of the control system. This
entire activity is called Abnormal Event Management (AEM), a key component of
supervisory control (Venkatasubramanian, 2003).
In the area of plant-wide control at the supervisory level, the process fault
detection and diagnosis system plays a key role. Foreseeable, the important of
supervisory system and the potential of computer to provide closer supervision and
better information of process safety by monitoring critical parameters and, when
circumstances warrant it, initialing and carrying out a safe shutdown. Due to the
problems stated, that is the next grand challenges for control engineers in implementing
a sophisticated system that can be able to monitor process operation.
Diagnosis consists of two different but closely related procedures. The first step
is to receive response of the system through measuring device. The second step is to
make a decision on the state of the system based on the sensory values. Researchers
seek a way of using a computer to mimic human reasoning. There are different search
techniques that can be applied to perform diagnosis based on the available process
knowledge. Venkatasubramanian (2003) and his partner has summarized the basic
approach in implementing diagnostic system. Knowledge engineering and advanced
software tools such as expert systems can also be used for process supervision purposes.
4
1.3 Research Objective
The objectives of the research are as follows:
i. To develop a fault detection and diagnosis algorithm for the safety purposes
in fatty acid fractionation column operation by using Univariate Statistical
Process Control techniques, x-MR chart and Hazard and Operability (HAZOP)
Study supported by rule-based approaches.
ii. To develop a fault diagnostic advisory system using Borland C++ Builder 6.0.
1.4 Scope of Research
The scope of the work consists of the following:
i. To develop Fault Diagnostic Advisory algorithm focusing on a fatty acid
precut fractionation column.
ii. To develop fault detection algorithm – discriminator base on Univariate
Statistical Process Control, x-MR chart.
iii. To diagnose common possible causes and consequences of process
deviations using HAZOP study.
iv. To develop and integrate rule-based fault detection and diagnosis algorithm.
v. To verify and validate the designed fault detection and diagnosis algorithm
by developing a prototype using Borland C++ Builder 6.0.
5
1.5 Contribution of Research
Process Hazard Analysis (PHA) has been proven to be an important tool in
improving the safety of plant designs and operation. This thesis provides improvement
to the existing method of fault diagnostic algorithm by introducing Fault Diagnostic
Advisory (FDA) system which combines SPC and HAZOP study approaches. A case
study involving precut fractionation column was used to illustrate the capability of the
proposed methodology. Besides, a third party application – external database was used
to improve the advisory results by giving detail information of fault position - causes
and consequences.
The following publications are results from the work described in the thesis:
i. H. Y. Heng, M. W. Ali and M. Z. Kamsah. (2003). Fault Detection and
Diagnosis Using Rule-Based Support System on Fatty Acid Fractionation
Colum. International Conference on Chemical and Bioprocess Engineering
(ICCBPE 2003). 103 - 108.
ii. H. Y. Heng, M. W. Ali and M. Z. Kamsah. (2003). Fault Detection and
Diagnosis Support System for Fatty Acid Fractionation Column. 17th
Symposium of Malaysian Chemical Engineers (SOMChE 2003). 767 - 772.
iii. H. Y. Heng, M. W. Ali and M. Z. Kamsah. (2004). Fault Diagnostic
Algorithm for Precut Fractionation Column. 18th Symposium of Malaysian
Chemical Engineers (SOMChE 2004). 1 - 6.
6
1.6 Layout of the Thesis
The thesis is divided into seven chapters. Literature review and fundamental
theory of knowledge that covered in this study are discussed in Chapter II. In this
chapter, x-MR chart, HAZOP study, expert system and people works on fault detection
and diagnosis are elaborated. This is followed by the discussion of research
methodology in Chapter III; presented the algorithm to build the fault diagnostic system.
Chapter IV gives the results and discussion of the developed diagnostic system. Finally,
Chapter V concludes the thesis and recommends some future works.
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