Made available in DSpace on 2014-12-17T14:08:44Z (GMT). No. of bitstreams: 1 AndreLA_DISSERT.pdf: 1426214 bytes, checksum: 62fdf46525d28318b6138490219276ba (MD5) Previous issue date: 2010-12-21 / In the operational context of industrial processes, alarm, by definition, is a warning to the operator that an action with limited time to run is required, while the event is a change of state information, which does not require action by the operator, therefore should not be advertised, and only stored for analysis of maintenance, incidents and used for signaling / monitoring (EEMUA, 2007). However, alarms and events are often confused and improperly configured similarly by developers of automation systems. This practice results in a high amount of pseudo-alarms during the operation of industrial processes. The high number of alarms is a major obstacle to improving operational efficiency, making it difficult to identify problems and increasing the time to respond to abnormalities. The main consequences of this scenario are the increased risk to personal safety, facilities, environment deterioration and loss of production. The aim of this paper is to present a philosophy for setting up a system of supervision and control, developed with the aim of reducing the amount of pseudo-alarms and increase reliability of the information that the system provides. A real case study was conducted in the automation system of the offshore production of hydrocarbons from Petrobras in Rio Grande do Norte, in order to validate the application of this new methodology. The work followed the premises of the tool presented in ISA SP18.2. 2009, called "life cycle alarm . After the implementation of methodology there was a significant reduction in the number of alarms / No contexto de opera??o de processos industriais, alarme, por defini??o, ? um aviso ao T?cnico de Opera??o que uma a??o com tempo restrito para ser executada ? necess?ria, enquanto que evento ? uma informa??o de mudan?a de estado e n?o demanda a??o por parte do T?cnico de Opera??o, consequentemente n?o deve ser anunciada, sendo apenas armazenada para fins de an?lise de manuten??o, incidentes e utilizadas para sinaliza??o/monitora??o (EEMUA, 2007). Por?m, alarmes e eventos s?o frequentemente confundidos e configurados inadequadamente de forma semelhante por programadores de sistemas de automa??o. Esta pr?tica resulta em uma elevada quantidade de pseudo-alarmes durante a opera??o de processos industriais. O elevado n?mero de alarmes configurados ? um dos principais entraves para a melhoria da efici?ncia operacional, dificultando a identifica??o de problemas e aumentando o tempo de resposta ?s anormalidades. As principais conseq??ncias desse quadro s?o o aumento do risco ? seguran?a das pessoas, instala??es, meio ambiente e o agravamento das perdas de produ??o. O objetivo principal deste trabalho ? apresentar uma filosofia de configura??o de um sistema de supervis?o e controle, desenvolvida com o intuito de diminuir a quantidade de pseudo-alarmes configurados e aumentar a confiabilidade das informa??es que o sistema fornece. Um estudo de caso foi realizado no sistema de automa??o das plataformas mar?timas de produ??o de Hidrocarbonetos da Petrobras no Rio Grande do Norte, de forma a validar a aplica??o dessa nova metodologia. O trabalho seguiu as premissas da ferramenta apresentada na norma ISA SP18.2. 2009, denominado ciclo de vida de alarme . Ap?s a implanta??o da metodologia verificou-se uma redu??o significativa no n?mero de alarmes

La sécurité des installations industrielles implique une gestion intégrée de tous les facteurs pouvant causer des incidents. La gestion d’alarmes est une condition qui peut être formulée comme un problème de reconnaissance de motifs pour lequel les motifs temporels sont utilisés pour caractériser différentes situations typiques, en particulier liées au phases de démarrage et d'arrêt. Dans cette thèse, nous proposons une nouvelle approche de gestion des alarmes basée sur un processus de diagnostic. En considérant les alarmes et les actions des procédures d'exploitation standard comme des événements discrets, le diagnostic repose sur la reconnaissance de situation pour fournir aux opérateurs des informations pertinentes sur les défauts induisant les flux d'alarmes. La reconnaissance de situation est basée sur des chroniques qui sont apprises pour chaque situation. Nous proposons d'utiliser un modèle causal hybride du système et des simulations pour générer les séquences d'événements représentatives à partir desquelles les chroniques sont apprises automatiquement en utilisant l'algorithme « Heuristic Chronicle Discovery Algorithm Modified » (HCDAM). Une extension de cet algorithme est présentée dans cette thèse où les connaissances d'experts sont prises en compte comme des restrictions temporelles qui constituent une nouvelle entrée pour HCDAM. Deux cas d’étude illustratifs dans le domaine des procédés pétrochimiques sont présentés. / Industrial plant safety involves integrated management of all the factors that may cause incidents. Process alarm management is a requisite that can be formulated as a pattern recognition problem in which temporal patterns are used to characterize different typical situations, particularly at startup and shutdown stages. In this thesis, we propose a new approach of alarm management based on a diagnosis process. Assuming the alarms and the actions of the standard operating procedures as discrete events, diagnosis relies on situation recognition to provide the operators with relevant information about the faults inducing the alarm flows. Situation recognition is based on chronicles that are learned for every situation. We propose to use the hybrid causal model of the system and simulations to generate the representative event sequences from which the chronicles are learned using the Heuristic Chronicle Discovery Algorithm Modified (HCDAM). An extension of this algorithm is presented in this thesis where expert knowledge is included as temporal restrictions which are a new input to HCDAM. Two illustrative case studies in the field of petrochemical plants are presented.

Major accidents in the process industries occurred mostly as an outcome of multiple failures in different safety barriers and their interrelation with unsafe acts by frontline operators. This has become the reason why safety analyses in terms of plant technical aspects cannot be performed independently from analysing human response to the changing technology. Unsafe acts and errors by operators must be seen as a symptom of system insufficiencies and underlying problems, rather than as the cause of an accident. With this paradigm, the need to optimally configure the system and the whole working condition to understand human’s limitation and requirements becomes very evident. It is too naive to desire that human operators make zero error by asking them to change their behaviour and to perfectly adapt to the system. Human Factors (HF) attempts to cope with the need to understand the interrelation between human operators, the technology they are working with and the management system, with the aim to increase safety and efficiency. In achieving this goal, HF must be incorporated into the whole plant lifecycle, from the earliest design stage to plant operation and modifications. Moreover, HF analysis must comprise all kinds of operators’ activities and responsibilities in operating process plants, which can include manual works in field and supervisory control conducted remotely from a control centre/room. This work has developed techniques that provide systematic way to incorporate HF into process plant lifecycle. The new HF analysis technique, PITOPA-Design, in a combination with the classic PITOPA, is applicable for an implementation during design and operation of a plant. With the awareness that safety analysis and HF cannot be performed separately, an interconnection with HAZOPs is made possible by means of this new technique. Moreover, to provide a systematic analysis of operators’ work in control room, an additional technique, the PITOPA-CR was also developed. This HF technique can as well be integrated into a general HF analysis both during design phase and plant operation. In addition to it, results coming from PITOPA-CR will provide information required to optimally configure control and alarm system, as well as the whole alarm management system to better understand the limitation and requirements of control room operators. The structure of the development can be described as follows: i) Development of HAZOPA (the Hazards and Operator Actions Analysis), which provides the interconnection between HF analysis and HAZOPs, ii) Development of PITOPA-Design, a technique to incorporate HF consideration into design phase, which is differentiated into 3 stages to comprise the conceptual design, the basic engineering and the detail engineering phase, iii) Development of PITOPA-CR, a technique for HF analysis in control room, iv) Integration of PITOPA-CR into alarm management system, development of a technique for alarm prioritization. / Schwere Unfälle in der Prozessindustrie erfolgen meist aus einem Zusammenspiel mehrerer verschiedener Fehler und der gleichzeitigen Wechselwirkung mit falschem menschlichem Handeln. Dabei sind diese Fehlhandlungen nicht als Unfallursache anzusehen, sondern sie resultieren aus Fehlern, die in dem System selbst zu finden sind. Aus diesem Grund kann bei der Sicherheitsanalyse die technische Analyse nicht unabhängig von der Betrachtung des Human Factors (HF) durchgeführt werden. Um eine Reduzierung der Fehlhandlungen zu erreichen, müssen das Anlagendesign, die Bedienbarkeit und die Arbeitsumgebung an die menschlichen Fähigkeiten angepasst werden. Human Factors (HF) betrachtet die Interaktion zwischen menschlichen, technischen und organisatorischen Aspekten einer Anlage, mit dem Ziel die Sicherheit und Effektivität der Anlage zu optimieren. Dafür ist eine Einbindung von HF in den gesamten Lebenszyklus einer Anlage notwendig. So müssen HF- Analysen nicht nur während des Betriebs einer Anlage und bei Prozessmodifikationen durchgeführt werden, sondern auch während des gesamten Design- Prozesses, da gerade in den frühen Design-Phasen das Optimierungspotential besonders hoch ist. Eine solche Analysemethode muss alle Aufgaben eines Operators erfassen, so dass zwischen manueller Arbeit und der Arbeit in der Leitwarte unterschieden werden muss. In dieser Arbeit wurden Analysentechniken entwickelt, die einen systematischen Ansatz zur Berücksichtigung des HF über den gesamten Lebenszyklus einer verfahrenstechnischen Anlage darstellen. Mit Hilfe der neuen Analysemethode, PITOPA-Design, können Untersuchungen sowohl während der Designphase als auch während des Betriebs einer Anlage durchgeführt werden. Da solche HF-Analyse immer in Verbindung mit einer klassischen Sicherheitsanalyse erfolgen muss, bindet die neue Methode die HAZOP-Analyse direkt ein. Darüber hinaus wurde ein weiterer Ansatz für die Analyse von Operatorhandlungen in einer Messwartenarbeit entwickelt. Diese neue Analysentechnik, PITOPA-CR, bildet die Grundlage für Verbesserungen im Alarmsystem und wird in das Alarmmanagementsystem eingebunden. Die Arbeit ist wie folgt strukturiert: i) Entwicklung von HAZOPA (the Hazards and Operator Actions Analysis). Diese Methode stellt die Einbindung der HF-Analyse in HAZOP dar. ii) Entwicklung von PITOPA-Design, zur HF-Analyse während des gesamten Designprozesses einer verfahrenstechnischen Anlage. Die Methode wurde in 3 Teile eingeteilt, um die drei Designsphasen Conceptual-, Basic-, und Detail-Design zu erfassen. iii) Entwicklung von PITOPA-CR, zur HF-Analyse in der Messwarte. iv) Einbindung von PITOPA-CR in das Alarmmanagementsystem und Entwicklung einer Technik zur Alarmpriorisierung.

Made available in DSpace on 2014-12-17T14:56:12Z (GMT). No. of bitstreams: 1 DaniloCS_DISSERT.pdf: 3897603 bytes, checksum: cd98fa05a1dee36b5186c50e95b2f03b (MD5) Previous issue date: 2013-02-01 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / Operating industrial processes is becoming more complex each day, and one of the factors that contribute to this growth in complexity is the integration of new technologies and smart solutions employed in the industry, such as the decision support systems. In this regard, this dissertation aims to develop a decision support system based on an computational tool called expert system. The main goal is to turn operation more reliable and secure while maximizing the amount of relevant information to each situation by using an expert system based on rules designed for a particular area of expertise. For the modeling of such rules has been proposed a high-level environment, which allows the creation and manipulation of rules in an easier way through visual programming. Despite its wide range of possible applications, this dissertation focuses only in the context of real-time filtering of alarms during the operation, properly validated in a case study based on a real scenario occurred in an industrial plant of an oil and gas refinery / A opera??o de processos industriais vem se tornando mais complexa ao longo dos anos, e um dos elementos que possibilitam este aumento de complexidade ? a integra??o de novas tecnologias e solu??es inteligentes empregadas no setor, como ? o caso dos sistemas de apoio ? decis?o. Neste sentido, esta disserta??o visa o desenvolvimento de um sistema de aux?lio ? opera??o baseado em uma ferramenta computacional chamada de sistema especialista. O objetivo principal ? tornar a opera??o mais confi?vel e segura ao maximizar a quantidade de informa??es relevantes a cada situa??o atrav?s da utiliza??o de um sistema especialista baseado em regras pr?-moldadas para uma determinada ?rea de conhecimento. Para a modelagem de tais regras foi proposto um ambiente de alto-n?vel, que permite a cria??o e manipula??o de regras de forma facilitada atrav?s de programa??o visual. A despeito de sua ampla gama de poss?veis aplica??es, esta disserta??o tem como foco o contexto de filtragem em tempo real de alarmes durante a opera??o, devidamente validada em um estudo de caso baseado em um cen?rio real ocorrido em uma planta industrial de uma refinaria de petr?leo e g?s

Made available in DSpace on 2016-08-17T14:52:39Z (GMT). No. of bitstreams: 1 Heider Cristian Moura Quintao.pdf: 4191526 bytes, checksum: 3053fbdd9113f05514fce93fc176aa9e (MD5) Previous issue date: 2008-02-15 / When equipment failure occur in complex industrial plants, the automation and control system generates a great amount of alarms that can confuse the operators and lead them to take wrong decisions - the time for decision taking is very short and the amount of generated information is higher, being impossible for the operator read all of them before taking the correct decision. The new industrial systems have presented functionalities that try to minimize this deficiency presenting some support to the user, but still in an inefficient form. This work presents a proposal of an Alarm Management System based on Action Recommendation - SIGARA, a knowledge-based tool which aims supporting users of industrial control systems, when abnormal events occur. SIGARA is an action recommender multi-agent system, shaped on the basis of the described tasks and phases of the ONTORMAS ontology and MAAEM methodology. Beyond searching the solution of a problem of the real world in the industries, the proposed SIGARA presents some additional features not present on existing systems, as the application of information filtering techniques in different processing phases, and also the use of MAAEM and ONTORMAS in this new domain. / Quando ocorrem falhas de equipamentos em plantas industriais complexas, o sistema de automação e controle gera uma grande quantidade de alarmes que podem confundir os operadores e induzi-los a tomar decisões erradas. O tempo para a tomada de decisão é muito curto e a quantidade de informação gerada é muito grande, sendo impossível que o operador consiga ler todas antes de tomar a decisão correta. Os novos sistemas industriais têm apresentado funcionalidades que buscam minimizar essa deficiência apresentando algum suporte ao usuário, mas ainda de forma ineficiente. O presente trabalho apresenta como proposta um Sistema Informatizado de Gerenciamento de Alarmes baseado na Recomendação de Ações (SIGARA). É uma ferramenta baseada em conhecimento que objetiva suportar usuários de sistemas industriais de automação e controle, quando da ocorrência de alguma anomalia. O SIGARA é um sistema multiagente de recomendação de ações, modelado com base nas tarefas e fases descritas na ontologia ONTORMAS ( Ontology for Reusing Multi-agent Software ), conforme a metodologia MAAEM ( Multi-Agent Application Engineering Methodology ). Além de buscar a solução de um problema do mundo real presente nas indústrias, o SIGARA proposto apresenta alguns diferenciais frente aos existentes no mercado, como o uso de técnicas de filtragem de informação em várias etapas do processamento das informações, e também a aplicação da MAAEM e ONTORMAS que ainda não haviam sido utilizadas nesse domínio.

Major accidents in the process industries occurred mostly as an outcome of multiple failures in different safety barriers and their interrelation with unsafe acts by frontline operators. This has become the reason why safety analyses in terms of plant technical aspects cannot be performed independently from analysing human response to the changing technology. Unsafe acts and errors by operators must be seen as a symptom of system insufficiencies and underlying problems, rather than as the cause of an accident. With this paradigm, the need to optimally configure the system and the whole working condition to understand human’s limitation and requirements becomes very evident. It is too naive to desire that human operators make zero error by asking them to change their behaviour and to perfectly adapt to the system. Human Factors (HF) attempts to cope with the need to understand the interrelation between human operators, the technology they are working with and the management system, with the aim to increase safety and efficiency. In achieving this goal, HF must be incorporated into the whole plant lifecycle, from the earliest design stage to plant operation and modifications. Moreover, HF analysis must comprise all kinds of operators’ activities and responsibilities in operating process plants, which can include manual works in field and supervisory control conducted remotely from a control centre/room. This work has developed techniques that provide systematic way to incorporate HF into process plant lifecycle. The new HF analysis technique, PITOPA-Design, in a combination with the classic PITOPA, is applicable for an implementation during design and operation of a plant. With the awareness that safety analysis and HF cannot be performed separately, an interconnection with HAZOPs is made possible by means of this new technique. Moreover, to provide a systematic analysis of operators’ work in control room, an additional technique, the PITOPA-CR was also developed. This HF technique can as well be integrated into a general HF analysis both during design phase and plant operation. In addition to it, results coming from PITOPA-CR will provide information required to optimally configure control and alarm system, as well as the whole alarm management system to better understand the limitation and requirements of control room operators. The structure of the development can be described as follows: i) Development of HAZOPA (the Hazards and Operator Actions Analysis), which provides the interconnection between HF analysis and HAZOPs, ii) Development of PITOPA-Design, a technique to incorporate HF consideration into design phase, which is differentiated into 3 stages to comprise the conceptual design, the basic engineering and the detail engineering phase, iii) Development of PITOPA-CR, a technique for HF analysis in control room, iv) Integration of PITOPA-CR into alarm management system, development of a technique for alarm prioritization.:ACKNOWLEDGEMENT i ABSTRACT iii ZUSAMMENFASSUNG iv CONTENTS v TABLE OF FIGURES viii LIST OF TABLES x NOMENCLATURE xi ACRONYMS AND ABBREVIATIONS xii CHAPTER 1 1 INTRODUCTION 1 1.1 Background 1 1.2 Objectives 2 1.3 Scope of Work 3 CHAPTER 2 5 THEORETICAL BACKGROUND 5 2.1 Fundamentals of Human Error 5 2.2 Human Factors (HF) 8 2.3 Motivations to Consider HF in Process Safety 9 2. 3. 1 Accidents that Address HF in Process Safety 11 2. 3. 2 Regulation and Legal Requirements 16 2. 3. 3 Business Value 19 2.4 Work of Operators in Complex Systems 19 2. 4. 1 Role of Operators in Complex Systems 20 2. 4. 2 Problems with Computerisation and Automation 24 2. 4. 3 Allocation of Functions and Levels of Automation 25 2.5 Performance Influencing Factors (PIFs) 27 2.6 Distributed Control System (DCS) and Alarm Systems 29 2. 6. 1 Alarm, Alarm System and Alarm Management 30 2. 6. 2 Most Common Alarm Problems 33 2. 6. 3 Improving Alarm Performance through Prioritization 34 2.7 Safety Analysis Methods 38 2.7.1 Qualitative Safety Analysis 39 2.7.2 Quantitative Safety Analysis 43 2.8 Mathematical Algorithms 44 2.8.1 Techniques for Multi-Criteria Decision Making (MCDM) 44 2.8.2 Classification Methods 47 CHAPTER 3 50 RECENT DEVELOPMENTS IN HF STUDIES 50 3. 1 Methods for HF analysis 50 A. Task Analysis 50 B. Techniques for Operators Actions Analysis 51 3. 2 Human Reliability Analyses (HRA) 52 3. 3 Consideration of Human Error in HAZOP 53 3. 4 HF in Process Plant Design 54 3. 5 HF in Alarm Management and DCS-Design 55 3. 6 The Need for Further Development of HF Methods 57 CHAPTER 4 58 MOTIVATION OF THE WORK 58 CHAPTER 5 61 PROCESS INDUSTRY TOOL FOR OPERATOR ACTIONS ANALYSIS (PITOPA) 61 5.1 The New Technique for Operator Actions Analysis (OAA) 64 5.2 Technique for Performance Influencing Factors (PIFs) Evaluation 65 5.3 Validation of PITOPA in the Process Industry 67 CHAPTER 6 71 EXTENDING HAZOP TO INTEGRATE HF INTO 71 GENERAL SAFETY ANALYSIS 71 6.1 Development of HAZOPA (The Hazard, Operability and Operator Actions Analysis) 72 6.2 Case Study 75 CHAPTER 7 85 APPROACH TO INCORPORATING HF CONSIDERATION 85 INTO PLANT DESIGN 85 7.1 Development of an Approach for HF Analysis in Design – The PITOPA-Design 85 7.1.1 HF Analysis in Conceptual Design Phase (HFAD–Conceptual) 88 7.1.2 HF Analysis in Basic Engineering (HFAD – Basic) 93 7.1.3 HF Analysis in Detail Engineering (HFAD-Detail) 107 7.2 Technique for HF-Design Parameters Evaluation 109 7.3 Intermediate Summary 114 CHAPTER 8 115 IMPLEMENTATION OF THE NEW PITOPA-DESIGN: 115 A CASE-STUDY 115 8.1 Conceptual Design 115 8.2 Basic Engineering 123 8.3 Detail Engineering 127 CHAPTER 9 132 APPROACH FOR IMPROVING OPERATOR PERFORMANCE 132 IN CONTROL ROOM 132 9.1 Performance Influencing Factors (PIFs) for Supervisory & Monitoring Tasks 134 9.2 Development of PITOPA-Control Room (PITOPA-CR) 140 9.2.1 Analysis of Normal Operation 142 9.2.2 Analysis of Abnormal Operation 150 9.3 Alarm Prioritization 156 9.3.1 A survey on Alarm Prioritization 156 9.3.2 Incorporation of CROAA into Alarm Prioritization 157 9.4 Intermediate Summary 165 CHAPTER 10 167 INCORPORATION OF OPERATOR ACTIONS ANALYSIS INTO ALARM MANAGEMENT 167 CHAPTER 11 171 RESULTS AND FUTURE WORKS 171 11. 1 Results 171 11. 2 Future Works 172 BIBLIOGRAPHY 174 APPENDIX A A-1 APPENDIX B B-1 / Schwere Unfälle in der Prozessindustrie erfolgen meist aus einem Zusammenspiel mehrerer verschiedener Fehler und der gleichzeitigen Wechselwirkung mit falschem menschlichem Handeln. Dabei sind diese Fehlhandlungen nicht als Unfallursache anzusehen, sondern sie resultieren aus Fehlern, die in dem System selbst zu finden sind. Aus diesem Grund kann bei der Sicherheitsanalyse die technische Analyse nicht unabhängig von der Betrachtung des Human Factors (HF) durchgeführt werden. Um eine Reduzierung der Fehlhandlungen zu erreichen, müssen das Anlagendesign, die Bedienbarkeit und die Arbeitsumgebung an die menschlichen Fähigkeiten angepasst werden. Human Factors (HF) betrachtet die Interaktion zwischen menschlichen, technischen und organisatorischen Aspekten einer Anlage, mit dem Ziel die Sicherheit und Effektivität der Anlage zu optimieren. Dafür ist eine Einbindung von HF in den gesamten Lebenszyklus einer Anlage notwendig. So müssen HF- Analysen nicht nur während des Betriebs einer Anlage und bei Prozessmodifikationen durchgeführt werden, sondern auch während des gesamten Design- Prozesses, da gerade in den frühen Design-Phasen das Optimierungspotential besonders hoch ist. Eine solche Analysemethode muss alle Aufgaben eines Operators erfassen, so dass zwischen manueller Arbeit und der Arbeit in der Leitwarte unterschieden werden muss. In dieser Arbeit wurden Analysentechniken entwickelt, die einen systematischen Ansatz zur Berücksichtigung des HF über den gesamten Lebenszyklus einer verfahrenstechnischen Anlage darstellen. Mit Hilfe der neuen Analysemethode, PITOPA-Design, können Untersuchungen sowohl während der Designphase als auch während des Betriebs einer Anlage durchgeführt werden. Da solche HF-Analyse immer in Verbindung mit einer klassischen Sicherheitsanalyse erfolgen muss, bindet die neue Methode die HAZOP-Analyse direkt ein. Darüber hinaus wurde ein weiterer Ansatz für die Analyse von Operatorhandlungen in einer Messwartenarbeit entwickelt. Diese neue Analysentechnik, PITOPA-CR, bildet die Grundlage für Verbesserungen im Alarmsystem und wird in das Alarmmanagementsystem eingebunden. Die Arbeit ist wie folgt strukturiert: i) Entwicklung von HAZOPA (the Hazards and Operator Actions Analysis). Diese Methode stellt die Einbindung der HF-Analyse in HAZOP dar. ii) Entwicklung von PITOPA-Design, zur HF-Analyse während des gesamten Designprozesses einer verfahrenstechnischen Anlage. Die Methode wurde in 3 Teile eingeteilt, um die drei Designsphasen Conceptual-, Basic-, und Detail-Design zu erfassen. iii) Entwicklung von PITOPA-CR, zur HF-Analyse in der Messwarte. iv) Einbindung von PITOPA-CR in das Alarmmanagementsystem und Entwicklung einer Technik zur Alarmpriorisierung.:ACKNOWLEDGEMENT i ABSTRACT iii ZUSAMMENFASSUNG iv CONTENTS v TABLE OF FIGURES viii LIST OF TABLES x NOMENCLATURE xi ACRONYMS AND ABBREVIATIONS xii CHAPTER 1 1 INTRODUCTION 1 1.1 Background 1 1.2 Objectives 2 1.3 Scope of Work 3 CHAPTER 2 5 THEORETICAL BACKGROUND 5 2.1 Fundamentals of Human Error 5 2.2 Human Factors (HF) 8 2.3 Motivations to Consider HF in Process Safety 9 2. 3. 1 Accidents that Address HF in Process Safety 11 2. 3. 2 Regulation and Legal Requirements 16 2. 3. 3 Business Value 19 2.4 Work of Operators in Complex Systems 19 2. 4. 1 Role of Operators in Complex Systems 20 2. 4. 2 Problems with Computerisation and Automation 24 2. 4. 3 Allocation of Functions and Levels of Automation 25 2.5 Performance Influencing Factors (PIFs) 27 2.6 Distributed Control System (DCS) and Alarm Systems 29 2. 6. 1 Alarm, Alarm System and Alarm Management 30 2. 6. 2 Most Common Alarm Problems 33 2. 6. 3 Improving Alarm Performance through Prioritization 34 2.7 Safety Analysis Methods 38 2.7.1 Qualitative Safety Analysis 39 2.7.2 Quantitative Safety Analysis 43 2.8 Mathematical Algorithms 44 2.8.1 Techniques for Multi-Criteria Decision Making (MCDM) 44 2.8.2 Classification Methods 47 CHAPTER 3 50 RECENT DEVELOPMENTS IN HF STUDIES 50 3. 1 Methods for HF analysis 50 A. Task Analysis 50 B. Techniques for Operators Actions Analysis 51 3. 2 Human Reliability Analyses (HRA) 52 3. 3 Consideration of Human Error in HAZOP 53 3. 4 HF in Process Plant Design 54 3. 5 HF in Alarm Management and DCS-Design 55 3. 6 The Need for Further Development of HF Methods 57 CHAPTER 4 58 MOTIVATION OF THE WORK 58 CHAPTER 5 61 PROCESS INDUSTRY TOOL FOR OPERATOR ACTIONS ANALYSIS (PITOPA) 61 5.1 The New Technique for Operator Actions Analysis (OAA) 64 5.2 Technique for Performance Influencing Factors (PIFs) Evaluation 65 5.3 Validation of PITOPA in the Process Industry 67 CHAPTER 6 71 EXTENDING HAZOP TO INTEGRATE HF INTO 71 GENERAL SAFETY ANALYSIS 71 6.1 Development of HAZOPA (The Hazard, Operability and Operator Actions Analysis) 72 6.2 Case Study 75 CHAPTER 7 85 APPROACH TO INCORPORATING HF CONSIDERATION 85 INTO PLANT DESIGN 85 7.1 Development of an Approach for HF Analysis in Design – The PITOPA-Design 85 7.1.1 HF Analysis in Conceptual Design Phase (HFAD–Conceptual) 88 7.1.2 HF Analysis in Basic Engineering (HFAD – Basic) 93 7.1.3 HF Analysis in Detail Engineering (HFAD-Detail) 107 7.2 Technique for HF-Design Parameters Evaluation 109 7.3 Intermediate Summary 114 CHAPTER 8 115 IMPLEMENTATION OF THE NEW PITOPA-DESIGN: 115 A CASE-STUDY 115 8.1 Conceptual Design 115 8.2 Basic Engineering 123 8.3 Detail Engineering 127 CHAPTER 9 132 APPROACH FOR IMPROVING OPERATOR PERFORMANCE 132 IN CONTROL ROOM 132 9.1 Performance Influencing Factors (PIFs) for Supervisory & Monitoring Tasks 134 9.2 Development of PITOPA-Control Room (PITOPA-CR) 140 9.2.1 Analysis of Normal Operation 142 9.2.2 Analysis of Abnormal Operation 150 9.3 Alarm Prioritization 156 9.3.1 A survey on Alarm Prioritization 156 9.3.2 Incorporation of CROAA into Alarm Prioritization 157 9.4 Intermediate Summary 165 CHAPTER 10 167 INCORPORATION OF OPERATOR ACTIONS ANALYSIS INTO ALARM MANAGEMENT 167 CHAPTER 11 171 RESULTS AND FUTURE WORKS 171 11. 1 Results 171 11. 2 Future Works 172 BIBLIOGRAPHY 174 APPENDIX A A-1 APPENDIX B B-1

L’industrie microélectronique est un domaine compétitif, confronté de manière permanente à plusieurs défis. Pour évaluer les étapes de fabrication, des tests de qualité sont appliqués. Ces tests étant discontinus, une défaillance des équipements peut causer une dégradation de la qualité du produit. Des alarmes peuvent être déclenchées pour indiquer des problèmes. D’autre part, on dispose d’une grande quantité de données des équipements obtenues à partir de capteurs. Une gestion des alarmes, une interpolation de mesures de qualité et une réduction de données équipements sont nécessaires. Il s’agit dans notre travail à développer des méthodes génériques d’analyse multi-variée permettant d’agréger toutes les informations disponibles sur les équipements pour prédire la qualité de produit en prenant en compte la qualité des différentes étapes de fabrication. En se basant sur le principe de reconnaissance de formes, nous avons proposé une approche pour prédire le nombre de produits restant à produire avant les pertes de performance liée aux spécifications clients en fonction des indices de santé des équipement. Notre approche permet aussi d'isoler les équipements responsables de dégradation. En plus, une méthodologie à base de régression régularisée est développée pour prédire la qualité du produit tout en prenant en compte les relations de corrélations et de dépendance existantes dans le processus. Un modèle pour la gestion des alarmes est construit où des indices de criticité et de similarité sont proposés. Les données alarmes sont ensuite utilisées pour prédire le rejet de produits. Une application sur des données industrielles provenant de STMicroelectronics est fournie. / The microelectronics industry is a highly competitive field, constantly confronted with several challenges. To evaluate the manufacturing steps, quality tests are applied during and at the end of production. As these tests are discontinuous, a defect or failure of the equipment can cause a deterioration in the product quality and a loss in the manufacturing Yield. Alarms are setting off to indicate problems, but periodic alarms can be triggered resulting in alarm flows. On the other hand, a large quantity of data of the equipment obtained from sensors is available. Alarm management, interpolation of quality measurements and reduction of correlated equipment data are required. We aim in our work to develop generic methods of multi-variate analysis allowing to aggregate all the available information (equipment health indicators, alarms) to predict the product quality taking into account the quality of the various manufacturing steps. Based on the pattern recognition principle, data of the degradation trajectory are compared with health indices for failing equipment. The objective is to predict the remaining number of products before loss of the performance related to customer specifications, and the isolation of equipment responsible for degradation. In addition, regression- ased methods are used to predict the product quality while taking into account the existing correlation and the dependency relationships in the process. A model for the alarm management is constructed where criticality and similarity indices are proposed. Then, alarm data are used to predict the product scrap. An application to industrial data from STMicroelectronics is provided.