Human Factor and Reliability Analysis to Prevent Losses in Industrial Processes: An Operational Culture Perspective

Front Cover
Human Factor and Reliability Analysis to Prevent Losses in Industrial Processes
Copyright Page
Contents
About the authors
Preface
	1 Paradigms
	2 Book structure
	3 Products related to the chapters
Acknowledgment
1 Introduction
	1.1 A brief discussion
		1.1.1 The organization inserted in the social environment
		1.1.2 Conceptual and mathematical models in human and operational reliability
		1.1.3 Risk management in complex processes and environments
		1.1.4 Competency analysis and task planning
		1.1.5 Analysis and diagnosis of human factors
	1.2 Discussion timeline and schools
		1.2.1 A historical vision about schools related to origin of human reliability
			1.2.1.1 Mass production in the United Kingdom and Fordism in the United States
			1.2.1.2 Toyota, total quality control and total productivity maintenance in Japan
			1.2.1.3 Psychology and safety of work, discussion originated in the United States and Europe
			1.2.1.4 Psychodynamics at work in France and England
			1.2.1.5 Cognitive ergonomics (France)
			1.2.1.6 American anthropometrical ergonomics
			1.2.1.7 Human reliability in nuclear power plants (CNSI/ONU)
			1.2.1.8 Analysis of the task in England, Manchester, and Sheffield
			1.2.1.9 Cognitive models about human error in Nordic countries
			1.2.1.10 Human error analysis in US aviation
			1.2.1.11 Chemical industry and oil industry in the United States
			1.2.1.12 Rail transportation in the United States and maritime transportation in the United Kingdom
			1.2.1.13 Medical error in the US health services
	1.3 Worker role in job and society: human error
		1.3.1 Human activity
		1.3.2 Role at work and society
		1.3.3 Departments, functions, and human reliability
			1.3.3.1 Strategic area—business environment
			1.3.3.2 Technology and projects
			1.3.3.3 Organizational culture
			1.3.3.4 Production management profile
			1.3.3.5 People management
			1.3.3.6 Selection by function
			1.3.3.7 Education and training
			1.3.3.8 Leadership
			1.3.3.9 Operation profile
			1.3.3.10 Maintenance profile
			1.3.3.11 Laboratory profile
			1.3.3.12 Health, security and environment profile
			1.3.3.13 Logistics
			1.3.3.14 Client
			1.3.3.15 Summarizing
			1.3.3.16 Example of the possibility of the same error caused by different functions
	1.4 Risk management on material losses and operations
		1.4.1 Material loss risk
	References
2 Human reliability and cognitive processing
	2.1 Human reliability
		2.1.1 Why study human reliability?
		2.1.2 Classic concepts of human reliability
			2.1.2.1 The worker is guilty!!!
			2.1.2.2 The injured is guilty!
		2.1.3 Modeling—first, second, and third generation
			2.1.3.1 First generation—Technique for Human Error-Rate Prediction
			2.1.3.2 Proposed exercise
			2.1.3.3 Second generation—TESEO e CREAM
			2.1.3.4 Exercise
			2.1.3.5 Third generation—Using fuzzy logic to change performance factors
		2.1.4 Standardized plant analysis. risk-human reliability analysis method and a case in a chemical facility
		2.1.5 Human reliability involved in the cultural link
			2.1.5.1 Human design and reliability
			2.1.5.2 Structures, processes, and environments
			2.1.5.3 Operational failure exergy
			2.1.5.4 Human reliability database
			2.1.5.5 Fuzzy mechanics in human reliability
		2.1.6 Operator discourse—fuzzy
			2.1.6.1 Operator discourse
			2.1.6.2 Dynamic risk management and operator discourse
			2.1.6.3 Assumptions for investigation of operator discourse
				2.1.6.3.1 The phenomenon of omission and commission (reason)
				2.1.6.3.2 Operational mode differences for task analysis
				2.1.6.3.3 Patterns and culture
				2.1.6.3.4 Process interconnects
				2.1.6.3.5 Multicultural
				2.1.6.3.6 Fluctuation of behavior
				2.1.6.3.7 Skills analysis
			2.1.6.4 Abnormality mapping and operator discourse
				2.1.6.4.1 Complexity
				2.1.6.4.2 Failure logic in complex systems
				2.1.6.4.3 Influence of technology on human error
				2.1.6.4.4 Operational factors for failure
				2.1.6.4.5 Normal state of process
				2.1.6.4.6 Process disorders
			2.1.6.5 Identification of types of operational factors in failure
				2.1.6.5.1 Cause, signal and consequence
				2.1.6.5.2 Potentiating factor, false factor, and anticipated factor
				2.1.6.5.3 Failure energy feedback
				2.1.6.5.4 Corrective action and preventive action
			2.1.6.6 Failure analysis in production systems
				2.1.6.6.1 Identify the moment of failure materialization
		2.1.7 Third-generation application in the calculation of organizational efficiency, Oil & Gas
			2.1.7.1 The method
			2.1.7.2 Generic algorithm
			2.1.7.3 Situation simulation
	2.2 Human reliability and cognitive processing
		2.2.1 Cognitive processing
		2.2.2 Introduction to cognitive processing, cases, failure, and skill knowledgement, rules
			2.2.2.1 Case 1: Divided attention in the oil industry
			2.2.2.2 Case 2: Illusions of memory in the workplace
			2.2.2.3 Case 3: Primary memory capacity and memory with association
			2.2.2.4 Case 4: Mind map, perception and mnemogram
			2.2.2.5 Case 5: Slips, organizational and individual values
			2.2.2.6 Case 6: Defragmenting the operator’s discourse to investigate accident
			2.2.2.7 Case 7: Human error, automatism and excessive self-confidence
			2.2.2.8 Case 8: Gender conflict and ethnicity prejudice in shift operator accident
			2.2.2.9 Case 9: Conflict of generations and supervision of shifts
				2.2.2.9.1 The supervisor asked the expert how to proceed in this situation of generational conflict?
			2.2.2.10 Failure analysis with cognitive nature cause
			2.2.2.11 Case 10: Culture biases and population stereotypes
			2.2.2.12 Case 11: Emergency, Three mile island (nuclear fission versus utilities), and Fukushima (tidal wave)
			2.2.2.13 Discussion on skills, knowledge, and rules
		2.2.3 Cognitive functions and decision processes
			2.2.3.1 Routine of a field operator in chemical industry
		2.2.4 Learning and skill
		2.2.5 Motivation and decision
		2.2.6 Decision-making process
		2.2.7 Cognitive model discussion
			2.2.7.1 Rules, gaps, and questions
			2.2.7.2 Model I—Cognition and memory operations
			2.2.7.3 Model II—Decision model for the observer and the controller
			2.2.7.4 Model III—Simple model for cognition
		2.2.8 Human behavior dynamics in the company
			2.2.8.1 Classification of psychological/psychic functions in the informational process
			2.2.8.2 Movements: functions and behaviors in the organization
			2.2.8.3 Organizational processes
				2.2.8.3.1 The moment of reality and cognitive processes
				2.2.8.3.2 The moment of elaboration of causal nexus
				2.2.8.3.3 The moment of fantasies and unconscious processes
				2.2.8.3.4 Thought and fantasy can provoke verbal and motor actions
				2.2.8.3.5 The moment of the organic nexus
				2.2.8.3.6 The moment of causal and organic nexus
				2.2.8.3.7 The moment of information processing, memory and action
			2.2.8.4 Johnny Big Head’s life cycle in the organization
	References
3 Factors affecting the performance of tasks
	3.1 Human and social typology
		3.1.1 Human typology
			3.1.1.1 Types of organizational behavior
		3.1.2 Social typology and archetypes
		3.1.3 Classification of human error
		3.1.4 Concepts and the investigation of latent failure
		3.1.5 Socioeconomic environment: human reliability analysis
			3.1.5.1 Social culture, globalization, and human types of the society for work
			3.1.5.2 Influence on the workplace
			3.1.5.3 Organizational environment: human reliability and efficiency
		3.1.6 Investigation of socioeconomic-affective cycle and cycle of thinking and decision making—utility
			3.1.6.1 Cycle of socioeconomic-affective utility
			3.1.6.2 Thinking cycle
			3.1.6.3 Decision–action cycle
		3.1.7 Executive function analysis
		3.1.8 Environments in human and operational reliability: human error
		3.1.9 Diseases, bad habits and cognitive academy
			3.1.9.1 Case study—sulfuric acid and oil industry
	3.2 Task assessment
		3.2.1 General
			3.2.1.1 Task control complexity
			3.2.1.2 Methods in task analysis
			3.2.1.3 Requirements for task analysis
			3.2.1.4 Knowledge base and skill in planning and adjusting the task
			3.2.1.5 Means and goals in task planning
		3.2.2 PADOP—environments and cognitive aspects in task preparation-execution
			3.2.2.1 IAT—task environment identification
			3.2.2.2 PCET—cognitive processing
			3.2.2.3 PCET—task execution
			3.2.2.4 ATEE—assessment of the task, efficacy, and effectiveness
			3.2.2.5 PADOP—implementation process
		3.2.3 Tools for planning the standard task
			3.2.3.1 Practical case—fertilizer industry
			3.2.3.2 Case study—oil and gas processing
			3.2.3.3 Dynamic investigation of task in failure
			3.2.3.4 Case study—oil and gas processing
			3.2.3.5 Case study—oil and gas processing
			3.2.3.6 Case study—oil and gas processing
			3.2.3.7 Operational failure connectivity
			3.2.3.8 Principles and concepts of connectivity analysis
			3.2.3.9 Case study. Cooling system
		3.2.4 Decision analysis under stress: emergency simulation
			3.2.4.1 Organization of techniques for task review
		3.2.5 Application of PADOP for the sulfuric acid plant case
			3.2.5.1 Introduction
			3.2.5.2 Operational context of the case of sulfuric acid facility
			3.2.5.3 Task and barriers analysis
			3.2.5.4 Critical analysis
	3.3 Discussion about API 770
		3.3.1 Concepts and assessment from API 770
			3.3.1.1 Are managers capable or not?
		3.3.2 Analysis of the API 770 survey
			3.3.2.1 Policy analysis and guidelines
			3.3.2.2 Analysis of assignments in tasks
			3.3.2.3 Analysis of the human machine interface
			3.3.2.4 Analysis of written procedures
			3.3.2.5 Analysis of the worker
	References
4 Process loss assessment
	4.1 Context
	4.2 Competencies to assess process losses
		4.2.1 Premises and competencies
	4.3 Losses in the process industries
		4.3.1 Process losses in the oil industry
		4.3.2 Process losses in gas industry for energy
		4.3.3 Process losses in the biofuels industry
		4.3.4 Process losses in the petrochemical industry
		4.3.5 Process losses in the chemical industry
		4.3.6 Process losses in the polymer industry
		4.3.7 Process losses in the metallurgy industry
		4.3.8 Risk of loss due to technology
	4.4 Diagnosis of process losses
		4.4.1 Introduction
		4.4.2 Knowledge of the production process
		4.4.3 Collecting data—inputs
			4.4.3.1 Production area
			4.4.3.2 Project and process engineering area
			4.4.3.3 Integrated management, occupational, and environmental area
			4.4.3.4 Commercial
			4.4.3.5 Personnel area
			4.4.3.6 Institutional relations area
		4.4.4 Measuring results—outputs
			4.4.4.1 Satisfaction and loss of customers
			4.4.4.2 Revenue
			4.4.4.3 Sales amount
			4.4.4.4 Fines
			4.4.4.5 Image
			4.4.4.6 Insurance
			4.4.4.7 Process variables
			4.4.4.8 Yield
			4.4.4.9 Unit operations efficiency and chemical reaction
			4.4.4.10 Product quality
			4.4.4.11 Consumption index
			4.4.4.12 Effluent quantity and quality
			4.4.4.13 Industrial plant load (capacity) and continuity (availability)
				4.4.4.13.1 (II) End activities—production: maintenance
			4.4.4.14 Maintainability and availability of critical equipment
			4.4.4.15 Spare parts costs
			4.4.4.16 Overtime—extra hour
			4.4.4.17 Maintenance re-work
				4.4.4.17.1 (II) End activities: production, operation and management
			4.4.4.18 Load (capacity) and continuity (availability)
			4.4.4.19 Production
			4.4.4.20 Quantity and reprocessing costs
			4.4.4.21 Consumption index
			4.4.4.22 Extra hour/overtime
			4.4.4.23 Repeated process and efficiency of operating procedures
			4.4.4.24 Product quality
			4.4.4.25 Effluent quantity and quality
			4.4.4.26 Turnover
				4.4.4.26.1 (II) End-activities: people or human resources
			4.4.4.27 Absences at work
			4.4.4.28 Turnover
			4.4.4.29 Image on the workplace
			4.4.4.30 Motivation
			4.4.4.31 Accidents, incidents and morale changes
				4.4.4.31.1 (III) Support areas: integrated management and project engineering
			4.4.4.32 Accidents and incidents
			4.4.4.33 Effluent and waste treatment
			4.4.4.34 Effluent quality and quantity
			4.4.4.35 Fines
			4.4.4.36 Costs from inadequate projects
		4.4.5 Introduction to tools and methods
			4.4.5.1 Abnormal event qualitative-quantitative map based on operator’s discourse
				4.4.5.1.1 Period for investigation
				4.4.5.1.2 Process abnormalities
				4.4.5.1.3 Operational factors
				4.4.5.1.4 Description of failure & history resulting in process loss
			4.4.5.2 Mapping losses
			4.4.5.3 Mass balance
			4.4.5.4 Statistical analysis of process and effluent variables
			4.4.5.5 Statistical analysis of abnormal events
			4.4.5.6 Production audits
	4.5 Cases: diagnostics with quantitative and qualitative analysis
		4.5.1 Chemical and polymer case: diagnosis based on operator’s discourse
			4.5.1.1 Phase 1—Abnormal event map and chain of abnormalities
			4.5.1.2 Phase 4—Statistical analysis of process variable and effluent
			4.5.1.3 Phase 5—Statistical analysis of abnormal event
			4.5.1.4 Phase 6—Audit for production and actions
			4.5.1.5 Phase 7—Discussion of results
				4.5.1.5.1 Improving liquid effluent quality
				4.5.1.5.2 Increasing the availability of critical equipment
				4.5.1.5.3 Decreasing spare part consumption
				4.5.1.5.4 Decreasing overtime expenses
				4.5.1.5.5 Decreasing reworked hours
				4.5.1.5.6 Improving motivation
				4.5.1.5.7 Decreasing accidents and incidents
				4.5.1.5.8 Minimizing waste at source
				4.5.1.5.9 Improving product quality
				4.5.1.5.10 Increasing load and continuity
				4.5.1.5.11 Improving consumption rate
				4.5.1.5.12 Improving the company’s image
				4.5.1.5.13 Increasing customer satisfaction
		4.5.2 Case of metallurgy based on manager discourse and technical issues
			4.5.2.1 Phase 1—Loss mapping through manager’s speech
			4.5.2.2 Phase 2—Mass balance for materials
		4.5.3 Discussion
	References
5 Learned lessons: human factor assessment in task
	5.1 Routine, environments, human types, and class of errors
		5.1.1 Routine management case GR: director's behavior (511—GR1)
		5.1.2 Technical and operational culture: solution for waste in the reaction (512—CTO2)
		5.1.3 Emergency case: situation in reaction stoichiometry (513—ER3)
		5.1.4 Practical skills case: perception and monitoring (514—HP4, HP5)
			5.1.4.1 Acid plant (HP4)
			5.1.4.2 Chemical plant (HP5)
		5.1.5 Routine management case: meeting to change time in shift group
		5.1.6 Operational—process control: investigation of the process and wastewater (516—COP7)
		5.1.7 Problem analysis: diagnosis and process mapping (517—AP8)
		5.1.8 Problem analysis: negotiation for preventive action (518–AP9)
		5.1.9 Operational safety case: about safety culture (519—SO10, SO11, SO12)
			5.1.9.1 Failure of personal protective equipment, occupational health risk: new standard (SO10)
			5.1.9.2 Tools and requirements for the task: new standards (SO11)
			5.1.9.3 Guilt and information for Environmental, Health and Safety: new standards
		5.1.10 Inappropriate design and operation: technological solutions (5110–PJ13)
		5.1.11 Organizational change: change in practice without consulting past ritual (5111-PR14)
		5.1.12 Routine management, technical-operational culture: bias in execution (5112–VIO15/19)
			5.1.12.1 Intentional violation: false sampling of effluent masking abnormality
			5.1.12.2 Violation: great loss of solvent with group omission (VIO16)
			5.1.12.3 Intentional violation: forced vaporization of solvent in the area, occupational ethics or false environmental imag...
			5.1.12.4 Violation: unaccompanied drainage, aqueous phase and sea oil (VIO18)
			5.1.12.5 Risk analysis in critical systems, cost or security decisions? (VIO19)
		5.1.13 Accident cases in contractors: inadequate standard for services (5113–AC20 a 21)
			5.1.13.1 Accident with death during service in TDI tank (AC20)
			5.1.13.2 Accident with death when handling forklifts (AC21)
	5.2 Routine learning: guidelines for human reliability
		5.2.1 Learning points
			5.2.1.1 Culture
				5.2.1.1.1 Cultural vices
				5.2.1.1.2 Corporate policy, management profile, and conflicting priorities
				5.2.1.1.3 Culture of safety and guilt
				5.2.1.1.4 The new technical-operational culture
			5.2.1.2 Knowledge, commitment, and standards
				5.2.1.2.1 Knowledge and investigation of causal link
				5.2.1.2.2 Skills
				5.2.1.2.3 Changing procedures and standards
				5.2.1.2.4 Education, commitment, and health risks
			5.2.1.3 Social relations at work
				5.2.1.3.1 Group work and communication
				5.2.1.3.2 Organizational integration and social inclusion
			5.2.1.4 Operation, project, maintenance, and contractors
				5.2.1.4.1 Operation and design standard
				5.2.1.4.2 Task and shutdown planning
				5.2.1.4.3 Contractors and commitments
			5.2.1.5 Summary
		5.2.2 Route of human, group and organizational error
	5.3 Lessons learned and validation of the guidelines
		5.3.1 Cognitive and behavioral academy: routine and program friends of emergency pool
			5.3.1.1 Diseases and vaccines in the study of behavioral aspects
			5.3.1.2 Cognitive and behavioral academy program (Avila et al., 2016)
			5.3.1.3 Fertilizer case, public industry: PROGRAM friends of emergency pool
				5.3.1.3.1 Step 1: Problem definition
				5.3.1.3.2 Step 2: Formation of training multipliers agents
				5.3.1.3.3 Step 3: Preparation material
				5.3.1.3.4 Step 4: Technical visits
				5.3.1.3.5 Step 5: Maintenance and evaluation
				5.3.1.3.6 Step 6: Results
			5.3.1.4 PROGRAM friends of emergency pool’s activities description
			5.3.1.5 Polycarbonate case: reduction of methylene chloride losses (Ávila, 2004)
			5.3.1.6 Comments about the chart in Fig. 5.10
		5.3.2 Application of tools for archetype analysis and executive function in the industry
		5.3.3 Communication in routine—environmental accident with HCl (Souza et al., 2018b)
			5.3.3.1 Normality status (human factors and operational control)
			5.3.3.2 Chronological description of occurrences with HCl in chemical installations
			5.3.3.3 Some final considerations
				5.3.3.3.1 Cognition
				5.3.3.3.2 Culture and behavior
				5.3.3.3.3 Level 5 of leadership and operational discipline
				5.3.3.3.4 Human-machine interface
		5.3.4 Investigation of technical failure and human error in the sulfuric acid plant
			5.3.4.1 Process description and main events
			5.3.4.2 Critical discussion on the culture of operational safety (Ávila, 2004; Avila et al., 2016)
			5.3.4.3 Work and people
			5.3.4.4 Investigation of abnormalities
			5.3.4.5 Activities carried out on the team to achieve self-management
		5.3.5 Industry alarms and shutdown (Ammonia, HDT, Cyclohexane): H2 and CO compressors
			5.3.5.1 Hydrogen compressor in benzene hydrogenation unit (FMEAH Ávila et al., 2012)
			5.3.5.2 Hydrogen compressor failure analysis in HDT unit, refinery (Souza et al., 2018b)
			5.3.5.3 Failure analysis of CO compressor in ammonia unit, fertilizers
				5.3.5.3.1 Step 1: Preliminary analysis
				5.3.5.3.2 Step 2: Operational context and task analysis
				5.3.5.3.3 Step 3: Ishikawa diagram and components reliability
				5.3.5.3.4 Step 4: Human reliability
				5.3.5.3.5 Case study
		5.3.6 Task complexity, low efficiency, and accident investigation
			5.3.6.1 Critical task complexity, forklift accident
			5.3.6.2 Qualitative approach for risk and task complexity
			5.3.6.3 Case study of a forklift in the oil industry
		5.3.7 Just culture in metallurgy and oil industries
			5.3.7.1 General aspects about Brazilian culture
			5.3.7.2 Discussion on fair, organizational and safety culture in metallurgy
			5.3.7.3 Discussion on fair, organizational and safety culture on the OFFSHORE oil platform
			5.3.7.4 Final considerations
	5.4 Human reliability, sociotechnical reliability, culture of safety demands
		5.4.1 Chemical industry and electricity distribution
			5.4.1.1 General cases
				5.4.1.1.1 Caprolactam industry
				5.4.1.1.2 Alkylamines
				5.4.1.1.3 Industry of aromatic amine, isocyanate and sulfuric acid
				5.4.1.1.4 Polycarbonate industry
				5.4.1.1.5 Electricity distribution (DEE) in Brazil
			5.4.1.2 Real cases of chemical industry demand and electricity distribution demand
			5.4.1.3 Proposal for the blast-explosive industry (USA)
			5.4.1.4 Human reliability research proposal for electricity distribution (BR), private industry
			5.4.1.5 Proposal for chemical industry of acrylates (2019–20), private industry
				5.4.1.5.1 1st Task—training and preliminary diagnosis—risk perception: cases and practices
				5.4.1.5.2 Main topics in training
				5.4.1.5.3 organization of the preliminary diagnosis
				5.4.1.5.4 2nd Task—diagnosis for changes in safety culture: phase 2 of risk perception diagnosis
		5.4.2 Petrochemical industry
			5.4.2.1 Real cases of petrochemical industry, demand for services
		5.4.3 Onshore offshore oil and gas industry
			5.4.3.1 Real cases of demand for research and services
			5.4.3.2 Proposal for onshore, Brazil, 2008, public industry
			5.4.3.3 Proposal for offshore, Brazil, 2010, public industry
			5.4.3.4 Proposal for onshore, Brazil, 2012, private industry
			5.4.3.5 Proposal for offshore, Brazil, 2016–17, public O&G industry
			5.4.3.6 Proposal for onshore, Brazil, 2017–18, public industry, research
			5.4.3.7 Proposal for offshore, USA, 2018–19
		5.4.4 Fertilizer industry and refining units
			5.4.4.1 Real cases of demand
		5.4.5 Metallurgical industry and chicken manufacturing
			5.4.5.1 Real cases of demand
			5.4.5.2 Metallurgy, mining and cellulose industry proposal, 2018–19
		5.4.6 Public security agencies: security, mobility and health, firefighter—PuA
			5.4.6.1 Real cases of demand
			5.4.6.2 Master's degree proposal for the Bahia firefighters department, 2018–19
		5.4.7 Context conclusion: research versus society's demand
	References
6 Human reliability: SPAR-H cases
	6.1 Introduction
		6.1.1 Reviewing the chapters
		6.1.2 Human errors in the context of critical activities
	6.2 Concepts and SPAR-H calibration
		6.2.1 Operational context
			6.2.1.1 Operational context in the manufacturing or intermittent process industry
			6.2.1.2 Operational context in the continuous process industry
			6.2.1.3 Culture and operational safety
		6.2.2 Calculation of human error probability
		6.2.3 SPAR-H calibration
		6.2.4 Discussion of performance shaping factors and calibration
	6.3 Case studies
		6.3.1 Chicken industry
			6.3.1.1 Industrial context
			6.3.1.2 Process analysis
			6.3.1.3 Equipment
			6.3.1.4 Application of the SPAR-H method
			6.3.1.5 Considerations of the case of the chicken industry
		6.3.2 Uranium industry
			6.3.2.1 Industrial context
			6.3.2.2 Process analysis
			6.3.2.3 Application of SPAR-H
			6.3.2.4 Considerations for the uranium ore extraction industry
		6.3.3 Chemical industry
			6.3.3.1 Industrial context
			6.3.3.2 Process analysis
			6.3.3.3 Application of SPAR-H
			6.3.3.4 Considerations for the case of the chemical industry
		6.3.4 Refining industry
			6.3.4.1 Industrial context
			6.3.4.2 Process analysis
			6.3.4.3 Task
			6.3.4.4 Equipment
			6.3.4.5 Application of SPAR-H
			6.3.4.6 Considerations for the case of the refining industry
		6.3.5 Fertilizer industry
			6.3.5.1 General context
			6.3.5.2 Process analysis
			6.3.5.3 Task
			6.3.5.4 Equipment
			6.3.5.5 Application of SPAR-H
			6.3.5.6 Considerations for the fertilizer industry case
		6.3.6 Coconut industry
			6.3.6.1 Operational context
			6.3.6.2 Task
			6.3.6.3 Application of SPAR-H
			6.3.6.4 Considerations for the coconut processing industry case
		6.3.7 Packing list services in the manufacture of sports products
			6.3.7.1 General context
			6.3.7.2 Process analysis
			6.3.7.3 Task
			6.3.7.4 SPAR-H application
			6.3.7.5 Scenario with original calibration
			6.3.7.6 Considerations on the new calibration of performance shaping factor and NHEP
	6.4 Comparative analysis
		6.4.1 Recommendations
	6.5 Integrated reliability: the beginning
		6.5.1 Control and metrics to achieve integrated or sociotechnical reliability
		6.5.2 Simulation of the application of the integrated reliability method
			6.5.2.1 Illustrative questions from experts
			6.5.2.2 Complexity
			6.5.2.3 Process reliability
			6.5.2.4 Human reliability
			6.5.2.5 Equipment reliability
			6.5.2.6 Operational reliability
		6.5.3 Results
	References
7 Human reliability: chemicals and oil and gas cases
	7.1 Methodology description
		7.1.1 Guiding the algorithms to apply technological tools and social environment
		7.1.2 Concept, tool, and procedure for technical, social, environment, and human typologies
			7.1.2.1 Concepts—C1 to C9
			7.1.2.2 TOOLS—T1 to T11
			7.1.2.3 Assessment procedures—PA1 to PA16
	7.2 Chemical industry case application
		7.2.1 Abnormalities inventory
		7.2.2 Abnormality logic in complex processes
		7.2.3 Aliphatic amines
			7.2.3.1 Technology
			7.2.3.2 Operational context
			7.2.3.3 EVA—Statistic abnormal events
		7.2.4 Aromatic amines
			7.2.4.1 Technology
			7.2.4.2 Operational context
			7.2.4.3 Statistical monitoring of processes
			7.2.4.4 Operational diagnosis on standard
		7.2.5 Polycarbonates
			7.2.5.1 Technology
			7.2.5.2 Operational context
			7.2.5.3 Statistical monitoring of processes
	7.3 Oil and gas case application
		7.3.1 Context identification: company, experience, rituals and organizational culture
			7.3.1.1 Application of methodology at an oil and gas company
			7.3.1.2 Knowledge of technology and experience in the process
		7.3.2 Survey of technical, human and social data
			7.3.2.1 Routine, task, process and safety
			7.3.2.2 Human and social data collection (T7 to T10)
		7.3.3 Abnormal event mapping, signs, and failure mode (MEA and FMEA)
		7.3.4 Process analysis, logistics, operations, maintenance and safety (T3—AEP, T4—EVA)
			7.3.4.1 Process analysis
			7.3.4.2 Logistics analysis
			7.3.4.3 Operations analysis
			7.3.4.4 Maintenance, safety and environmental analysis
		7.3.5 Analysis of the task and results
			7.3.5.1 Types of task analysis
			7.3.5.2 Routine standard procedures
			7.3.5.3 Failure task analysis (PA2)
			7.3.5.4 Risk and behavior analysis in emergency or extreme stress (PA3/PA7)
		7.3.6 Analysis of human and social data in the work environment
			7.3.6.1 About social data
			7.3.6.2 About individual data
			7.3.6.3 About the individual’s behavior data for cooperation
			7.3.6.4 About group behavior and leadership
			7.3.6.5 About the social data collected in the company’s human resources
		7.3.7 Competence analysis and results
			7.3.7.1 Demand based on technology
			7.3.7.2 Competence offering—dynamics and identification of technical culture
			7.3.7.3 Examination for technical–operational culture identification
		7.3.8 Standard for behavior analysis
			7.3.8.1 Performance in the workplace
			7.3.8.2 Analysis of behavior in the workplace
			7.3.8.3 Human error in the task
			7.3.8.4 Analyses for the type of human and social behavior
		7.3.9 Abnormal event cluster analysis (T14)
			7.3.9.1 Cluster analysis and relationships in process abnormalities
			7.3.9.2 Cluster analysis and relationships in equipment abnormalities
	7.4 Qualitative results: chemical industry cases
		7.4.1 Management aspects for decision
			7.4.1.1 Objective function: gross profit margin
			7.4.1.2 Comparison between cases already applied
			7.4.1.3 Management programs to increase human reliability
			7.4.1.4 Stabilization program for processes, opportunities and suggested techniques
	7.5 Quantitative results: oil and gas case
		7.5.1 Failure energy analysis
			7.5.1.1 Rules of technical behavior, environment, and individuals
			7.5.1.2 Criteria and selection for measuring organizational efficiency
			7.5.1.3 Analysis and choice of principal component analysis groupings: partial conclusions
			7.5.1.4 Choice of input data and organizational efficiency for application
			7.5.1.5 Data processing, results
			7.5.1.6 Mapping organizational efficiency and simulating situation: failure energy
			7.5.1.7 Final observations for the application of the principal component analysis
	7.6 Future work: task cross-assessment based on particle swarm model
		7.6.1 The path of workers
		7.6.2 The bridge to the future
	References
8 Conclusion and products
	8.1 Conclusion
	8.2 Future book: human factor routine and emergency analysis
	8.3 Products in general
	8.4 Product 1 (Chapter 4)—process loss mapping
		8.4.1 Introduction and methods
		8.4.2 Methods
		8.4.3 Discussion
		8.4.4 Calculation based on metallurgy case (Section 4.5.2)
	8.5 Product 2—task assessment—PADOP
		8.5.1 Introduction
		8.5.2 PADOP standard and review
			8.5.2.1 Environment assessment—IAT
				8.5.2.1.1 A1 context
				8.5.2.1.2 Context PLG facility
					A2 risk and complexity
					Risk PLG facility
					Complexity refinery facility
				8.5.2.1.3 A3 labor organization rules
					Rules and heuristics sulfuric acid plant
				8.5.2.1.4 A4 task control indicators
					Control indicators in PLG column dryer
			8.5.2.2 Cognitive processing and execution—PCET
				8.5.2.2.1 B5 PCET. develop causal relationship process with task
					Causal nexus—equipment, process and operation in caprolactam industry
					Breakage event on glass pipeline in a sulfuric acid plant
				8.5.2.2.2 B6 PCET. Apply GMTA-PADOP to requirements, actions, goals and checking
					Operational instruction to clean acid tank in aromatic amine site
				8.5.2.2.3 B7 PCET. After planning task, write procedure, test physical-cognitive-organizational
					Procedure revision in startup of a sulfuric acid stripping column
				8.5.2.2.4 A8 IAT identify environmental threats and the industrial and cognitive process→ deviance
					Self-management, barrier in organization and technology polycarbonate plant
			8.5.2.3 Task efficiency control—ATEE
				8.5.2.3.1 Objective function weight calibration—the case of PLG plant
				8.5.2.3.2 C9 ATEE. Task control indicators for reviewing procedures—manuals, training and checklist
				8.5.2.3.3 Process control and procedure review, aromatic amines
				8.5.2.3.4 C10 ATEE. Task control indicators for behavior review, C4t
					Measuring behavior for change—LPG industry Ávila thesys (2010)
				8.5.2.3.5 C11 ATEE. Task control indicators for technology and culture review
					Prospecting just culture—a case of the metallurgy industry
		8.5.3 Task failure assessment
			8.5.3.1 D12 PADOP, FMEAH, FTAH of the observed failed task
				8.5.3.1.1 Operational continuity in the fertilizer and caprolactam industry
			8.5.3.2 D13 failure logic and connectivity
				8.5.3.2.1 Reduction of methylene chloride loss in the polycarbonate industry
			8.5.3.3 D14 failure materialization and energy
				8.5.3.3.1 Cause mode and solid in PLG plant
				8.5.3.3.2 Accident with fire ball, in the LPG industry
		8.5.4 Task emergency assessment
			8.5.4.1 E15 LODA—progressive and accident stress
	8.6 Product 3—cognitive quality
		8.6.1 Introduction
		8.6.2 Cognitive quality elements, functions and subfunctions
		8.6.3 Static cognitive quality—COGNQe
		8.6.4 Discussion about dynamic cognitive quality—COGNQd
	8.7 Product 4—human reliability SPARH
		8.7.1 Operational context
		8.7.2 Performance factors assessment
		8.7.3 Calculation and calibration
	8.8 Product 5—social-technical reliability
		8.8.1 Culture and manager profile
		8.8.2 Complexity of parametric equations
		8.8.3 Individual reliabilities
			8.8.3.1 Equipment reliability (Req)
			8.8.3.2 Operational reliability (Rop)
			8.8.3.3 Process reliability (Rpr)
			8.8.3.4 Human reliability (Rhu)
		8.8.4 Social-technical reliability calculation
			8.8.4.1 Hydrochloric acid case
	8.9 Product 6—operational-technical culture and prediction
		8.9.1 Introduction
		8.9.2 Methodology and products
		8.9.3 Management programs for human and sociotechnical reliability
			8.9.3.1 Program 1: human error and technical failure trend indicators
				8.9.3.1.1 Implement program to increase human reliability
				8.9.3.1.2 Analyze critical tasks in routine and emergency situations
				8.9.3.1.3 Defining behavior patterns with elements of human reliability
				8.9.3.1.4 Human error classification
			8.9.3.2 Program 2—routine management for environment favorable to human reliability
			8.9.3.3 Program 3—management to validate the failure analysis model
			8.9.3.4 Program 4—opportunities for logistics and safety operations management
	References
Annex
	A.1 Process loss map (Product 01)
	A.2 Calibration of the SPAR-H method
		A.2.1 Survey about operational context—operators/staff
		A.2.2 PSF calibration
	A.3 Task assessment
		A.3.1 Standard task data collection—PADOP
		A.3.2 Collection of failure data for task assessment—MEA (abnormal events)
		A.3.3 Collection of failure data for task assessment—PADOP
		A.3.4 Collection of failure data for task assessment
		A.3.5 Data collection for emergency assessment
	A.4 Technical survey
		A.4.1 Data collection of technical: OTC O&G—general
		A.4.2 API 770 data & operational–technical culture
		A.4.3 Continuous process technology requirements
		A.4.4 Routine data collection
		A.4.5 Process and production variables collection
	A.5 Social typology
		A.5.1 Data collection for political aspects and practices
		A.5.2 Data collection for social relations
	A.6 Human typology
		A.6.1 Data collection to emotional imbalance
		A.6.2 Data collection for cognition quality & CTO
		A.6.3 Data collection for cooperation level of operational team
		A.6.4 Data collection for leadership assessment
		A.6.5 Data collection for commitment assessment
	A.7 Exercise
		A.7.1 Exercises task assessment in sulfuric acid facility
		A.7.2 Exercise API770
List of Abbreviations
Glossary
Index
Back Cover