Human reliability is an issue that is increasingly discussed in the process and manufacturing industries to check factors that influence operator performance and trigger errors. Human Factor and Reliability Analysis to Prevent Losses in Industrial Processes: An Operational Culture Perspective provides a multidisciplinary analysis of work concepts and environments to reduce human error and prevent material, energy, image, and time losses.
The book presents a methodology for the quantification and investigation of human reliability, and verification of the influence of human factors in the generation of process losses, consisting of the following steps: contextualization, data collection, and results; performing task and loss observation; socio-technical variable analyses; and data processing. Investigating human reliability, concepts, and models in situations of human error in practice, the book identifies where low reliability occurs and then visualizes where and how to perform an intervention. This guide is an excellent resource for professionals in chemical, petrochemical, oil, and nuclear industries for managing and analyzing safety and loss risks and for students in chemical and process engineering.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Human reliability and cognitive processing - p. 25 3. 2.1: Human reliability - p. 25 4. 2.2: Human reliability and cognitive processing - p. 52 5. Chapter 3 Factors affecting the performance of tasks - p. 85 6. 3.1: Human and social typology - p. 85 7. 3.2: Task assessment - p. 106 8. 3.3: Discussion about API 770 - p. 143 9. Chapter 4 Process loss assessment - p. 151 10. 4.1: Context - p. 151 11. 4.2: Competencies to assess process losses - p. 155 12. 4.3: Losses in the process industries - p. 157 13. 4.4: Diagnosis of process losses - p. 169 14. 4.5: Cases: diagnostics with quantitative and qualitative analysis - p. 194 15. Chapter 5 Learned lessons: human factor assessment in task - p. 213 16. 5.1: Routine, environments, human types, and class of errors - p. 213 17. 5.2: Routine learning: guidelines for human reliability - p. 228 18. 5.3: Lessons learned and validation of the guidelines - p. 235 19. 5.4: Human reliability, sociotechnical reliability, culture of safety demands - p. 277 20. Chapter 6 Human reliability: SPAR-H cases - p. 307 21. 6.1: Introduction - p. 307 22. 6.2: Concepts and SPAR-H calibration - p. 310 23. 6.3: Case studies - p. 324 24. 6.4: Comparative analysis - p. 347 25. 6.5: Integrated reliability: the beginning - p. 349 26. Chapter 7 Human reliability: chemicals and oil and gas cases - p. 359 27. 7.1: Methodology description - p. 359 28. 7.2: Chemical industry case application - p. 391 29. 7.3: Oil and gas case application - p. 404 30. 7.4: Qualitative results: chemical industry cases - p. 451 31. 7.5: Quantitative results: oil and gas case - p. 457 32. 7.6: Future work: task cross-assessment based on particle swarm model - p. 468 33. Chapter 8 Conclusion and products - p. 473 34. Annex - p. 509 35. List of Abbreviations - p. 541 36. Glossary - p. 543 37. Index - p. 545
Human reliability is an issue that is increasingly discussed in the process and manufacturing industries to check factors that influence operator performance and trigger errors. Human Factor and Reliability Analysis to Prevent Losses in Industrial Processes: An Operational Culture Perspective provides a multidisciplinary analysis of work concepts and environments to reduce human error and prevent material, energy, image, and time losses.
The book presents a methodology for the quantification and investigation of human reliability, and verification of the influence of human factors in the generation of process losses, consisting of the following steps: contextualization, data collection, and results; performing task and loss observation; socio-technical variable analyses; and data processing. Investigating human reliability, concepts, and models in situations of human error in practice, the book identifies where low reliability occurs and then visualizes where and how to perform an intervention. This guide is an excellent resource for professionals in chemical, petrochemical, oil, and nuclear industries for managing and analyzing safety and loss risks and for students in chemical and process engineering.
1. Chapter 1 Introduction - p. 1 2. Chapter 2 Human reliability and cognitive processing - p. 25 3. 2.1: Human reliability - p. 25 4. 2.2: Human reliability and cognitive processing - p. 52 5. Chapter 3 Factors affecting the performance of tasks - p. 85 6. 3.1: Human and social typology - p. 85 7. 3.2: Task assessment - p. 106 8. 3.3: Discussion about API 770 - p. 143 9. Chapter 4 Process loss assessment - p. 151 10. 4.1: Context - p. 151 11. 4.2: Competencies to assess process losses - p. 155 12. 4.3: Losses in the process industries - p. 157 13. 4.4: Diagnosis of process losses - p. 169 14. 4.5: Cases: diagnostics with quantitative and qualitative analysis - p. 194 15. Chapter 5 Learned lessons: human factor assessment in task - p. 213 16. 5.1: Routine, environments, human types, and class of errors - p. 213 17. 5.2: Routine learning: guidelines for human reliability - p. 228 18. 5.3: Lessons learned and validation of the guidelines - p. 235 19. 5.4: Human reliability, sociotechnical reliability, culture of safety demands - p. 277 20. Chapter 6 Human reliability: SPAR-H cases - p. 307 21. 6.1: Introduction - p. 307 22. 6.2: Concepts and SPAR-H calibration - p. 310 23. 6.3: Case studies - p. 324 24. 6.4: Comparative analysis - p. 347 25. 6.5: Integrated reliability: the beginning - p. 349 26. Chapter 7 Human reliability: chemicals and oil and gas cases - p. 359 27. 7.1: Methodology description - p. 359 28. 7.2: Chemical industry case application - p. 391 29. 7.3: Oil and gas case application - p. 404 30. 7.4: Qualitative results: chemical industry cases - p. 451 31. 7.5: Quantitative results: oil and gas case - p. 457 32. 7.6: Future work: task cross-assessment based on particle swarm model - p. 468 33. Chapter 8 Conclusion and products - p. 473 34. Annex - p. 509 35. List of Abbreviations - p. 541 36. Glossary - p. 543 37. Index - p. 545
