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Methods to assess and manage process safety in digitalized process system /

Methods to Assess and Manage Process Safety in Digitalized Process System, Volume Six, the latest release in the Methods in Chemical Process Safety series, highlights new advances in the field, with this new volume presenting interesting chapters written by an international board of authors.- Provid...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Khan, Faisal (Chemical engineer) (Editor), Pasman, Hans (Editor), Yang, Min (Editor)
Format: eBook
Language:English
Published: Cambridge, Massachusetts : Elsevier, [2022]
Series:Methods in chemical process safety ; Volume 6.
Subjects:
Chemical processes > Safety measures.
Procédés chimiques > Sécurité > Mesures.
Chemical processes > Safety measures
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Methods to Assess and Manage Process Safety in Digitalized Process System
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Chapter One: Opportunities and threats to process safety in digitalized process systems-An overview
  • 1. Definition of digital process, digitalization, and safety
  • 2. Brief history of process safety and reasons why digitalization can support process safety
  • 2.1. A brief history of process safety
  • 2.2. Why can digitalization support process safety?
  • 3. Process safety treated in digitalized process systems
  • 3.1. Human and management errors
  • 3.2. Process monitoring and control
  • 3.3. Massive data processing
  • 3.4. Cyber-attacks
  • 4. Purpose and organization of this MCPS Volume 6
  • References
  • Chapter Two: State-of-the-art in process safety and digital system
  • 1. Background
  • 1.1. Definition
  • 1.2. Drivers of digital transformation: A historical perspective
  • 1.3. Opportunities and challenges
  • 2. Current progress in digital process safety
  • 2.1. Bibliographic information collection
  • 2.2. Results
  • 2.2.1. Annual publication volume
  • 2.2.2. Key research areas
  • 3. A future roadmap in digital process safety
  • 3.1. Safety in cyber-Physical process system
  • 3.1.1. Industrial internet of things (IIoT)
  • 3.1.2. Big data/cloud computing
  • 3.1.3. Artificial intelligence
  • 3.1.3.1. Development of AI-based algorithms
  • 3.1.3.2. Tuning of the AI models using optimization algorithms
  • 3.1.3.3. Development of robust AI-based algorithms for handling uncertainties and low-quality data
  • 3.1.3.4. Development of hybrid models based on data-driven and first principle-based models
  • 3.1.4. Predictive maintenance
  • 3.2. Security in cyber-physical process system
  • 3.3. Integrating cybersecurity with process safety in cyber-physical process systems
  • References
  • Further reading.
  • Chapter Three: Data-driven approaches: Use of digitized operational data in process safety
  • 1. Importance of data in process safety
  • 2. Data source
  • 3. Data preparation stages
  • 3.1. Two stages of data preparation
  • 3.1.1. Error detection
  • 3.1.2. Error repairing
  • 3.2. Preparation of four types of error
  • 3.2.1. Inconsistency error
  • 3.2.2. Incompleteness error
  • 3.2.3. Structural conflict
  • 3.2.4. Duplication error
  • 3.2.5. Tools
  • 3.2.6. Data cleaning for big data
  • 4. Data-driven model for process safety
  • 4.1. Definition of the data-driven model
  • 4.2. Statistical models
  • 4.3. Artificial intelligence models
  • 5. Applications of data-driven models
  • 5.1. Process monitoring in process safety
  • 5.2. Risk assessment in process safety
  • 5.3. Process prognosis in process safety
  • 5.4. Emergency response in process safety
  • 6. Summary and perspective
  • References
  • Chapter Four: Industry 4.0 based process data analytics platform
  • 1. A brief overview for industrial revolution
  • 2. Overview of process data analytic platforms &amp
  • Industry 4.0
  • 3. Hardware and sensing layer
  • 3.1. What is IoT/IIoT?
  • 3.2. Edge devices
  • 3.3. Location of data processing
  • 3.4. Technology enablers
  • 4. Data management layer
  • 4.1. Data preprocessing
  • 4.2. Data analytics tools
  • 4.3. Process data analytics platforms
  • 4.4. Data processing hardware platforms
  • 5. Networking layer
  • 6. Application layer
  • 6.1. Predictive maintenance
  • 6.2. Remaining useful life estimation
  • 6.3. Cyber physical systems
  • 7. Cybersecurity
  • 8. Summary
  • References
  • Chapter Five: Digital process safety management
  • 1. Introduction
  • 2. Basic digital concepts
  • 3. The case for digital process safety management
  • 4. Application of digital technologies in PSM
  • 4.1. Process hazard analysis element
  • 4.2. Mechanical integrity element.
  • 4.2.1. Applying targeted corrosion approach through digital radiography
  • 4.2.1.1. Introduction
  • 4.2.1.2. Methodology
  • 4.2.1.3. Wellhead piping
  • 4.2.1.4. Separator drain collar ports
  • 4.2.1.5. Economics
  • 4.2.1.6. Conclusion
  • 4.2.2. Tank internal inspection using aerial drones
  • 4.2.2.1. Challenges
  • 4.2.2.2. Drone technology
  • 4.2.2.3. Trial on sales oil tank
  • 4.2.2.4. Trial outcome
  • 4.2.2.5. Next steps
  • 4.2.2.6. Conclusion
  • 4.3. Management of change element
  • 4.4. Pre-startup review element
  • 5. Future improvements in process safety through digital technology: Limited literature survey
  • Acknowledgment
  • References
  • Chapter Six: Statistical approaches and artificial neural networks for process monitoring
  • 1. Introduction
  • 1.1. Background
  • 1.2. Common data-driven models
  • 1.2.1. Principal component analysis and partial least squares-based FDD methods
  • 1.2.2. Independent component analysis-based FDD methods
  • 1.2.3. Copula-based FDD methods
  • 1.2.4. Gaussian mixture models of FDD
  • 1.2.5. Bayesian network and hidden Markov models of FDD
  • 1.2.6. Machine learning-based models for FDD
  • 1.2.7. Hybrid data-driven FDD models
  • 2. Statistical process monitoring
  • 2.1. Generalized framework for statistical process monitoring
  • 2.1.1. Data characterization
  • 2.1.2. Data pre-processing
  • 2.1.3. Model selection
  • 2.1.4. Model training and validation
  • 2.1.5. Online monitoring
  • 2.1.6. Process maintenance
  • 2.1.7. Model update
  • 2.2. A comparative performance analysis
  • 3. Artificial neural network for process monitoring
  • 3.1. Neural network basic model
  • 3.2. Neural network for supervised learning
  • 3.2.1. Procedure to apply ANN for supervised learning
  • 3.2.2. An overview of the application of supervised neural network modeling in process monitoring
  • 3.3. Neural network for unsupervised learning.
  • 4. Conclusions
  • References
  • Further reading
  • Chapter Seven: Alarm management techniques to improve process safety
  • 1. Philosophy of alarm management
  • 2. Conventional alarm systems vs. advanced alarm systems
  • 3. Conventional alarm management techniques with improvements
  • 3.1. Methods of data analytics for alarm management
  • 3.2. Recognition of nuisance alarms
  • 3.2.1. Chattering alarms
  • 3.2.2. Oscillating alarms
  • 3.2.3. Recognition of nuisance alarms based on the comparison of the sample mean and alarm threshold
  • 3.3. Recognition of stale alarms
  • 3.4. Univariate alarm processing strategies
  • 3.4.1. Filtering
  • 3.4.2. Deadband
  • 3.4.3. Delay timers
  • 3.5. Improvement of univariate alarm processing techniques
  • 4. Advanced alarm management techniques
  • 4.1. State-based and dynamic alarming
  • 4.2. Pattern recognition of alarm floods
  • 4.2.1. Detection of alarm floods
  • 4.2.2. Pairwise sequence alignment of alarm floods
  • 4.2.3. Accelerated method for alarm flood sequence alignment
  • 4.2.4. Multiple sequence alignment of alarm floods
  • 4.2.5. Online sequence alignment of alarm floods
  • 4.2.6. Cross-process alarm flood sequence alignment
  • 4.3. Alarm prioritization and alarm response procedure
  • 4.3.1. Basics of alarm priorities
  • 4.3.2. Alarm prioritization methods
  • 4.3.3. Alarm response procedures
  • 5. Applications of alarm management
  • 5.1. Steps of applications
  • 5.2. Basics of the power generation unit
  • 5.3. Comparison of the current and advanced alarm systems
  • 6. Conclusions
  • References
  • Chapter Eight: Performance evaluation of digitalized safety barriers
  • 1. Introduction
  • 2. Failure and operational analysis of safety barriers
  • 3. Measures for performance evaluation
  • 4. Modeling approaches
  • 4.1. Fault tree analysis (FTA) and reliability block diagram (RBD)
  • 4.2. Bayesian network.
  • 4.3. Markov method
  • 4.4. Petri net
  • 5. Integrity analysis and evaluation of safety barriers
  • 6. Digital twin approach for condition-based evaluation and prognosis
  • 7. Closure and further reading
  • References
  • Chapter Nine: Dynamic operational risk assessment in process safety management
  • 1. Necessity of dynamic risk assessment
  • 2. Dynamic risk assessment methodology
  • 2.1. A Bayesian approach
  • 2.1.1. Accident scenario modeling
  • 2.1.2. Information collection
  • 2.1.3. Nodes probabilities estimation
  • 2.1.4. Dynamic risk assessment
  • 2.2. Data-driven approach
  • 2.2.1. Selecting model input and output variables
  • 2.2.2. Dividing sample sets for model training and testing
  • 2.2.3. Selection and optimization of model parameters
  • 2.2.4. Model training and testing
  • 3. Case Study 1: Dynamic risk assessment of subsea pipeline leak
  • 3.1. Scenario development
  • 3.2. Nodes probabilities estimation
  • 3.3. Probability updating
  • 3.4. Probability adapting
  • 3.5. Risk assessment
  • 4. Case Study 2: Dynamic prediction of subsea pipelines corrosion degradation
  • 4.1. Index selection
  • 4.1.1. Corrosion influencing factors of subsea pipelines
  • 4.1.2. Principal component analysis of corrosion influencing factors
  • 4.2. Division of sample sets
  • 4.3. Comparison of predicted results
  • 4.3.1. Compared with SVR and PCA-SVR
  • 4.3.2. Compared with PCA-GA-SVR and PCA-PSO-SVR
  • 5. Conclusions
  • References
  • Chapter Ten: Risk of cascading effects in digitalized process systems
  • 1. Introduction
  • 2. Definition of cascading event
  • 3. Specific causes of cascading events in digitalized process systems
  • 4. Layers of protection (LOP) in chemical and process facilities
  • 5. Unintentional cascading effects in digitalized process systems
  • 6. Intentional cascading events in digitalized process systems.