Method of Process Systems in Energy Systems.

Method of Process Systems in Energy Systems: Emerging Energy Systems, Part II, Volume Nine, the latest release in the Methods in Chemical Process Safety series, highlights new advances in the field, with this new volume presenting interesting chapters on topics such as Cybersecurity of Energy System...

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Bibliographic Details
Main Author:	Khan, Faisal Irshad
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Pistikopoulos, Efstratios
Format:	eBook
Language:	English
Published:	Chantilly : Elsevier Science & Technology, 2025.
Edition:	1st ed.
Series:	Methods in Chemical Process Safety Series.
Subjects:	Hydrogen as fuel. Chemical processes. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Front Cover
Methods in Chemical Process Safety
Copyright
Contents
Contributors
Preface
Chapter One: Energy systems analysis for process safety
1 Introduction
2 Energy outlook and emerging process safety
3 Hydrogen as an energy carrier
3.1 Hydrogen properties
3.2 Hydrogen production
3.3 Hydrogen hazards and safety issues
3.3.1 Hydrogen embrittlement of materials
3.3.2 Hydrogen permeation and leak consequences
3.3.3 Hydrogen self-ignition
3.3.4 Hydrogen autoignition temperature
3.4 Hydrogen storage safety challenges
3.5 Environmental considerations
3.6 Hydrogen technologies codes and standards
4 Hydrogen substitutes: Ammonia, methanol, and others
5 Ammonia as an energy carrier
5.1 Comparison of ammonia (NH₃) and hydrogen (H₂) as energy carriers
5.2 Ammonia production technologies
5.3 Ammonia as a fuel
5.4 Ammonia safety incidents
5.5 Ammonia regulations
6 Batteries and other means of energy storage
6.1 Battery systems
6.2 Alternative battery technologies
7 Electrification of process industry
8 Risk assessment and hazard identification in emerging technologies
9 Conclusion
References
Chapter Two: Advancing operational risk assessment in emerging energy systems: Methods and applications
1 Introduction
2 Status of energy industry
2.1 Fossil fuel-based energy systems
2.1.1 Coal
2.1.2 Oil and gas
2.2 Low-carbon and renewable energy system
2.2.1 Nuclear power
2.2.2 Hydropower
2.2.3 Wind power
2.2.4 Solar power
2.2.5 Bioenergy
2.2.6 Hydrogen and ammonia as energy carrier
2.2.7 Hybrid energy systems
3 Operational risks in energy systems
3.1 Environmental and climate-related risks
3.2 Aging assets
3.3 Mechanical deterioration and fatigue
3.4 Cybersecurity and digital risks
3.5 Supply chain disruptions.
3.6 Human and organizational factors
3.7 Regulatory and policy-related risks
4 Operational risk assessment methods
4.1 Foundational standards and guidelines
4.2 Qualitative risk assessment methods
4.2.1 Risk matrix
4.2.2 Hazard and operability study (HAZOP)
4.2.3 Checklist
4.3 Semi-quantitative and quantitative risk assessment methods
4.3.1 Failure modes and effects analysis (FMEA/FMECA)
4.3.2 Fault tree analysis (FTA)
4.3.3 Event tree analysis (ETA)
4.3.4 Bow tie analysis (BTA)
4.3.5 Monte Carlo simulation (MCS)
4.4 Hybrid and advanced methods
4.4.1 Risk-based inspection (RBI)
4.4.2 Bayesian network model
5 AI-driven risk assessment
6 Conclusion
References
Chapter Three: From process design to control: Advancing instrumented energy system for sustainable and efficient operations
1 Introduction to energy systems
1.1 Sustainable energy systems
1.2 Challenges in sustainable energy systems
1.3 Risk in sustainable energy systems
1.3.1 Value chain risk in renewable energy
1.3.2 Long-term effects of climate change and natural hazards on renewable energy infrastructure
1.3.3 Fire risks in renewable energy infrastructure
1.3.4 Safety risks in hydrogen storage
2 From traditional risk management to smart system intelligence: Bridging the gap with DAQs, IoT, and Edge Computing
2.1 Understanding DAQs, IoT, and Edge Computing
2.1.1 Data acquisition systems (DAQs)
2.1.2 Internet of Things (IoT)
2.1.3 Edge Computing
2.2 Smart technologies vs traditional control mechanisms
2.3 Smart-technology strategies for risk mitigation in energy projects
3 Evolving methods for risk assessments for energy systems
3.1 Classification
3.2 Qualitative methods
3.2.1 Hazard and Operability Analysis (HAZOP)
3.3 Semi-quantitative analysis
3.4 Quantitative analysis.
3.4.1 Applications of Quantitative risk assessment (QRA) in energy systems
3.4.2 Case study 1: Quantitative risk assessment of an urban hydrogen refueling station in seoul
3.4.3 Case study 2: QRA of hydrogen transport infrastructure
3.5 Hybrid analysis/methodologies
3.5.1 Fault tree analysis (FTA)
3.5.2 Event tree analysis (ETA)
3.5.3 The bowtie method
4 Conclusion
References
Chapter Four: Reliability-informed economic assessment of blue hydrogen production design in a low-carbon energy system
1 Introduction
1.1 Overview of carbon capture and storage (CCS) projects and capacity
1.2 Multi-criteria performance evaluation of process design alternatives
1.2.1 Problem statement
2 Description of framework
3 System/process analyzed
4 Case study
4.1 Estimation of system reliability, availability, and maintainability (RAM) levels
4.2 Assessment of RAM-integrated economic performance
5 Conclusions
References
Chapter Five: Pipeline repurposing for energy transition: Hazards and safety considerations
1 Introduction
2 Technical and operational adaptations
2.1 Materials and compatibility for hydrogen and CO2
2.2 Modifications needed/requirements
2.3 Safety protocols
3 Risk assessment and management
3.1 Qualitative risk assessment
3.2 Quantitative risk assessment
3.3 Semi-quantitative risk assessment
4 Monitoring and safety systems
4.1 Risk-based inspection and maintenance
4.2 Leak detection and real-time pipeline monitoring and management systems
5 Future perspectives
5.1 Potential for large-scale hydrogen infrastructure
5.2 Carbon capture, utilization, transportation, and storage
6 Repurposing of pipeline for hydrogen/CO2 transportation: Case studies
6.1 Hazards in repurposing pipelines
6.1.1 Hydrogen-specific risks
6.1.2 CO2 related risks.
6.1.3 Hazards associated and safety considerations with using existing pipelines for CO2 transportation
6.2 Regulatory policies and standards driving pipeline repurposing
6.3 Technical adjustment required for hydrogen transmission through existing pipelines
6.4 Reliability analysis
6.5 Mitigation strategy
7 Conclusions
7.1 Technical and operational adjustment
7.2 Risk and safety considerations
7.3 Regulatory and social consequences
7.4 Social and environmental effects
7.5 Prospective outlook
Acknowledgments
References
Chapter Six: Automated fusion and labelling of process data
1 Introduction
2 Background
2.1 Data fusion
2.2 Segmentation
2.3 Clustering
2.4 Distance measures
3 Proposed workflow
3.1 Synchronization
3.2 Aamp
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E Log transformation
3.2.1 ON/OFF approach
3.2.2 Severity level approach
3.3 Overall system univariate time series (OV-TS)
3.4 Segmentation
3.5 Clustering
4 Case studies
4.1 Tennessee Eastman process
4.1.1 Single fault scenarios
Reactor cooling water inlet temperature (IDV4)
Condenser cooling water inlet temperature (IDV5) fault
4.1.2 Mixed faults scenarios
Two faults (IDV4 + IDV5, simulation run=1)
4.1.3 Validation
4.2 Refinery hydrocracking unit
5 Conclusion
Glossary
Appendix A Evaluation of search and cost functions
Appendix B Segmentation specific evaluation metrics
References
Chapter Seven: AI-based prediction models for dispersions in energy system safety
1 Introduction
2 Conventional dispersion prediction methods
2.1 Empirical method
2.2 Computational fluid dynamics (CFD) method
2.3 Integral method
3 AI-based prediction models on dispersions
3.1 Machine learning on dispersion prediction.
3.2 Quantitative property-consequence relationship (QPCR) model
3.2.1 Consequence database
3.2.2 Property descriptors
3.2.3 AI-based algorithms
3.3 Other AI-based prediction models for dispersions
4 Challenges and future directions
References
Chapter Eight: Safe and reliable AI in modern energy systems
1 Overview of artificial intelligence (AI) in energy systems
1.1 Foundational AI paradigms for energy applications
1.2 Key AI techniques in energy applications
1.2.1 ML in energy systems
1.2.2 DL in energy systems
1.2.3 Gen-AI in energy systems
2 The role of AI in energy systems
2.1 AI in energy systems decarbonization
2.2 AI in energy systems decentralization
2.3 AI in energy systems digitalization
3 Risks and challenges of AI deployment in energy systems
4 Reliability of AI
4.1 Defining the reliability of AI
4.1.1 Probabilistic framing of AI reliability in energy
4.2 Dimensions of AI reliability in practice
4.2.1 Temporal degradation and performance drift
4.2.2 Safety-constrained AI reliability
4.2.3 Interpretability and operational trust
4.2.4 Domain adaptability and reliability
4.2.5 Limitations of traditional testing in AI-driven energy systems
4.2.6 Toward a holistic reliability framework for energy AI
5 The imperative for AI governance in energy systems
5.1 Complexity and interconnectivity
5.2 Ethical and societal considerations
5.3 International regulatory frameworks
5.4 Standards development organizations
6 Conclusion
References
Chapter Nine: Identifying cyber risks in energy systems: Challenges and strategies
1 Introduction
2 Challenges in identifying cyber risks in OT industrial facilities
2.1 Review of existing gap in gathering lessons learnt from past incidents.