Multiscale Modeling for Process Safety Applications

Arnab Chakrabarty, Sam Mannan, Tahir Cagin

• Publisher: Butterworth-Heinemann
• Year: 2015
• Print ISBN: 9780123969750
• eBook ISBN: 9780123972835
• Topic: Technology & Engineering
• Subtopic: Chemical & Biochemical Engineering
• Index: Technology & Engineering

Multiscale Modeling for Process Safety Applications is a new reference demonstrating the implementation of multiscale modeling techniques on process safety applications. It is a valuable resource for readers interested in theoretical simulations and/or computer simulations of hazardous scenarios.As multi-scale modeling is a computational technique for solving problems involving multiple scales, such as how a flammable vapor cloud might behave if ignited, this book provides information on the fundamental topics of toxic, fire, and air explosion modeling, as well as modeling jet and pool fires using computational fluid dynamics.The book goes on to cover nanomaterial toxicity, QPSR analysis on relation of chemical structure to flash point, molecular structure and burning velocity, first principle studies of reactive chemicals, water and air reactive chemicals, and dust explosions.Chemical and process safety professionals, as well as faculty and graduate researchers, will benefit from the detailed coverage provided in this book.- Provides the only comprehensive source addressing the use of multiscale modeling in the context of process safety- Bridges multiscale modeling with process safety, enabling the reader to understand mapping between problem detail and effective usage of resources- Presents an overall picture of addressing safety problems in all levels of modeling and the latest approaches to each in the field- Features worked out examples, case studies, and a question bank to aid understanding and involvement for the reader

Chapter 1

Introduction

Abstract

The increased complexity of existing and emerging technologies has resulted from complicated interactions between process parameters, as well as intangible and hidden correlations. Hence, quantitatively defining the underlying root of a process safety phenomenon, or predicting the same scenario, requires a great deal of experience, extensive knowledge, and a structured approach. Experimentally validated multiscale modeling methodologies are powerful tools that have the potential to offer answers that are often expensive or unreachable through experiments. The significant rise of computational power in recent times, along with a better understanding of the physics, has led to implementation of higher-resolution models. Accordingly, gaining insights and refining existing models to assess hazardous scenarios have become more practical and achievable than before. This chapter lays out the basis for the book, which explores the applicability of modeling and simulation approaches in process safety applications.

Keywords

CFD; Chaos theory; Computational chemistry; Finite element; Molecular dynamics; Multiscale modeling; Multivariate analysis; Process safety; Process simulation; Quantum mechanics

Concern for man himself and his safety must always form the chief interest of all technical endeavors. Never forget this in the midst of your diagrams and equations.

Albert Einstein

Numerous incidents such as the Flixborough explosion resulting from the release of flammable hydrocarbons in June 1974, the methyl isocyanate release in Bhopal in December 1984, and the Macondo disaster in 2010 continue to remind us of the important role of process safety in the design and operations of process facilities. Over the years, process safety has emerged as a discipline in itself and has continued to play a dominant role in any process technological development. While a focus on process safety model development, experiments at various scales have gained momentum over the years and had a positive impact on the industry, the safety incident databases are anything but stagnant (MARSH, 2014). Even recently, in August 2013, in response to recent catastrophic chemical facility incidents in the United States, President Obama issued an executive order to enhance the safety and security of chemical facilities, and consequently reduce the risks associated with hazardous chemicals, for owners, operators, workers, and communities.

Given the complexity of existing and emerging technologies, interactions of process parameters, and intangible and hidden correlations, quantitatively defining the underlying root of a process safety phenomenon, or predicting the same scenario, requires a great deal of experience, extensive knowledge, and a structured approach. Additionally, while experimentally quantifying process safety parameters is often insightful, it is resource intensive and often not comprehensive owing to the number of experiments that would be required to achieve the same. Experimentally validated multiscale modeling methodologies are powerful tools that have the potential to offer answers which are otherwise often expensive or unreachable through experiments. For example, think about large-scale jet fires or deflagration-to-detonation events. Typical current practice in modeling process safety scenarios is to substitute the lack of understanding by introducing conservativeness to a safety model. Unfortunately, conservative safety parameters not only increase plant costs, but may not ensure the safety of a facility. The insufficient details or coarser resolution of the solution has the potential to offer more risk than is mitigated by adding conservativeness to it. As we have demonstrated in a later chapter, mixtures exhibiting minimum flash point behaviors pose such a risk, and therefore exercising conservativeness alone may not be sufficient.

With significant rise of computational power in recent times along with better understanding of the underlying physics, implementation of higher-resolution models in order to gain insights and refine existing models to assess hazardous scenarios has become more practical and achievable. Owing to the multiscale nature of processes, multiscale modeling has emerged as a new set of tools that provides insight into important features at multiple times and lengths of a physical phenomenon. Thus, in several applications, it has become crucial to incorporate information from a range of length and time scales into a model (Cameron and Gani, 2011). As an outcome of modeling product and process issues, a growing number of modeling efforts are resulting in multiscale modeling approaches. The strategies discussed throughout the book, albeit in the context of process safety, attempt to capture inherently important properties at various scales of a system and correlate them accurately to the system’s macroscale properties. A reasonable amount of work has been done in that respect in other areas, as demonstrated in several publications (Lépinoux, 2000; Kwon et al., 2007; Derosa and Cagin, 2010; Maekawa et al., 2008). Multiscale modeling techniques have been successfully employed in material design to rationally develop and accurately predict the performance of systems with the building blocks of their macroscopic-level performance residing at much smaller scales. Similar to other fields, it is only appropriate to extend the advancement in materials theory at different time and length scales to address less understood safety concerns by incorporating an adequate level of detail. In the context of process safety, while some sporadic work exists to assess problems at different time and length scales, to date those have been mostly isolated efforts. The primary objective in this book is to group these existing efforts on a common platform. This book aims to provide a review of the current status in this area, discuss potential implementation of multiscale modeling, and help refine existing computational approaches used for safety analysis. It attempts to provide an overall picture of how safety issues are addressed at all scales of modeling, and discusses the latest methods in the field.

Chapter 2 serves as the introduction to process safety. It touches upon the status of current industrially accepted state-of-the-art modeling approaches in process safety analysis. As appropriate, the chapter introduces or reintroduces the reader to process safety fundamentals such as the physics, consequences, and risks of fire, explosion, and toxic hazards in process industries. Concepts of flammability, ignition phenomena, fire, dispersion of flammable and toxic gases, deflagration and detonation, risk assessment of fire, toxic, and explosion hazards, are also covered in this chapter.

Chapters 3–6 deal with the use of modeling methodologies as applicable to process safety applications within various time and length scales. Chapter 3 demonstrates the applicability, relevance, and benefits of molecular modeling methods such as quantum mechanics, molecular dynamics, quantitative structure–property relationship (QSPR), and quantitative structure–activity relationship (QSAR) in process safety applications at various capacities. Chapter 4 moves into greater time and length scales and examines the effectiveness and benefits of implementing computational fluid dynamics (CFD) in the development of consequence models of process facilities. It looks into use of CFD in assessing the consequence of various types of fires such as jet, pool, and flash fires. It also looks at the modeling of explosion and blast waves using CFD. In a similar fashion, Chapter 5 illustrates the practicality of using finite element methods in process safety applications. Finite element methods have been implemented in understanding flare systems, storage and transportation of flammable materials, and other concerns in process hazard analysis. Accessing larger time and length scales phenomena are demonstrated through implementation of dynamic process simulations in Chapter 6. The transient nature of a process is typically crucial in modeling plant start-up and shutdown phenomena. In the same chapter, chaos theory and statistical analysis are introduced within the context of addressing process safety concerns. Chaos theory can be applied to investigate runaway reactions and has the potential to provide early warning detection of same. Statistical analysis has been utilized to monitor real-time plant data. Multivariate statistical analysis can be applied to plant data that can in turn help in incident investigation.

The chapters referred to above insights into implementing modeling approaches for the accurate estimation of consequences from fire, explosion, or toxic emission at various scales. Risk assessment, the succeeding part of a consequence modeling study that estimates the likelihood of a consequence, is covered in Chapter 7. The chapter illustrates approaches to gain insight into risk-related parameters through multiscale modeling. Approaches such as Bayesian logic, Bayesian-LOPA methodology for risk assessment are analyzed in this chapter. Process and material characteristics that directly affect equipment failure rate, such as fracture, corrosion and similar phenomena are examined in this chapter in the context of quantitative risk assessment. For example, beyond purely monetary value, corrosion profoundly affects the safety of advanced products and processes. Chapter 8 deals with inherently safer design, an emerging and increasingly important process safety topic. Addressing safety concerns at the design level has gained momentum in recent years and is a promising path forward for the process safety community. The subject of industrial hygiene is covered in Chapter 9. While the chapter typically addresses molecular modeling approaches in assessing the toxicity of plant chemicals, the perspective is different than that of Chapter 3, part of which also addresses assessing toxicity through implementation of molecular modeling.

The book concludes with Chapter 10, followed by exercise problems at various scales related to the topics discussed in the book. The aim is to represent multiscale modeling methodology as a set of crucial tools for answering questions and gaining insights in process safety applications.

References

Cameron Ian T, Gani Rafiqul. Product and Process Modelling. Elsevier; 2011.

Derosa Pedro, Cagin Tahir. Multiscale Modeling. CRC Press; 2010.

Kwon Young, Allen David H, Talreja Ramesh R. Multiscale Modeling and Simulation of Composite Materials and Structures. Springer Science & Business Media; 2007.

Lépinoux Joël. Multiscale Phenomena in Plasticity: From Experiments to Phenomenology, Modelling and Materials Engineering. Springer Science & Business Media; 2000.

Maekawa Koichi, Ishida Tetsuya, Kishi Toshiharu. Multi-scale Modeling of Structural Concrete. CRC Press; 2008.

MARSH, 2014. The 100 Largest Losses (In the Hydrocarbon Industry) 1974-2013. Uk.Marsh.com. http://uk.marsh.com/NewsInsights/Articles/ID/37406/The-100-Largest-Losses-in-the-Hydrocarbon-Industry-1974-2013.aspx (Accessed March 26.).

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Chapter 2

Process Safety

Abstract

Preventing process disasters require constant vigilance. When a plant does not experience a major mishap for a reasonable period, people tend to become complacent. They stop appreciating the importance of safety systems and control measures. This is a major reason for a disaster. The ...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Preface
• Acknowledgments
• Chapter 1. Introduction
• Chapter 2. Process Safety
• Chapter 3. Molecular-Level Modeling and Simulation in Process Safety
• Chapter 4. Computational Fluid Dynamics Simulation in Process Safety
• Chapter 5. Finite Element Analysis in Process Safety Applications
• Chapter 6. Dynamic Simulation, Chaos Theory, and Statistical Analysis in Process Safety
• Chapter 7. Equipment Failure
• Chapter 8. Inherently Safer Design
• Chapter 9. Application of Modeling for Industrial Hygiene and Toxicological Issues
• Chapter 10. Conclusion
• Chapter 11. Exercises
• Index
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