Arnab Chakrabarty

Inherently Safer Design

The application of a series of principles to inherently mitigate the potential hazards during process development is popularly known as inherently safer design (ISD). ISD principles can be categorized as the following four: minimization (intensification), substitution, moderation (attenuation), and simplification. Based on these principles, protection systems are adopted in designing an inherently safer process. However, protection measures that are needed to be adopted for ISD are considered as either active or passive protection systems. A passive protection measure is usually considered as an ISD rather than an active protection measure. Optimization of facility layout and siting, accurate quantitative risk analysis, proper process control, material design, and reactor design are some of the advanced features of ISD. This chapter presents a general briefing on the state-of-the-art tools to perform ISD.

Chapter 2 – Process Safety

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 advancement of industry, science, and technology has given rise to new problems. The constant change in the industry demands a continuous change in addressing process safety concerns. Maintaining sustained process safety performance without compromising on plant production is a formidable mission. In lieu of that, the first chapter introduces the readers to the fundamentals of process safety and its common components. It then illustrates current approaches in the industry in addressing process safety problems, along with future challenges and opportunities in the field.

Molecular-Level Modeling and Simulation in Process Safety

Molecular modeling is the science (or art) of representing molecular structures numerically and simulating their behavior with the equations of quantum and classical physics. Investigating chemical and material problems at an atomistic scale through capturing and understanding molecular interactions requires effective implementation of molecular modeling or computational chemistry techniques. Enabling ourselves to perform modeling at the molecular level empowers us with the ability to dig deeper into the characteristics of hazardous chemicals and processes, which are otherwise difficult and expensive to get through experimental observations. Application of molecular modeling has significantly increased in recent years and among others has been used in design and development of novel materials, drug discovery, protein engineering, microelectronics, and hydrogen storage. In here, we demonstrate how we can benefit from proven molecular modeling techniques for addressing process safety concerns.

Application of Modeling for Industrial Hygiene and Toxicological Issues

Industrial hygiene deals with workplace conditions to prevent workers from injury or illness. Chemical hazards, especially toxic hazards, are one of the main health concerns in the workplace. Hazardous chemicals in the workplace should be accordingly identified, evaluated, and controlled to ensure safety and health in the workplace. Chemicals can be hazardous due to their toxic (health), physical, and environmental properties. The Globally Harmonized System of Classification and Labeling of Chemicals provides an internationally agreed-on system developed by the United Nations that requires the classification of chemicals according to the hazardous properties with similar categories. This chapter demonstrates the application of modeling methods such as quantitative structure–activity relationship aligned with the objective of better industrial hygiene.

Chapter 7 – Equipment Failure

In previous chapters, the primary area of interest was consequence modeling at multiscale levels such as assessment of thermal hazards from fire, dispersion modeling of hazardous gases, consequence modeling of explosion, and estimation of safety parameters relevant to flammable and toxic chemicals. In safety analysis, consequence modeling is necessary but not a sufficient step. Consequence modeling addresses the question: How severe can a process safety incident be? It does not address: How often could it happen? The likelihood of a process safety event is thus very important for performing a quantitative risk assessment study of a given scenario. Some of the information that helps estimate the likelihood of an event are ignition probability (early for fire and late for explosion), weather data (wind speed, direction, temperature, etc.), and equipment failure rate. Among these, the failure of equipment such as that of a pump, a compressor, a pressure vessel, or a specific-diameter pipeline typically initiates a process safety event. In the following, various approaches applicable to estimation of equipment failure rate are explored. In addition, modeling methodologies that can help understand the underlying mechanism behind equipment failure is demonstrated.

Chapter 10 – Conclusion

In many applications, incorporation of a range of time and length scales in to its mathematical model formulation has become crucial. The focus on complex multiscale phenomena and their capture in models is one of the most significant developments in modeling methodology in the past three decades. Modeling has played and will continue to play an increasingly important role in a broad range of application areas in the industry. However, the industry has been slow to reflect a similar level of growing interest and implementation of multi-scale modeling approaches as that of academia. The current typical outlook in process safety applications in the industry is similar, and that implementation of multi-scale modeling approaches is minimal. This chapter finishes the book with the hope that the book has introduced its readers to the effective application of multi-scale modeling approaches in process safety applications. Similar to any active area of science, the application of multis-cale modeling methodologies in process safety discussed here is incomplete―further development in the field is expected. However, multi-scale modeling approaches are established powerful tools as proved in several areas of science, and process safety challenges are too big a concern for the industry to simply miss the boat.

Finite Element Analysis in Process Safety Applications

Finite element analysis is a dominant computational method in science and engineering. It is a numerical procedure that can be applied to obtain solutions to a variety of problems in engineering including steady, transient, linear, or nonlinear problems. Advancement of computational power along with advanced computational methods, applied mathematics, has aided the broad implementation of finite element–based approaches in designing products and processes. Within the context of process safety applications, finite element analysis has been proved to be a useful tool for gaining valuable insights as illustrated in this chapter. The chapter demonstrates the use of finite element analysis as a powerful tool in process safety applications through addressing a diverse set of problems including fluid dynamics–related problems such as dispersion modeling and problems dealing with both fluid flow and structure such as for studying interaction between fire and structure. In addition, it has been shown that finite element–based analysis enables one to perform estimation of heat release rate and to make informed decision regarding storage and transportation of flammable gases and provides methodologies for detection of damage via monitoring of a macro-scale property.

Computational Fluid Dynamics Simulation in Process Safety

Computational fluid dynamics (CFD) simulation has been applied extensively in process safety to assess possible catastrophic consequences such as fire, explosion, and dispersion of flammable and toxic materials from accidental release due to loss of primary containment events. If conducted properly, CFD simulations provide not only accurate hazard assessments such as radiation level, overpressure contour, and distribution of toxic cloud, but also detailed information about the spatial and temporal evolution of accidental events. A typical CFD simulation for a consequence modeling requires an extensive geometric setup, multiple input parameters, and related physical models representing physical phenomena behind the event.

Dynamic Simulation, Chaos Theory, and Statistical Analysis in Process Safety

A refinery or chemical facility spends less than 10% of its time on transient operations but more than 50% of process safety incidents have occurred during such activities. Major plant accidents are more than five times likely to occur during abnormal operations. In that regard, understanding dynamic behavior of processes through studying its fundamental principles or analyzing process data is of significant importance. In this chapter, we have introduced application of data analysis-based methodologies and dynamic modeling and simulation based approaches within the context of process safety. The primary objective in this chapter is to provide an overview of the available tools in this area to the readers. The methodologies discussed here should be treated as brief introductions and are by no means presented comprehensively.

Shockwave Response of Polymer and Polymer Nanocomposites

We present non-equilibrium molecular dynamic simulations of
the shock compression of polyurethane and its graphene-based nanocomposite systems. Using the projectile/wall approach, planar shock waves with piston velocity range from 0.1 to 2.5 km/s is applied for both systems. In this study, direct molecular-level simulations of shock-wave generation and propagation are utilized in order to construct the appropriate shock-Hugoniot relations. Through this study, we determined that inclusion of graphene into the polyurethane system has a significant effect on the shock propagation behavior when incorporated in the polymer matrix