PROCESS SAFETY IMPLEMENTATION
Moving Towards a Hydrogen Economy through Safer and More Reliable, Design and Operation of Proton Exchange Membrane Fuel Cells
PhD CHEN – Process Safety Implementation
Proton exchange membrane fuel cells (PEMFC) are considered as the best alternative for hydrocarbon-based combustible engines in vehicular applications due to their higher efficiency and cleaner utilization and can serve as a pathway towards implementing a hydrogen economy. However, one of the major hindrance towards their commercialization is the reliability of PEMFC. The deterioration in reliability of PEMFC is caused by the degradation of the polymer electrolyte membrane. More importantly, apart from affecting the performance of the fuel cell, degradation of the membrane can be a contributing cause for an explosion in PEMFC. Therefore, to mitigate the risk associated with explosion in PEMFC and to achieve the required levels of performance, it is essential to study the degradation of this membrane in depth. This study deals with understanding the fundamentals of PEMFC degradation through modeling using computational fluid dynamics (CFD) and minimizing the degradation mechanism by altering design and operating conditions of the PEMFC system. The study will also focus on experimentally determining the rate of degradation for different membrane materials and the membrane material that provides optimized performance and improves the overall safety of the system.
Comparative Study of Nano-Powder Synthesis Methods for Process Performance and Exposure Hazards
MS Seng – PS Implementation
Nanomaterials exhibit unique physical and chemical properties that impart specific characteristics essential in making engineered materials, but little is known about what effect these properties may have on human health. Research has shown that the physicochemical characteristics of particles can influence their effects in biological systems. These characteristics include particle size, shape, surface area, charge, chemical properties, solubility, oxidant generation potential, and degree of agglomeration. Nanomaterial-enabled products such as nanocomposites, surface-coated materials, and materials comprised of nanostructures are unlikely to pose a risk of exposure during their handling and use as materials of non-inhalable size. However, some of the processes used in their production (e.g., formulating and applying nanoscale coatings) may lead to exposure to nanomaterials, and the cutting or grinding of such products could release respirable-sized nanoparticles. This research will focus on comparative analysis of nano-powder synthesis methods for the performance and potential hazards of engineered nanoparticles with the aim to identify measures that can be taken to minimize workplace exposures.
Developing Leading Indicators Framework for Predicting and Preventing Offshore Blowouts
PhD CHEN – PS Implementation
Leading indicators are effective organizational tools that can identify vulnerabilities in a system. Offshore drilling operations and well activities have always been very challenging due to technological and operational complexities and it is quite difficult to develop well specified risk indicators for these high risk operations. This research work aims to develop leading risk indicators-based probabilistic models for offshore drilling and other well operations (e.g., workover) to predict gas kicks and possible blowout scenarios. This work proposes a cause-based approach to develop sets of leading indicators for different categories and organizational levels. Probabilistic models are developed for evaluating the relative importance of different leading indicators and for assessing their impacts on the key causal factors of well control barrier failure. This work continues to build comprehensive risk models combining real-time indicators with operational and organizational factors to predict and prevent blowout incidents.
Facility Safety Study of LNG Offshore System
MS SENG – PS Implementation
The motivation of this research is to establish a general offshore operation system to involve both marine transportation process and chemical safety process. The purpose of this research is to apply chemical process safety methodology to shipping and offshore industry, trying to support decision-makers facing LNG-FSRU siting problems and to arouse public concern of nearshore/offshore safety. GIS-based multi-attribute decision analysis (MADA) was adopted as the main logic of this research, and the evaluation framework was constituted by four layers (objective layer, process layer, hazard layer and attribute layer) by considering the data availability. Furthermore, three consecutive processes were determined for the whole system. For navigational process and berthing process, the operation process was realized by statistic tools and ship simulators. For LNG transferring process, the simulation runs were carried out by chemical consequence software. The whole evaluation framework was structured by the identified hazards and attributes. Based on the value of final LNG offshore system, the preferred alternative can be determined accordingly.
Process Safety in Nanomaterials
PhD CHEN – PS Implementation
The large-scale production of nanomaterials is of increasing commercial and academic interest due to its immense potential in diverse applications. As nanomaterials are gaining prominence, it is important to investigate its potential process safety hazards. One of such nanomaterials is graphene. Graphene is desirable for energy storage and for composite filler applications among others. There is no economically viable method to produce graphene in large quantities and therefore, graphene oxide (GO) route is predominantly used. This method involves oxidation of graphite to GO and its subsequent reduction to a produced graphene-like material called reduced graphene oxide (rGO). The GO route has shown potential for bulk production at high yield. However, prior studies have shown that GO can undergo explosive decomposition under certain conditions and its synthesizing steps involve producing unstable oxides. This research will study (1) thermal stability of GO for safer storage and handling conditions; (2) hazards during GO synthesis to recommend safer operating conditions.
Incorporating Process Safety into Heat Exchanger Network Synthesis and Operation
PHD CHEN – Implementation
The development of heat exchanger networks (HEN) has fundamentally changed the way processes are designed. By allowing process plants the ability to recover waste energy, the profitability of a process can be greatly increased. While there exists considerable research on further improving HEN, little attention has been paid to analyzing the safety of a HEN. Failing to consider safety in the design of HEN may result in an increased risk to a facility. This work examines overpressure analysis and the potential for overpressure of heat exchanger networks. A safety metric for heat exchangers and HEN is used to assess the risk of overpressure resulting in failure of the equipment. An application of this metric and its relevance is shown by comparing two HEN designs. Areas throughout the lifecycle of an exchanger in which an exchanger can be modified to reduce the severity of overpressure is discussed. An optimization formulation is then developed which incorporates an overpressure safety metric for heat exchangers. The results obtained indicate a Pareto-like curve relating the safety of an exchanger with its cost.
Thermal Risk Analysis of Nitro Aromatic Compounds in the Presence of other Chemicals
PhD CHEN – Calorimetry
Mononitrotoluene (MNT) is a typical nitro aromatic compound used in the production of dyes, rubber chemicals, flexible foams and agricultural chemicals. However, incident history demonstrates inadequate recognition and evaluation of reactive hazards of nitro aromatic compounds. Advanced reactive system screening tool (ARSST) and automatic pressure tracking adiabatic calorimeter (APTAC) have been used to experimentally study this reaction in order to better understand the mechanisms that result in the explosion of MNT. Key parameters such as “onset” temperature, “onset” pressure, self-heat rate and pressure rate are reported. The effect of certain additives has been screened and autocatalytic kinetics of the reaction has been developed. All of the related hazard-information in this study helps to determine safer operation conditions for handling, storage and transportation of MNT in the chemical process industry.
Study of Adiabatic Calorimeter Variability
MS SENG – Calorimetry
Adiabatic calorimetry is a reliable technique to obtain both thermodynamic and kinetic parameters of a reactive chemical. The APTAC and ARC are popular types of adiabatic calorimeters for evaluating these parameters. Consistent and reliable thermal hazard assessment is critical for the design and control of industrial processes, which contain thermal hazards from reactive chemicals. However, there is often variability between the values reported in literature for various calorimeters and between laboratories. Often this was believed to be due to age, contamination, improper reagent preparation, passivation, incorrect thermocouple calibration, or non-adiabatic conditions and heat loss. This research study will examine the potential differences between the results reported for various calorimeters.
Thermal Hazard Assessment of Styrene System during Polymerization and Storage
PhD CHEN – Calorimetry
Polymerization reactions are highly exothermic in nature, and are one of the industrial processes that are most frequently involved in thermal runaway incidents. Polymerization runaway reaction can take place in reactors, process tanks, and storage vessels. Once initiated, the reaction termination rate will decrease dramatically due to the increase of system viscosity and immobility of the polymer chain ends. As a result, for most polymerization processes, the runaway reaction auto-accelerates and generates a huge amount of heat, leading to loss of containment, fire, and explosion. In order to prevent polymerization runaway incidents, contributing factors to uncontrolled reactions and their relative importance need to be carefully analyzed in a systematic way. This study focuses on identifying and evaluating thermal stability and runaway behavior of crucial components (monomer, initiator, inhibitor, and contaminant) and their combinations in styrene polymerization process using calorimetry. Various calorimetry results will be integrated with kinetic models to determine the safe operating envelope of the styrene system during reaction, transportation, and storage.
Process Hazards Evaluation and Safer Design for Oxidation of Secondary Alcohol to Ketone Using Hydrogen Peroxide
PhD CHEN – Calorimetry
Ketones are produced on massive scale in industry as solvents, polymer precursors, and pharmaceuticals. Current ways of producing ketone through alcohol dehydrogenation are energy intensive and usually expensive, hazardous and toxic due to employing strong oxidizing agents. In 1997, Kazuhiko Sato, et al., found a way of producing ketones via 30% hydrogen peroxide oxidation of alcohol, which is considered as ‘green chemistry’ since it is organic solvent and halide-free. A great deal of laboratory work was subsequently done focusing on different catalysts and experimental conditions as well as their effects on yields of different ketones. However, there are safety concerns for scale-up regarding this reaction system because of its runaway potential. Thus, the purpose of this work is to conduct a comprehensive study of thermal and kinetic behavior of this reaction system. Calorimeters, such as DSC, Phi-TEC and RC1e, are used to help study, experimentally and theoretically, these reactions under normal operating and runaway conditions. The findings will be further used to propose measures for safer design and scale-up of this reaction process in order to avert a potential runaway.
Thermal Stability Analysis of Benzoyl Peroxide System
PhD CHEN – Calorimetry
Benzoyl peroxide (BPO) is a nontoxic, colorless, odorless, and tasteless granular solid widely used in the petrochemical industry to initiate free radical polymerization. The flammable and explosive features of benzoyl peroxide have led to a number of thermal explosions and runaway reaction incidents globally. This research is a comprehensive study of the runaway behavior of a benzoyl peroxide hybrid system using Advanced Reactive System Screening Tool (ARSST) and automatic pressure tracking adiabatic calorimeter (APTAC). The aim of this research is the advancement of understanding of thermal decomposition of BPO under various conditions, such as concentration, confinement, and contaminants. More specifically, this research will systematically study and develop thermodynamic and kinetic parameters related to BPO, and further the use of this particular knowledge to mitigate the risks in storage, transportation and manufacturing process.
Comprehensive Testing Hierarchy for Reactive Chemical Identification
PhD CHEN – Calorimetry
Reactive chemicals are a major hazard affecting the processing, storage, and handling of chemicals that can lead to serious consequences such as fires, explosions, and toxic gas releases. In the Chemical Safety Board’s Reactive Hazard Investigation of 167 serious accidents, over 40 classes of chemicals were identified with no clear dominating class and most were not rated as reactive chemicals. While most research focuses on classes already in use, this research plans to address the issue of how to identify a reactive chemical by developing a hierarchy of testing procedures through experiments and molecular modeling. The goal of this research is to produce a testing procedure for the vast majority of reactive chemicals that can be used by industry to identify potential risk before production.
ADNORMAL SITUATION MANAGEMENT
Risk-Based Optimization of Alarm Systems Used in Industrial Applications
PhD CHEN – Adnormal Situation Management
Alarm systems serve a critical role in the safe operation and control of plants by alerting operations staff of possible process deviations from normal operation. However, even with alarm systems installed, up to 90% of process incidents are attributable to human error. Thus, user-friendly alarm systems are crucial to ensure effective operator responses and thus safe plant operation. Alarms may fall into the categories of process alarms or critical safety alarms. However, there often exist a large number of alarms that fall in the range between these two categories. At times, the role of alarm management for operations staff can become unclear, particularly when multiple alarms occur simultaneously. This research aims to provide a framework to determine the optimum combination of audible and visual signals so as to prioritize operator response time to safety issues and process upsets based on relative risk and minimize the likelihood of alarm flooding.
A Systematic Approach to Alarm Identification with Application to Tennessee Eastman
Joshiba Ariamuthu Venkidasalapathy
PhD CHEN – Adnormal Situation Management
Abnormal event management (AEM) of process plants has garnered attention in recent years. It has been estimated that $20 billion are lost due to abnormal situations each year. Efficient monitoring of process variables and timely corrective measure are at the crux of AEM. The most process monitoring techniques in current practice involves the use of alarms to alert the operator, thereby requiring a corrective action to restore normal operation. The ‘alarm identification’ is an important aspect of the alarm system design, which is the focus of this research project. It concerns the selection of a potential subset of process measurements to configure to the alarm system. Most of the previous works on alarm identification involves a qualitative approach. This project aims to develop an alarm identification design technique that incorporates quantitative aspects. The operator-centered approach aims at providing the operator ample time to respond while having fewer active alarms. The proposed approach is implemented on the benchmark industrial casestudy: The Tennessee Eastman process control problem.
Decision Support System for Abnormal Situation Management : A data-driven approach
PhD EEN – Abnormal Situation Management
Modern industrial plants have thousands of sensors deployed in the field connected to the control system units such as Distributed Control Systems (DCS), Programmable Logic Controllers (PLC), and Emergency Shutdown Systems (ESD) for routine operations. In addition, due to the ease in configuring alarms in control systems in the past few decades, the number of alarms in plants has increased significantly. This has resulted in decreased system performance and additional workload on operators, which worsens during an abnormal situation. In this research, it is assumed that the alarm rationalization process has been completed by the organization and that the number of alarms during normal operations is under manageable conditions. However, the alarm flood condition still occurs during an abnormal or upset state. This research is focused on making a decision support system for the operators using data-driven methodologies. Such a system will improve early detection, prediction of abnormal situation and provide assistance in decision making for the operators during abnormal operations. The process includes data acquisition, data mining and analysis, generating information from the available datasets and creation of a smart dashboard to provide details to the operator. The resultant proactive system would ensure safer, reliable, and productive operations.
Measurement and Prediction of Minimum Ignition Energy (MIE) of combustible dusts
PhD CHEN – Dust Explosions
Dust Minimum Ignition Energy (MIE) is a critical safety parameter in industries, influencing dust explosion mitigation. The main objective of this work is equipment design and improved test method for accurate MIE measurement and develop MIE prediction models for compounds using molecular structure information.
A novel add-on purge device to the MIKE3 MIE testing device has been designed and patented which increased MIE measurement accuracy by 80%. The current ASTM E2019-03 MIE test standard does not mandate the gas composition in the Hartmann tube to be same as the gas composition used for dust dispersion, wich can result in misleading MIE measurements. To overcome this, an alternate MIE test method has been proposed which has shown to significantly change the measured MIE in inert and flammable gas atmospheres. Additionally, computational fluid dynamic (CFD) simulations of purge flow have been conducted to validate the proposed test method and determine a pre-ignition purge time. An amendment to the existing ASTM E2019-03 has been a major outcome of this work.
MIE prediction of combustible dusts based on just molecular structure information can result in saving costs for rare and expensive materials in industries. Molecular models were developed for a set of pharmaceutical dusts employing Quantitative Structure-Property Relationships (QSPR) relating inherent quantum molecular properties of the compounds to their MIE values. A machine learning framework was developed which accurately predicted the most important parameters affecting the MIE of these dusts. Thus, this work has attempted to address some of the most application oriented challenges in solids handling in industries through experimentation and modeling.
Effect of Dispersion and Morphology on Dust Explosion Hazard
PhD CHEN – Dust Explosions
Dust explosions are a serious and persistent problem for process industries (> 1 incident per month since 1980 in the United States of America). The threat of dust explosions can be perceived by parameters such as maximum explosion overpressure (Pmax), deflagration index (Kst), minimum ignition energy (MIE), minimum explosible concentration (MEC), limiting oxygen concentration (LOC), etc. It is important that these parameters are measured accurately to better understand the risk and take proper safety measures. These parameters are measured using a standard 20-L or 1-m3 spherical dust explosion vessel in accordance with ISO and ASTM standards. In all these standards, it is assumed that the dust cloud particle size distribution in the air is the same as the particle size distribution of the pre-dispersed dust sample. Recent studies have shown this is not necessarily true. Because dust explosion parameters are significantly dependent on particle size distribution, this general assumption can lead to erroneous results, which can cause severe underestimation/overestimation of dust explosion risk. In addition, the role of particle shape/morphology on these explosion parameters is not understood, but can be very important in understanding the combustion mechanism and explosion risk. This research addresses challenges such as: What stage of dispersion process (outlet valve, nozzle, and dispersion cloud turbulence) contributes to the change in particle size distribution ? Does the dust concentration impact size distribution change? How much shift in size distribution occurs for different materials? What is the consequence of this shift on explosion parameters? Can the change in size distribution for different materials be predicted? And what is the role of particle/shape morphology on dust explosion parameters? The results from this research will lead to better understanding of dust explosions, and highlight gaps in the current risk assessment methods, paving way to improve risk assessment and dust explosion safety.
Effect of Morphology and Polydispersity on Dust Explosions
MS SENG – Dust Explosions
Dust explosion has a history that extends back to 18th century.Although dust explosion might have occurred several centuries earlier, no one considered the possibility of dust explosion. Since the first dust explosion recorded in 1785, the world has seen at least a major dust explosion every year.
Several parameters like deflagration index(KST)-telling us the severity of an explosion, (Pmax)- the maximum pressure generated due to explosion, Minimum Ignition Energy(MIE), Minimum Explosion Concentration(MEC) etc. To get the information about these parameters we use, MIKE3 and 20-L dust explosion apparatus. Several work has been done of the factors influencing Dust explosion. Based on past work done, we know that shape size does, effect the different parameters of Dust explosion. But, till date we don’t have an answer as to how different shape or morphology effects these parameters. So, influence of morphology of dust on dust explosion sums up the first part of my research. Moving forward, how the different particle size distribution(polydispersity) of different dusts influences the energy required for the explosion.
Influence of Particle Size and Humidity on Sulfur Dust Explosion Properties
PhD CHEN – Dust Explosions
Dust Explosions are a widely occurring phenomenon that negatively impact several industries, including the sulfur industry. There is very little information available in literature on sulfur dust explosion properties. This research aims to perform an experimental investigation of sulfur dust explosion properties; specifically, the minimum explosible concentration (MEC), maximum explosion overpressure (Pmax), and dust deflagration index (Kst). A particular emphasis of this research is to determine the effect of particle size and humidity on the explosion properties. This research also aims to develop a theoretical model that predicts the impact of particle size and humidity on the dust explosion properties that will assist to improve the management of sulfur dust explosion risks.
Sour Gas Dispersion Modeling
PhD CHEN – Release Modeling
Heavy gases, such as hydrogen sulfide and chlorine, are hazardous materials that maximize the dangerous effects on nearby people and the ecosystem, because the gases tend to travel near the ground, where wind speed decreases and gases dilute slowly. Heavy gas dispersion models are crucial to provide decision makers with quick assessment of potential impacts as well as land use planning. Many heavy gas models are available, but some of them may not be fully validated against experimental data in complex situations. DEnse GAs DISpersion (DEGADIS) and CFD models are widely used in current industries. However, they both have their limitations: the DEGADIS model cannot consider different wind directions and varying wind speeds while CFD models generally take a large amount of time for one scenario simulation. More importantly, the near-field (within 100 meters) results obtained from the DEGADIS model are conservative, with predictions being two to five times larger than the experimental data, whereas CFD models sometimes underestimate the results. We need to overcome the disadvantages of current models. The objective of this research is to develop a semi-empirical mathematics equation to estimate hydrogen sulfide gas concentration profile in the near field. The proposed model will consider obstacles, varying wind speed and humidity.
Dynamic Simulations of the Impact of Depressurization of a Vessel on Interconnected Vessels
MS CHEN – Release Modeling
Thermal runaway may occur due to an uncontrolled exothermic reaction in reactors or storage vessels. An inadvertent pressurization of a vessel following a thermal runaway may pose an explosion hazard. Emergency Relief Systems (ERS) are commonly used for risk reduction measures to protect the reactor or vessel from the consequences of a runaway reaction. ERSs can be connected to effluent handling systems that may include the use of catch tanks. Sizing of the ERSs requires the understanding of the reaction kinetics, thermodynamics, and fluid dynamics of the reactive system before and during venting. Such phenomena are quite complex and yet to be fully understood. The aim of this research is to develop a model to perform dynamic simulation of the depressurization of a vessel to a catch tank, and to validate the simulator using experimental data.
Modeling of a Gas Release from Underground pipeline
Ibrahim Lokmane Daoudi
MS CHEN – Release Modeling
Buried pipelines are commonly used for transporting natural gas. Any failure due to corrosion or breakage of the pipe, might lead to a gas leak, which can cause significant human and physical damage. Many models are available for gas dispersion into the atmosphere, but not as many are available for underground gas leak. The modeling of such cases heavily depends on the flow mechanism of the gas through the soil. Thus, it is important to understand and model different types of flow mechanisms, in order to effectively design for prevention and mitigation, as well as to prepare for emergency response. The objective of this research is to model gas releases from underground pipelines by focusing on the gas release rates on soil displacement, and the effect of the orifice orientation of the leak.
Subsea Gas Release Consequence Modeling
MS CHEN – Release Modeling
Subsea gas releases can result from different causes including blowouts from oil and gas wells, drilling operations, leakage from transport pipelines, or malfunction of subsea processing equipment. Subsea releases will result in the dispersion of the hydrocarbon as it rises to the sea surface leading to potentially catastrophic impacts on human life, the environment and offshore installations. Consequence analysis provides quantitative information on the risk and potential hazards that could be caused by these events. Although many models have been developed for subsea releases, these models present limitations for representing underwater plume turbulences, gas dissolution and high flowrates environment. In addition, phenomena like hydrates formation, bubble size distributions and releases containing toxic gases like hydrogen sulfide (H2S) have not received much attention. The objective of this research is to develop a comprehensive CFD-based model for subsea gas releases.
Development of a Hazard Identification Method to Determine Safe Distance of Chemical Fire for the Preparedness of First Responders
MS SENG – Release Modeling
The first emergency responders, whether it is a fire department of a city or a fire brigade of an industrial facility, are directly in harm’s way. They are trained to run to a fire while everyone else is running away. But clearly, there are times when even the emergency responders should not approach a fire. A couple of recent tragedies are a stark reminder of this, such as the West, TX fertilizer plant fire and explosion and the Tianjin port explosion. In this study, several process hazard analysis and consequence modeling software (e.g. Phast by DNV GL and ALOHA® by EPA) will be utilized to better understand the characters of hazard material, the effective range of a chemical fire or explosion, and the time window to safely put out the fire. The objective of this work is to develop a hazard identification method to pre-determine a safe distance to fight a chemical fire.
RELEASE MODELING AND MITIGATION
Optimize Ventilation Systems in Confined and Unconfined Workplaces Using Computational Fluid Dynamics (CFD)
MS CHEN – Release Modeling and Mitigation
Ventilation is the most common method to control toxic and explosive airborne materials in confined and unconfined spaces. The purpose of ventilation is to dilute or remove toxic and explosive vapors with air to prevent potential poisoning or explosion. In this study, Computational Fluid Dynamics (CFD) tools will be utilized to better understand the efficiency and mechanisms of ventilation systems. The objective of this work is to evaluate the optimized ventilation systems in both confined and unconfined workplaces, by considering the installation location and exhaust air flow rate. The results of the CFD simulation can serve as reference to maximize ventilation efficiency and minimize the energy cost.
Experimental and theoretical study on stability of high expansion foam used for LNG vapor risk mitigation
PhD CHEN – Release Modeling and Mitigation
The consumption of natural gas is expected to increase by nearly 40 percent over the next few decades, as it is a cleaner source of energy compared to oil or coal. Liquefaction of natural gas can be an effective way of storage and transport because its volume is around 600 times lower in liquid form. However, a leak of liquefied natural gas (LNG) can result in a catastrophic scenario. It may form a vapor cloud, which can migrate downwind near ground level due to its dense gas behavior and potentially ignite. The National Fire Protection Association (NFPA) recommends the use of high expansion foam to mitigate LNG release vapor risk. The objectives of this research are to study the effects of forced convection and thermal radiation on high expansion foam breakage, study the effect of addition of nanoparticles to improve the stability of high expansion foam, and to develop a heat transfer model to predict how much high expansion foam needs to applied.
CFD Analysis of a Tube Rupture Scenario
PhD CHEN – Release Modeling and Mitigation
Shell and tube exchangers are commonly used in the oil and gas, chemical, and nuclear industries. One fault that may occur with heat exchangers is a tube rupture- an overpressure scenario in which high pressure fluid flows into the low pressure region. This overpressure event may compromise the mechanical integrity of the exchanger and can lead to the equipment’s failure. To protect against such scenarios, overpressure protection measures such as relief devices are installed on the low pressure side of the exchanger. To determine the size of the relief device that must be installed, API 521 recommends a dynamic analysis to be performed when there are large differences in pressure between the shell and tube side. Using ANSYS Fluent, the peak pressures from a 2D model are compared against a dynamic analysis. Finally, both results are then compared against known experimental data.
QSPR Methods to expedite Reactive Chemicals Hazards Assessment Processes
PhD CHEN – Reactive Chemicals
Reactivity tests for identification and understanding of reactive chemical hazards are conducted using different experimental tools of calorimetry. These attempt to predict chemical properties and unsafe process conditions. Moreover, there is often an under prediction of reactivity-related properties (e.g. over pressure), and experimental techniques face significant challenges for its extensive duration and high costs. Quantitative Structure Property Relationships (QSPR) on the other hand are defined as mathematical correlations to predict chemical properties from a series of descriptors inherent from the substances.
A systemic approach is proposed to develop prediction tools based on QSPR. This, using laboratory test data and molecular modeling techniques. This will expedite reactive hazard assessment, thus reducing the related costs in industry. Functional groups will be chosen based on different criteria including incident history. Therefore, by using molecular properties and kinetic parameters obtained from experimental testing, reactivity descriptors will be developed and models validated using existing data, followed by defining the limits of applicability and the development of a tool/software for the application of the model.
Roadmap of Transferring Batch to Continuous Fine/pharmaceutical Synthesis: A Case Study on Alkylpyridine N-oxide Synthesis
PhD CHEN – Reactive Chemicals
One of the common root causes for fine/pharmaceutical thermal runaway incidents in batch reactor is lack of understanding on the reactive chemical hazards and insufficient cooling/pressure relief systems. In contrast to batch reactor, continuous reactor is inherently safer with the advantage of reduced equipment size, smaller instantaneous accumulation of hazardous chemicals, enhanced heat/mass transfer efficiency. The objective of this research is to apply both experimental and computational approach to transfer traditional batch to continuous synthesis with a focus on alkypyridines N-oxide synthesis. Experimentally, response surface methodology is applied to determine the relationship between operating input variables such as different catalyst amounts/dosing rates/temperatures with process output response such as product yield and reactor pressure in isothermal calorimetry RC1e. Computationally, kinetics and thermodynamics model are developed based on experimental data and further applied for continuous reactor design and modelling. Parametric sensitivity analysis is performed to investigate the critical operation parameters affecting the reactor safety and efficiency. Quantitative risk assessment and techno-economic analysis are finally used to compare the continuous/batch flowsheet.
Mitigation of Thermal Runaway Risk for Polymerization
PhD CHEN – Reactive Chemicals
Thermal runaway has been a major threat to the process industry for decades. In light of numerous failure modes that lead to runaway and its catastrophic consequences, it is necessary to develop methods to mitigate thermal runaway risk. Reports show that the polymerization process is where thermal runaway occurs most often. The unique kinetic and flow behavior of polymerization escalates the difficulty of preventing thermal runaway. The present research focuses on the development of a polymerization reaction inhibition system (PRIS) and thermal runaway early detection techniques. By deep understanding of kinetic-transport interactions in the polymerization process, theoretical insights are expected to guide industrial applications of mitigation methods.
Developing a Framework for Dynamic Inherent Safety Assessment
PhD CHEN – Design Concepts – Inherent Safety
The most cost-effective approach to prevent safety incidents is to add process design features to prevent hazardous situations, rather than having to rely on mitigative or emergency response measures to deal with process upsets once they occur. This is the fundamental principle behind inherent safety. Current methods used to measure inherent safety rely on consequence models that estimate worst-case scenarios at steady-state conditions. However, processes are dynamic and prone to disturbances that can change the potential consequences. The current, steady-state inherent safety assessments currently used do not address this. This research seeks to create a framework to dynamically measure the inherent safety of chemical processes in the early stages of design. This framework will be used to optimize process designs for cost, safety, and operability.
Integrated Framework for Process Intensification with Safety, Control and Operability
PhD CHEN – Design Concepts – Process Intensification
Process intensification technologies are promising tools to create the next generation of modular, intensified, and innovative manufacturing technologies and processes. Despite the increasing interests in the field of process intensification from both industry and academia, the development of a systematic approach for intensified designs is still in its incipient stage. Challenges include the large set of feasible process intensification options, proper criteria to evaluate the controllability and reliability of a novel process technology, and accurate risk assessment with lack of precedent. With the aid of modeling and simulation tools, this research aims to promote an integrated framework for process intensification incorporating safety, control and operability.
Resilience Analysis Framework for Process Design and Operations
PhD CHEN – Design Concepts – Resilience
Increasing process safety and risk management challenges in the process industries and change in the public perception of hazards and risk globally have necessitated exploring tools for efficient risk management. The application of the resilience engineering perspective is gradually being explored as an approach for considering the dynamics of socio-technical aspects based on systems theory. The resilience methodology emphasizes non-linear dynamics, new types of threats, uncertainty, and recovery from upset or catastrophic situations. The main focus of this research is to propose a holistic method to integrate both technical (process parameters variations) and social (policy/regulations, human and organizational) factors including prediction, survival, and recovery analysis for process facilities. The framework developed in this research is called Process Resilience Analysis Framework (PRAF) comprising of four aspects: early detection, error tolerant design, plasticity, and recoverability. PRAF would be applicable to both onshore and offshore installations, primarily focusing on early detection of unsafe zones, assessment of aggregate risks and prioritization of safety barriers during abnormal situations, and reduction in response time resulting in enhanced recovery and mitigation of consequences.
A Systematic Sustainable Process Design Approach in Early-Stage Chemical Process Design with Inherently Safer Index and the DAHAZID
PhD CHEN – Design Concepts
In order to have a more systematic sustainable process design, this study will utilize the concept of Inherently safer design (ISD). ISD is a proactive strategy for preventing possible incidents. By seeking hazard reduction rather than risk reduction, ISD enables the chemical process sites to reduce potentially harmful situations in advance. It is a win-win business strategy in terms of safety and cost-optimization for a company to incorporate ISD, especially during the preliminary design phases.
This study will propose an advanced sustainable process design during the preliminary design phase with an inherently safer index and a data-based semi-automatic hazard identification tool — the DAHAZID. The inherently safer index will look at the correlation among hazardous chemical characteristics and its amounts including natural disaster aspects. Then, the DAHAZID will represent how to utilize the proposed inherently safer index with multiple case studies. This research not only will focus on the safety aspect but also will take into account profit, environment, and societal elements. As an extension version of traditional sustainability— based on balancing three primary targets, environmental protection, economic growth and societal equity— this study will establish an framework for a quick and practical appraisal in the preliminary design phase. This research will contain practical aspects for common causes of the chemical facilities accidents caused by natural disasters, and safety concepts for unit operation at an early design stage. The ultimate goal of this work is to help a chemical company to develop cost-optimal safety solutions with a reasonable guideline.
REALIABILITY AND MAINTENANCE
Maintenance Planning Using Machine Learning and Multi-Objective Stochastic Optimization
PhD CHEN – Reality and Maintenance
Maintenance planning and process operations in chemical manufacturing plants are subject to several sources of uncertainty ranging from volatile feedstock prices to uncertainty in equipment failure times. In the context of assuring the mechanical integrity of assets in ageing plants, the present research employs process systems engineering principles to develop novel optimization algorithms for preventive and predictive maintenance planning in the presence of uncertainty. The research spans different approaches to plant maintenance and consists of three aspects: (1) predictive maintenance using deep neural networks and support vector machines, (2) scheduling of turnaround activities subject to resource constraints using global event-based continuous-time optimization, and (3) preventive maintenance planning using multi-objective multi-stage stochastic programming with integer recourse. The results of the research can be used to prioritize maintenance actions, to improve overall equipment availability, and to maximize plant productivity.
A Framework for Reliability, Availability, Maintainability Optimization in Chemical Plant
PhD CHEN – Reliability and Maintenance
The problem of integrating RAM assessment and optimization into the conceptual design phase and techno-economic analysis reports is examined in this work. Integrating RAM analysis into process design has a management side and an engineering side (Grievink et al. 1993). The management side is concerned with introducing changes and affecting a transition in an organization in order to use RAM tools in design. Moen (2000) explored the challenges a major oil company dealt with when it incorporated reliability goals into the project development phase.
As for the engineering side, it is concerned with the development of reliability engineering tools to improve system effectiveness. Goel (2003) developed a framework for integrating RAM attributes into the conceptual design stage to obtain qualitative RAM targets. Thus, this work is distinguished from other existing works in that it presents a framework that can be used to determine an economically optimum level of reliability for a system/subsystem based on its costs and benefits. Reliability costs include capital and labor costs associated with improved reliability, while benefits include minimizing downtime and thus increasing revenue. Also, this work is not solely concerned with the reliability assessment of a system/subsystem, but allows for design modifications which can be utilized in process synthesis to optimize RAM. Finally, this work will introduce a modified return on investment metric incorporating RAM aspects that can be utilized in the optimization of design modification options.
Integration of Electron Impedance Spectroscopy and Microfluidics for Investigating Microbially Influenced Corrosion Using Co-Culture Biofilms
Susmitha Purnima Kotu
PhD CHEN – Reliability and Maintenance – Corrosion
Microorganisms can carry out corrosion reactions on metal surfaces resulting in microbiologically influenced corrosion (MIC). MIC is a significant problem in the oil and gas, water pipelines and tanks and costed about $460 billion globally in 2013. MIC has been extensively studied using batch reactors or large scale circulating loops. Both suffer from nutrient limitation and do not correctly represent the MIC environment. Thus, a small-scale continuous flow system that better represents flowing conditions for studying MIC is desirable.
A microfluidics-based flow system, consisting of microscale flow channels in a manner similar to pipelines where MIC is a major concern, is an attractive alternative. We developed a microfluidic flow cell by coating a pair of metal electrodes on glass. A microfluidic channel placed on this electrode pair allows corrosive microbes to be cultured on metal. This configuration allows MIC to be directly monitored using electrochemical impedance between the two electrodes while visualizing microbial community growth using confocal microscopy. Thus, this microfluidic MIC model enables integration of microbe-driven corrosion mechanisms along with microbial community growth at the metal surface.
Effects of Hydrogen on the Strength and Fracture Characteristics of Multigrain Metals
PhD MSEN – Reliability and Maintenance – Corrosion
The presence of atomic hydrogen in metals plays an important part in metals in the process industries; Metal grain structure also plays an important role in determining the strength and mechanical properties of metals. The generation, migration, and diffusion of hydrogen is difficult to experimentally study. Hydrogen’s interaction in multi-grain systems is less-well understood. By developing a hydrogen multi-grain model and mapping changes in properties to failure through a model system of palladium-hydrogen, insight is developed into the hydrogen concentration and grain-size effects on properties of metals exposed to hydrogen.
Operational Risk Assessment of Routing Flare Gas to Boiler for Cogeneration
MS SENG – Risk Assessment
Flaring is a controlled combustion process in which unwanted or excess hydrocarbon gas is released to flare stack for disposal. Flaring has a significant impact on environment and leads to economic losses. Flare gas integration to cogeneration plants is an economic alternative to mitigate flaring, benefiting from utilizing waste flare gas as fuel. Earlier studies have proved the process and economic sustainability. However, the impact of fuel quality on cogeneration plants are yet to be identified. This paper studies the effect of flare gas composition from an ethylene plant to an existing boiler during abnormal flaring. The study proposes a framework which identifies the hazards associated with variation of boiler fuel through process simulation, compares the critical operational event occurrences and the probability of incidents with base case natural gas fuel. Major outcome of the study is identifying significant effect of higher hydrogen containing flare gas on fire tube boiler radiation zone, increasing the probability of arch zone temperature, rich fuel mixture in firebox and leading to flame impingement and tube rupture in boiler.
Study of Dependence of Control Layers for Rare Events and its Application to Dynamic Risk Assessment
PhD CHEN – Risk Assessment
Process variable upsets in a process operation is caused by different sources, such as variation in feed specifications, wrong settings, control system malfunctions and operator error. These upsets lead to unprofitable process operation due to production of sub-quality products and increased energy usage resulting from a deviated process variable, and subsequently to near miss or incidents if not stopped by control layers and safety system in place. The near misses or incidents are the outliers originating from the failure of control layers. This project first aims to identify the significant process variable associated with an incident, and then study the dependence of control layers for the identified process variable upset in outlying region and predict the frequency and probability of near misses based on that.
Cumulative Risk Assessment Model to Analyze Increased Risk Due to Impaired Barriers in Offshore Oil and Gas Facilities
PhD CHEN – Risk Assessment
Most large-scale disasters occurred due to failure of multiple factors which can be technical, operational, human or organizational in nature. Even though we carry out investigations to learn from these incidents, little is done to incorporate the learnings into our risk assessment models. Most models use expert opinion to consider the contribution of human and organizational factors to risk which are difficult to collect and update and are not always accurate. Consequently, dynamic changes in risk due to deviations of various factors are not considered. This research focuses on bridging the gap for a better cumulative risk assessment that will remove complete reliance on expert opinion and use statistical learnings from past incidents and current plant data to update the risk information and predict barrier failure so that they can be better managed. The work is divided into three steps: 1. Developing a methodology for learning from incidents though analysis of incident investigation reports and generating statistical data for contributing factors behind incidents. 2. Providing a methodology for updating failure rates of technical, operational, human and organizational factors using data extracted from investigation reports 3. Developing a model that integrates all the findings and the modifications suggested in the previous steps to predict the cumulative risk existing in a facility.
Effects of Unequal Blockage Ratio and Obstacle Spacing on Flame Propagation Regime and Explosion Severity during Hydrogen Combustion
PhD CHEN – Explosions
Flame propagation and explosion behavior of hydrogen-based mixtures remain critical issues for explosion safety in nuclear power plants and refineries. Research in this area has shown that the presence of confinement and obstruction in the flame path may enhance flame acceleration due to an increase in flame instabilities resulting from flame-obstacle interaction . In addition, as the combustion process progresses, unburnt gases ahead of the flame are put into motion, generating turbulence downstream of an obstacle. This induced turbulence increases the reaction rate, further accelerating the flame. Extremely fast explosion flames can be caused by this mechanism, giving rise to severe overpressures. From the perspective of explosion safety, it is fundamental to understand what conditions a premixed deflagration accelerates and eventually leads to more severe overpressures and even, as the worst case, to a transition to detonation. Although extensive efforts have been made to understand the underlying mechanisms affecting flame acceleration in obstructed enclosures, most of the studies address obstacles with uniform conditions. This uniformity is characterized by constant obstacle spacing, shape, and blockage ratio, and may not be representative of the layout in actual industrial facilities. Therefore, this study aimed to investigate the influence of unequal area blockage and obstacle spacing on the leading shock wave speed and overall overpressure during flame propagation of hydrogen-based mixtures.
LEARNING FROM INCIDENTS AND DATA
Weak Signal Identification Using Data Mining Techniques
PhD CHEN – Learning from Incidents and Data
Catastrophic incidents in process industries still occur, even though people have been developing analysis tools of safety management to improve process safety. Learning from incident investigations could be an approach to understand unexpected emerging phenomena in the complex systems and identify the weak signals, but it only allows people learning from what had happened, and highly depends on the quality of the incident investigations. In order to understand the complex system proactively, a systematic framework is needed to understand how a complex system is supported by different functions and how the functions interact with each other leading to emergent failures. Thus, the study is first to model and simulate chemical plant operation based on a system-based technique integrating physical process, organization and human factors. With the simulation, a comprehensive set of emergent system failures, with the corresponding interactions among the functions, can be generated and exhaustively explored. Data mining techniques will be used to develop a predictive model The predictive model will help industrial people recognize and interpret the weak signals of the emergent system failures before the failures occur as early as possible, thus preventive measures can be taken. In the study, methyl-methacrylate (MMA) polymerization process is used as case study due to its runaway hazard.
Identifying Resilient Performance of an Incident Management Team during a Disaster
PhD ISEN – Resilience
Disasters, either natural or man-made, have long been a persistent threat to social systems including organization, community, and government. Uncertainty and complexity of the disasters gave rise to resilience in emergency management during disasters. Resilience is generally defined as a system’s capacity to adjust the system’s performance and to meet both routine and non-routine work demands from a disaster. To identify and enhance resilience of an Incident Management Team (IMT), the research conducts various methods including naturalistic observation, interview, and experimental study. As an initial attempt to understand the IMT’s actual operations and identify resilient performance, a novel approach called Interaction Episode Analysis (IEA) is developed and applied to a naturalistic training environment. The IEA identifies emergent team performance as an instance of work-as-done (WAD) as opposed to work-as-imagined (WAI). In addition to providing narrative accounts of adaptive team performance, the IEA enables the comparisons between WAI and WAD by focusing on essential elements of interactions such as Context, Characteristics, and Content (Three C’s). The gaps obtained from the comparison inform how to make the IMTs more resilient against unexpected incidents.
Counterfactual Thinking and Safety Behavior
PhD I/O PSYC – Human Factors
Workplace safety has been receiving increasing attention by both researchers and organizations, as it is a source of substantial costs to organizations. Incidents may result in bodily injuries and property damage. One individual factor that may be responsible for incidents is safety behavior. Little is known about how individual mental models influence safety behaviors and the mechanism of such relationships. The current study aims to examine the impact of one kind of mental thought, counterfactual thinking, on safety behavior and several important predictors of safety behavior (e.g., safety knowledge and safety motivation) proposed as explanatory mechanisms for this association. This study will provide important evidence regarding the underlying mechanisms explaining why counterfactual thinking promotes safety behavior.
Aerosol Combustion and Explosion Study
PhD CHEN – Flammability- Aerosols
Liquids can be ignited below their flash point if they are in the form of aerosol, which has attracted more attention recently because many incidents involve the explosion of aerosol. The other reason aerosol attracts more attention is the explosion of aerosol has the so called air fuel bomb effect, which results in a more dangerous situation. However, there is no established standard study procedure for aerosol explosion and combustion. Based on the similarity between a dust explosion and an aerosol explosion, we adopted the procedure of studying a dust explosion to investigate an aerosol explosion. This research involves the modification of 36-L dust explosion apparatus to 36-L aerosol explosion apparatus. The consequence quantification parameters, such as Pman and deflagration index, also have been adopted to quantify the consequence of aerosol explosion. The characteristics of the aerosol cloud are characterized through the droplets size distribution, measured through a Malvern laser, and the overall equivalence ratio within 36-L vessel. The second phase of this study is applies the CFD software, Ansys Fluent, to simulate the three processes within the experiments; droplets dispersion, ignition of the droplets, and the explosion process.
CFD Modeling of Liquefied Carbon Dioxide Discharging Through a Pipeline Full Bore Rupture
PhD CHEN – Consequence Analysis
Large amounts of substances are transported in pipelines worldwide. This activity represents a hazard that needs to be quantitatively assessed through discharge models that predict the pipeline outflow. When a pipeline transporting a pressurized liquefied gas ruptures (e.g., CO2, LPG pipelines), the expansion generates a phase transition which results in a two-phase release. Numerous researchers have developed one-dimensional models to describe the discharge of liquefied carbon dioxide when a pipeline full bore rupture occurs. However, a systematic study on how the accuracy of different equations of state affects the depressurization prediction is lacking for this case. The objective of this research is to develop a two-dimensional depressurization model using Computational Fluid Dynamics (CFD) to predict the pressure profiles along the pipeline, as well as the decompression wave speed, discharge rate, and phase transition; while investigating the effect of different equations of state. To validate the 2-D decompression model, full bore rupture experiments with dense-phase CO2 pipelines are used. This validation step evaluates the adequacy of the assumptions implemented to model the transient phenomenon.
Effects of Flow Conditions on the Performance of Corrosion Inhibitors in Pipelines
Major process safety incidents have been caused by corrosion all over the world. These incidents are usually linked to leakage of highly flammable liquids or gases, causing severe damage to the environment, affecting people, and ultimately resulting in monetary losses. Despite the increasing knowledge of corrosion, efforts are still needed to understand different damage mechanisms and their control methods. One of the most common active corrosion mitigation techniques for internal corrosion in pipelines is the use of corrosion inhibitors. Film-forming corrosion inhibitors create a protective layer that can be influenced by different flow characteristics and flow regimes. These changes in hydrodynamic conditions have an effect on the performance of corrosion inhibitors. This work focuses on the fundamental understanding of the hydrodynamics of the system coupled with analysis of the corrosion behavior using electrochemical corrosion techniques. The objective of this research is to study the influence of different flow parameters on the efficiency of corrosion inhibitors under corrosive environments such as CO2.
Nanowires for Thermoelectric Applications
Nanowires are one-dimensional nanostructures with diameters less than one hundred nanometers and a length-to-width ratio greater than one thousand. Nanowires can be used in a wide variety of applications, including thermoelectric power generation, which involves applying a temperature gradient across a thermoelectric material to produce current. Thermoelectric generators can be a viable source of clean energy and bolster the nation’s energy security amid dwindling reserves of conventional fossil fuels. A major advantage of using thermoelectric generators is that they have no moving parts, making them silent, robust, and reliable. However, these generators currently operate at very low efficiencies. Nanowires are promising for thermoelectric electricity generation as they have been shown to improve the efficiency of thermoelectric generators over bulk materials. An increase in the efficiency of thermoelectric generators will lead to an increase in the economic feasibility of processes such as waste heat harvesting in aircraft and cars, resulting in accelerated commercialization of the generators. Innovation in the production of thermoelectric generators is especially important in space related research, as these devices can be used to reliably power probes in deep space, where photovoltaic cells are ineffective. In fact, radioisotope thermoelectric generators currently power NASA’s Voyager and Cassini spacecraft. Thus, nanowires produced from novel materials have the potential to increase the efficiencies of thermoelectric generators and pave the way for widespread use of these generators in both terrestrial and space applications. Currently, research is being performed to synthesize and characterize nanowires composed of magnesium silicide and pseudo-binary alloys of magnesium silicide and magnesium stannide. These materials are attractive candidates for thermoelectric power generation due to their low toxicity, low density, and high relative abundance.
Biodegradable Low-Temperature Oil Herder
When oil is spilled offshore, the harsh conditions at the remote location may make the oil spill response challenging. The weathering effect and evaporation of oil can also slow down the cleaning process. In the open water region, the spilled oil could spread much quicker due to gravity, wind, current and wave effects, which would hinder the mitigation process. The countermeasures of open-water oil spillage generally include mechanical recovery, oil herders combining in-situ burning, and dispersant biodegradation. Among those methods, oil herder is more applicable at remote location with limited resources, i.e. arctic area. We are developing a biodegradable herder formula based on Konjac powder to increase the crude oil clean up ratio. The herder/cosurfactant formula is easily applicable in terms of effectiveness, cost, and nontoxic nature. Comparing to other products in the market, the Konjac based oil herder has a better oil herding performance at low temperature.
A Novel Approach of Large-Scale Synthesis and Assembly of Nanostructured Materials
Large-scale synthesis and assembly of nanomaterials, e.g., nanowires, is one of the major challenges of commercial applications of nanotechnology. For instance, zinc phosphide (Zn3P2) nanowires, which are synthesized on a zinc foil and manually scraped from the substrate, contain an appreciable amount of bulk zinc metal when they are assembled into a pellet form by hydraulic press. This results in loss in quantity of Zn3P2 nanowires and reduction in performance of the device. Herein, a simple approach to synthesizing a bulk-scale assembly of Zn3P2 nanowires is developed. In this method, a zinc pellet with sacrificial salt is directly converted to a Zn3P2 nanowire pellet. This technology can be applied to other types of nanomaterials and allows the assembly of nanostructured materials into bulk form without introducing any byproduct.