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The complexity of process industry and the consequences that Na-Tech events could produce in terms of damage to equipment, release of dangerous substances (flammable, toxic, or explosive), and environmental consequences have prompted the scientific community to focus on the development of efficient methodologies for Quantitative Seismic Risk Analysis (QsRA) of process plants. Several analytical and numerical methods have been proposed and validated through representative case studies. Nevertheless, the complexity of this matter makes their applicability difficult, especially when a rapid identification of the critical components of a plant is required, which may induce hazardous material release and thus severe consequences for the environment and the community. Accordingly, in this paper, a screening methodology is proposed for rapid identification of the most critical components of a major-hazard plant under seismic loading. It is based on a closed-form assessment of the probability of damage for all components, derived by using analytical representations of the seismic hazard curve and the fragility functions of the equipment involved. For this purpose, fragility curves currently available in the literature or derived by using low-fidelity models could be used for simplicity, whereas the parameters of the seismic hazard curve are estimated based on the regional seismicity. The representative damage states (DS) for each equipment typology are selected based on specific damage states/loss of containment (DS/LOC) matrices, which are used to individuate the most probable LOC events. The risk is then assessed based on the potential consequences of a LOC event, using a classical consequence analysis, typically adopted in risk analysis of hazardous plants. For this purpose, specific probability classes will be used. Finally, by associating the Probability Class Index (PI) with Consequence Index (CI), a Global Risk Index (GRI) is derived, which provides the severity of the scenario. This allows us to build a ranking of the most hazardous components of a process plant by using a proper risk matrix. The applicability of the method is shown through a representative case study.
The process industry is composed of a large number of types of equipment; a seismic event could cause the simultaneous damage of several units and the consequent release of dangerous substances, as well as the development of multiple accidental chains. The classical consequence-based methods for risk assessment, largely employed for the evaluation of the risk of process industries (
In the presence of Na-Tech events (technological accidents triggered by natural hazard as earthquakes), damage and loss of containment (LOC) depend on the structural behavior of the equipment; seismic events could induce structural damage that is not known
A new probabilistic method for the seismic risk assessment in the process industry, based on the Monte Carlo simulations technique, has been recently proposed by
Despite the numerous advantages previously mentioned, this methodology requires a large amount of data and a high computational cost. Processing time increases with the amount of the equipment because it grows the number of possible initial seismic damage scenarios. Generally, it is quite prohibitive to expect that the plant manager might include in the risk analysis the individuation of seismic starting damage scenarios with their probability of occurrence and the domino effects, mainly because of the large time necessary for the data elaboration and the recognized complexity of these operations, due to the high uncertainty of the models and the interpretation of the results. Therefore, it is usually required to assess only the risk of the single equipment with respect to DS that generate LOC events. Since the total number of installations in hazardous facilities can be very large and not all installations contribute significantly to the risk, it is not worthwhile to include all types of equipment in a quantitative seismic risk assessment. Therefore, a preliminary selection method appears necessary.
Accordingly, in this paper, a screening methodology for the identification of the most critical units for seismic risk analysis of major-hazard facilities is proposed. This methodology has a double advantage: it responds to the needs of plant managers and increases the efficiency of a Quantitative Seismic Risk Analysis (QsRA) by reducing the amount of relevant equipment and the computational time.
A critical analysis of QsRA methodologies and some relevant issues can be found in (
Nevertheless, several issues are still under discussion. For example, the definition of nominal life is a controversial point that should be analyzed with the due attention because of the extremely harsh conditions in which the equipment of a process plant usually works. Typically, an important factor is adopted to account for the criticalities of these structures, even though this does not favor the harmonization of the uniform risk conditions required by the codes (
Fragility Analysis of industrial equipment and relevant LOC conditions is crucial for a credible risk and consequence-based analysis of process plants. A large number of methodologies for deriving fragility curves, especially for the most diffused equipment like storage tanks, can be found in the literature (
Finally, the risk analysis of a process plant can be quantified by combining seismic hazard, vulnerability, and consequence analyses. In addition, given that the seismic action could generate a multiplicity of damage conditions, the mutual interaction should also be accounted for, including the possible domino effects (
Consequently, in what follows, a short-cut methodology for a decision-making analysis implying the selection of critical equipment of major-hazard industries is proposed and applied to a realistic case study. The selection method here allows us to assess also the risk of the single equipment with respect to DS that generate LOC events.
In this section, a screening methodology for the identification of the most critical units in major-hazard industrial plants under seismic loading is proposed and described. The method is based on the following main steps: 1. Preliminary identification of the critical units of the industrial plant. 2. Estimation of site-specific seismic hazard. 3. Fragility analysis of each critical unit previously selected. 4. LOC events identification with DS/LOC correlation matrices. 5. Evaluation of the mean annual frequency of LOC events. 6. Decision-making analysis and ranking of scenarios.
In the first step, a preliminary selection of the units is performed through the use of an index method, which is based on the idea of using synthetic indexes to account for seismic hazard, vulnerability, and exposition (consequences). A ranking of all possible critical equipment is then built, which allows the preliminary identification of the most critical units. Subsequently, the seismic hazard analysis and the fragility analysis of each critical unit previously identified can be performed; the mean annual frequency of their most relevant DS is then calculated using a closed-form solution for seismic hazard and vulnerability. Based on the identification of a DS-LOC matrix, it is possible to evaluate LOC events and quantify the consequence class to which the analyzed equipment belongs. Starting from the mean annual frequency of the damage of each unit, a ranking of the most critical units can be drawn up, and consequently, the seismic risk conditions can be managed through a decision-making analysis and the selection of proper seismic mitigation strategies.
Flowchart of the selection method of critical units in major-hazard plants.
Since the total number of installations in the major-hazard industrial plant can be very large, a preliminary selection of critical components is a very important task of the methodology because it allows the selection of the most critical units, decreasing the amount of equipment to be considered in the following steps and drastically reducing the computational time. According to ( • Slim vessels: cylindrical equipment with a large height-to-diameter ratio (usually from 5 to 30). They can be vertical elements anchored to the foundation, both free and restrained along the height, or horizontal vessels on saddle supports. Columns, stacks, reactors, and horizontal vessels are also included. • Equipment directly placed on the ground: this category consists of equipment characterized by comparable dimensions in the three directions and high masses. The most important category is represented by storage tanks. • Equipment on support structures: this category includes equipment supported by columns (furnaces, spherical tanks, compressors, tanks on legs, air-cooler, etc.\enleadertwodots) or elevated equipment placed on metallic frames. • Piping systems. • Buildings: in this paper, this category will be excluded since the risk assessment methods are well established in the literature.
Structural typologies of a process plant.
Special attention should be paid to elements that have no direct consequences because, for example, they contain nonhazardous material but are crucial for the safety of the plant (e.g., firefighting water tank), or they represent secondary structural elements. In the first case, they should be clearly included in the ranking list, discussed in
The identification of the most critical equipment of a major-hazard industrial plant depends on several factors: 1) level of expected seismic hazard, 2) seismic design and vulnerability of the equipment, 3) conservation and maintenance status of the equipment, 4) physical effects related to the dangerousness of the stored material, 5) exposition to the external zones, and 6) domino effects.
A reasonable selection criterion should necessarily integrate all these aspects in a rational manner. At this end, the international literature provides at least three methods ( • Preliminary calculation of the damage probability for each equipment, evaluation of the relevant consequences, and risk recomposition ( • Use of vulnerability curves. • Risk Index methods (
The first two methods require a high level of information and analysis that generally is not suitable in this step for quick screening of numerous equipment. For this reason, a model based on risk indices appears more reasonable. Sometimes equipment design and other pieces of information are not available, especially in an older process plant, so that the choice of a simplified method such as Risk Index methods becomes mandatory. Starting from the definition of seismic risk, whose main ingredients are hazard, vulnerability, and exposure, a synthetic
Usually, the Seismic Hazard Index
The Seismic Vulnerability Index • equipment typology; • state of preservation; • degradation phenomena; • typical DS.
The most direct way to define
Because the significant DS for the risk analysis of a process plant are those associable with a material release (LOC), it is reasonable to adopt fragility curves related to an “extensive DS” that could reasonably generate a LOC (
For each unit, the vulnerability class and the corresponding Vulnerability Index (
According to
Fragility functions of plant equipment.
Vulnerability classes of process plant equipment.
Typology | Vulnerability class | ||||
---|---|---|---|---|---|
— | 4 | 3 | 2 | 1 | |
— | Columns/reactors | x | x | — | — |
Slim vessels | Stacks | — | — | x | x |
— | Horizontal vessels | — | x | x | — |
Squat eq. on ground | Unanchored tanks | — | x | x | — |
— | Anchored tanks | — | — | — | x |
— | Compressors | — | x | x | — |
Squat eq. on support structures | Heat exchangers | — | x | x | — |
— | Support structures | — | — | x | — |
Piping systems | Pipes and pipe racks | x | x | — | — |
The highest class represents structures with the highest seismic vulnerability. Given that each structural category could be characterized by a certain geometrical and mechanical variability, it has been deemed necessary to individuate, for each structural category, adjacent vulnerability classes. For example, the highest columns are typically more vulnerable than the other (
Finally, the Exposure Index
In order to identify and complete the preliminary identification of the most critical units, the Risk Index values of
Risk Index and risk level for process plant equipment.
Risk Index | Risk level |
---|---|
12 |
High risk |
9 |
Medium risk |
6 |
Low risk |
0 ≤ |
Limited risk |
The seismic hazard of a site is usually derived through a full PSHA, which is expressed as a mean annual frequency of exceeding a certain intensity measure (IM) (
As a simplified alternative, many technical codes, as the Italian one (
A crucial aspect is the local seismic response that could strongly affect the risk assessment. Consequently, the local seismic response should be always conducted, including the soil effects. This is possible by adopting a frequency-dependent amplification factor or directly integrating the site effects into PSHA (
The seismic vulnerability of a process plant equipment can be effectively quantified using the fragility curves. Fragility functions provide the probability of exceeding a certain LS, given a ground-shaking intensity. For this type of structure, the PGA is usually considered an efficient and sufficient IM (
Static or dynamic seismic analysis carried out with proper low-fidelity or high-fidelity models can be used to build fragility curves. For a given number of return periods, consistent with the expected damage, a minimum number of seven accelerograms (
The collapse condition, as defined in the codes, refers to conditions that potentially could cause a structural collapse. Specific collapse fragility curves have been introduced recently, which are based on models able to follow step-by-step the structural collapse. Therefore, the term “collapse” must be intended as a condition of potential collapse. This conservative choice guarantees appropriate margins with respect to phenomena considered more disastrous of the mere collapse, being involved in potentially catastrophic consequences with the release of hazardous material. The collapse LS for each category of equipment are listed in
Vulnerability classes of process plant equipment.
Structural typology | Damage state (DS) | Engineering demand parameter (EDP) | Limit state (LS) | LOC1 | LOC2 | LOC3 |
---|---|---|---|---|---|---|
— | — | — | — | Continuous release from a 10 mm hole | Continuous release from the connected pipe section | Instantaneous release of the whole content |
Slim vessels | Collapse of the base connection | Base stresses and rotation | Complete plasticization | No | No | Yes |
— | Excessive rotation of pipe flange joint | Rotation at the pipe attachment | First release rotation | Yes | No | No |
— | Excessive rotation of pipe flange joint | Rotation at the pipe attachment | Collapse rotation | No | Yes | No |
Squat equipment on ground | Elastic buckling (EB) | Meridional stress | Buckling limit | No | Yes | No |
— | Elephant-foot buckling (EFB) | Meridional stress | Buckling limit | No | Yes | No |
— | Sliding | Base shear | Sliding force | No | Yes | No |
Squat equipment on support structures | Excessive plasticization of the support structure | Drift and shear forces | Structural collapse | No | Yes | No |
— | Excessive rotation of the flange joint | Rotation at the pipe attachment | First release rotation | Yes | No | No |
— | Excessive rotation of the flange joint | Rotation at the pipe attachment | Collapse rotation | No | Yes | No |
Pipes and pipe racks | Yielding of pipe | Stress and strain | Yielding stress and deformation | Yes | No | No |
— | Collapse of pipe | Stress and strain | Ultimate stress | No | Yes | No |
— | Damage to the support structures due to excessive plasticization | Drift and shear forces | Structural collapse | No | No | No |
Concerning the LS associated with the failure of pipes connected to the equipment and coming from other units, literature definitions have been adopted, which are associated with standard LOC conditions often used in the QRA of process industries. Three different LOC events are herein adopted, whose definition is reported in
Definition of LOC events in process plant equipment.
LOC1 | LOC2 | LOC3 | |
---|---|---|---|
Definition | Continuous release from a 10 mm hole | Continuous release from a full bore of the pipe | Instantaneous release of full content |
Effects on the equipment | Limited damage of the structure and limited material release | Consistent damage and release, with possible domino effects | Structural collapse, catastrophic losses, and domino effects |
For the definition of LOC events from pipes, the results of experimental tests can be used; for example, in (
In case of process plants, the risk calculation necessary involves the effects of the content release from a critical unit (tank, pipe, etc.\enleadertwodots), which can cause important effects on the surrounding equipment and community. Thus, only DS involving the release of dangerous substances (toxic, flammable, explosive, or polluting) or critical equipment for emergency phases (e.g., firefighting water tanks) are important in seismic risk analysis, while the other DS can be neglected. As stated before, nonhazardous equipment, whose collapse may induce failure in some surrounding elements, should be included in the list of elements at risk.
The aim of this part of the framework is to use a reasonable criterion to correlate seismic DS and LOC resulting from the structural damage analysis for different categories of industrial equipment. In this respect, the probability of LOC for a given level of seismic damage should be calculated. Nevertheless, the contributions in this direction have been particularly limited. The few contributions present in literature are typically based on empirical approaches (
In case of slim vessels category, LOC may occur due to the excessive rotation of bolted flange joints or to the failure of the anchorage; the first one is caused by the structure deformation and the plasticization of the base plate and/or of the skirt, while the second one is due to the excessive rotation and stresses in the anchorage systems. Squat equipment placed on the ground is mainly represented by storage tanks. For this kind of equipment, LOC may occur due to the following DS: elastic buckling (EB), elephant-foot buckling (EFB), sliding, and overturning. The first three DS can cause the pipes’ detachment, while the overturning DS can generate a complete release of the full content. For the equipment on support structures, the DS mainly related to the LOC are the excessive rotation of pipe connection and the failure of the support structure, with following overturning and loss of the whole content.
The definition of DS for pipe racks is carried out in terms of lateral deformation (
The MAF of exceeding a given DS can be performed by applying the total probability theorem, combining the seismic hazard curve with the fragility curves. The general equation is as follows:
Consequently, the risk calculation can be estimated using the following formula:
Thus, the calculation of the MAF of exceeding the damage can be immediately derived. Finally, by means of the above-mentioned DS/LOC matrices, it is possible to obtain the annual frequency of exceeding the LOC events (LOC1, LOC2, and LOC3) and thus to evaluate the degree of exposure of the equipment.
From the calculation of the MAF of exceeding the LOCs, it is possible to derive the ranking of the most critical components, identifying the ones for which it may be necessary to intervene with appropriate mitigation systems. The reference values of the LOC probability are not easily identifiable from the literature. In the present work, it is suggested to evaluate the consequences of a given LOC event through the correlated event tree. Once the frequency of the different scenarios is known, it is possible to evaluate a consequence-based risk level. For this purpose, it may refer to the probability classes characterized by the Probability Class Index (PI) shown in
Probability classes (PI) (
Mean annual frequency (MAF) of an event | ||||
---|---|---|---|---|
p |
10E-6 ≤ p |
10E-4 ≤ p |
10E-3 ≤ p |
p ≥ 10E-1 |
Rare | Rather unlikely | Unlikely | Quite likely | Likely |
PI = 1 | RI = 2 | PI = 3 | PI = 4 |
|
By associating each PI with a Consequence Index (CI), it is possible to derive a Global Risk Index (GRI) product of the first two, which provides a measure of the severity of the scenario. The definition of the CI is provided in
Damage thresholds and Consequence Index (CI).
Damage threshold ( |
||||
---|---|---|---|---|
Accidental scenario | High lethality and structural damage | Initial lethality | Irreversible injury | Reversible injury |
Stationary thermal radiation | 12.5 |
7.0 |
5.0 |
3.0 |
Flashfire | LFL | 1/2 LFL | — | — |
UCVE | 0.3 bar | 0.14 bar | 0.07 bar | 0.03 bar |
Toxic release | L50 | — | IDLH | — |
CI | 5 | 4 | 3 | 2 |
The ranking and the related priorities can be established in the form of a risk matrix (
Risk matrix for decision-making analysis.
The proposed selection method of critical units and their risk assessment have been carried out for an idealized case study (
Plan view of the case study.
In addition, the plant is equipped with an emergency system, which includes a blown-down pipeline and a fire protection system with compressors, water storage tanks, and a buried pipeline network. A series of seven return periods (
The plant has more than 400 units, but not all of them contain hazardous materials; for this reason, the safety report has been firstly analyzed, which allows us to extract a list of 139 units, potentially at risk. Subsequently, for the preliminary identification of the most critical units, the proposed screening methodology has been applied. For each unit, the Risk Index
Risk Index
For the derivation of fragility curves, a linear dynamic analysis has been performed. This choice depends essentially on the hypothesis that a short-cut methodology is generally based on low-fidelity models. For each equipment, a FE model has been built using MIDAS software (
Examples of finite element models of the main equipment.
These models have been employed to build fragility curves by means of Cloud Analysis. In this respect, by adopting linear models, the response spectrum analysis would be deemed sufficient rather than using T-H analysis. Nevertheless, given the complexity of the equipment, even in the linear field, it is preferable to perform analysis in the time domain to better account for this dynamic complexity.
Examples of fragility curves for a stabilization oil column are shown in
The results show that the slug catcher is extremely vulnerable against damage conditions that imply structural collapse (LOC3). This is due to the weakness of the anchor bolts of the foundation, whereas LOC1 and LOC2 events are less frequent. In fact, the structure itself is particularly rigid, and high rotations of the bolted flange joints are unlikely. Differently, the columns are more flexible structures; this indicates a more likely excessive rotation of the bolted flange joints at the pipe-column connections. As a matter of fact, LOC1 has the highest frequency. Elevated equipment could result vulnerable to earthquakes due to the filtering effect of the support structure. For example, the heat exchanger of
It is clear from this framework that the plant contains equipment particularly vulnerable to earthquake that could also generate severe scenarios with important consequences. As can be seen in
MAF of LOC events.
Units | LOC1 | LOC2 | LOC3 |
---|---|---|---|
Column | 1.90E-03 | 5.37E-03 | 6.62E-04 |
Slug catcher | 3.30E-09 | 2.91E-09 | 7.53E-03 |
Oil storage tank | — | 3.34E-04 | 7.94E-06 |
Vertical separator | 1.43E-03 | 4.74E-04 | 7.46E-04 |
Elevated heat exchanger | 2.55E-03 | 4.60E-04 | 7.30E-04 |
Contour lines of the impact: LOC2 scenario due to the oil spillage from a storage tank.
Parameters for the scenario simulation.
Fixed parameters | Scenario |
---|---|
Pipe section (inches) | 24 |
Model release | Continuous release from a pipe section |
Temperature (°c) | 28 |
Relative humidity (%) | 45 |
Total volume stored ( |
20.000 |
Scenario | Chemical burns inducing a pool fire |
The continuous release of crude oil material from the connected pipe section of the oil tank (LOC2) has a MAF of occurrence equal to 3.34E-04. According to
By analyzing all possible damage scenarios triggered by the critical unit, GRI can be evaluated to create a ranking of events with decreasing level of risk and draw up a list of priorities.
In conclusion, a dedicated decision-making analysis with the indication of the most suitable mitigation strategies would be necessary.
The paper deals with a new selection method for risk analysis of critical process plant components under seismic loading. An index-based approach is proposed to identify the most critical damage scenarios entailing hazardous material release. Using closed-form solutions for seismic hazard and fragility analysis, a simplified solution for the risk assessment is proposed.
Based on predefined DS/LOC matrices, the MAF of standardized LOC events is evaluated. Finally, a decision-making analysis with the use of a simple risk-consequence matrix allows the identification of the most critical equipment.
The methodology has been applied to a realistic case study, demonstrating its simplicity and novelty in evaluating the most frequent LOC events that could generate hazardous consequences.
Future developments will concern the application of the last stage of the method and the application of more rigorous procedures for its validation. Moreover, the domino effects will be implemented to complete the framework of risk analysis of hazardous facilities.
In the knowledge of the authors, this is the first attempt to summarize in a simplified but complete framework a complex matter as the risk assessment of existing process plants under seismic loading.
The original contributions presented in the study are included in the article/Supplementary Material; further inquiries can be directed to the corresponding author.
FP developed the conceptual aspects of the methodology; DC implemented the methodology; SC dealt with the analysis of the case study.
This project has received funding from the Italian Ministry of Education, University and Research (MIUR) 482 in the frame of the Departments of Excellence (Grant L. 232/2016).
SC was employed by the company Safeplant Srl.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations or those of the publisher, the editors, and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
The company Safeplant Srl is also acknowledged for the discussions and help in developing the procedure.