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Accident modeling of toxic gas-containing flammable gas release and explosion on an offshore platform
Journal of Loss Prevention in the Process Industries 65 (2020) 104118
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Accident modeling of toxic gas-containing flammable gas release and explosion on an offshore platform Dongdong Yang , Guoming Chen *, Ziliang Dai Centre for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Toxic gas-containing flammable gas Leakage Vapor cloud explosion Comprehensive consideration Accident modeling CFD
Toxic gas-containing flammable gas leak can lead to poisoning accidents as well as explosion accidents once the ignition source appears. Many attempts have been made to evaluate and mitigate the adverse effects of these accidents. All these efforts are instructive and valuable for risk assessment and risk management towards the poisoning effect and explosion effect. However, these analyses assessed the poisoning effect and explosion effect separately, ignoring that these two kinds of hazard effects may happen simultaneously. Accordingly, an inte grated methodology is proposed to evaluate the consequences of toxic gas-containing flammable gas leakage and explosion accident, in which a risk-based concept and the grid-based concept are adopted to combine the effects. The approach is applied to a hypothetical accident scenario concerning an H2S-containing natural gas leakage and explosion accident on an offshore platform. The dispersion behavior and accumulation characteristics of released gas as well as the subsequent vapor cloud explosion (VCE) are modeled by Computational Fluid Dy namics (CFD) code Flame Acceleration Simulator (FLACS). This approach is concise and efficient for practical engineering applications. And it helps to develop safety measures and improve the emergency response plan.
1. Introduction Leakage and explosion are major potential chain accidents with high frequency (Ramsay et al., 1994; Kalantarnia et al., 2010; BP, 2010; Ottem€ oller and Evers, 2010; Yang et al., 2019). Several efforts have been made to predict the consequences of leakage and explosion accidents. In the field of evaluation of flammable gas dispersion (Gupta and Chan, 2016), conducted a study to model the leakage and dispersion of flammable gas with a time-varying leakage rate. Instantaneous variation of flammable gas cloud was obtained. And the dispersion results ob tained by the time-varying leak rate were compared with those obtained using the time-averaged constant leak rate. The study confirms that using the constant leakage rate may be relatively reasonable for systems with slow depressurization rates. But it may lead to inaccurate estima tion results when it comes to systems that depressurize at a high rate. In a study conducted by (Dadashzadeh et al., 2013), FLACS was utilized to model the dispersion of blowout gas and the subsequent vapor cloud explosion (VCE) against the “Deepwater Horizon” accident. Comparing the research results with the investigation results of BP, the authors demonstrated that FLACS was applicable to predict the conse quences of dangerous gas leakage and explosion.
In a study conducted by (Savvides et al., 2001), taking the offshore modules as the object, a series of experiments with different configu rations were simulated by FLACS. The simulations results were compared with measured data obtained by the JIP Module Experiments. The comparison has confirmed that FLACS can predict the dispersion behavior and accumulation characteristics of released gas with good accuracy. (Middha et al., 2009) conducted validation effort for FLACS where a series of hydrogen dispersion experiments with different leakage con ditions were simulated. Subsonic jets, sonic jets, impinging jets with low and high momentum, and liquid hydrogen releases were considered, respectively. In general, the modeling results are in reasonable agree ment with the experimental data. In a study conducted by (Li et al., 2018a, 2018b), a systematic CFD-based simulation was performed to predict the consequences of a subsea release. Accidental released gas from a subsea gas pipeline re leases into seawater and rises to the sea surface. Then a gas pool is generated on the sea surface. The gas pool turns into a leakage source, and the flammable gas disperses into the atmosphere. Besides, a jack-up drilling platform is assumed to be located in the downwind area of the gas pool. The impact of dispersion and deflagration on this drilling
* Corresponding author. E-mail addresses: [email protected] (D. Yang), [email protected] (G. Chen). https://doi.org/10.1016/j.jlp.2020.104118 Received 13 October 2019; Received in revised form 16 February 2020; Accepted 20 March 2020 Available online 22 March 2020 0950-4230/© 2020 Elsevier Ltd. All rights reserved.
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platform was discussed. The corresponding risk management measures were also recommended by (Li et al., 2018a, 2018b). In the field of toxic gas dispersion evaluation (Lovreglio et al., 2016), proposed an integrated approach to predict the consequences of toxic gas dispersion. The approach was applied to a hypothetical scenario where the dynamic assessment results were compared with the results obtained by the static approach which ignoring the evacuation move ment, and the proposed approach proved to improve the accuracy of assessment results significantly. (Lyu et al., 2018) pointed out the importance of performing the off-site consequence analysis for all chemical dealing companies. Moreover, the effect of safety measure regarding mitigation barriers was confirmed by two representative accident scenarios. The consequences of flammable gas or toxic gas release, dispersion and consequent explosion accidents were extensively studied by Centre for Offshore Engineering and Safety Technology of China University of Petroleum (East China) (Deng et al., 2012; Liu et al., 2015; Zhu et al., 2010; Shi et al., 2019). Different research methods, such as experiments and numerical simulations, were used. A series of accident scenarios based on different parameters or different installations were studied (Li et al., 2019; Shi et al., 2018a,b; Wei et al., 2014; Zhang and Chen, 2010; Zhu and Chen, 2010). However, assuming that the flammable gas containing toxic gas, and exposed to ignition, the poisoning effect and explosion effect will occur continuously. There are comprehensive studies on consequence pre diction about flammable gas containing toxic gas leakage accidents. E.g., a blowout accident involving hydrogen sulfide was simulated by (Yang et al., 2017). Poisoning risk area and explosion risk area are identified separately, i.e., the poisoning effect and explosion effect are considered respectively. Also, there are some attempts focusing on the domino ef fect (Khakzad et al., 2013, 2018; Ni et al., 2016), predicting the cumu lative adverse effects due to accident escalation. However, the theory of domino effect is of inadequacy to the current work, since the domino effect always refers to the accident starting from one unit and spreading to different units with great randomness and strong uncertainty. The current study focuses on the flammable gas containing toxic gas leakage and explosion accident, in which only a facility is involved and the cu mulative adverse effects are inevitable. The above studies did not consider the dual consequences against flammable gas containing toxic gas release and explosion accident, i.e., the poisoning effect and the explosion effect were considered respec tively. A combination of these two kinds of effects is imperative because the poisoning effect and explosion effect are inevitable during flam mable gas containing toxic gas leakage and explosion accident. Accordingly, comprehensive consideration of the explosion effect and the poisoning effect is emphasized, and a systematic approach is pro posed against similar accidents. Then the proposed approach is pre sented through a hypothetical scenario concerning H2S-containing natural gas leakage and explosion chain accidents on an offshore plat form. The main innovation of the current study is that the explosion effect and the poisoning effect are considered comprehensively, while the previous studies take these two hazard effects into account sepa rately. The dispersion of the released gas and the subsequent VCE are predicted by FLACS. FLACS is a leading 3D CFD software which has been widely used in process industry (Huser and Kvernvold, 2000; Qiao and Zhang, 2010; Li et al., 2014; Huang et al., 2017; Shi et al., 2018a,b), and the availability and accuracy of the tool have been validated against numerous experiments with different scales and different scenarios (Savvides et al., 2001; Middha et al., 2009; Hansen et al., 2010; Bleyer et al., 2012).
unknown source of ignition and the vapor cloud. The consequences caused by fire and hydrogen sulfide were 21 deaths. In January 2018, a tanker collision accident between bulk freighter CF Crystal and tanker Sanchi happened in the East China Sea (Li et al., 2018a, 2018b), which caused a cargo tank of tanker Sanchi failure and sulfur condensate oil release. The sulfur condensate oil then vapourized and a fire accident followed which lasted for several days. Under the influence of fire and sulfide, a total of 32 crew members on tanker Sanchi lost their lives. In another accident in 2014, a coal gas leakage accident occurred at a coal coking plant in Shanxi Province, China (People’s Daily Online, 2014). The escaped gas formed a flammable gas cloud and ignited by a source of ignition, which caused severe fire and explosion. There were 4 deaths and 31 injuries in this accident. The ‘‘12.2300 sour gas well blowout is another notorious accident, which occurred in Kai County, Sichuan, China (Ma Q and Zhang L, 2011). A large amount of hydrogen sulfide-containing natural gas burst from Luo 16H well after the failure of well control. The accidents resulted in the loss of 243 lives and nearly 10,000 poisoned. Fortunately, potential fire & explosion accidents did not happen. It is not difficult to imagine that if fire or explosion accidents had happened after the acci dental blowout accident, the consequences of the accident would have been severer. In the above accidents, the sufferers suffered from both poisoning effects and potential explosion effects. The sufferers may mainly have affected by one of these two hazard effects, but the other hazard effect increases the difficulty of the emergency evacuation. Therefore, it is meaningful to conduct the comprehensive evaluation of the poisoning effect and explosion effect to develop an appropriate response plan, provide guidance to prevent and mitigate the impact of similar accidents. 3. Integrated methodology Fig. 1 introduces the proposed methodology for toxic gas-containing flammable gas leakage and explosion accident consequence modeling. The detailed procedure is described below. The first step is geometric modeling. In this step, the geometric modeling is conducted by the pre-processor of FLACS. All structural components can be converted into primitives, such as boxes, cylinders, ellipsoids, generally truncated cones, and complex polyhedrons. How ever, in general, only the basic primitives, including boxes and cylin ders, are recommended. Because there are limitations for the rest of primitives: (1) they have little contribution to turbulence and drag force
2. Review of past accidents In 2007, a natural gas containing hydrogen sulfide leakage accident occurred in the Kab-121 platform in the Gulf of Mexico (Yang et al., 2019). Then a fire accident followed due to the contact between an
Fig. 1. Methodology framework. 2
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for there are not sub-grid models against them; (2) it takes more time for porosity calculation with these primitives (GexCon, 2015). In addition, Anticipated Congestion Method (ACM) is suggested by adding small geometrical details to meet the anticipated congestion (Davis et al., 2011). The second step is the ventilation and dispersion simulation. Ventilation simulation helps to get a stable wind field and ventilation distribution, where the areas with poor ventilation can be identified. Then the dispersion simulation is conducted. The leak source parame ters, wind condition parameters, and boundary conditions are set ac cording to the accident scenarios. Both constant leakage and timedependent leakage can be considered. In addition, FLACS can define constant wind profiles as well as fluctuating wind profiles. In the third step, the concentration field of released gas, including toxic gas profile and the flammable gas profile, is obtained. 2D contour plots, 3D spatial distribution, and equivalent cloud volume (ESC) (Hansen et al., 2013; Qi et al., 2017; Li et al., 2017) can be extracted as required. The toxic gas profile contributes to identifying the poisoning consequences, while the explosion consequences can be gotten by igniting the flammable gas. In the fifth step, the explosion simulation is performed based on the flammable gas profile. FLACS is utilized to model the VCE. To improve the accuracy and continuity of numerical calculation, the data-dump technique (Li et al., 2016) is suggested to be applied to gas explosion simulations. The explosion overpressure distribution can be obtained in this step. In the sixth step, the consequences caused by the poisoning effect and explosion effect are integrated. A risk-based concept is adopted to calculate the overall risk, the grid-based concept and isoline are intro duced to represent the overall risk. According to (Assael and Kakosimos, 2010), the effects of VCE on human mainly include 1) direct or primary effects, 2) indirect effects. Direct or primary effects refer to the adverse impacts caused by explosion overpressure, which can cause damage to human organs or result in deaths (such as eardrum rupture or death from lung damage). Indirect effects refer to the adverse effects caused by dynamic explosion fragments or stationary objects. The direct or pri mary effects of VCE are merely considered in the current study. Only death is considered when studying the adverse effects of toxic gas on humans. To simplify the risk assessment process, the consequences severity is mapped into scores (S), as shown in Table 1. According to (CCPS, 1999), the probit model is introduced to calculate the probabilities of different adverse effects (Pr) of H2S and VCE (Eq. (1) and (2)).
1 Pr ¼ pffiffiffiffiffi 2π
Y 5
Adverse effects H2S VCE
(4)
R ¼ Rvce þ RH2 S
(5)
The gas dispersion process satisfies the continuity equation, the laws of mass conservation, momentum conservation, and energy conserva tion. The dispersion process also satisfies mixture fraction and fuel mass fraction transport equation considering the multiple compositions in released gas. All these conversation equations can be represented in general as (GexCon, 2015): � � � ∂ ∂ ∂ ∂φ þ Sφ ρuj φ ¼ ρΓ φ (6) ðρφÞ þ ∂t ∂xj ∂xj ∂xj where ϕ represents the general variable, including variables such as mass, momentum, energy, turbulent kinetic energy and so on; ρ repre sents the gas mixture density; t represents time; uj represents the velocity component in j-direction; Гϕ represents the dispersion coefficient of the general variable ϕ; Sϕ represents the source term. Turbulence is modeled by the k ε model. It is an eddy viscosity model that solves turbulent kinetic energy and dissipation of turbulent kinetic energy. An eddy viscosity is introduced to model the Reynolds stress tensor. The turbulent kinetic energy equation, the dissipation of turbulent kinetic energy equation and the Reynolds stress tensor are shown as follows (GexCon, 2015): � � � ∂ ∂ ∂ ueff ∂k βj ρuj k ¼ (7) βj þ βv Pk βv ρε ðβv ρkÞ þ ∂t ∂xj ∂xj σ k ∂xj �
Table 1 Hazard effects and their severity scores.
Severity scores
Death
Eardrum rupture
Lung damage (death)
10
6
10
�
� ∂ ∂ ∂ ueff ∂ε β þ β v Pε β ρu ε ¼ ðβ ρεÞ þ ∂t v ∂xj j j ∂xj j σε ∂xj �
VCE
3.008 1.93 6.91
4.1. Turbulence model
(3)
H2S
31.42 15.6 77.1
Rvce ¼ maxðRe ; Rl Þ
ρu’’i u’’j ¼ ueff
Hazard effects
k2
4. Theoretical model
where Y denotes probit variable; k1 and k2 are constants; V denotes exposure dose of hazardous factors; x denotes integral variable. The value of different constants and computing methods of V are summa rized in Table 2 (Zhang and Chen, 2010; Assael and Kakosimos, 2010). where C denotes the concentration of the H2S, ppm or mg/m3; T denotes the time interval, min; ΔP denotes maximum overpressure, N/m2. Further, the corresponding risk can be calculated as follows. Rj ¼ Prj � Sj
k1
where Rvce denotes the risk of VCE; Re denotes the risk when only considering the eardrum rupture; Rl denotes the risk when only considering the lung damage.
(2)
2 e ðx =2Þ dx
∞
Death Eardrum rupture Lung damage (death)
V P 1.43 C T ΔP ΔP
where Rj denotes the risk of different adverse effects caused by H2S or VCE; Prj denotes the probabilities and Sj represents the consequence severity of different adverse effects; j denotes different adverse effects, which contains death caused by H2S, eardrum rupture and lung damage (death) caused by VCE. The higher value between two kinds of effects caused by VCE is chosen to represent the risk of VCE (Eq. (4)). Once the risk of each effect is determined, the risk-based concept is adopted to combine the indi vidual effect based on the additivity characteristic of the risk-based concept. The cumulative risk at any location is calculated by combining individual risks (Eq. (5)).
(1)
Y ¼ k1 þ k2 ln V Z
Table 2 The value of different constants and computing method of V.
∂ui ∂uj þ ∂xj ∂xi
�
2 ρkδij 3
C2ε β v ρ
ε2 k
(8) (9)
where k represents the turbulent kinetic energy; ε represents turbulent kinetic energy dissipation rate; βv represents volume porosity; βj repre sents area porosity in the j-direction; ueff represents the effective tur bulence viscosity; δij represents the stress tensor; Pk and Pε represent the production of turbulent kinetic energy and the production of dissipation 3
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respectively; σ k and σε represents Prandtl–Schmidt number of k and ε, which are taken as 1.0 and 1.3; C2ε is a constant, which is taken as 1.92.
Table 3 Dangerous gas composition for the hypothetical accident.
4.2. Combustion model The burning process of the flammable gas cloud involves three stages: laminar burning regime, quasi-laminar burning regime, and turbulent burning regime. When a combustible cloud in quiescent con ditions is ignited, the laminar burning arises. Then under the influence of instability effect, e.g., flow dynamics, Rayleigh-Taylor, etc., the burning speed increases and becomes quasi-laminar. Constantly hin dered by obstacles, the flame changes into a turbulent state. The Burning velocity models in each stage are shown as follows (GexCon, 2015): � �γp P SL ¼ SL0 (10) P0 � � Sql ¼ SL 1 þ Cql Raf ST ¼ 1:81SL0:784 u’0:412 l0:196 μ 1
Component
Methane
Ethane
Propane
Pentane
Hydrogen sulfide
Concentration (%)
27
32
16
23
2
(11) 0:196
(12)
where SL represents the laminar burning velocity; P represents the pressure produced by combustion; P0 represents the atmospheric pres sure; Sql represents the quasi-laminar burning velocity; Cql is a constant associated with gas clouds; Rf represents the flame radius; a represents a constant, which is taken as 0.5; ST represents the turbulent burning velocity; u’ represents the turbulence rate fluctuation; l1 represents the turbulence integral scale; μ represents the assumed constant.
Fig. 2. Transient leakage profile.
5. Case study
equipment is omitted in Fig. 3. Point A, B, C are feature points.
The proposed methodology is applied to a hypothetical H2S-con taining natural gas leakage and explosion accident on an offshore plat form. The offshore platform consists of a process module and an accommodation module. There are three decks in the process module, i. e., the main deck, the middle deck, and the lower deck. Process equip ment is mainly concentrated on the middle deck and the lower deck, including power device, compressor, fuel gas device, acid gas, dehy dration & mercury removal device, and some pipe & pipe rack. The accommodation module is located at the bow. In this hypothetical ac cident scenario, a high-pressure system located at the middle deck fails to cause the dangerous gases to release. The released gas spreads and enters the lower deck. A VCE is triggered when the gas then reached an ignition source at 150s after the leak. Throughout the entire process of the hypothetical accident, the individuals are threatened by poisoning effect due to H2S at the beginning, and then explosion effect risk be comes the major threat after ignition. It should be noted that the domino theory is inadequate for the current study since only the direct effects or primary effects on humans are considered, whereas the effect of VCE on facilities and the effect of explosion fragments on humans or other equipment are not taken into account. Therefore, chain accidents with a certain development trajectory rather than domino accidents are studied in this paper. The wind direction which blows in the accommodation module is adopted to consider the worst wind scenario, where the reference wind speed is 3 m/s and Pasquill class is D at an ambient temperature of 20 � C. The gas composition is summarized in Table 3. The leakage rate is timevarying with the interference of the emergency shutdown system (ESD) and blowdown system. The detection and initiation time for the ESD system costs 60s, and isolation costs 30s. Then the blowdown system starts up 50s later. The transient leakage rates are calculated according to the reference by (Spouge, 1999), and illustrated in Fig. 2. In this section, the module in the lower deck is of interest to illustrate the proposed approach. Fig. 3 provides the layout of the modules in the lower deck. The accommodation module and the life module are sepa rated by a blast wall. There are two cabins in the lower deck. The process
5.1. Geometric modeling All these objects are modeled by the pre-processor of FLACS. In the process of constructing the geometric model, only the spatial layout and external structure are of concern, while the internals of cabins and the accommodation module are not of interest. To meet the needs of accu racy in geometric details and efficiency in computational time, the distributed porosity concept is used to represent geometric details and the sub-grid model is employed to represent the flame smaller than grid size. Fig. 4 represents the geometry model and the layout of the modules of the offshore platform. The dimension of the model is 30 m � 60 m � 40 m and there is a total of 24,116 components in this model. The monitoring points matrix covering the whole process module of the lower deck is set. After the geometry model is imported, the grid model based on grid sensitivity analysis is built. The process of building the grid model is followed (Yang et al., 2019). 5.2. Ventilation According to standard procedures defined in GexCon and NORSOK, ventilation simulation should be performed before dispersion simulation to establish a stable wind field in the computational domain. The areas with ventilation condition where dangerous gas tend to gather can also be identified in the ventilation simulation. 8 different wind directions are utilized to perform ventilation simulation (NORSOK, 2010). In this hypothetical accident scenario, the reference wind speed is 3 m/s and the reference height is 10 m. Thereby the wind speed profile at different height can be calculated according to wind profile function (GexCon, 2015). Besides the wind condition parameters, the setting of other param eters are as follows: the boundary condition type of inflow and parallel boundary is Wind, while the boundary condition type of the outflow and sea bottom surface is Nozzle. CFLC and CFLV, which are CourantFriedrich-Levy number based on sound velocity and fluid flow velocity 4
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respectively, are taken as 20 and 2. The value of the aerodynamic roughness length is 0.0002 m. MODD and DTPLOT are both set as 1, which means that the time interval for data output is 1s. All the above parameters are also applied to dispersion simulation. 5.3. Dispersion The data from dispersion simulation mainly include the flammable gas profile and the toxic gas profile. The flammable gas profile is dum ped and used as the input parameter for explosion simulation, while the toxic gas profile is extracted to calculate the effect of poisoning. Fig. 5 depicts the dispersion results of flammable gas, including 3D spatial distribution of flammable gas at different times and the Q9 (Gupta and Chan, 2016; Yang et al., 2019) time-varying curve. The explosive limits of flammable gas are calculated according to the Lechteilier Principle where the lower and upper limits of monitoring interval (2.46%– 11.85%) for flammable gas are adopted. As can be seen in Fig. 5a)–5c), the spatial distribution of flammable gas varies with time. At the steady leakage stage, the spatial distribution of flammable gas increases with time due to insufficient ventilation. Then the spatial distribution of flammable gas decreases gradually at the leakage rate attenuation stage because the ventilation plays an increasingly important role with the decrease of leakage rate. The variation of Q9 represents a similar tra jectory of the spatial distribution of flammable gas according to Fig. 5d). The Q9 reaches its maximum of 3251 m3 at 107.6s and then drops down. Fig. 6 illustrates 2D contour plots of H2S (1.5 m above the lower deck). Both the spatial distribution and areas with high concentration H2S first increase and then decrease. The H2S on the plane of 1.5 m above the lower deck reaches the accommodation module at about 30s after the leakage. Almost the whole plane is occupied by H2S with concentration over 3200 mg/m3 at 90s. Comparing to the dispersion result of H2S at 90s, the spatial distribution of H2S, especially the areas with a concentration of H2S over 5000 mg/m3 decreases significantly at 150s. 5.4. Explosion The data-dump technique is adopted to perform explosion simula tion. In this hypothetical accident scenario, the flammable gas exposed to the ignition source at 150s after the gas leak. Therefore, the dispersion simulation results are dumped at 150s. After defining a new grid model for explosion simulation, an explosion scenario is set up by mapping the dumped dispersion simulation results into this new grid model. The dispersion simulation analysis is restarted at the beginning of the ex plosion simulation, by which a real gas cloud is detonated. Thereby the calculation accuracy is improved. Fig. 7 depicts the explosion overpressure distribution at different time points on the plane of 1.5 m above the lower deck. The explosion lasts for a short time. The explosion overpressure wave propagates to the lower deck at about 0.44s after ignition and reaches its maximum of 6.385 bar at about 0.53s after ignition. Then the explosion overpressure on this plant vanishes at about 0.6s after ignition. The distribution of explosion overpressure is irregular since there are numerous equipment, pipes, and pipe racks. Complex layout and high congestion/confinement lead to the increase of explosion overpressure partially, which has been discussed by (Huser et al., 2009).
Fig. 3. The layout of the modules in the lower deck.
6. Results and discussion 6.1. Poisoning assessment The monitored data for the concentration of H2S of each monitoring point is extracted, and the concentration of H2S at every moment is of concern, i.e., the H2S concentration-time curve at each coordinate is expected rather than the maximum concentration. Then the probit variables of toxic gas can be calculated according to Eq. (2). Converting
Fig. 4. Geometric model of an offshore platform.
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Fig. 5. Dispersion results of flammable gas.
Fig. 6. Dispersion results of H2S.
the probit variables into probabilities of fatality (Eq. (3)), then the risk index of the poisoning effect based on the probit model can be estimated (Eq. (4)), which is illustrated in Fig. 8. The risk indexes are in a range from 0 to 10. The higher values of poisoning risk are mainly below the leakage source. It is interesting to note that the H2S risk profile in Fig. 8 is not consistent with the H2S dispersion result in Fig. 6. The main reason is that the integral concentration and time is of concern instead of the maximum concentration. In certain areas, although the maximum H2S
concentration is considerable, the H2S spreading is a time-consuming process which makes the integration time decrease. Thereby, the probit variables decrease, further leading to a reduction in the proba bility of fatality and risk index. For example, although the maximum concentration of point A is about 2516.73 mg/m3 (Fig. 9), the duration of concentration over 2500 mg/m3 is less than 20s, and a comparably low-risk index less than 1 is derived. However, if continuous exposure to H2S with 2500 mg/m3 lasted for 150s, the corresponding risk index 6
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Fig. 7. Explosion overpressure over the plant.
would reach 4.964. 6.2. Explosion assessment Fig. 10 depicts the explosion risk profile according to the proposed approach. Similarly, the explosion risk profile is distinguished from the explosion overpressure distribution (Fig. 7) at any time. Instead of focusing on the explosion overpressure profile at a fixed time, the maximum overpressure for each monitoring point throughout the ex plosion process is of concern. The probabilities of ear rupture and lung damage (death) are calculated respectively based on the maximum ex plosion overpressure at each monitoring point. Afterward, the corre sponding risk indexes can be calculated considering the severity scores of different hazard effects. The higher risk index at each monitoring point is chosen and plotted on the plant (Fig. 10). 6.3. Integration of individual consequences The poisoning effect and explosion effect are integrated in this sec tion. The grid-based concept is introduced to combine these two kinds of hazard effects. Fig. 11 illustrates the contour plot for the cumulative risk indexes with a range from 0 to 20. It is noteworthy that although there is a low-risk index in some areas considering individual hazard effects (Figs. 8 and 10), the risk index may have increased significantly by considering another hazard effect. For example, Fig. 12 gives the time-
Fig. 8. H2S risk profile.
Fig. 9. H2S concentration time-history curve of point A.
Fig. 10. Explosion risk profile. 7
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Fig. 13. H2S concentration time-history curve of point C.
7. Conclusions Fig. 11. Cumulative risk profile.
Toxic gas-containing flammable gas leakage is very dangerous, and catastrophic accidents may be induced if the gas is exposed to an ignition source. Both the poisoning effect and explosion effect do indeed exist and cannot be neglected in the chain accident. Moreover, these two hazard effects have nothing with the domino effect. An integrated approach is proposed to fill the gap that the poisoning effect and explosion effect are considered separately in the current ef forts. To evaluate the cumulative hazard effects, the poisoning effect and explosion effect are predicted firstly. Then the risk-based concept is introduced to combine the hazard effects. And the grid-based concept is adopted to represent the total hazard effects. The proposed approach is applied to H2S-containing flammable gas leakage and explosion chain accidents on an offshore platform. Some interesting phenomena have been noticed. In some areas, the individual hazard effect may be insig nificant, but the cumulative hazard effects can be remarkable in view of both the poisoning effect and explosion effect. And there may be both significant poisoning effects and explosion effects in some areas. Thus, terribly appalling results can be derived. In general, more conservative evaluation results are obtained by comparing to only considering indi vidual hazard effects. The evaluation results confirm that the proposed approach is comprehensive and outperforms the approach concerning the individual hazard effect. Considering cumulative hazard effects contributes to intrinsic safety design and improves emergency plan to wards the potential chain accidents referring to multiple hazard effects. The integrated approach can also be extended to other accident conse quences evaluation which involving two or more hazard effects.
Fig. 12. H2S concentration time-history curve of point B.
history curve for the H2S concentration of point B (Fig. 3), the corre sponding risk index for poisoning is 0.432 as per Eq. (1)- (3). The maximum explosion overpressure of point B is 1.991 bar, the risk index for the explosion which is 9.865 can be calculated accordingly. Although there is a lower risk index for the poisoning effect, the cumulative risk index cannot be ignored. Besides, there are significant risk indexes in some areas for both poisoning effect and overpressure effect, which implies that these areas are extremely hazardous. For example, Fig. 13 depicts the H2S concentration time-history curve of point C, and its poisoning risk index is 9.987. The maximum explosion overpressure and explosion risk index of point C are 2.432 bar and 9.998, respectively. Both the poisoning effect and explosion effect are considerable. Hence, the cumulative risk index is significant. The results demonstrate that the proposed approach is effective for evaluating the cumulative effects of chain accidents. It is inadequate to consider individual hazard effects concerning the toxic gas-containing flammable gas leakage and explo sion accident since either of poisoning risk or explosion risk may be dominant in the total risk. Conversely, both the poisoning risk and the explosion risk are crucial and should be considered when assessing the consequences of similar accidents.
CRediT authorship contribution statement Dongdong Yang: Writing - review & editing, Writing - original draft, Investigation, Formal analysis, Methodology, Conceptualization. Guoming Chen: Supervision, Project administration, Funding acquisi tion. Ziliang Dai: Validation, Data curation. Acknowledgments The authors gratefully acknowledge the financial support provided by the National Key R&D Program of China (No: 2017YFC0804501). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jlp.2020.104118. 8
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Journal of Loss Prevention in the Process Industries 65 (2020) 104118
Declaration of interests
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Assael, M., Kakosimos, K., 2010. Fires, Explosions, and Toxic Gas Dispersions: Effects Calculation and Risk Analysis. CRC Press. Bleyer, A., Taveau, J., Djebaïli-Chaumeix, N., et al., 2012. Comparison between FLACS explosion simulations and experiments conducted in a PWR Steam Generator casemate scale down with hydrogen gradients. Nucl. Eng. Des. 245 (3), 189–196. BP, 2010. Deepwater Horizon Accident Investigation Report. http://www.wash ingtonpost.com/wpsrv/politics/docume-nts/Deepwater Horizon Accident Investigation Report Executive summary.pdf. CCPS, 1999. Guidelines for Consequence Analysis of Chemical Releases. American Institute of Chemical Engineers. Dadashzadeh, M., Abbassi, R., Khan, F., et al., 2013. Explosion modeling and analysis of BP Deepwater Horizon accident. Saf. Sci. 57 (Complete), 150–160. Davis, S., Hansen, O., Rogstadkjernet, L., et al., 2011. Benefits of risk-based design through probabilistic consequence modeling. In: Proceedings of the 7th Global Congress on Process Safety, Chicago, IL, USA. Deng, H., Chen, G., Zhu, Y., et al., 2012. Simulation and experiment of gas leakage and dispersion in complex topography. J. China Univ. Petrol. (Edit. Nat. Sci.) 36 (1), 122–126þ135. GexCon, 2015. FLACS v10.4 User’s Manual, Norway. Gupta, S., Chan, S., 2016. A CFD based explosion risk analysis methodology using time varying release rates in dispersion simulations. J. Loss Prev. Process. Ind. 39, 59–67. Hansen, O., Gavelli, F., Ichard, M., et al., 2010. Validation of FLACS against experimental data sets from the model evaluation database for LNG vapor dispersion. J. Loss Prev. Process. Ind. 23 (6), 857–877. Hansen, O., Gavelli, F., Davis, S., et al., 2013. Equivalent cloud methods used for explosion risk and consequence studies. J. Loss Prev. Process. Ind. 26 (3), 511–527. Huang, Y., Ma, G., Li, J., 2017. Multi-level explosion risk analysis (MLERA) for accidental gas explosion events in super-large FLNG facilities. J. Loss Prev. Process. Ind. 45, 242–254. Huser, A., Kvernvold, O., 2001. Explosion risk analysis - development of a general method for gas dispersion analyses on offshore platforms. Parall. Comput. Fluid Dyn. 517–524. Kalantarnia, M., Khan, F., Hawboldt, K., 2010. Modelling of BP Texas City refinery accident using dynamic risk assessment approach. Process Saf. Environ. Protect. 88 (3), 191–199. Khakzad, N., Khan, F., Amyotte, P., et al., 2013. Domino effect analysis using Bayesian networks. Risk Anal.: Int. J. 33 (2), 292–306. Khakzad, N., Amyotte, P., Cozzani, V., et al., 2018. How to address model uncertainty in the escalation of domino effects? J. Loss Prev. Process. Ind. 54, 49–56. Li, J., Madhat, A., Ma, G., 2014. New correlation for vapor cloud explosion overpressure calculation at congested configurations. J. Loss Prev. Process. Ind. 31, 16–25. Li, J., Ma, G., Abdel-Jawad, M., et al., 2016. Gas dispersion risk analysis of safety gap effect on the innovating FLNG vessel with a cylindrical platform. J. Loss Prev. Process. Ind. 40, 304–316. Li, J., Ma, G., Hao, H., et al., 2017. Optimal blast wall layout design to mitigate gas dispersion and explosion on a cylindrical FLNG platform. J. Loss Prev. Process. Ind. 49, 481–492. Li, X., Chen, G., Zhu, H., et al., 2018a. Gas dispersion and deflagration above sea from subsea release and its impact on offshore platform. Ocean Eng. 163, 157–168. Li, C., Ren, H., Zhou, X., et al., 2018b. Deduction and analysis of the cause of the sinking of the Sanchi tanker. J. Harbin Eng. Univ. 39 (7), 1123–1131.
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Accident modeling of toxic gas-containing flammable gas release and explosion on an offshore platform Dongdong Yang , Guoming Chen *, Ziliang Dai Centre for Offshore Engineering and Safety Technology, China University of Petroleum (East China), Qingdao, 266580, Shandong, China
A R T I C L E I N F O
A B S T R A C T
Keywords: Toxic gas-containing flammable gas Leakage Vapor cloud explosion Comprehensive consideration Accident modeling CFD
Toxic gas-containing flammable gas leak can lead to poisoning accidents as well as explosion accidents once the ignition source appears. Many attempts have been made to evaluate and mitigate the adverse effects of these accidents. All these efforts are instructive and valuable for risk assessment and risk management towards the poisoning effect and explosion effect. However, these analyses assessed the poisoning effect and explosion effect separately, ignoring that these two kinds of hazard effects may happen simultaneously. Accordingly, an inte grated methodology is proposed to evaluate the consequences of toxic gas-containing flammable gas leakage and explosion accident, in which a risk-based concept and the grid-based concept are adopted to combine the effects. The approach is applied to a hypothetical accident scenario concerning an H2S-containing natural gas leakage and explosion accident on an offshore platform. The dispersion behavior and accumulation characteristics of released gas as well as the subsequent vapor cloud explosion (VCE) are modeled by Computational Fluid Dy namics (CFD) code Flame Acceleration Simulator (FLACS). This approach is concise and efficient for practical engineering applications. And it helps to develop safety measures and improve the emergency response plan.
1. Introduction Leakage and explosion are major potential chain accidents with high frequency (Ramsay et al., 1994; Kalantarnia et al., 2010; BP, 2010; Ottem€ oller and Evers, 2010; Yang et al., 2019). Several efforts have been made to predict the consequences of leakage and explosion accidents. In the field of evaluation of flammable gas dispersion (Gupta and Chan, 2016), conducted a study to model the leakage and dispersion of flammable gas with a time-varying leakage rate. Instantaneous variation of flammable gas cloud was obtained. And the dispersion results ob tained by the time-varying leak rate were compared with those obtained using the time-averaged constant leak rate. The study confirms that using the constant leakage rate may be relatively reasonable for systems with slow depressurization rates. But it may lead to inaccurate estima tion results when it comes to systems that depressurize at a high rate. In a study conducted by (Dadashzadeh et al., 2013), FLACS was utilized to model the dispersion of blowout gas and the subsequent vapor cloud explosion (VCE) against the “Deepwater Horizon” accident. Comparing the research results with the investigation results of BP, the authors demonstrated that FLACS was applicable to predict the conse quences of dangerous gas leakage and explosion.
In a study conducted by (Savvides et al., 2001), taking the offshore modules as the object, a series of experiments with different configu rations were simulated by FLACS. The simulations results were compared with measured data obtained by the JIP Module Experiments. The comparison has confirmed that FLACS can predict the dispersion behavior and accumulation characteristics of released gas with good accuracy. (Middha et al., 2009) conducted validation effort for FLACS where a series of hydrogen dispersion experiments with different leakage con ditions were simulated. Subsonic jets, sonic jets, impinging jets with low and high momentum, and liquid hydrogen releases were considered, respectively. In general, the modeling results are in reasonable agree ment with the experimental data. In a study conducted by (Li et al., 2018a, 2018b), a systematic CFD-based simulation was performed to predict the consequences of a subsea release. Accidental released gas from a subsea gas pipeline re leases into seawater and rises to the sea surface. Then a gas pool is generated on the sea surface. The gas pool turns into a leakage source, and the flammable gas disperses into the atmosphere. Besides, a jack-up drilling platform is assumed to be located in the downwind area of the gas pool. The impact of dispersion and deflagration on this drilling
* Corresponding author. E-mail addresses: [email protected] (D. Yang), [email protected] (G. Chen). https://doi.org/10.1016/j.jlp.2020.104118 Received 13 October 2019; Received in revised form 16 February 2020; Accepted 20 March 2020 Available online 22 March 2020 0950-4230/© 2020 Elsevier Ltd. All rights reserved.
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platform was discussed. The corresponding risk management measures were also recommended by (Li et al., 2018a, 2018b). In the field of toxic gas dispersion evaluation (Lovreglio et al., 2016), proposed an integrated approach to predict the consequences of toxic gas dispersion. The approach was applied to a hypothetical scenario where the dynamic assessment results were compared with the results obtained by the static approach which ignoring the evacuation move ment, and the proposed approach proved to improve the accuracy of assessment results significantly. (Lyu et al., 2018) pointed out the importance of performing the off-site consequence analysis for all chemical dealing companies. Moreover, the effect of safety measure regarding mitigation barriers was confirmed by two representative accident scenarios. The consequences of flammable gas or toxic gas release, dispersion and consequent explosion accidents were extensively studied by Centre for Offshore Engineering and Safety Technology of China University of Petroleum (East China) (Deng et al., 2012; Liu et al., 2015; Zhu et al., 2010; Shi et al., 2019). Different research methods, such as experiments and numerical simulations, were used. A series of accident scenarios based on different parameters or different installations were studied (Li et al., 2019; Shi et al., 2018a,b; Wei et al., 2014; Zhang and Chen, 2010; Zhu and Chen, 2010). However, assuming that the flammable gas containing toxic gas, and exposed to ignition, the poisoning effect and explosion effect will occur continuously. There are comprehensive studies on consequence pre diction about flammable gas containing toxic gas leakage accidents. E.g., a blowout accident involving hydrogen sulfide was simulated by (Yang et al., 2017). Poisoning risk area and explosion risk area are identified separately, i.e., the poisoning effect and explosion effect are considered respectively. Also, there are some attempts focusing on the domino ef fect (Khakzad et al., 2013, 2018; Ni et al., 2016), predicting the cumu lative adverse effects due to accident escalation. However, the theory of domino effect is of inadequacy to the current work, since the domino effect always refers to the accident starting from one unit and spreading to different units with great randomness and strong uncertainty. The current study focuses on the flammable gas containing toxic gas leakage and explosion accident, in which only a facility is involved and the cu mulative adverse effects are inevitable. The above studies did not consider the dual consequences against flammable gas containing toxic gas release and explosion accident, i.e., the poisoning effect and the explosion effect were considered respec tively. A combination of these two kinds of effects is imperative because the poisoning effect and explosion effect are inevitable during flam mable gas containing toxic gas leakage and explosion accident. Accordingly, comprehensive consideration of the explosion effect and the poisoning effect is emphasized, and a systematic approach is pro posed against similar accidents. Then the proposed approach is pre sented through a hypothetical scenario concerning H2S-containing natural gas leakage and explosion chain accidents on an offshore plat form. The main innovation of the current study is that the explosion effect and the poisoning effect are considered comprehensively, while the previous studies take these two hazard effects into account sepa rately. The dispersion of the released gas and the subsequent VCE are predicted by FLACS. FLACS is a leading 3D CFD software which has been widely used in process industry (Huser and Kvernvold, 2000; Qiao and Zhang, 2010; Li et al., 2014; Huang et al., 2017; Shi et al., 2018a,b), and the availability and accuracy of the tool have been validated against numerous experiments with different scales and different scenarios (Savvides et al., 2001; Middha et al., 2009; Hansen et al., 2010; Bleyer et al., 2012).
unknown source of ignition and the vapor cloud. The consequences caused by fire and hydrogen sulfide were 21 deaths. In January 2018, a tanker collision accident between bulk freighter CF Crystal and tanker Sanchi happened in the East China Sea (Li et al., 2018a, 2018b), which caused a cargo tank of tanker Sanchi failure and sulfur condensate oil release. The sulfur condensate oil then vapourized and a fire accident followed which lasted for several days. Under the influence of fire and sulfide, a total of 32 crew members on tanker Sanchi lost their lives. In another accident in 2014, a coal gas leakage accident occurred at a coal coking plant in Shanxi Province, China (People’s Daily Online, 2014). The escaped gas formed a flammable gas cloud and ignited by a source of ignition, which caused severe fire and explosion. There were 4 deaths and 31 injuries in this accident. The ‘‘12.2300 sour gas well blowout is another notorious accident, which occurred in Kai County, Sichuan, China (Ma Q and Zhang L, 2011). A large amount of hydrogen sulfide-containing natural gas burst from Luo 16H well after the failure of well control. The accidents resulted in the loss of 243 lives and nearly 10,000 poisoned. Fortunately, potential fire & explosion accidents did not happen. It is not difficult to imagine that if fire or explosion accidents had happened after the acci dental blowout accident, the consequences of the accident would have been severer. In the above accidents, the sufferers suffered from both poisoning effects and potential explosion effects. The sufferers may mainly have affected by one of these two hazard effects, but the other hazard effect increases the difficulty of the emergency evacuation. Therefore, it is meaningful to conduct the comprehensive evaluation of the poisoning effect and explosion effect to develop an appropriate response plan, provide guidance to prevent and mitigate the impact of similar accidents. 3. Integrated methodology Fig. 1 introduces the proposed methodology for toxic gas-containing flammable gas leakage and explosion accident consequence modeling. The detailed procedure is described below. The first step is geometric modeling. In this step, the geometric modeling is conducted by the pre-processor of FLACS. All structural components can be converted into primitives, such as boxes, cylinders, ellipsoids, generally truncated cones, and complex polyhedrons. How ever, in general, only the basic primitives, including boxes and cylin ders, are recommended. Because there are limitations for the rest of primitives: (1) they have little contribution to turbulence and drag force
2. Review of past accidents In 2007, a natural gas containing hydrogen sulfide leakage accident occurred in the Kab-121 platform in the Gulf of Mexico (Yang et al., 2019). Then a fire accident followed due to the contact between an
Fig. 1. Methodology framework. 2
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Journal of Loss Prevention in the Process Industries 65 (2020) 104118
for there are not sub-grid models against them; (2) it takes more time for porosity calculation with these primitives (GexCon, 2015). In addition, Anticipated Congestion Method (ACM) is suggested by adding small geometrical details to meet the anticipated congestion (Davis et al., 2011). The second step is the ventilation and dispersion simulation. Ventilation simulation helps to get a stable wind field and ventilation distribution, where the areas with poor ventilation can be identified. Then the dispersion simulation is conducted. The leak source parame ters, wind condition parameters, and boundary conditions are set ac cording to the accident scenarios. Both constant leakage and timedependent leakage can be considered. In addition, FLACS can define constant wind profiles as well as fluctuating wind profiles. In the third step, the concentration field of released gas, including toxic gas profile and the flammable gas profile, is obtained. 2D contour plots, 3D spatial distribution, and equivalent cloud volume (ESC) (Hansen et al., 2013; Qi et al., 2017; Li et al., 2017) can be extracted as required. The toxic gas profile contributes to identifying the poisoning consequences, while the explosion consequences can be gotten by igniting the flammable gas. In the fifth step, the explosion simulation is performed based on the flammable gas profile. FLACS is utilized to model the VCE. To improve the accuracy and continuity of numerical calculation, the data-dump technique (Li et al., 2016) is suggested to be applied to gas explosion simulations. The explosion overpressure distribution can be obtained in this step. In the sixth step, the consequences caused by the poisoning effect and explosion effect are integrated. A risk-based concept is adopted to calculate the overall risk, the grid-based concept and isoline are intro duced to represent the overall risk. According to (Assael and Kakosimos, 2010), the effects of VCE on human mainly include 1) direct or primary effects, 2) indirect effects. Direct or primary effects refer to the adverse impacts caused by explosion overpressure, which can cause damage to human organs or result in deaths (such as eardrum rupture or death from lung damage). Indirect effects refer to the adverse effects caused by dynamic explosion fragments or stationary objects. The direct or pri mary effects of VCE are merely considered in the current study. Only death is considered when studying the adverse effects of toxic gas on humans. To simplify the risk assessment process, the consequences severity is mapped into scores (S), as shown in Table 1. According to (CCPS, 1999), the probit model is introduced to calculate the probabilities of different adverse effects (Pr) of H2S and VCE (Eq. (1) and (2)).
1 Pr ¼ pffiffiffiffiffi 2π
Y 5
Adverse effects H2S VCE
(4)
R ¼ Rvce þ RH2 S
(5)
The gas dispersion process satisfies the continuity equation, the laws of mass conservation, momentum conservation, and energy conserva tion. The dispersion process also satisfies mixture fraction and fuel mass fraction transport equation considering the multiple compositions in released gas. All these conversation equations can be represented in general as (GexCon, 2015): � � � ∂ ∂ ∂ ∂φ þ Sφ ρuj φ ¼ ρΓ φ (6) ðρφÞ þ ∂t ∂xj ∂xj ∂xj where ϕ represents the general variable, including variables such as mass, momentum, energy, turbulent kinetic energy and so on; ρ repre sents the gas mixture density; t represents time; uj represents the velocity component in j-direction; Гϕ represents the dispersion coefficient of the general variable ϕ; Sϕ represents the source term. Turbulence is modeled by the k ε model. It is an eddy viscosity model that solves turbulent kinetic energy and dissipation of turbulent kinetic energy. An eddy viscosity is introduced to model the Reynolds stress tensor. The turbulent kinetic energy equation, the dissipation of turbulent kinetic energy equation and the Reynolds stress tensor are shown as follows (GexCon, 2015): � � � ∂ ∂ ∂ ueff ∂k βj ρuj k ¼ (7) βj þ βv Pk βv ρε ðβv ρkÞ þ ∂t ∂xj ∂xj σ k ∂xj �
Table 1 Hazard effects and their severity scores.
Severity scores
Death
Eardrum rupture
Lung damage (death)
10
6
10
�
� ∂ ∂ ∂ ueff ∂ε β þ β v Pε β ρu ε ¼ ðβ ρεÞ þ ∂t v ∂xj j j ∂xj j σε ∂xj �
VCE
3.008 1.93 6.91
4.1. Turbulence model
(3)
H2S
31.42 15.6 77.1
Rvce ¼ maxðRe ; Rl Þ
ρu’’i u’’j ¼ ueff
Hazard effects
k2
4. Theoretical model
where Y denotes probit variable; k1 and k2 are constants; V denotes exposure dose of hazardous factors; x denotes integral variable. The value of different constants and computing methods of V are summa rized in Table 2 (Zhang and Chen, 2010; Assael and Kakosimos, 2010). where C denotes the concentration of the H2S, ppm or mg/m3; T denotes the time interval, min; ΔP denotes maximum overpressure, N/m2. Further, the corresponding risk can be calculated as follows. Rj ¼ Prj � Sj
k1
where Rvce denotes the risk of VCE; Re denotes the risk when only considering the eardrum rupture; Rl denotes the risk when only considering the lung damage.
(2)
2 e ðx =2Þ dx
∞
Death Eardrum rupture Lung damage (death)
V P 1.43 C T ΔP ΔP
where Rj denotes the risk of different adverse effects caused by H2S or VCE; Prj denotes the probabilities and Sj represents the consequence severity of different adverse effects; j denotes different adverse effects, which contains death caused by H2S, eardrum rupture and lung damage (death) caused by VCE. The higher value between two kinds of effects caused by VCE is chosen to represent the risk of VCE (Eq. (4)). Once the risk of each effect is determined, the risk-based concept is adopted to combine the indi vidual effect based on the additivity characteristic of the risk-based concept. The cumulative risk at any location is calculated by combining individual risks (Eq. (5)).
(1)
Y ¼ k1 þ k2 ln V Z
Table 2 The value of different constants and computing method of V.
∂ui ∂uj þ ∂xj ∂xi
�
2 ρkδij 3
C2ε β v ρ
ε2 k
(8) (9)
where k represents the turbulent kinetic energy; ε represents turbulent kinetic energy dissipation rate; βv represents volume porosity; βj repre sents area porosity in the j-direction; ueff represents the effective tur bulence viscosity; δij represents the stress tensor; Pk and Pε represent the production of turbulent kinetic energy and the production of dissipation 3
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respectively; σ k and σε represents Prandtl–Schmidt number of k and ε, which are taken as 1.0 and 1.3; C2ε is a constant, which is taken as 1.92.
Table 3 Dangerous gas composition for the hypothetical accident.
4.2. Combustion model The burning process of the flammable gas cloud involves three stages: laminar burning regime, quasi-laminar burning regime, and turbulent burning regime. When a combustible cloud in quiescent con ditions is ignited, the laminar burning arises. Then under the influence of instability effect, e.g., flow dynamics, Rayleigh-Taylor, etc., the burning speed increases and becomes quasi-laminar. Constantly hin dered by obstacles, the flame changes into a turbulent state. The Burning velocity models in each stage are shown as follows (GexCon, 2015): � �γp P SL ¼ SL0 (10) P0 � � Sql ¼ SL 1 þ Cql Raf ST ¼ 1:81SL0:784 u’0:412 l0:196 μ 1
Component
Methane
Ethane
Propane
Pentane
Hydrogen sulfide
Concentration (%)
27
32
16
23
2
(11) 0:196
(12)
where SL represents the laminar burning velocity; P represents the pressure produced by combustion; P0 represents the atmospheric pres sure; Sql represents the quasi-laminar burning velocity; Cql is a constant associated with gas clouds; Rf represents the flame radius; a represents a constant, which is taken as 0.5; ST represents the turbulent burning velocity; u’ represents the turbulence rate fluctuation; l1 represents the turbulence integral scale; μ represents the assumed constant.
Fig. 2. Transient leakage profile.
5. Case study
equipment is omitted in Fig. 3. Point A, B, C are feature points.
The proposed methodology is applied to a hypothetical H2S-con taining natural gas leakage and explosion accident on an offshore plat form. The offshore platform consists of a process module and an accommodation module. There are three decks in the process module, i. e., the main deck, the middle deck, and the lower deck. Process equip ment is mainly concentrated on the middle deck and the lower deck, including power device, compressor, fuel gas device, acid gas, dehy dration & mercury removal device, and some pipe & pipe rack. The accommodation module is located at the bow. In this hypothetical ac cident scenario, a high-pressure system located at the middle deck fails to cause the dangerous gases to release. The released gas spreads and enters the lower deck. A VCE is triggered when the gas then reached an ignition source at 150s after the leak. Throughout the entire process of the hypothetical accident, the individuals are threatened by poisoning effect due to H2S at the beginning, and then explosion effect risk be comes the major threat after ignition. It should be noted that the domino theory is inadequate for the current study since only the direct effects or primary effects on humans are considered, whereas the effect of VCE on facilities and the effect of explosion fragments on humans or other equipment are not taken into account. Therefore, chain accidents with a certain development trajectory rather than domino accidents are studied in this paper. The wind direction which blows in the accommodation module is adopted to consider the worst wind scenario, where the reference wind speed is 3 m/s and Pasquill class is D at an ambient temperature of 20 � C. The gas composition is summarized in Table 3. The leakage rate is timevarying with the interference of the emergency shutdown system (ESD) and blowdown system. The detection and initiation time for the ESD system costs 60s, and isolation costs 30s. Then the blowdown system starts up 50s later. The transient leakage rates are calculated according to the reference by (Spouge, 1999), and illustrated in Fig. 2. In this section, the module in the lower deck is of interest to illustrate the proposed approach. Fig. 3 provides the layout of the modules in the lower deck. The accommodation module and the life module are sepa rated by a blast wall. There are two cabins in the lower deck. The process
5.1. Geometric modeling All these objects are modeled by the pre-processor of FLACS. In the process of constructing the geometric model, only the spatial layout and external structure are of concern, while the internals of cabins and the accommodation module are not of interest. To meet the needs of accu racy in geometric details and efficiency in computational time, the distributed porosity concept is used to represent geometric details and the sub-grid model is employed to represent the flame smaller than grid size. Fig. 4 represents the geometry model and the layout of the modules of the offshore platform. The dimension of the model is 30 m � 60 m � 40 m and there is a total of 24,116 components in this model. The monitoring points matrix covering the whole process module of the lower deck is set. After the geometry model is imported, the grid model based on grid sensitivity analysis is built. The process of building the grid model is followed (Yang et al., 2019). 5.2. Ventilation According to standard procedures defined in GexCon and NORSOK, ventilation simulation should be performed before dispersion simulation to establish a stable wind field in the computational domain. The areas with ventilation condition where dangerous gas tend to gather can also be identified in the ventilation simulation. 8 different wind directions are utilized to perform ventilation simulation (NORSOK, 2010). In this hypothetical accident scenario, the reference wind speed is 3 m/s and the reference height is 10 m. Thereby the wind speed profile at different height can be calculated according to wind profile function (GexCon, 2015). Besides the wind condition parameters, the setting of other param eters are as follows: the boundary condition type of inflow and parallel boundary is Wind, while the boundary condition type of the outflow and sea bottom surface is Nozzle. CFLC and CFLV, which are CourantFriedrich-Levy number based on sound velocity and fluid flow velocity 4
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respectively, are taken as 20 and 2. The value of the aerodynamic roughness length is 0.0002 m. MODD and DTPLOT are both set as 1, which means that the time interval for data output is 1s. All the above parameters are also applied to dispersion simulation. 5.3. Dispersion The data from dispersion simulation mainly include the flammable gas profile and the toxic gas profile. The flammable gas profile is dum ped and used as the input parameter for explosion simulation, while the toxic gas profile is extracted to calculate the effect of poisoning. Fig. 5 depicts the dispersion results of flammable gas, including 3D spatial distribution of flammable gas at different times and the Q9 (Gupta and Chan, 2016; Yang et al., 2019) time-varying curve. The explosive limits of flammable gas are calculated according to the Lechteilier Principle where the lower and upper limits of monitoring interval (2.46%– 11.85%) for flammable gas are adopted. As can be seen in Fig. 5a)–5c), the spatial distribution of flammable gas varies with time. At the steady leakage stage, the spatial distribution of flammable gas increases with time due to insufficient ventilation. Then the spatial distribution of flammable gas decreases gradually at the leakage rate attenuation stage because the ventilation plays an increasingly important role with the decrease of leakage rate. The variation of Q9 represents a similar tra jectory of the spatial distribution of flammable gas according to Fig. 5d). The Q9 reaches its maximum of 3251 m3 at 107.6s and then drops down. Fig. 6 illustrates 2D contour plots of H2S (1.5 m above the lower deck). Both the spatial distribution and areas with high concentration H2S first increase and then decrease. The H2S on the plane of 1.5 m above the lower deck reaches the accommodation module at about 30s after the leakage. Almost the whole plane is occupied by H2S with concentration over 3200 mg/m3 at 90s. Comparing to the dispersion result of H2S at 90s, the spatial distribution of H2S, especially the areas with a concentration of H2S over 5000 mg/m3 decreases significantly at 150s. 5.4. Explosion The data-dump technique is adopted to perform explosion simula tion. In this hypothetical accident scenario, the flammable gas exposed to the ignition source at 150s after the gas leak. Therefore, the dispersion simulation results are dumped at 150s. After defining a new grid model for explosion simulation, an explosion scenario is set up by mapping the dumped dispersion simulation results into this new grid model. The dispersion simulation analysis is restarted at the beginning of the ex plosion simulation, by which a real gas cloud is detonated. Thereby the calculation accuracy is improved. Fig. 7 depicts the explosion overpressure distribution at different time points on the plane of 1.5 m above the lower deck. The explosion lasts for a short time. The explosion overpressure wave propagates to the lower deck at about 0.44s after ignition and reaches its maximum of 6.385 bar at about 0.53s after ignition. Then the explosion overpressure on this plant vanishes at about 0.6s after ignition. The distribution of explosion overpressure is irregular since there are numerous equipment, pipes, and pipe racks. Complex layout and high congestion/confinement lead to the increase of explosion overpressure partially, which has been discussed by (Huser et al., 2009).
Fig. 3. The layout of the modules in the lower deck.
6. Results and discussion 6.1. Poisoning assessment The monitored data for the concentration of H2S of each monitoring point is extracted, and the concentration of H2S at every moment is of concern, i.e., the H2S concentration-time curve at each coordinate is expected rather than the maximum concentration. Then the probit variables of toxic gas can be calculated according to Eq. (2). Converting
Fig. 4. Geometric model of an offshore platform.
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Fig. 5. Dispersion results of flammable gas.
Fig. 6. Dispersion results of H2S.
the probit variables into probabilities of fatality (Eq. (3)), then the risk index of the poisoning effect based on the probit model can be estimated (Eq. (4)), which is illustrated in Fig. 8. The risk indexes are in a range from 0 to 10. The higher values of poisoning risk are mainly below the leakage source. It is interesting to note that the H2S risk profile in Fig. 8 is not consistent with the H2S dispersion result in Fig. 6. The main reason is that the integral concentration and time is of concern instead of the maximum concentration. In certain areas, although the maximum H2S
concentration is considerable, the H2S spreading is a time-consuming process which makes the integration time decrease. Thereby, the probit variables decrease, further leading to a reduction in the proba bility of fatality and risk index. For example, although the maximum concentration of point A is about 2516.73 mg/m3 (Fig. 9), the duration of concentration over 2500 mg/m3 is less than 20s, and a comparably low-risk index less than 1 is derived. However, if continuous exposure to H2S with 2500 mg/m3 lasted for 150s, the corresponding risk index 6
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Fig. 7. Explosion overpressure over the plant.
would reach 4.964. 6.2. Explosion assessment Fig. 10 depicts the explosion risk profile according to the proposed approach. Similarly, the explosion risk profile is distinguished from the explosion overpressure distribution (Fig. 7) at any time. Instead of focusing on the explosion overpressure profile at a fixed time, the maximum overpressure for each monitoring point throughout the ex plosion process is of concern. The probabilities of ear rupture and lung damage (death) are calculated respectively based on the maximum ex plosion overpressure at each monitoring point. Afterward, the corre sponding risk indexes can be calculated considering the severity scores of different hazard effects. The higher risk index at each monitoring point is chosen and plotted on the plant (Fig. 10). 6.3. Integration of individual consequences The poisoning effect and explosion effect are integrated in this sec tion. The grid-based concept is introduced to combine these two kinds of hazard effects. Fig. 11 illustrates the contour plot for the cumulative risk indexes with a range from 0 to 20. It is noteworthy that although there is a low-risk index in some areas considering individual hazard effects (Figs. 8 and 10), the risk index may have increased significantly by considering another hazard effect. For example, Fig. 12 gives the time-
Fig. 8. H2S risk profile.
Fig. 9. H2S concentration time-history curve of point A.
Fig. 10. Explosion risk profile. 7
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Fig. 13. H2S concentration time-history curve of point C.
7. Conclusions Fig. 11. Cumulative risk profile.
Toxic gas-containing flammable gas leakage is very dangerous, and catastrophic accidents may be induced if the gas is exposed to an ignition source. Both the poisoning effect and explosion effect do indeed exist and cannot be neglected in the chain accident. Moreover, these two hazard effects have nothing with the domino effect. An integrated approach is proposed to fill the gap that the poisoning effect and explosion effect are considered separately in the current ef forts. To evaluate the cumulative hazard effects, the poisoning effect and explosion effect are predicted firstly. Then the risk-based concept is introduced to combine the hazard effects. And the grid-based concept is adopted to represent the total hazard effects. The proposed approach is applied to H2S-containing flammable gas leakage and explosion chain accidents on an offshore platform. Some interesting phenomena have been noticed. In some areas, the individual hazard effect may be insig nificant, but the cumulative hazard effects can be remarkable in view of both the poisoning effect and explosion effect. And there may be both significant poisoning effects and explosion effects in some areas. Thus, terribly appalling results can be derived. In general, more conservative evaluation results are obtained by comparing to only considering indi vidual hazard effects. The evaluation results confirm that the proposed approach is comprehensive and outperforms the approach concerning the individual hazard effect. Considering cumulative hazard effects contributes to intrinsic safety design and improves emergency plan to wards the potential chain accidents referring to multiple hazard effects. The integrated approach can also be extended to other accident conse quences evaluation which involving two or more hazard effects.
Fig. 12. H2S concentration time-history curve of point B.
history curve for the H2S concentration of point B (Fig. 3), the corre sponding risk index for poisoning is 0.432 as per Eq. (1)- (3). The maximum explosion overpressure of point B is 1.991 bar, the risk index for the explosion which is 9.865 can be calculated accordingly. Although there is a lower risk index for the poisoning effect, the cumulative risk index cannot be ignored. Besides, there are significant risk indexes in some areas for both poisoning effect and overpressure effect, which implies that these areas are extremely hazardous. For example, Fig. 13 depicts the H2S concentration time-history curve of point C, and its poisoning risk index is 9.987. The maximum explosion overpressure and explosion risk index of point C are 2.432 bar and 9.998, respectively. Both the poisoning effect and explosion effect are considerable. Hence, the cumulative risk index is significant. The results demonstrate that the proposed approach is effective for evaluating the cumulative effects of chain accidents. It is inadequate to consider individual hazard effects concerning the toxic gas-containing flammable gas leakage and explo sion accident since either of poisoning risk or explosion risk may be dominant in the total risk. Conversely, both the poisoning risk and the explosion risk are crucial and should be considered when assessing the consequences of similar accidents.
CRediT authorship contribution statement Dongdong Yang: Writing - review & editing, Writing - original draft, Investigation, Formal analysis, Methodology, Conceptualization. Guoming Chen: Supervision, Project administration, Funding acquisi tion. Ziliang Dai: Validation, Data curation. Acknowledgments The authors gratefully acknowledge the financial support provided by the National Key R&D Program of China (No: 2017YFC0804501). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.jlp.2020.104118. 8
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Declaration of interests
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