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Energy Procedía 1 (2009) 1571-1578 ^^^^^^^^^^
www.elsevier.com/locate/procedia
GHGT-9
A Consideration of the jet-mixing effect when modelling CO2
emissions from high pressure CO2 transportation facilities
12 1 Alberto Mazzoldi *, Tim Hill , Jeremy Colls
1 School of Biosciences, University of Nottingham, Nottingham, NG7 2RD, United Kingdom
2 E.ON Engineering, Ratcliffe-on-Soar, United Kingdom
Abstract
Safe transportation of carbon dioxide is an important issue in the developing field of Carbon Capture and Storage (CCS). A leakage from a high-pressure transportation facility could result in damage to the environment and be a hazard to people, depending on the total amount of carbon dioxide released to the atmosphere and the concentrations achieved. In the field of risk assessment, the atmospheric dispersion of gases has often been undertaken using Gaussian models. The starting point for the model calculations has often been a static CO2 cloud, avoiding the initial dispersion phase involving the jet. From high-pressure transportation facilities in CCS projects, any significant CO2 leak would initially be at high speed, with rapid entrainment of air. This would provide an early means of dispersion of the gas, mainly dependent on the speed of the flow. In this study on leakage from facilities within a modular transportation system, a CFD model is used, enabling the initial jet to be explicitly modeled. A comparison is made of the results with the no-jet case and suggestions for future risk assessment given. © 2009 Elsevier Ltd. All rights reserved.
Keywords: Carbon Sequestration; Transportation; Atmospheric dispersion; Risk assessment; CFD model; Jet release
1 Introduction
It is now widely accepted that the planet is undergoing climate change, with man-made gases, primarily carbon dioxide produced from fossil fuels, such as coal, gas and oil, being one of the major causes of global warming. Reducing these greenhouse gases is therefore a high priority. While some of the answer comes from increasing use of renewable technologies such as onshore and offshore wind, biomass and marine power, fossil fuel usage, particularly for power generation, will continue for the foreseeable future. Therefore, technologies which facilitate the capture of CO2 and its geological confinement are of great interest to the global community. Carbon dioxide can be sequestered in geological media via utilization in enhanced oil recovery operations, displacement of methane in coal beds, storage in depleted oil and gas reservoirs, injection into deep saline aquifers and storage in salt caverns (IPCC, 2005). Carbon dioxide would be captured at large point emission sources (e.g. power plants), and
* Corresponding author. Tel.: +44.9770.701.4380; E-mail address: sbxam0@nottingham.ac.uk.
doi:10.1016/j.egypro.2009.01.206
transported at high pressure (~10 MPa) via pipeline (on- and off-shore), sea-carrier (off-shore) or a combination of these (Svensson et al., 2004) to suitable locations where it can be sequestered underground.
If Carbon Capture and Storage (CCS) technology is to be widely introduced, then extensive networks of CO2 transportation facilities will be needed (Gale and Davison, 2004). There is a possibility of leakage from this infrastructure through component failure or infrastructure damage. The failure probability of some parts of the high-pressure transportation system has been well documented in the oil industry literature (Burgherr and Hirschberg, 2005, Hirschberg et al., 2004, Townes et al., 2004), and the principal causes of natural gas / CO2 pipeline incidents have been classified - i.e. relief valve failure, weld/gasket/valve packing failure, corrosion and external forces. In their study, Vendrig et al. (2003) reported an overall failure probability from a CCS transportation facility of about 0.371 per year (for failure of any magnitude, significant failure being much rarer than this), irrespective of its location (underground or above the surface) but with much higher likelihood for surface components (i.e. CO2 recovery at source, booster stations and injection plants).
Gaseous CO2 is an asphyxiant, a cerebral vasodilator and at high concentrations (i.e. > 70,000 ppm) can cause circulatory insufficiency leading to coma and death. Carbon dioxide is about 1.5 times denser than air at ambient temperature and therefore may remain close to the surface under some conditions, posing a potential health hazard. Moreover, an adiabatic (quasi instantaneous) pressure drop - such as those expected from high pressure transportation facility failures - reduces the temperature by more than 100oC (Joule-Thomson effect), raising its density to about 2.8 kg m-3 (Mazzoldi et al., 2007). The tendency of the gas to stay close to the ground would be enhanced, amplifying the risk it poses to humans and the environment, particularly in situations of complex topography and low wind speed. It is therefore important, as CCS facilities are developed, to understand the dispersion of CO2 from a containment failure, and hence be able to quantify the associated risks. Transportation facilities (e.g. Compressors, CO2 pipeline, injection infrastructure) may be in the proximity of CO2 sensitive receptors such as inhabited areas, and undertaking modelling studies of worst case scenarios associated with leaks from these types of infra-structure is focussed on in this paper.
Several studies regarding potential atmospheric dispersion of CO2 leaked from CCS transportation facilities have been drawn up in the last decade (IEA, 2003, Kruse and Tekiela, 1996, Turner et al., 2003, Vendrig et al., 2003). These investigations were carried out utilizing Gaussian/dense gas models. Gaussian tools are widely used in risk analysis procedures, providing fast dispersion estimations and usually reliable results when describing unobstructed gas flow over flat terrain (Hanna, 2003, Reynolds, 1992, Smith, 1999). Owing to the advance in computational power it is now practicable to apply Computational Fluid Dynamics (CFD) models for short- and medium-range gas dispersion scenarios. Although currently impracticable for real-time simulations because of the relatively lengthy scenario set-up and computational times required, CFD is particularly useful when modelling plume dispersion over complex topography (Burman, 1998, McBride et al., 2001, Scargiali et al., 2005) and among buildings (Milliez and Carissimo, 2006, Pullen et al., 2004, Tang et al., 2006, Yamada, 2004).
Another advantage of this kind of software is that it can simulate high-pressure leaks in a way which is much closer to reality than Gaussian models are able to, because it is possible to account more realistically for the high speed leaking flow at the source. Also, Gaussian models normally base their predictions on the solution of a 1-dimensional time-independent equation and are therefore not suited to describing the complex variability associated with dispersion over complex terrain, or the large temperature, pressure or velocity gradients in existence near the source of the leak. Previous risk analyses have assumed a near zero velocity at the source and a prescribed size for the source term. In this study the CFD tool Fluidyn PANACHE has been used for comparing simulations with 0 m s-1 and jet release speeds.
2 The transportation system
Precise information on CO2 properties can be found elsewhere (Atkins, 1981). Carbon dioxide would be transported on-shore in surface/shallow facilities, probably beginning in its supercritical state (P > Pc and T > Tc, where Pc = 7.4 MPa and Tc = 31.1o C), before cooling below its critical temperature. Whenever a leakage from a high pressure
This result is valid for a modular pipeline system composed of CO 2 recovery at source, Converging pipelines, one Booster station, 10km pipeline and one injection plant. Singular modules have lower probability but one integral transportation system would have a higher failure probability (it would consist of more than 10 km of pipeline and maybe more than one booster station).
facility occurs, carbon dioxide may at least partially, freeze into its solid state (dry ice) in passing to ambient pressure. For non-downward releases CO2 is expected to return entirely to the gaseous state, due to subsequent sublimation as outside air entrains rapidly into the jet.
Specific details of CO2 transportation systems and capture/sequestration plants are not readily available and so, in order to undertake a risk analysis, it is necessary to identify generic sections of plant and piping that, combined appropriately, can describe the majority of potential infra-structure designs. CO2 will be recovered from sources, such as power stations; it will be transported on-shore by surface/shallow pipeline systems at a pressure of about 100 atm (10 M Pa, well into its supercritical state (Atkins, 1981)) and eventually injected below the surface.
2.1 The modular system
There are eight modules in the generic delivery system, as described by Vendrig et. al (2003). Some will appear once in a system (e.g. recovery at source) while others can be repeated tens of times over (e.g. specified lengths of piping). A description of each module is provided in table 1, with indications of the length of piping each is assumed to include and failure rates for representative leak sizes. These last are further described in the next paragraph.
Module no. Module description Module pipe length Failure rate (year1) Small Medium Large Full-bore
1 C02 recovery at source 500 m 1.5 * 101 9.6 * 10"2 5.1 * 10"2 2.0 * 10"3 5.6 * 10"3
2 Converging pipelines 100 m 4.6 * 10"3 3.5 * 10"3 8.8 * 10"4 1.0 * 10"4 1.5 * 10"4
3 Booster station 100 m 4.0 * 10 2 3.5 * 10"2 3.8 * 10"3 3.0 * 10"4 8.8 * 10"4
4 Pipelines 10km 3.4 11<H 1.4 * 10"4 5 5 * 10-s 2.0 * 10"5 8.5 * 10"5
5 Injection plant. 500 m 1.8 * 101 1.2 * 10"1 5.3 * 10"2 2.1 * 10"3 5.8 * 10"3
Table 1 - Descriptions of modules in the generic engineered system and their failure rates distribution (Vendrig et al., 2003).
In the present study, Modules 6, 7 and 8 (respectively, CO2 raiser to offshore platform, line down to containment region and Tanker transport) were not considered. For Modules 1 to 5, the bulk of the data is derived from the databases of offshore incident frequencies and the American Gas Association (Gale, 2001, Skovholt, 1993, Smith and Warwick, 1981, Vendrig et al., 2003). Each of the modules is described in more detail in Vendrig et al. (2003).
2.2 Failure cases of the engineered system
The first stage in any risk analysis is to identify the potential accidents that could result in the release of a hazardous material (e.g., carbon dioxide) from its normal containment.
The modular approach to the engineered system is based around process components such as pipe-work, equipments and vessels. Process failure data is well established and data is available to define representative accident scenarios for all of the generic items included in the modular system. The range of possible releases from a given component covers a broad spectrum, from a pinhole leak up to a catastrophic pipe or vessel rupture. Representative failure cases are generated: for each module and each component four different leak severities have been addressed:
• Full-bore pipe rupture (applied to all leaks of equivalent diameter >150 mm)
• Large leaks, 100 mm equivalent diameter (covering leaks from 50 to 150 mm)
• Medium leaks, 30 mm equivalent diameter (10 to 50 mm), and
• Small leaks, 7 mm equivalent diameter (3 to 10 mm)
In this study, it is assumed that every leak from a module reaches the surface (whether the module is on the surface or buried), disperses in the atmosphere and poses a possible hazard (IEA, 2003, Turner et al., 2003, Vendrig et al., 2003). Time varying releases were modelled by using a constant rate corresponding to the time at which 25% of the inventory has been released.
2.3 Frequency Analysis
The data for failure frequency have been derived from DNV's library of failure data (Vendrig et al., 2003), spanning the last forty years of oil, gas and waste product transportation. Although they are based primarily on hydrocarbon transport systems, they are considered to be equally applicable to carbon dioxide, particularly for generic systems. Table 1 summarises failure rates per year for each module and the breakdown of failure rates associated with each module by the representative leak size given in the previous paragraph.
Other important parameters such as leak rates, releasable inventory for each module, automatic detection and isolation times after leakage, specific pressure and temperature conditions for modules, separation of Emergency Shut Down valves and pipeline diameters for each module are also taken from Vendrig et al. (2003).
3 Material and methods
Fluidyn-PANACHE (version 3.4.1) is a computer code for numerical simulation of atmospheric flows and pollution in short and medium-range scales. PANACHE uses CFD tools (i.e. Navier-Stokes equations and turbulence models) in a finite volume-based approach, solving the differential equations governing mass, momentum, and energy transfer on discrete control volumes, provided by a non-uniform mesh generator that accounts for the presence of obstacles or topographical features (i.e. with generation of a finer mesh in critical areas).
3.1 PANACHE numerical scheme
The continuity equation for total fluid density is given by
dp/dt +V»[pu] = 5s + 5p (1)
where V denotes the gradient of the considered quantity on the three dimensions, other symbols are as described in nomenclature. The appropriate SI units are implicitly assumed for all quantities. The momentum equation for the f uid mixture is
dpu/dt + V» [puu ct] = VP + Fs +Fg + Fp (2)
where ct = Newtonian viscous stress tensor (= |a[Vu+(Vu)T]+X(V*u)i, where |a,X = first and second coefficients of viscosity, X = -2/31a; T = matrix transpose; i = unit dyadic - product of vectors). The internal energy equation is
SpI/dt + V*[pu I J] = V*u + pe + Qs + Qp + Qh (3)
where J = heat flux vector = kVT + pZ[hmV(pm/p)].
PANACHE solves the governing equations described above both in three-dimensional space and in time. Further description of PANACHE boundary conditions definition and turbulence models used can be found elsewhere (Mazzoldi et al., 2008).
3.2 Leaking flow release speed
The aim of this work is to give a general idea of the differences obtained within results of risk assessments when considering the jet release speed of a leak formed as a consequence of its high pressure, rather than assuming a 0 release speed.
As it leaks from a high pressure facility, a gas/supercritical fluid would develop a velocity of up to the speed of sound (Kuprewicz, 2007), entraining an amount of air of many tens of times its own weight (Wakes et al., 2002). In the scientific community, work is mainly focused on the means of leak detection and on measuring the total amount of leaked gas/liquid (Scott and Barrufet, 2003). In this study, attention is raised on the calculation of the release speed for high-pressure pipelines/transportation facilities, as a mean of early dispersion of the gas, particularly considering the case of CO2 within CCS projects.
For this purpose, three different equations have been used to compute the leak speed, taking advantage of Bernoulli's principle (equation 4, assuming the supercritical fluid as incompressible), Venturi effect (5) and the equation for calculating the mass flow rate of a gas flowing through an orifice in choked conditions (6), which is the limiting case of the Venturi effect:
.2 n „2
r v; _ p
+ gh + -i- = + gh + (4)
2 p 2 p
(rr - r ) = f (v2 - v2)
Q = CA
(k+1)/(k-1)
v P vf Pf
Equation (4) can also be written as + gh + -J- = -J~ + gh + , where v,, v, P, and Pf are respectively the initial
2 p 2 p
and final flow velocities (inside the pipeline, taken as zero, and soon after the release, outside the pipeline) and the initial and final pressure (P,- = 10 MPa, Pf = 0.1 MPa - atmospheric pressure), p is the density of CO2 (950 kg m-3, supercritical density taken as constant before and soon after the leak), the term gh is equal to zero (h = 0). Applying this equation to our case and resolving in v, gives v = ~102 m s-1
Equation (5) represents another way of calculating the velocity of a flow through a restriction. The terms have the same value as for equation (4). Resolving in v, gives v = ~144 m s-1.
Equation (6) is used for calculating the mass flow through a leaking orifice and is valid only for choked conditions. Q is the mass flow rate (kg s-1); C the discharge coefficient (usually 0.72); A the discharge orifice cross-sectional area (m2); k = cp/cv the ratio of specific heats of the gas (kCO2 = 1.29, at STP, in supercritical conditions, while values for cp and cv will be changed, this ratio is not supposed to vary much); p is the fluid density at T and P for supercritical CO2 (p = 950 kg m-3); P is the absolute upstream pressure (10 MPa) (Pa). After calculating the value of Q, knowing that Q = vA, it is easy to find the speed of the flow when considering representative leak dimensions. The value obtained using this method is v = ~49 m s-1.
Each one of these equations does not consider a change in the leak velocity with changing the dimension of the leak, giving the same value both for e.g. a 5 mm leak and a full-bore rupture. Of the three values obtained by the usage of the different equations, the lowest one (choked flow, equation 6)) was chosen for computer simulations. For typical pipeline conditions, the flow is likely to be choked, but this choice is also justified by the fact that a full bore leak (probably the most significant in terms of risk to people or environment) is not likely to develop a very high release speed (because of the quick depressurization of the pipeline). For smaller leaks the leaked gas velocity would reach the speed of sound, so that the values considered are not much representative of this case. A value of 49 m s-1 is therefore used in this work.
In this study, the releases were all considered to have an initial direction perpendicular to the wind's, starting at 0 m height and with a 4o upward angle from the horizontal.
3.3 Risk analysis
The risk involved in the transportation of potentially dangerous substances (e.g. oil, natural gas, CO2) is quantified by considering:
• Frequency of occurrence of accidental releases of given amount of the substance by statistical analysis of historical observations (Burgherr and Hirschberg, 2005, PMgroup, 2005);
• Potential effects on human receptors exposed to different concentrations of the gas for different periods
• Areas covered by modelled plumes, representing toxic concentrations of the gas of interest, after the occurrence of the leak and under particular atmospheric conditions (IEA, 2003, Vendrig et al., 2003). In the work described in this paper, two combinations of wind speed and atmospheric stability class have been used: D5 (neutral stability, D, and 5 m s-1 wind speed) and F2 (high stability, F, and 2 m s-1 wind speed). The prevailing atmospheric conditions within the UK were approximated by 80% D5 and 20% F2, broadly representing the critical dispersion conditions which are generally, high wind speed for short-duration releases and low wind speed with stable stratification for long duration releases (HSE, 2008)
3.3.1 Consequence assessment methodology
In their study, Vendrig et al. (2003) considered three CO2 concentration envelopes for performing risk analyses within the field of carbon dioxide transportation in CCS projects. The tolerable concentration is identified as 2,000
(HSE, 2001);
ppm or 0.2 %. For humans, the STEL (Short-Term Exposure Limit) level of 1.5 % or 15,000 ppm is used as a guide for maximum exposure. 100,000 ppm (10%) was used as an upper limit. This study has not been pursued with the intention of drawing up a risk assessment, instead a comparison on plume downwind length results between 0 m s-1 and ~50 m s-1 release velocity simulations has been drawn up.
4 Results
Four releases covering a range of flow rates within the five modules have been modelled, for the two atmospheric conditions considered (D5 and F2). For the trials described, PANACHE has been used for simulating releases in two different ways: with a near zero release speed and with a release speed of 49 m s-1. Table 2 displays downwind extent of the modelled concentration surfaces, against the four differently sized leaks considered, for the two atmospheric conditions regarded and for the different modules, for zero and 49 m s-1 release speeds.
Leak case Release parameters MAX downwind distance (m), □ m s"1 MA^X downwind distance (m), 49 m s'1
Duration CO Rate (kg/s) Inventory (kg) 100,000 £|>m 15,000 ggm 100,000 œil 15,000 gjm
MOD 1 D5 F2 D5 F2 D5 F2 D5 F2
Full-bore 600 95 57000 45 161 149 471 12 10 133 38
Large 816 43 35329 39 66 111 354 11 8 104 88
Medium 3600 4 14040 7 32 98 3 2 35 28
Small 3600 2 7560 2 16 36 2 1.8 22 21
MOD 2, 3,4 Duration CO Rate (kg/s) Inventory (kg) 100,000 gum 15,000 100,000 g|jm 15,000 jijm
D5 F2 D5 F2 D5 F2 D5 F2
Full-bore 600 1800 1080000 315 852 817 1290 52 33 374 263
Large 3600 633 2278800 242 335 660 775 31 18 158 233
Medium 3600 57 205200 20 58 169 200 12 S 32 87
Small 3600 3.1 11160 4 24 37 2.4 3 32 25
MOD 5 Duration (0 Rate (kg/0 Inventory (kg) 100,000 £jmi 15,000 100,000 g|jm 15,000 gjm
D5 F2 D5 F2 D5 F2 D5 F2
Full-bore 600 35 57000 45 161 169 471 12 10 130 88
Large 600 95 57000 45 161 169 471 12 10 130 88
Medium 3600 57 205200 20 58 149 200 11 8 102 88
Small 3600 3.1 11160 13 23 46 2.4 2 32 26
Table 2 - Hazard ranges for representative releases from Module 1 to 5. PANACHE simulations for zero release speed against jet release predictions
Modules 2, 3, 4 were considered as having the same releasable inventory, for there is no specific isolation associated with Modules 2 and 3, so consequences of releases are the same as for the Pipeline Module 4 (Vendrig et al., 2003). Figure 1 is the graphical elaboration of data in Table 2 for the 100,000 ppm concentration plumes. From both the table and the picture it can be seen how the jet release would be responsible of a major part of the dispersion of gaseous carbon dioxide as it leaks out of a transportation facility. Figure 2 is an image of a jet release as modelled by PANACHE (Module 2, large leak, D5 atmospheric conditions). As expected, the shape and position of high concentration envelopes (e.g. 100,000 ppm) is influenced more by the jet release direction than by atmospheric parameters (wind speed and direction and atmospheric stability), D5 conditions generally gave the largest downwind length to the concentration envelopes. From figure 2 it can be seen how actually the plume of 100,000 ppm concentration has got a much larger length in the direction of the release
Figure 1 - Individual Risk of 100,000 ppm downwind to each of the five Modules. Predictions with 0 release speed (a) and jet release (b). Note large change of scale on x-axis.
5 m S"
psleass
than downwind. Thus, for estimating the at risk areas of the plume, it is suggested not to use the 30o sector downwind of the source as it is usually done in risk assessment when using near zero release speed.
Conclusion
This paper has illustrated the usefulness of using a CFD code to examine near-field dispersion of CO2 gas leaked from a high-pressure facility, and, in particular, demonstrates the importance of understanding the effects of a high velocity release on the atmospheric dispersion of the gas. The initial jet is not explicitly accounted for in current risk assessment methodologies.
The entrainment of large quantities of air by the high-speed flow created after the occurrence of a leak on a high-pressure facility causes a major early dispersion of the gas. The jet mixing effect could provide even a higher dilution of the gas, than is currently assumed in dispersion modelling of high-pressure releases.
100,000 ppm surface
15,000 ppm surface
Figure 2 - Jet release plumes as modelled by PANACHE
Plume concentrations near the source are more dependent on the angle made by the leaking jet-flow direction with the wind direction, than on the wind speed and atmospheric stability; the wind speed assuming a major role in determining the shape and dimension of concentrations distributions when modelling concentrations further downwind.
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