Parametric Modeling of Slurry Wear in a Pipeline with Bed Flowmlipsett/Slurry-pipe-wear...  · Web...

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Parametric Modeling of Wear in a Slurry Pipeline with Bed Flow S. El Sayed, M.G. Lipsett * Department of Mechanical Engineering University of Alberta, Edmonton Alberta, Canada T6G 2G8 ABSTRACT Slurry transport is a key process in a number of industries. In transporting mined oilsand, slurry pipelining promotes conditioning to release and aerate bitumen prior to separation from water and solids. Reliability of slurry transport pipelines is a major ongoing problem for operating oilsands companies due to unexpected piping failures. To date, no accurate model has been developed to predict wear rates in slurry transport pipelines, although previous studies have shown that some process variables are important such as flow rate, slurry density, and particle size distribution. This work investigates erosion wear mechanisms causing inner pipe wall wear due to sand slurry flow in a horizontal section of pipe under steady-state conditions. A lumped- parameter erosion wear model is presented based on a simplification of the physics of oilsands slurry flow. An apparatus was developed and tested to measure the forces acting on the pipe inner wall to monitor forces related to erosion in a laboratory-scale sand slurry loop. Preliminary results are presented with some recommendations for future work that would be required to validate the model. Keywords: Slurry wear, pipelines, modeling, prognosis Introduction * Corresponding author. Email: [email protected]

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Parametric Modeling of Wear in a Slurry Pipeline with Bed Flow

S. El Sayed, M.G. Lipsett*

Department of Mechanical Engineering University of Alberta, Edmonton

Alberta, Canada T6G 2G8

ABSTRACTSlurry transport is a key process in a number of industries. In transporting mined oilsand, slurry pipelining promotes conditioning to release and aerate bitumen prior to separation from water and solids. Reliability of slurry transport pipelines is a major ongoing problem for operating oilsands companies due to unexpected piping failures. To date, no accurate model has been developed to predict wear rates in slurry transport pipelines, although previous studies have shown that some process variables are important such as flow rate, slurry density, and particle size distribution.

This work investigates erosion wear mechanisms causing inner pipe wall wear due to sand slurry flow in a horizontal section of pipe under steady-state conditions. A lumped-parameter erosion wear model is presented based on a simplification of the physics of oilsands slurry flow. An apparatus was developed and tested to measure the forces acting on the pipe inner wall to monitor forces related to erosion in a laboratory-scale sand slurry loop. Preliminary results are presented with some recommendations for future work that would be required to validate the model.

Keywords: Slurry wear, pipelines, modeling, prognosis

Introduction

Alberta oilsands deposits comprise one of the world’s largest oil reserves. Significant investments have been made in oilsands mining and processing operations, which have resulted in oilsands-derived synthetic crude oil supplying 60% of Canada’s needs. Oilsands contain mixture of bitumen, sharp-edged silica sand with average diameter of approximately 120 um, clay fines of less than 44 um diameter, and water. The bitumen content ranges between 1% and 20% depending on the quality of the ore body.

Shallow oilsands deposits are mined using front shovels and transported using large off-road haul trucks to a collection point. At this location, the oilsands are crushed and screened to remove oversized rocks and any foreign material that may harm equipment downstream. Warm treated water is added to the mixture, and the resulting oilsands slurry is transported by pipeline to a separation plant. During slurry transport, turbulent mixing causes entrained air bubbles to collide with bitumen droplets that have been liberated from the oilsand. In favourable process conditions, the bubbles attach to the bitumen, which promotes efficient separation of bitumen from water and solids. Sand and fine tailings are subsequently transported from the plant to settling basins. An oilsands

* Corresponding author. Email: [email protected]

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mining operation typically employs approximately 90 km of slurry piping, with up to10,000 tonnes per hour of oilsands being transported in a single pipeline.

Wear is the dominant damage mechanism for steel slurry pipelines transporting oilsands. Any failure to a pipeline component results in downtime and lost production. Conservative maintenance strategies are used to reduce risk of failure, because mean time between failures is not predictable due to variable operating conditions and ore types. As well, there is a lack of understanding of the wear mechanisms affecting oilsands slurry pipelines. The current industry practice is to predict wear in a hydrotransport pipeline based on a linear relationship relating pipeline metal loss to the number of operating hours, based solely on prior operating experience but not the variability of process conditions. Consequently, operating costs are substantially increased, with much piping removed from service with some remaining usable wall thickness, and yet sudden pipeline failures still occur on occasion.

For these reasons, a predictive model for hydrotransport pipeline wear rate would help maintainers to schedule maintenance activities more cost effectively and reduce the occurrence of sudden failures.

1.1 Oilsand Slurry Properties

A slurry of oilsand in water tends to be heterogeneous, with a significant fraction of its particles settling to the bottom of a container due to their own weight. The particle size distribution of oilsand varies depending on the mined oilsand location, with sand particles generally larger in deeper deposits. Transported oilsands slurries have an average concentrated of approximately 35% by volume, and the particle size diameter varies between 0.18 to 0.3mm [1]. The flow regime of heterogeneous slurries varies with the slurry average flow velocity. In order to prevent any accumulation of solid particles at the bottom of the pipeline, the mixture average velocity must exceed the slurry deposition velocity. Oilsands slurries are transported above deposition velocity at velocities between 3 m/s and 5.5 m/s with the minimum deposition velocity being around 2 m/s [1]. As the velocity of the mixture is increased, more particles are suspended by fluid turbulence, and as a result the velocity and concentration profile across the pipe diameter changes as well.

Slurry pipeline wear is complex, due mainly to three wear processes: erosion, corrosion, and combined erosion-corrosion. Slurry pipeline wear is difficult to predict because of the challenges to isolate and quantify the contribution of each of the wear processes and their synergistic effects. Erosion is usually the dominant wear process, with corrosion playing a minor role, especially in fresh water slurry applications. On the other hand, the combined erosion-corrosion wear process can be the dominant wear process in some slurry applications. The combined erosion-corrosion wear process becomes dominant when there is continuous removal of pipe wall material due to erosion and as a result exposing new material to corrode [2].

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The wear profile around the circumference of slurry piping may not be uniform, especially for heterogeneous slurry flow, likely due to the changes on concentration and flow conditions in different regions of the cross-section. There may also possibly be changes in chemistry near the wall, particularly in parts of a dense bed where there is not a lot of mixing.

Oilsands hydrotransport pipeline wear measurements are taken regularly as part of equipment monitoring programs. In one case, the maximum wear rate reported in a straight section of 76 cm diameter low-carbon steel pipe occurred at the bottom of the pipe, with average velocity of 4.5m/s, bitumen content of between 11% and 12%, and fines content between 21% and 26%. The material loss rate was mainly due to bottom bed friction where the slurry bed concentration was believed to be a main contributor [1]. Similarly, Roco and Addie reported the maximum wear rate to be at the bottom of the pipe in a sand slurry test loop, with pipe diameter dpipe = 200mm and concentration of 10% by volume [3]. Gupta, Singh, and Seshadri reported on the uneven wear rate in a brass-pipe / sand-slurry test loop, with average velocity between 1.95 to 2.75 m/s and concentration between 17.23 to 34.5% by weight and a wide particle size distribution range; again, the maximum wear rate was found to be at the bottom of the pipe [4].

There have been several attempts to model slurry pipeline wear, using either empirical models or energy approaches.

An empirical model based on a large number of tests was presented by Salama to predict the erosion wear rate in multiphase gas/liquid well production pipelines [5]. Similarly, Gupta, Singh, and Seshadri developed an empirical model to predict the erosion wear rate in a slurry brass test loop around the pipe circumference [4]. The empirical correlation produced was a function of the average slurry velocity, average particle size, and average slurry in-situ concentration. The empirical correlation values developed for wear rates appear to apply only to narrow ranges of operating conditions and pipe materials. No insight was provided to the wear mechanisms affecting slurry pipelines; and, as a result, these equations cannot be used with confidence to predict wear in slurry pipelines operating at different conditions.

Other researchers have used computer simulations to predict uneven wear rates in slurry pipelines. For example, Roco and Addie divided erosion wear mechanisms in a heterogeneous slurry pipeline into directional impact, random collision, and Coulombic friction. Directional impact was found to be dominant in elbows and bends, while Coulombic friction was dominant in flows with a dense bed. A small-scale test loop was constructed to verify results based on simulated near wall particle velocities and concentration profile, for particle diameter dparticle = 0.5mm and concentration of 10% by volume [3]. Similarly, Wood, Jones, and Miles used computer simulations to predict wear in a 78mm straight stainless steel pipe with slurry flow at 10% slurry concentration [6].

The key contribution in the work of Roco et al. is the mechanistic approach utilized, although the contribution of corrosion and erosion/corrosion was not isolated from the

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erosion wear mechanisms. Consequently, one can not call the model presented a pure erosion model. There is room for improving erosion models of slurry pipelines. The work of Wood et al. was limited by the computer power available and the number of particles included in the simulation [6]. In case of high slurry concentrations, more powerful simulations are needed. Numerical simulations of dense slurries remain challenging to generate and validate especially with broad ranges of particle size distributions and in larger pipe diameters.

Since wear in oilsands hydrotransport pipelines is a complex process that is difficult to observe, an alternative approach is necessary to reduce the complexity of the wear model by tackling each of its aspects separately. The main objective of this work is to introduce a simple parametric model for erosion wear based on the physics of sand slurry flow in a straight section of pipe at steady-state conditions. While there are good models of heterogeneous slurry flow, there is uncertainty about the forces associated with wear in a slurry pipeline. An apparatus was designed and tested to measure the overall forces on a section of pipe wall, due to fluid-wall and particle-wall interactions.

Sand Slurry Erosion Wear Model Development

In the improved two layer model presented by Gillies, Shook, and Wilson, heterogeneous slurry flow at moderate flow velocities was divided into two layers [7].

In the top layer, particles are fully suspended by mixing turbulence where there is only kinematic friction. In the bottom layer, a fraction of the particles is fully suspended by turbulence. The remaining fraction of particles is supported through contact by pipe wall. As a result, both kinematic friction and Coulombic friction exist in the bottom layer. The idealized concentration and velocity profile in the two-layer model of heterogeneous slurry flow is shown in Figure 1.

Figure 1. Two layer model concentration and velocity profiles [9]

A simple lumped-parameter flow model was developed for a straight section of an oilsands hydrotransport pipeline, based on the improved two layer model for high concentration slurries at moderate flow velocities [8]. A graphical representation of the model presented is shown in Figure 2.

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Figure 2. Physical system model of oilsands slurry pipeline section [8]

This model uses the convention of dividing the flow in a straight section of a dense slurry pipeline into two layers: a top layer and a bottom layer. In both layers it was assumed that the flow processes occur independently and in sequence [8]. It was also assumed that the flow conditions are steady-state. Three processes occur in the top layer. There is top layer friction represented by resistance component R4, by top layer mixing represented by R5, and bitumen conditioning in the top layer is represented by resistance component R6. The flow is each process is assumed to be the same, and resistance is with respect to the average flow in the layer rather than any near-wall effect [8].

Three flow processes occur in the bottom layer. There is bottom layer friction represented graphically by resistance component R1 followed by bottom layer digestion represented by R2. Mixing in the bottom layer is represented by R3.

There may be some transfer of material between the bottom and top layers. During transportation of oilsands in hydrotransport pipelines, larger particles (also known as lumps) lose cohesion and ablate or even break apart; smaller particles are released into the flow. Some smaller particles are transferred to the top layer due to carrier fluid turbulence. In the case of oilsands, bitumen in the bottom layer gets released during transportation and it is also carried by carrier fluid turbulence to the top layer. If aeration occurs, the bitumen has a net migration from the bed to the less dense top layer. This transitional flow process between the bottom layer and the top layer is represented by flow component R7 [8].

The pressure difference between the top and bottom layers at the upstream end of the horizontal pipe section is represented by Ph, and downstream by Pl [8]. PA, PB, and PD represent the average pressure in the top layer of the upstream, intermediate and downstream locations of the pipe section [8]. PC represents the average pressure in the bottom layer at an intermediate state where bitumen and digested particles flow to the top layer [8].

The oilsands hydrotransport pipeline lumped parameter flow model can be simplified further to represent sand slurry flow in a straight section of pipe. A graphical representation of the simplified sand slurry flow model can be seen in Figure 3.

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Resistance components related to bitumen conditioning can be safely neglected in sand slurry flow since very little bitumen is present in the sand slurry [10], and so resistance component R6 is removed. In addition, a sand slurry does not contain as many large lumps as an oilsands slurry. As a result, resistance component R2 representing solid particle digestion and bitumen conditioning in the bottom layer can also be safely neglected in a sand slurry. (During experiments to determine the coefficient, sand particles shape and size would be monitored over the course of each experiment to ensure that this assumption is valid.) Resistance component R7 can also be removed, because no particle digestion and no bitumen conditioning occurs in the bottom layer [10]. Consequently, it can be assumed that no transfer of solid particles or bitumen occurs between the bottom layer and the top layer.

Figure 3. Simplified physical model of sand slurry pipeline section [10]

As seen from Figure 3, the sand slurry flow model is similar to the oilsands hydrotransport flow model, in that the flow is divided into two layers: a top and a bottom layer. Sand particles are fully suspended in the top layer due to carrier fluid turbulence. Some particles are fully suspended in the bottom layer due to carrier fluid turbulence, with the remaining particles directly supported by the pipe wall. The latter fraction of particles slides against the bottom of the pipe wall forming a sliding bed. Pressure drop in the top layer is mainly due to top layer kinematic stress and top layer mixing flow processes. These flow processes are graphically represented in figure 2 by resistance components R4 and R5. Similarly, pressure drop in the bottom layer is attributed to bottom layer kinematic and Coulombic friction represented by R1 and bottom layer mixing represented by R3. To isolate the contributions of kinematic friction from Coulombic friction, resistance component R1 can be split into two components R11 and R12 representing kinematic and Coulombic friction respectively [10].

There are two flow processes in each of the layers in sand slurry flow; and energy is dissipated in each of the resistance components. For example, kinetic energy is dissipated by the kinematic friction component in the top flow layer. Kinetic energy is dissipated due to the mixing flow process. A fraction of the energy dissipated by friction is lost in the form of heat, which heats up the slurry flow and raises its temperature without causing any damage to the pipe inner wall.

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There may be another fraction of the friction energy that causes damage to the pipe if the energy transfer exceeds a threshold for the pipe material. Part of the energy lost in suspending the slurry particles in the top layer or mixing energy causes some particles to exit the flow streamlines and impact randomly on the straight pipe inner wall. A fraction of the energy transmitted to the pipe inner wall by these random impact causes damage to the pipe [10]. These random impacts hit the pipe wall at low angles [6]. A similar process occurs in the bottom layer of flow; however, in the bottom layer, the energy lost due to friction can be divided into kinematic and Coulombic friction terms.

The wear contribution s′ of each of the wear components was described by Roco and Addie [3] as:

, (1)

where wall shear stress and wear coefficient measurements are necessary to quantify the contribution of each of the wear mechanisms on the inner pipe wall for both top and bottom layers. In a slurry, there may be a difference between the bulk velocity of the fluid in the region and the effective velocity near the wall that results in damage accumulation.

As a result, wear coefficients can be introduced to an energy model with a coefficient proportional to the energy Ed , which is the energy transferred to the pipe above some threshold, and , which attenuates the damage due to a difference between process velocity and local velocity of impacts. For the different processes in the pipe section there will be a set of damage terms, as illustrated in Figure 4. The overall erosion wear rates in a straight section of sand slurry pipeline can be estimated if the contribution of each of the erosion wear mechanisms can be quantified.

Figure 4. Simplified wear damage model of sand slurry pipeline section

The total energy dissipated in the system can be expressed as:

(2)

(3)

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(4)

(5)

The energy contributing to damage can therefore be expressed as:

(6)

The damage mechanism is related to damage of the pipe wall by a wear coefficient; and the damage rate can be expressed as:

(7)

(8)

Model Validation Issues

In order to validate the proposed model it is necessary to evaluate the parameters R, α, β, and for each of the erosion wear mechanisms. It is also required to isolate each of the

wear mechanisms so as to calculate the corresponding parameters of interest. Isolation of the kinematic friction and random impact of particles from the Coulombic friction can be achieved in a fully suspended heterogeneous slurry flow by increasing the flow velocity. In a fully suspended flow condition, a symmetric concentration and velocity profile exists in the pipe, and Coulombic friction can be ignored. The resulting flow no longer consists of two layers, but rather fully suspended slurry flow within the entire pipe. In that case, the kinematic friction and random impact of particles can be lumped together into a single equation: q0 = R1345P1345, as illustrated in Figure 5. This relationship can then be exploited later when validating the flow model for dense bed cases, when the velocity of the homogeneous slurry case matches that of the top layer for two-layer conditions.

Figure 5. Simplified physical model of sand slurry pipeline section for fully suspended flow

q0R1345

ΔP1345

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The resistance coefficient R1345 corresponding to the kinematic friction and random impact of particles due to slurry mixing in the fully suspended slurry flow can be determined for a variety of flow conditions by measuring the pressure drop across a laboratory test section in a slurry loop. By controlling the slurry flow rate, the in situ particle concentration, density of particles, carrier fluid viscosity, and particle size distribution, a variety of near wall particle velocities and concentrations around the pipe circumference can be achieved. A set of R1345 values can be determined for a range of flow conditions. At this stage, it is important to prevent damage accumulation from occurring in the pipe by covering the pipe with an abrasion-resistant overlay. Care must be taken to choose an overlay material that has the same roughness as the pipe itself. This will ensure that no energy is lost due to pipe wall erosion wear damage process, while still maintaining the same head loss due to friction.

Erosion damage in a fully suspended heterogeneous slurry flow condition causes additional pressure drop in the piping system above a threshold point which can be represented as shown in 6 [12].

Figure 6. Parametric representation of sand slurry flow and erosion damage for fully suspended flow

The damage caused is a function of the pressure drop and the flow rate, which is also a function of velocity and concentration [12]. The threshold energy for incipient pipe wear can be estimated for a variety of flow conditions by coating the pipe wall with a thin surface stain. Flow can be increased until the stain paint layer begins to be ablated to determine the threshold point for incipient wear. Another potential method to determine the threshold energy for incipient wear is by recording acoustic emissions in the vicinity of the pipe [13]. The amplitude of some features in the acoustic signature for damage should correlate well with the threshold stress for incipient wear; but the acoustic emission method needs to be tested in a slurry application to identify the unique signatures at which damage occurs.

By measuring the total pressure drop in the pipe section, velocity, and concentration at the same flow conditions used to find R1345 values, Rt1345 values can be determined. The total energy in the system and the threshold energy for incipient wear can then be computed using Equation 3.

Also, by measuring the pipe erosion damage rate using an ultrasonic thickness measurement device, and the stress experienced by the pipe wall, and the threshold stress for incipient wear, the erosion wear coefficient α1345 can be determined using Equation 8.

q0 R1345 Rd1345

ΔPd1345ΔP1345

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Once the threshold for incipient wear, α1345 values, R1345 values, and Rt1345 values for a wide range of flow conditions are determined, the contribution of kinematic friction to erosion pipe wear in the bottom layer of heterogeneous sand slurry flow can be determined. Similarly, erosion damage in heterogeneous slurry flow due to Coulombic friction causes additional pressure drop in the piping system which can be represented as shown in Figure 7. Since kinematic friction, Coulombic friction, and random impact of particle due to bottom slurry mixing do not occur sequentially, isolation of Coulombic friction contribution to pipe erosion damage is difficult.

Figure 7. Parametric representation of sand slurry flow and erosion damage for sliding bed

In order to determine α12, an ASTM G65 rubber wheel test can be used, or another similar wear test that can emulate process conditions, such as the oscillating table test [3] and the wet sand rubber wheel test [14]. The ASTM G65 test [14] is a standard wear test in the oilsands industry because it yields fairly repeatable relative wear results, provided that the solid particle distribution used in the test resembles that in pipe flow conditions, and the normal force of the particle and the tangential speed of the wheel are close to how a particle slides along the actual pipe. The apparatus is illustrated in Figure 8.

Figure 8. A simplified schematic illustration of the ASTM G65 test.

By knowing the suspended arm weight, the sliding friction shear stress can be calculated by multiplying the normal force by a dynamic friction factor (usually assumed to be 0.5 for sand particles). The velocity of the flowing sand particles can be assumed to be equal to the velocity at the tip of the rubber wheel. Therefore, the wear coefficient for sliding wear in the bottom layer can be calculated for the slurry/material combination, since the wear rate experienced by the specimen can be measured directly. Once the threshold conditions for incipient pipe wear are determined for a range of wheel velocities and normal forces imposed by the weight, a curve of the wear coefficients calculated at

ql R12 Rd12

ΔPd12ΔP12

Once-through flow of abrasive solids

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different particle velocities can be constructed and used to estimate wear in the bottom layer of the pipe due to particle sliding abrasion.

To predict wear due to sliding bed abrasion, wall stress measurements need to be performed around the circumference of pipe. The contribution of kinematic friction to damage around the circumference of the pipe by knowing α1345 values, R1345 values, and Rt1345 values obtained in the fully suspended flow tests. The velocity and concentration profile across the pipe wall should be also developed in order to isolate the contribution of kinematic friction to pipe wall damage. To understand local wall effects, additional flow monitoring and visualization techniques are required to measure the near wall velocity and concentrations of suspended particles and sliding bed. Advanced flow visualization techniques may include a high-speed camera estimating change in particle speed near the wall at a viewing section (which will admittedly not likely yield realistic results for the actual pipe material) or a boroscope introduced into the flow (which will affect the flow and will require calibration); but a correlation based on the estimate of bulk velocity in the layer may be sufficient for

The threshold stress for incipient wear due to Coulombic friction wear mechanism can be determined by measuring the stress required to start wear in the laboratory-scale tests. Once the stress, threshold stress for incipient wear, particle sliding velocity, and wear coefficient are determined, a model for erosion due to Coulombic forces can be validated and used for estimating erosion wear rates around the circumference of the pipe during heterogeneous slurry flow.

Experimental Setup and Testing Methodology

Experiments are needed to find the constitutive relationships for each of the elements of the wear model. A slurry loop of 2-inch inner diameter has been constructed and commissioned at the University of Alberta. The process flow diagram of the test loop is shown in Figure 9. The purpose of the test loop is to be able to produce various slurry pipeflow conditions in a controlled environment. The construction material of the pipe loop was chosen as SA-106 grade B seamless carbon steel pipe, which is a common piping construction material. The loop also has a removable section to test the wear resistance of other materials such as polymers and composites. The slurry loop consists of a 7.5 kW pump equipped with a variable frequency drive, a Coriolis flow meter, a slurry tank, and a straight run test section, with a total loop length of approximately 12 m. The loop is also equipped with a clear sight-glass section for flow visualization and particle tracking using particle image velocimetry to estimate the momentum loss during impacts against the pipe wall.

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Figure 9. Preliminary process diagram of the slurry loop at the University of Alberta

Since wall wear rate is a function of wall shear stress exerted by the slurry on the pipe inner wall, a floating element sensor assembly was designed and constructed to measure shear stress on a small area of pipe wall. The floating element sensor assembly is shown in Figures 10 and 11.

Figure 10. Cross section view of the floating element sensor assembly

In [11], Kiewicki, Saric, Marusic, and Eaton describe the floating element sensor as the simplest sensor used in measuring wall stress due to its simple working principle. The sensor is equipped with a floating wear sample mounted on elastic supports. The floating wear sample can translate slightly due to friction force because of the small gap present between the wear sample and the pipe wall; the displacement is proportional to the force on the floating element.

Wear Sample

Strain Gage location on Cantilever Supports

Gap

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The wear sample was machined out of the same pipe material using Electrical Discharge Machining Technology in order to eliminate alignment concerns. The material of the wear sample can be varied in order to measure the difference in overall force measurement for different materials. The difference between readings may be used to give an indication of the magnitude of force contribution due to random collisions of particles and the sliding bed. Two flush water connections were added to the sides of the floating element assembly to flush out trapped sand particles between the wear sample and the pipe. This can be performed by maintaining a small positive differential pressure between the flush water and the slurry flow at small flow rates. Alignment of the wear sample was performed prior to commissioning of the floating element assembly.

Figure 11. Photo of the floating element assembly

The cantilever supports were equipped with two full Wheatstone bridges. The shear force bridge provides strain measurements due to the slurry friction and eliminates the contribution of the axial forces due to pipe internal pressure according to standard formula relating simple beam loading to strain gauge output:

. (9)

The axial force bridge measures the strain experienced by the cantilever support due to axial force from high angle impacts and slurry turbulence while eliminating the strain contribution of the friction and random impact force, as

. (10)

A strain gauge conditioner fabricated at the University of Alberta was used to balance the axial and shear strain gage bridges installed on the floating element cantilever supports. The strain gauge conditioner contains two channels with separate circuits and an output voltage variable amplifier with a gain range between 0 and 2100 for each channel. The output voltage of each of the strain gauge bridges was acquired using a computer data acquisition system.

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In order to keep the gap from plugging with sand particles, flush water was used. By maintaining a small positive differential pressure between the flush water and the slurry flow, sand particles are prevented from entering the gap. The effect of flush water flow across the gap can be minimized provided that the pressure difference is kept small. If necessary, the force-strain relationship can be corrected empirically for circumstances when the flush rate through the gap is high.

The floating element bridge outputs were calibrated using known forces applied in both shear and axial loading as shown in Figure 12. After bridge balancing, the friction and axial bridge outputs were recorded for each axial and shear force applied and the corresponding [2x2] calibration matrix was calculated in Matlab. After calibration was completed, the element was ready for laboratory testing in a slurry flow loop.

Figure 12. Calibration of Floating Wear Sample Using Known Weights

Preliminary testing of the floating element assembly was conducted on a test loop at Syncrude, constructed using commercially available SA-106 Gr. B seamless carbon steel pipe with nominal diameter of 76 mm and a test section of 50 mm diameter. The loop is driven by a 3/2 AH Warman gland sealed belt driven pump with 40 HP Hyundai heavy duty AC motor equipped with variable frequency drive. A Coriolis Krohne flow meter and venturi flow meter measure flow rate. There is a hopper with a 50 mm outlet valve for loading particulate solids into the loop.

Figure 13. Photo of the floating element assembly installed on Syncrude slurry loop

Downstream Clear Section

Floating Element Assembly

Upstream Clear Section

2” x 3” Transition Pipe Spool

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Test #1 was conducted with water flow without flush water supply. The water bulk flow rate was increased from 0 USGPM to 250 USGPM and then decreased back down to 0 USGPM. A repeatability test (Test #2) to assess the variation in measured data by the axial and friction bridges was also conducted. During test #2, the pump rpm was varied from 0 to 706 rpm and then back to 0 rpm again. This process was repeated one more time while continually recording the shear and axial full Wheatstone bridges, volume flow rate, density, temperature, pump rpm, and pump discharge pressure. During the second round of preliminary testing (test #3), heterogeneous sand slurry was pumped through the test loop at average flow rates between 110 and 220 USGPM. Before the start of test #3, the floating element assembly was removed from the piping system and cleaned to ensure no sand was present in the chamber of the assembly.

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Table 1. Test Summary

Test # Description Flow rateUSGPM

DensityKg/m3

d50

mmVelocity

m/s1 Water Flow 0 to 250 1014 N/A 0 to 7.32 Repeatability 0 to 136 1014 N/A 0 to 4.03 Slurry Flow 110 to 220 1350 0.27 3.2 to 6.4

Results from test #1 and test #3 are represented in Figures 14, 15, 16, and 17.

Figure 14. Plot of measured water friction force versus bulk velocity

Figure 15. Plot of measured slurry friction force versus bulk flow rate

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Similar to test #1, the friction force was measured for the duration of test #3, and measured friction force increased with increasing flow rate as expected. The difference between the starting zero point and the final zero point is due to a small drift in both of the axial and friction bridge sensor output.

Figure 16. Plot of measured water axial force versus bulk flow rate

Figure 17. Plot of measured slurry axial force versus bulk flow rate

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The increase in the axial force measured was more substantial compared to the water flow. The axial final zero point was also plotted on the same plot.

The theoretical kinematic stress due to water flow in the pipeline is given by. (11)

Knowing the water density, velocity and the corresponding fanning friction factor, the theoretical kinematic stress at the pipe wall was calculated (using water viscosity value

= 10-3 Pa.sec). The corresponding theoretical friction force was computed by multiplying the theoretical stress calculated by the area of the floating element wear sample in contact with the flow. The comparison graph shown in Figure 18 highlights the difference between the theoretical friction force and the measured friction force.

Figure 18. Theoretical and measured water friction force versus flow bulk velocity

The small difference between the theoretical and measured water friction force can be attributed to several factors. First of all, the viscosity of the water in the theoretical calculations was approximated to be 0.001 Pa.sec for water at 20C. The actual viscosity of the tested water must be measured in the lab in order to verify this assumption. There may be slight error in the volume flow rate and density measurements made by the Coriolis flow meter. Similarly, the theoretical kinematic stress due to slurry flow in the pipeline was calculated according to the two-layer model assuming fully stratified flow conditions:

(12)

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According to the improved two-layer model [9], the kinematic stress experienced by the flow is increased due to the presence of solid particles in the mixture, which can be computed by calculating a modified friction factor . The mean diameter of sand particle size distribution used was 0.27 mm. A plot showing the slurry flow theoretical and measured friction force is shown in Figure 19.

Figure19. Theoretical and measured slurry friction force versus flow bulk velocity

The measured slurry friction force is high for the measured flow conditions compared to the friction force calculated from the kinematic stress based on the two-layer model. The presence of the gap between the floating element wear sample and the pipe has contributed to unaccounted discrepancy in the measurement. Fouling of the assembly and the flow of flush water through the gap contributed to error in the measurements, but this error was not quantified. These predicted sources of error are considered to be the main sources of error in the measurements made.

Due to Coriolis flow meter malfunctioning at higher slurry densities, flow rate was measured using a backup venturi flow meter. The flow rate measurement of the Venturi meter was thought to be slightly out of calibration for correcting the venturi flow rate estimate for slurry, because the slurry flow rate was measured using a venturi flow meter which is calibrated to measure water flows, and the meter had previously given slightly erroneous measurements compared to the Coriolis output for the same water flow rate.

The measured slurry friction force was 10 times higher than the theoretical friction force. Since the area of the element is constant, the measured kinematic stress acting on the floating element is 10 times higher than the theoretical kinematic stress. The required flow rate would have to be 2.2 times the measured flow rate in the pipe loop to produce the same stress level experienced by the floating element sensor. The error in the flow measurement could not be that high, as the pump motor could not produce enough power to drive such a high flow rate in the loop. The decrease in water viscosity due to test

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temperature increase during testing is small and as a result its effect on the kinematic stress value is also considered negligible. It was assumed that the errors in the and are negligible relative to the error in the flow rate measurement; and it was assumed that the error in the solids density and carrier fluid density is negligible. (Additional velocity and concentration profile measurements must be made during future testing in order to verify the floating element friction measurements.)

There may be errors in relating strain measurements to forces on the element surface. The floating element sensor measures both axial and shear forces at the same time. As a result, cross channel interference occurs between the axial and shear bridges. Errors in the axial bridge measurement get transferred due to cross-channel interference to the shear bridge output. Consequently, it would improve the design of the floating element to conduct the axial and shear force measurements separately.

Cross-channel interference error can be attributed to the pressure drop across the floating element sensor, which applies an axial load. The axial load applied causes errors in the axial and friction force measurement because the element was calibrated using a combination of axial and shear loads. The pressure drop across the floating element wear sample during slurry flow is estimated to be 600 Pa using the two layer model equation at 220 USGPM flow rate. This pressure drop causes an additional axial force equal to 1.8 N knowing that the surface area of the floating wear sample is 0.003 m2. By inspection of calibration Table 1, the voltage difference due to the pressure drop is 0.019 V in the axial bridge and 0.027 V in the friction bridge which causes a maximum relative error in the bridge output equal to 3% in the axial bridge output and 15% in friction bridge output.

Post test calibration of the floating element sensor was conducted to make certain that the element was not damaged during testing. Initial visual inspection of the assembly indicated there was no visible damage done on the element since all the components were in good condition. The post calibration shear force results agreed with the pretest shear calibration results. No axial force post test calibration was performed since the sensor was in good condition.

During test #2, the friction force measured was repeatable with a standard deviation in the measured data equal to 4%, as shown in Figure 20. The axial force output was less repeatable compared to the friction force with a calculated standard deviation equal to 5%, as shown in Figure 21.

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Figure 20. Friction force versus time during repeatability test #2

Figure 21. Axial force versus time during repeatability test #2

The dynamic response of the sensor was also investigated. The first natural frequency of the moving components of the floating element assembly was found by mounting the wear sample assembly on a shaker table, shown in Figure 22, consisting of an articulating drum that vibrates a shaker table placed on a lubricated surface. The frequency of vibrations was varied until the first natural frequency of the element was found to be 38 Hz.

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Figure 22. A photo of the frequency test setup

Both axial and shear bridge output signals were acquired at 500 samples per second data acquisition rate. A power spectral density (PSD) analysis of the friction bridge signal was developed in Matlab using Welch averaged spectral estimation method with several windowing functions such as Hanning, Blackman and Chebychev. The input signal was divided into segment sizes of 64 samples for all three window functions with default value of 50% overlap between segments. The PSD plot using Chebychev window showed the power spectrum without exhibiting apparent spectral leakage, and as a result it was chosen as the window of choice. The peak of the power spectral density plot occurs at 5 Hz for the friction signal as shown in Figure 23. The power spectral density plot drops sharply after 10 Hz and reaches a minimum at 30 Hz as the plot flattens out. Another small peak occurs at around 39 Hz due to the natural frequency of the floating element sensor. As a result, the amplitude of these fluctuations is amplified due to resonance resulting in another peak in the power spectral density. Fortunately, the magnitude of the latter peak due to resonance is very small compared to the dominant peak of fluctuation in the signal occurring at 5 Hz.

Shaker Table

Wear Sample Holder Assembly

Vibrating Drum

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Figure 23. Sample friction bridge power spectral density plot for both slurry and water flows

As shown in Figure 24, the peak of the power spectral density plot occurs at 5 Hz for the axial signal during water and slurry testing at 207 and 190 USGPM respectively. The power spectral density plot drops sharply after 10 Hz and reaches a minimum at 30 Hz as the plot flattens out. As a result, the signal dominant measurement fluctuations occur at around 5 Hz, which is well below the first natural frequency of the floating element in the axial directions as expected. Small amplitude fluctuations in the signal occurring above 30 Hz are likely due to mechanical force fluctuations from turbulence or low angle impact of particles, or may be an artifact of the low-pass filtering.

Figure 24. Sample axial bridge power spectral density plot for both slurry and water flow

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From these tests, it was concluded that the signal measurements were not distorted by excitation of a natural frequency of the element. Small amplitude fluctuations in the signal occurring above 30 Hz were likely due to mechanical force fluctuations from turbulence or low angle impact of particles.

Conclusions and Future Work

A model has been formulated for estimating erosion wear in a horizontal section of pipe under steady state conditions for sand slurry flow. The model is based on the physics of sand slurry flow represented by the two-layer model. The presented erosion wear model can be improved to embody erosion wear in oilsands straight horizontal section of pipe under steady state conditions by including the contribution of bitumen and particle digestion to the total erosion wear.

A floating element assembly has been designed and tested to measure axial and shear forces inside a straight pipe section. Both axial and shear forces acting on the floating element were measured during water and sand slurry experiments in addition to other flow variables such as flow rate, density, temperature, pump rpm, and pump discharge pressure. The results of the measured forces did not fully agree with theoretical results due to possible discrepancies in the flow rate measurement and a possible concentration and possibility of a concentration profile across the pipe cross section during slurry testing.

Future work will improve the design of the floating element sensor assembly so that the floating element sensor can be used in future slurry wear tests. The steady-state, lumped parameter model will be validated. The wear model will be enhanced to add additional elements to capture erosion wear in the transition zone between top layer and bottom layer, and to include the contribution of corrosion and combined erosion-corrosion to wear rate. Conditions under which particles contact the surface will be investigated, and a practical approach will be developed and field-tested to measure stresses experienced by industrial slurry transport pipelines.

Acknowledgments

Funding support is gratefully acknowledged from Syncrude Canada Ltd. and The Natural Sciences and Engineering Research Council of Canada (NSERC). Victor Jaimes contributed to the slurry loop design. Derek Loewen contributed to the spectral analysis of the sensor signals. Dan Wolfe coordinated the slurry loop experiments at Syncrude.

Nomenclature

PA is the averaged pressure of the top layer at the start of pipePD is the averaged pressure of the top layer at end of pipeq0 is the average flow rate of the slurry in pipelineql is the average flow rate of slurry in the lower layer of pipe

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qu is the average flow rate of slurry in the upper layer of pipeR1 is the resistance coefficient due to bottom layer friction (Columbic + kinematic friction)R3 is the resistance coefficient due to bottom layer mixing (Energy contributing to particle suspension and also to particle impact with the pipe inner wall in bottom layer)R4 is the resistance coefficient due to top layer friction (Kinematic friction)R5 is the resistance coefficient due to top layer mixing (Energy contributing to particle suspension and also to particle impact with the pipe inner wall in upper layer)

is the wear rate by the corresponding wear mechanism in units of (thickness/time)is the rate of energy transfer to the pipe wall contributing to damage in units of

(energy per unit area/time)is the total rate of energy transfer to the pipe wall in units of (energy per unit

area/time)is the threshold energy rate for incipient wear above which damage starts to

accumulate (energy per unit area/time)is the stress caused by the wear component on the pipe wall in units (force/area)is the threshold stress for incipient wear caused by the wear component on the pipe

wall in units of (force/area)is the local tangential velocity component for each wear mechanism in units of

(distance/time)α is the wear coefficient of each wear component in units of ((thickness/(energy per unit area))α11 is the wear coefficient due to bottom layer kinematic friction in the lower layer α12 is the wear coefficient due to bottom layer Columbic friction in the lower layer α3 is the wear coefficient due to particle impact with pipe inner wall in the lower layerα4 is the wear coefficient due to top layer kinematic friction in the top layerα5 is the wear coefficient due particle impact with pipe inner wall in the upper layer β3 is the energy fraction causing particles to exit flow streamlines and impact pipe inner wall in the lower layerβ5 is the energy fraction causing particles to exit flow streamlines and impact pipe inner wall in the upper layer

is the shear force in the y directionis the axial force in the x direction

is the moment of inertiais the cross sectional area of the cantilever elementis the distance between the shear force and strain gage 1

is equal to half of the cantilever element thicknessis the bridge output voltage

is the bridge excitation voltagestands for the gage factor

is the density of water = 1014 kg/m3is the water fanning friction factor

is the density of sand particles = 2650 kg/m3is the friction factor computed using the two layer model

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is the slurry flow velocity in the bottom layer of the flow

is the total energy dissipated in the system,

is the energy fraction contributing to damage in the system,

is the total pressure drop in the system in Pa,

is the pressure drop in the system due to slurry flow in Pa,

is the additional pressure drop in the system due to erosion damage in Pa,

is the resistance coefficient representing total pressure drop in the system,

is the resistance coefficient representing pressure drop in the system due to slurry

flow, and

is the resistance coefficient representing erosion damage

References

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