Process modelling and simulation of bitumen mining and recovery from oil sands

Minerals Engineering 134 (2019) 65–76

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Minerals Engineering journal homepage: www.elsevier.com/locate/mineng

Process modelling and simulation of bitumen mining and recovery from oil sands

T

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Manu Suvarnaa, , Mohanraj Divakaranb, Experience I. Nduaguc a

ANDRITZ Technologies Pvt. Ltd., Bangalore 560045, India ANDRITZ Inc, 125 Clairemont Ave, Suite 570, Decatur, GA 30030, USA c Department of Chemical and Petroleum Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada b

A R T I C LE I N FO

A B S T R A C T

Keywords: Bitumen extraction Oil sand Primary separation cell IDEAS Dynamic process simulation

The upstream operations in the oil sands industry comprise numerous interlinked processes with complex physical phenomena. To better understand the same, a high-fidelity dynamic process model, encompassing the major unit operations in bitumen extraction and recovery from oil sands, is developed in the IDEAS simulation platform. This model is validated against a reference plant data, and the relative error is less than 5%. In the validated model, three scenarios are devised to investigate the process variable-response relationship in a simulation environment. These include the effects of ore grade and ablation on bitumen recovery and hydrotransport length on secondary ablation. The predicted recovery of bitumen using low-grade ore is 89.30% and that for high-grade ore is 92.81%. For all the scenarios investigated, the model brings to fore the collective dynamic response of the interacting system of unit operations. Dynamic models such as these can be used as test beds by researchers for further analysis and also by plant personnel as an operational training tool.

1. Introduction Canada is home to one of the largest unconventional oil reserves in the world in the form of oil sands (Head et al., 2003; Gates and Larter, 2014). Bitumen being an unconventional fossil fuel, its recovery from oil sands is an expensive and technically challenging process in comparison to its conventional oil counterparts. Presently, the two main technologies contributing to the total production of bitumen are surface mining and in-situ operations (SAGD) (Wu and Dabros, 2012; Wang et al., 2014; Shah et al., 2010). Bitumen production by surface mining is the preferred extraction process when oil sands deposits are located at shallow reservoirs, usually up to depths of 65 m (Alberta Energy Regulator, 2018). Surface mining is characterized by its ease of operation as compared to in-situ methods that require drilling of wells and injection of high-pressure steam through wellbores to mobilize the bitumen underground. More so, bitumen mining is well established given that it has been applied commercially and has a higher bitumen recovery percentage than in-situ mining. Efficient and economic bitumen recovery involves two main stages: bitumen hot-water extraction (upstream) and froth treatment (downstream) (Zhou et al., 2004; Long et al., 2007; Masliyah et al., 2004). In-situ production of bitumen is beyond the scope of this work, and henceforth any reference to bitumen mining and recovery relates only to surface mining technology.

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Dynamic process simulators, which are based on robust analytical and empirical models, can aid the process industry to improve their process-related decision-making. In general, process simulators allow plant operators to test for operating conditions, start-up and shutdown transients and any abnormal or emergency procedures (Chen et al., 2017). The development and application of simulation models across various process domains has been reported by many authors which include thermal power plants (Chen et al., 2017; Colonna and van Putten, 2007; Pei et al., 2013; Sahin et al., 2016), mining operations (Bergh and Yianatos, 2013; Burgos and Concha, 2005; Yianatos et al., 2012), waste-water treatment (Heusch et al., 2010; Petrides et al., 1998), steam-reforming (Gangadharan et al., 2012; Sarvar-Amini et al., 2007; Zhang et al., 2013), biodiesel production (Lee et al., 2011; West et al., 2008), etc. Despite the significant progress and application of computer-aided process design and simulation tools in the process industry, only a few reports presenting the simulation of unit operations with respect to bitumen mining and recovery from oil sands processes are found in literature. Wan et al. (2000) used a computational fluid dynamics (CFD) model to show that the hydrodynamics in a primary separation cell (PSC) influences the separation efficiency of recovered bitumen. The model accounted for two parameters: the overall mean flow and the turbulent interactions of the particles inside the PSC and their significance in

Corresponding author at: ANDRITZ Technologies Pvt. Ltd., Manyata Embassy Business Park, Bangalore 560045, India. E-mail address: [email protected] (M. Suvarna).

https://doi.org/10.1016/j.mineng.2018.12.024 Received 17 May 2018; Received in revised form 15 November 2018; Accepted 24 December 2018 0892-6875/ © 2018 Published by Elsevier Ltd.

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Nomenclature

A50 ablation at 50 percent g acceleration due to gravity Dp particle diameter ρeffective effective particle density ρ_l carrier liquid density μ_(l_effective) effective carrier liquid viscosity NRe Reynolds number f(i) mass fraction of the particle in the ith size bin (PSD) k(i) Masliyah slip velocity factor μl pure component viscosity of the carrier liquid ρl density of the carrier liquid volume fraction of fines in the middlings layer Vffines %CLx percent lumps at x meters from the beginning of the conditioning line %CL0 percent lumps at the beginning of the conditioning line k rate constant, for exponential decay k values must be negative; x distance from the beginning of the conditioning line Kp controller proportional gain Ki controller integral gain Kd controller derivative gain

Abbreviations and parameters SCO SAGD IDEAS PSC HT Line wt% µm SPP PSD HGO AGO TPH mm I/F m/s km OS

synthetic crude oil steam-assisted gravity drainage integrated design engineering and advanced simulation primary separation cell hydrotransport line weight percentage microns slurry preparation plant particle size distribution high grade ore average grade ore tonnes per hour millimeters Interfacial layer meters per second kilometers oil sands

2. Theory & essential concepts

bitumen recovery. Friesen and Dabros (2004) modelled the conditioning of oil sands slurry using a Monte-Carlo algorithm to simulate the mean field kinetics of the coalescing bitumen drops and the air bubbles. Based on the developed model, they investigated the effects of bitumen concentration, the initial bitumen size and turbulent energy on the evolution of the coalescing drop size. Eskin et al. (2003) developed a model for granular flow in the regime of a moving bed during granular phase ablation. This model allowed for estimation of hydrotransport pipeline lengths required for total ablation of the ore lumps in a moving bed and axial pressure gradient distribution for different flow regimes. Notably, these simulations are devised at the level of the physical processes governing a particular unit operation under consideration. However, such models do not account for the overall process picture nor do they address the underlying interactions and dynamics among various inter-dependent unit operations. Thus, there is an evident lack of modelling tools for exploring the collective dynamics of the group of unit operations involved in bitumen mining and recovery processes. The fact that oil sands have become a dominant source of crude oil production in Canada - and will continue to remain so for the next decade – coupled with the deterioration of high-grade mineral ore deposits, mining of lower-grade ores is becoming the norm (Wang et al., 2014). Thus, research in dynamic process simulation, control and optimization is of interest to achieve the necessary mineral liberation and hence recovery of bitumen using conventional mineral processing. In this work, a high-fidelity dynamic process model is developed, encompassing major unit operations from oil sands ore mining, slurry preparation, slurry transport in hydrotransport lines and bitumen recovery in the PSC. The model is divided into three networks: the dry ore preparation plant, the wet ore preparation plant and a primary separation cell. Further, the model is validated against a steady-state data sourced from a full-scale surface mining operation. A few essential controllers are incorporated into the plant model and their working procedures briefly described. Furthermore, four scenarios are devised to investigate the process variable-response relationship in a simulation environment. These include (i) the effect of ore grade and ablation on the dynamic response of bitumen recovery (ii) the effect of ore ablation rates on the slurry properties (iii) the role of hydrotransport (HT) length on secondary ablation and (iv) the effect of slurry density on the dynamic response of slurry velocities in the HT lines.

2.1. Ore composition and classification Oil sands ore is a complex mixture of bitumen, sand and clay (frequently referred to as fines) and water. The physico-chemical properties of the oil sands constituents have significant effects on the bitumen extraction and recovery process (Dai and Chung, 1995; Sanford, 1983). Generally, oil sands ore quality is defined in terms of the bitumen and the fines content. Ores with high bitumen content and low fines are efficiently processed and usually yield a higher bitumen recovery, in the range of 85–90%. On the other hand, ores with higher fines and lower bitumen content are not only quite complex to process, but also have lower bitumen recovery rates (Romanova et al., 2006). Conventionally, the oil sands grade, a term that relates to the bitumen content, is defined as follows: rich (12–14 wt% bitumen), average (9–11 wt% bitumen), and lean (5–8 wt% bitumen) (Romanova et al., 2006). Another common descriptor is the percentage of fines (sand and clay particles < 44 µm in size) contained in the ore. An ore with high fines contains > 20% fines, while a low-fines ore contains < 8% fines (Kaminsky et al., 2008). From a process and recovery perspective, the ore should have fewer fines. 2.2. Ablation and hydrotransport lines Ablation is the process of breakdown of oil sands ore into its constituents by the application of mechanical shear. Bitumen, being highly viscous and gluey, holds the oil sands lumps together. However, on subjecting these lumps to hot water and mechanical shear in tumblers or mixing boxes, and during transportation in the hydrotransport lines, the ore’s outer layer becomes heated, thereby resulting in a reduction of bitumen viscosity and subsequent shearing away of the ore, a process known as ablation (Masliyah et al., 2004). The new surface is again heated and sheared away, as aforementioned, and this process repeats itself to a point where the entire lump is ablated. The HT pipelines, commonly referred to as slurry pipes are large diametric pipes that carry the slurry from the wet ore preparation plant (OPP) to the primary separation cells (PSC) and extend over two to five kilometers in length. They provide sufficient mechanical shear for the breakdown of the oil sands lumps, as typical slurry velocities range from 3 to 6 m/s. They also contribute towards enhanced bitumen recovery rates by providing the much-required residence time for the 66

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the mathematical models of the hydrotransport pipeline and primary separation cell are presented in this section. The dynamics of the hydrotransport pipeline is captured using the framework of a heterogeneous slurry flow model based on the two-layer pressure drop theory proposed by Saskatchewan Research Council (Sanders et al., 2004; Shook, 2002). This pipeline model captures the flow characteristics of slurries in the regimes where the rheology is highly influenced by the settling tendency. The model predicts the deposition velocity – a critical velocity below which the lift forces in the fluid are insufficient to maintain the solids in a suspended state. The operating velocity, when compared against the deposition velocity, provides an index of the degree of segregation of solids, which is further used to compute the extent of segregation of the fluid into two layers – a homogenous layer supporting the fines and a segregated layer with coarser solids. The pressure drop model predicts the stress interactions such as fluid-wall shear in both these layers, in addition to the stresses caused due to inter-particle interactions in the settling layer. The settling dynamics and the associated pressure drop are captured in an axially discretized pipeline model with plug flow zones. The extent of ablation along the length of the pipeline is modelled as a rate process with first-order kinetics.

attachment of air bubbles to the bitumen in the slurry mixture (Magazine, 2018). 2.3. Primary separation cell Primary separation cells are large diametric vessels with a coneshaped bottom that facilitates separation of the oil sands into a bitumen-rich froth and the rest of the slurry (Wan et al., 2000; Masliyah et al., 1981). The oil sands slurry from the hydrotransport pipeline enters the vessels through a feed well. Once the slurry enters the PSC, the bitumen fraction being light and buoyant rises to the top. This accounts for the froth product in the PSC, which is collected at the top or overflow of the vessel. Bitumen collected in the froth has densities around 1000 kg/m3 due to its very high air content (Magazine, 2018; Masliyah et al., 1981). Coarse solids present in the slurry sink to the bottom of the vessel and are collected and pumped to the tailings plant. The coarse solids are also referred to as PSC underflow. The coarser the solids, the faster they settle and the higher the underflow density (Wan et al., 2000; Magazine, 2018). Slurries with densities higher than 1600 kg/m3 do not flow and would likely lead to plugging of the conical section of the vessel. The portion of slurry in the middle section of the vessel (termed as middlings) comprises ore fractions that are not light enough to float at the top nor heavy enough to sink to the bottom. Middlings usually consist of fines and clays, which trap the bitumen and hinder attachment to the air bubbles. They have higher bitumen content than the underflow (usually 1–4%), and their densities range between 1100 and 1300 kg/m3. The middlings recovered from the PSC is usually sent to flotation units for reprocessing due to its significant bitumen content (Magazine, 2018; Masliyah et al., 1981).

Remaining% Lumps (CL): %CLx = %CL 0 ∗ exp(k ∗ x) where %CLx = percent lumps at x (m) from the beginning of the conditioning line %CL0 = percent lumps at the beginning of the conditioning line k = rate constant, for exponential decay k values must be negative x = distance from the beginning of the conditioning line The primary separation cell model captures the dynamics of heavy and light phase separation in a lumped system modelling framework of three discrete zones, viz. the froth, middlings and compaction zones. The conical vessel geometry represents the compaction zone, which is surmounted by a cylindrical vessel configuration to accommodate the middlings and froth zones (Wan et al., 2000). The constitutive laws of settling are derived from Masliyah’s settling velocity model (Masliyah et al., 1981). This model calculates the settling of particles governed by the inertial or viscous regime interactions based on the particle Reynolds number. The properties of the suspension used in the settling equation account for the fluidity and concentration of the particles in the continuum of the settling medium. The effective viscosity of the suspension is determined based on the carrier liquid viscosity and the volumetric concentration of the fine particles, which are not significantly influenced by the gravity force field. An increase in the fines content aids in increasing the viscosity of the suspension, as determined by the effective viscosity relation, and, in effect, impedes the settling of coarser particles due to the higher viscous drag imposed by the suspension on the settling species. The PSC model uses the Masliyah’s settling velocity expression (Masliyah et al., 1981) to determine the terminal velocity of the sand and bitumen particles as described below:

3. Methodology 3.1. Framework of the dynamic model The entire simulation is performed using IDEAS™ 600, a dynamic simulation platform developed by ANDRITZ Inc. All the unit operations and process models built in the software are based on the first principles of mass and energy conservation and population balance methodology (Parthasarathi et al.). The software offers an extensive list of libraries for modelling several unit operations and a database for physical properties comprising a wide variety of material components used in various industries (Cristoffanini et al.). The topological structure of the plant scale model presented in this work exhibits classification in the following two layers: 1. A layer of hydrodynamic components which includes ablation hydrotransport pipelines, centrifugal pumps, control valves and pipe fittings. 2. A layer of unit operations typically characterized by large residence times such as the primary separation cell, storage tanks and surge bins. The unit operation models involve solving the conservation laws of mass and energy, population balances of particles, kinetics of interacting species, constitutive relations and thermodynamic property correlations such as specific heat capacity, density, etc. The hydrodynamic components are modelled with emphasis on momentum balances, in addition to the mass and energy conservation. The temporal evolution of the network of unit operations and hydraulic components is solved in a sequential modular approach with time discretization. At every time step, all the conserved quantities are integrated within the design volume of the equipment representing the unit operations. The solutions of the unit operations serve as a boundary condition for solving the adjoining hydraulic network, where the pressure and flow relations are solved. The details of the physical processes captured in

Vsettling =

Gfactor g D2.0 p (ρp _effective − ρ l ) 18.0 μl_effective (1.0 + 0.15 N 0.687 Re )

where g = acceleration due to gravity Dp = particle diameter ρp_effective = effective particle density ρl = carrier liquid density μ l_effective = effective carrier liquid viscosity NRe = Reynolds number

67

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with the characteristics of the ore, is fed to the dump hopper. The maximum permissible ore flow rate is set at 20,000 TPH. However, the flow of ore is mainly regulated by the level of the surge bin present downstream in the dry OPP (Fig. 3). From the dump hopper, the ore is conveyed to the double-roll crusher via the apron feeder with a maximum design capacity of 28,500 TPH. The ore undergoes significant size reduction in the double–roll crushers. Ore particles with PSD less than 120 mm are screened and collected in the surge bin, while those greater than 120 mm are recycled back to the dump hopper for further processing and size reduction. The storage of the ore in the surge bin marks the end of the dry OPP process. The ore collected in the surge bin is conveyed to the slurry mix-box via a thirty-meter long slurry prep feed conveyors. Mixing of the ore with the warm water and caustic inside the slurry mix-box marks the beginning of the wet OPP (Fig. 4). Addition of water into the slurry mixbox is controlled using a density controller to manage the slurry density downstream of the pump box. Mixing of the ore with warm water and the mechanical shear subjected to it due to inter-ore collision and collision with the walls of the mix-box cause the ore to ablate and break down into its individual components. This is considered as primary ablation. Post primary ablation, the ore particles in the slurry with size less than 0.21 mm are screened and collected in the pump box. Finally, the slurry collected in the pump box is then pumped via the HT lines to the PSC as the last step in the wet OPP. The surface chemistry effects of bitumen liberation and the role of sodium hydroxide in bitumen extraction is not investigated in this study. As the slurry enters the PSC, it is further diluted with water to maintain a feed density of 1470–1480 kg/m3. In the PSC, a flow approximating 65% of the total mass fed into the PSC is withdrawn from the tailings section for both ore grades (Tech, 2018). Optimal control of the interface between the bitumen froth and the middlings in the PSC results in a significant improvement in bitumen recovery. Thus, the mass flow rate withdrawn from the middlings layer is controlled in order to maintain optimal interface level (Jampana et al., 2010; Narang et al., 2015).

For a verisimilitude representation of the dynamic settling nature in the middlings zone, a set of detailed interaction parameters are used to describe the influence of flow pattern on the settling characteristics. A slip velocity factor is used in the Masliyah settling velocity expression, which accounts for the influence of the various size populations in determining the effective settling velocity.

Gfactor =

[1 − ∑i k(i)f(i)]4.7 [1 − ∑i f(i)]2.0

where f(i) = mass fraction of the particle in the ith size bin (PSD) k(i) = Masliyah slip velocity factor The effective density of a particle of size ‘i’ is,

1.0 ⎞ 1.0 ⎞ + ρl ⎛1.0 − ρpeffective = ρp ⎛ k(i) ⎠ ⎝ k(i) ⎠ ⎝ ⎜

⎟

⎜

⎟

The viscosity of the carrier fluid is calculated using Shook-SRC equation (Shook, 2002), which accounts for the effect of the fines in the suspension.

μl_effective = μl (1.0 + 2.5(9.0Vffines) + 10.0(9.0Vffines )2.0) where μl & ρl = pure component viscosity and density of the carrier liquid, respectively Vfines = volume fraction of fines in the middlings layer. A few among the principle physical processes considered in this model include: hindered settling based on the size distribution of sand particles, influence of flow pattern on the settling components, buoyancy-driven rise of the aerated bitumen to the froth zone, recycling of tailings from the separation cascade of the process, instantaneous variation of the froth-middling interface level, and the effect of zone dilutions.

3.3. Control philosophies 3.2. Simulation of the process in IDEAS In a dynamic simulation, process controllers are modelled to analyze plant stability and regulation (Chen et al., 2017; Albalawi et al., 2018). In the dry OPP, the regulatory control structure includes PID controllers for the level control of the drum hopper and the surge bin. The surge bin controller adjusts the speed of the apron feeder to keep the surge bin level at 50%. The surge bin level is measured using a level transmitter, and the set-point is determined at 50% of the total level, regulated by actuating the speed of the apron feeder. Similarly, a PID controller is used to maintain the level of the drum hopper at 40% of the total level by regulating the ore feed flow rate. In the wet OPP, the regulatory control structure includes the PID controllers for level control of the pump box, the slurry mix-box and slurry density control. For the pump box control strategy, the slurry level inside the pump box is measured using a level transmitter – the set-point is determined at 70% with the downstream hydrotransport pump speed being the actuated quantity. The slurry mix-box level is maintained at 50% of the total level by using another PID controller, which would regulate the speed of the slurry preparation conveyor

The simulation model is developed based on a hypothetical extraction facility developed by TetraTech Canada Inc. and presented for use by Canada’s Oil sands Innovation Alliance (COSIA) (Tech, 2018). This generic process model developed by TetraTech provides the essential material and energy balances across each unit operation. The material balances provided in the generic plant are used in building the simulation model. The required engineering data such as physical dimensions and elevations of tanks, conveyors, pipes, PSC, pump sizing, feed characteristics, particle size distribution (PSD) of the ore, etc., are configured in the simulation. Much attention is given to keep these values to match industrial norms as close as possible (Magazine, 2018). Tables 1–4 lists the data and dimensions of the major units modelled in the dry and wet OPP as well as the PSC. Two types of ore, termed low-grade ore (LGO) and high-grade ore (HGO) are used in this study. The ore compositions and the PSD data for both the ore grades are listed in Tables 5 and 6. The ore characteristics are formulated based on available literature data and to match the ore features of the conceptual plant (Romanova et al., 2006; Tech, 2018; Kaminsky et al., 2006). Fig. 1 represents the process flow diagram of the simulated model along with mass balances sourced from the reference plant for HGO. Fig. 2 represents a simplified diagram of the controllers used in the simulated model. Figs. 3 and 4 provide the visual representation of the dry and wet OPP networks developed and modelled in IDEAS simulation platform. Much of the process in the dry OPP involves the movement of the ore from the dump hopper to the surge bin via conveyors. A source feed,

Table 1 Physical dimensions of tanks used in the simulation model.

Dump Hopper Surge Bin Slurry Box Pump Box

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Height (m)

Diameter (m)

L * B (m2)

Volume (m3)

6 35 10 15

15 – 10.2 10.5

– 45 * 24 – –

1032 37,800 785 1178

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Table 2 Physical dimensions of conveyors used in the simulation model.

Apron Feeder Surge Feed Conveyor Slurry Prep Conveyor

Capacity (t/h)

Length (m)

Belt width (m)

Belt Shape

Bed height (m)

Feeder width (m)

28,500 13,933 15,483

22 337 30

4.8 2.1 5

Flat with feed chute 3-idler, Trough angle = 45° Flat with feed chute

1.74 – 1.6

4.2 – 4.2

using a density transmitter, the set-point defined at 1550 kg/m3, and the actuator controls the opening of the feed water valve. Fig. 2(a–e) represents the simplified diagrams of the various controllers used in this study.

Table 3 Sizing capacities of pumps used in the simulation model.

Pump 1 Pump 2

Design Pump Head (m)

Design Pump Flow (m3/h)

Suction Diameter (in.)

65 65

19,000 19,000

36 36

3.4. Model objectives and scenarios Once the base model is developed, it is tuned to match the performance of the reference plant reported by Tech (2018) at steady state. The two ore grades are studied for their respective process flows, parameters and bitumen recovery and then compared with the reference plant data for model validation. Since there is no data for the plant under transient condition, and also such data not being easily accessible, a dynamic validation of the model becomes a challenging task. However, to evaluate the dynamic capability of the model, four scenarios are devised to understand the dynamics involved in the upstream bitumen mining and the recovery processes. These include evaluation and understanding of the response of a given unit operation when a particular process parameter of interest is varied. The scenarios are listed below:

Table 4 Physical dimensions of the PSC. Vessel Diameter (m) Height (m) Cone height (m) Tailing zone height (m) % Air in the froth (m) Froth outlet (m) Middling outlet (m) Tailing outlet (m)

28 24 19 7 40 24 18.4 0.3

Table 5 Ore composition in mass percentage. Mass Percentage

Low Grade Ore

High Grade Ore

Bitumen Water Sand

8.8 3.6 87.6

12 3.2 84.8

1. The influence of ore grade used as the feed source in the final bitumen recovery in the PSC. This is more of an extension of the model validation study. For all the conditions, a fixed ablation of 60 percent in the mix-box and constant slurry density of 1550 kg/m3 is maintained. 2. The effect of different ore ablation rates inside the mix-box on the properties of the slurry in the HT lines and on the bitumen recovery in the PSC is examined. Three different ablation percentages in the mix-box (50, 60 and 70) are tested using HGO at a constant slurry density of 1550 kg/m3. 3. The role of HT pipeline lengths on secondary ablation. Three pipelines of different lengths 1.6, 2.4 and 3.2 km are tested, and their effect on the ablation of ore is investigated using LGO and at a constant slurry density of 1550 kg/m3. 4. The effect of slurry density on the velocity and residence time of the slurry in the HT lines is assessed. Slurry densities of 1500, 1550 and 1575 kg/m3 are tested using HGO and at 60 percent ablation of the ore in the mix-box.

Table 6 Defined PSD characteristic for the ores. Diameter (microns)

Average Grade Ore (% passing)

High Grade Ore (% passing)

4 13 44 80 140 154 158 180 201 220 244 307 357 754 1041 2000 3000 4000 5000 6000 8000 10,000

2.04 8.94 16.81 29.6 49.2 55.61 59.8 66.93 71.69 74.78 81.18 89.83 96.2 100 100 100 100 100 100 100 100 100

1.48 7.12 10.41 24.09 42.62 44.68 46.72 51.84 56.96 59 63.6 78.44 88.36 94.71 98.25 100 100 100 100 100 100 100

d50

144.39

169.27

4. Results and discussion 4.1. Controller tuning & model validation at steady-state In the simulation environment, the PID controllers are tuned to stabilize the process and eventually bring it to steady-state. To achieve this, the controlled variables are maintained at their respective setpoints. The proportional, integral, and derivative terms of each controller are tuned by following the standard procedures (Åström and Hägglund, 2006). Initially, the value of Kp is either increased or decreased until the output signal started oscillating to value ranges close to the setpoint. Ki is then tuned by reducing its value until the output signal reached and maintained at the desired setpoint in an acceptably short time span. Table 7 presents the tuning parameters of all the PID controllers incorporated into the simulated model along with the process variables, control outputs, mode of action, set-points, minimum and maximum output ranges.

feeding the slurry into the slurry mix-box. The density of slurry in the HT line is regulated using a controller. This allows for adjusting the mass flow rate of the warm water mixed in the slurry mix-box by manipulating the warm feed water valve. The slurry density is measured 69

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Fig. 1. Process Flow Diagram for the simulation model using HGO.

used and the process conditions involved in mining, extraction and froth treatment. The bitumen recovery from surface mining operations is typically 90% (Masliyah et al., 2004; Magazine, 2018). The percentage of bitumen losses from mining activities is about 2–4%, primary bitumen extraction 6–8% and froth treatment 2%. The major causes of bitumen loss include variation in ore characteristics, water chemistry, mechanical reliability and improper slurry conditioning (Qiu, 2010; Ng et al., 2000). To improve bitumen recovery, all these factors should be addressed collectively. In this study, two different ore grades are used: a low-grade ore with bitumen mass content of 8.6% and a high-grade ore with bitumen mass content of 12%. The simulation results when the process is operated at standard conditions of slurry density (1550 kg/m3) and the HT line length of 2.4 km are listed in Tables 9 and 10. Evidently, and as anticipated, the high-grade ore with higher bitumen content yielded 92.81% recovery of bitumen in the froth layer. The low-grade ore yielded a bitumen recovery of 89.30%. Also, the mass percentage of bitumen in the froth layer is 62.46% and 53.6% in the case of highgrade and low-grade ores, respectively. Both the recovery percentage and the bitumen mass percentage in the froth layer are in agreement with the data published in literature (Magazine, 2018; Masliyah et al., 1981; Ng et al., 2000; Romanova et al., 2004). The addition of sodium hydroxide to either the warm water or the slurry water in the wet OPP results in higher bitumen recoveries. When sodium hydroxide interacts with the oil sands, the sand particles are liberated from the bitumen droplets (Dai and Chung, 1996). Despite the fact that this process is not included in the model, the simulated recoveries of bitumen closely match that of the reference plant bitumen recovery. This is made possible by tuning the Masliyah slip factor in the PSC formulation, such that the bitumen recovery is comparable to that of the reference data and also keeping the composition of the froth, middlings and tailings within ranges consistent with normal operations (Romanova et al., 2006; Magazine, 2018). Bitumen losses to the middling and tailing regions of the PSC are inevitable and must be kept to a minimum. In this study, it is observed

To evaluate the model, the results obtained from the simulation at steady-state are compared to that of the corresponding reference plant data. The model matched the essential process parameters of the reference plant such as ore and water flow rates, bitumen mass percentage in the respective streams, operating temperatures, etc., as seen in Table 8. The reference plant did not account for rejects in the dry OPP zone. With an assumption of typically 3–5% rejects in the dry OPP (Magazine, 2018), an initial ore flow rate of 19,200 and 13,600 TPH, respectively, for the low and high-grade ore is defined at the start of the simulation. This helped to match the required ore flow in the wet OPP for both ore grades. The hot water flow rate in the wet OPP slightly deviated from the reference data for both ore grades. This could be due to the controller action set to maintain a slurry density of 1550 kg/m3 in the simulated model. The reference model is an open-loop model without any controls described, and the density range is specified between 1500 and 1600 kg/m3. To establish a slurry density of 1550 kg/m3, the hot water requirement is calculated by the simulation model, and the same is reported in Table 8, which could explain the difference with respect to the reference data. The slurry flow, dilution water and cooling water streams entering the PSC are in good agreement with the reference data. In addition, the froth properties recovered from the top of the PSC closely matched the reference data in terms of parameters such as flow rate, temperature and bitumen percentage with a relative error of less than 2%. The overall bitumen recovery rates in the reference plant are 91.07% and 92.95% for the low and high-grade ores, respectively. The predicted values from the simulation model closely matched this reference data, with bitumen recovery rates of 89.30% and 92.81% for the low and high-grade ores, respectively. In summary, the simulated model is very much capable of emulating the reference plant process parameters to a good degree of accuracy as seen in Table 8.

4.2. Effect of ore grade on bitumen recovery The overall bitumen recovery rate depends on the quality of the ore 70

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Fig. 2. Simplified diagram of the controllers used in the simulated model (a) surge bin level control, (b) drum hopper level control, (c) pump box level control, (d) slurry mix box level control, (d) slurry density control.

4.3. Effect of ablation

that the respective mass percentage of bitumen losses to the middlings section are 1.79 and 2.41 for low and high-grade ores, respectively, and to the tailings, 0.63 and 0.54 for low and high-grade ores, respectively. The motion and distribution of solids/fines in the complex suspension inside the PSC is essential to get the right PSDs across all the three zones and is of utmost importance in the froth as it ultimately affects bitumen recovery. Ideally, the solids from the slurry feed should not be present in the froth layer because of the thermodynamic interfacial properties: the hydrophilic surface of the solids, hydrophobic surfaces of the air bubbles and the bitumen drops (Wan et al., 2000; Masliyah et al., 1981). However, in real extraction operations, water and solids always report to the froth layer. Typical compositions in the froth layer include bitumen (about 55–65%), water (about 20–30%) and solids (about 10–15%) (Romanova et al., 2006; Magazine, 2018; Ng et al., 2000; Romanova et al., 2004). And, the very similar composition is seen from the results of the simulated model for both the ore types, thus validating this study.

Primary ablation of the oil sands takes place in the slurry or mixboxes. Thereafter, the crushed and screened oil sands lumps, whose sizes range from 50 to 150 mm, are ablated inside the hydrotransport pipeline (Magazine, 2018). The rate of ablation is controlled by heat transfer from the slurry and the inter-lump collision within the pipe wall. The other important factors contributing to ablation of the ore in the hydrotransport pipeline are ore composition, initial lump size, temperature and mechanical shear imparted to the lump (Masliyah et al., 2004; Sanders et al., 2004; Shook, 2002). Ablation has also been found to have a remarkable significance in bitumen recovery. Three different ablation percentages (50, 60 and 70) are tested over the simulation using the high-grade ore, keeping the slurry density constant at 1550 kg/m3. Interesting observations are made on evaluating the response of bitumen recovery rates, with changes in ablation, for HGO. In the base conditions, a primary ablation of 60% in the mix-box gave a final yield of 92.81% bitumen recovery. When the ablation rate 71

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Fig. 3. Process schematic of the Dry OPP in IDEAS simulation platform.

fact that increased ablation had increased the bitumen liberation into the slurry, which is eventually recovered in the froth stream of the PSC, thereby enhancing recovery. Fig. 5 represents the role of primary ablation on bitumen recovery rates. An increase in ablation indicates not only enhanced liberation of bitumen but also introduces more fines into the slurry. This, in turn, would lead to a higher velocity (Friesen and Dabros, 2004) and lighter

is reduced to 50% in the mix-box, the bitumen recovery dropped to 92.21%, which is a marginal decrease as compared to the base condition. However, at 70% ablation in the mix-box, the bitumen recovery in the PSC increased up to 94.47%, which is a significant increase than anticipated. A closer look at the model revealed that the bitumen content in the froth had increased to 63.58% as compared to 62.46% in the base condition. The reasoning for this could be inferred from the

Fig. 4. Process schematic of the Wet OPP in IDEAS simulation platform. 72

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Table 7 PID controller description and tuning parameters. Parameters

PID 1

PID 2

PID 3

PID 4

PID 5

Controlled variable Manipulated variable Set Point with units Action mode Maximum output Minimum output Kp Ki Kd

Surge bin level Apron feeder speed 40% Reverse 100 rpm 20 rpm 0.1 0.75 1

Drum Hopper level Ore feed flowrate 30% Reverse 20,000 TPH 0 TPH 0.01 1 1

Pump box level Pump Speed 60% Direct 100 rpm 30 rpm 0.1 0.5 1

Slurry mix box level Slurry flow to pump box 70% Reverse 28,000 TPH 0 TPH 0.1 0.8 1

Slurry density Warm water flow 1550 kg/m3 Direct 1600 kg/m3 1400 kg/m3 2.28 0.75 1

Table 8 Comparison of reference plant and model plant. Zone

Parameters

LGO

HGO

Relative Error %

Reference Plant

IDEAS Model

Relative Error %

Reference Plant

IDEAS Model

Ore Flow (TPH) Ore Temp (°C) Rejects (TPH) Bitumen in ore (%)

– −3 – 8.6

19,200 −3 435 8.6

NA 0.00 NA 0.00

– 5 – 12

13,600 5 458 12

NA 0.00 NA 0.00

Wet OPP

Ore Flow (TPH) Ore Temp (°C) Hot Process Water Flow (TPH) Water Temp (°C) Slurry Flow Slurry Density Slurry Temp Rejects (TPH)

18,765 −3 8580 80 27,345 1500–1600 50 928

18,765 −3 8642 80 27,407 1550 51.1 974

0.00 0.00 0.72 0.00 0.23 NA 2.20 4.96

13,142 5 6162 80 19,304 1500–1600 50 441

13,142 5 6221 80 19,363 1550 50.8 527

0.00 0.00 0.96 0.00 0.31 NA 1.60 19.50

PSC

Slurry Flow (TPH) Slurry Temp (°C) Dilution Water Dilution Water (°C) Cooling Water (°C) Cooling Water (°C)

26,417 50 1716 45 7017 25

26,433 50.8 1716 45 7018 25

0.06 1.60 0.00 0.00 0.01 0.00

18,863 50 4396 45 5769 25

18,836 50.4 4400 45 5770 25

0.14 0.80 0.09 0.00 0.02 0.00

Froth

Flow rate (TPH) Temperature (°C) Bitumen (%) % Recovery

2773 40–50 53 91.07

2751 49.28 53.6 89.30

0.79 NA 1.13 1.94

2443 50–70 60 92.95

2425 58 62.46 92.81

0.74 NA 4.10 0.15

Dry OPP

Table 9 Result summary for bitumen recovery in the PSC using LGO.

Table 10 Result summary for bitumen recovery in the PSC using HGO.

Property

Units

Feeda

Froth

Middling

Tailing

Property

Units

Feeda

Froth

Middling

Tailing

Flow Temperature Pressure Density Enthalpy Water Sand Bitumen d50 (All Solids)

TPH °C kPa kg/m3 kJ/kg wt% wt% wt% microns

32,377 50.8 97.19 1480 −15810.5 42.97 51.70 5.33 144.39

2721 49.28 97.19 1134.6 −15819.5 25.49 20.91 53.6 19.66

10,361 50.71 98.15 1435.51 −15809.3 46.57 51.64 1.79 40.91

22,016 49.92 97.19 1502.26 −15808.6 49.83 49.54 0.63 158.9

Flow Temperature Pressure Density Enthalpy Water Sand Bitumen d50 (All Solids)

TPH °C kPa kg/m3 kJ/kg wt% wt% wt% microns

18,836 50.77 97.19 1480 −15815.4 40.77 50.58 8.65 169.27

2425 58.15 97.19 1008.16 −15826.4 24.17 13.37 62.46 9.4

8281 50.31 98.15 1133.55 −15808.5 49.27 48.32 2.41 33.69

18,300 49.47 97.19 1389.7 −15812.5 45.79 53.57 0.54 331.87

a An additional flow of 1716 TPH of dilution water at 45 °C and cooling water at 7018 TPH at 25 °C has not been accounted in feed stream. These streams enter the PSC separately.

a An additional flow of 4400 TPH of dilution water at 45 °C and cooling water at 5770 TPH at 25 °C has not been accounted in feed stream. These streams enter the PSC separately.

density as compared to the lumped ore in the case of lesser ablation. This phenomenon can be observed in Fig. 6. The simulation began with the HGO undergoing ablation at 60%, and the corresponding downstream slurry had a velocity of 5.05 m/s and density of 1550 kg/m3. When the extent of ablation is dynamically reduced to 50% after 2000 s of simulation time, the slurry density began to gradually drop and finally stabilized at 4.86 m/s. The corresponding slurry density dropped to 1523 kg/m3. Finally, when the extent of ablation is dynamically increased to 70% after 4000 s of simulation time, the slurry velocity

began to gradually increase until it reached a maximum of 5.25 m/s with a corresponding density of 1576 kg/m3. Thus, the model is capable of capturing the dynamic effects of changes in primary ablation in the mix-box (upstream) to that of slurry density and velocity in the downstream HT pipelines. 4.4. Role of HT length As the slurry passes through the HT pipelines, the ore lumps further 73

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95

than 44 µm in diameter and (ii) a coarse sand phase, which mainly includes oil sands lumps. The fines generally remain suspended in the water phase and are easily carried across the length of the pipeline. However, the coarse particles are dragged along the bottom of the pipeline and require higher velocities to keep moving (Masliyah et al., 2004; Eskin et al., 2003; Qiu, 2010). Sanding occurs if the velocity in the HT line is not fast enough to keep the coarse particles moving and is highly undesirable from an operations perspective. A general rule of thumb is to have oil sands slurries run at greater than 3.5 m/s. Slurry velocities close to 5 m/s are good to push even the coarsest of slurries across the HT pipelines (Magazine, 2018). Three different slurry densities for HGO at 1500, 1550 and 1575 kg/ m3 at 60% ablation are tested during the simulation. Since the slurry feed to the PSC is always maintained at a density between 1470 and 1480 kg/m3, there is no substantial change observed in bitumen recovery with the slurry density variations upstream in the wet OPP. However, variations in two significant process variables are observed in the HT line, viz. slurry velocity and slurry residence time as a function of density. An increase in slurry density reduced the slurry velocity, which eventually increases the residence time of the slurry in the pipeline. Based on the slurry composition and the sizing of the HT pipeline, the HT line formulation calculates the minimum required slurry flow velocity to prevent sanding, known as deposition velocity. At velocities below the deposition velocity, sanding takes places. Fig. 8 represents the profiles of density, deposition velocity and slurry velocity over a given simulation period. At a slurry density of 1550 kg/m3, the deposition velocity is 3 m/s, and the actual slurry velocity is 5.05 m/s. The slurry density is then dynamically changed to 1575 kg/m3 and finally to 1500 kg/m3 at 2400 and 4800 s of simulation time, respectively, to evaluate the response of slurry velocity in the HT line. In both cases, corresponding changes in the slurry velocity are observed as seen in Fig. 8. The slurry velocity is first reduced to 4.86 m/s on increasing the density to 1575 kg/m3, and then gradually increased to 5.26 m/s on reducing the density to 1500 kg/m3. In both cases, the slurry velocity is higher than the deposition velocity, indicating smooth operation in the HT line.

94

% Bitumen Recovery

93 92 91 90 A50

A60

A70

% Ablation Fig. 5. Bitumen recovery as function of ablation for HGO. 6

Density (kg/m3)

1570 1560

5.5

1550 5

1540 1530

4.5

1520 1510

Slurry Velocity (m/s)

1580

4

1500 0

2000

4000

Time (seconds)

6000

8000 Density Slurry Velocity

Fig. 6. Changes in slurry density and velocity as a function of ablation.

ablate depending on the length of the HT pipeline and the residence time provided (Romanova et al., 2006; Albalawi et al., 2018). For this study, three different scenarios with three pipeline lengths are tested: 3.2 km, 2.4 km and 1.6 km using LGO. Two equal length pipes are connected in series to meet the cumulative length in each of the cases. As anticipated, the longest pipe provided the maximum ablation. Post the pump box, 80.75% of the bitumen had liberated from the ore, and only 19.25% of the bitumen remained in lumps. Following the transport of slurry in each pipeline, the bitumen present in the lump underwent subsequent ablation. Fig. 7 represents the ablation of ore as a function of HT pipeline length. The longest pipe with a cumulative length of 3.2 km had 19.25 mass percent of bitumen in lumps in the slurry at its inlet. As the slurry passed through the first half of the pipeline, it underwent ablation, and 18.58% of bitumen lumps released into the slurry as free bitumen. Of the remaining 0.67 mass percent of bitumen present in lumps, which passed through the second half of the pipe, further ablation along the pipe length released 0.58 mass percent of bitumen as free bitumen and only 0.09 mass percent of bitumen was present in lumps at the end of the hydrotransport pipeline. Similar trends with reduced ablation are observed in the other two pipelines, mainly due to the reduction in lengths. The pipeline with a cumulative length of 2.4 km had 99.78 mass percent of bitumen liberated from the ore and only 0.22 mass percent present in lumps. The pipeline with a cumulative length of 1.6 km had 99.16 mass percent of bitumen liberated from the ore and 0.84 mass percent present in lumps. Overall, the simulated model is very well capable of showing the realistic and dynamic behaviour of the role of HT pipeline length on secondary ablation.

5. Conclusion The modern-day process industry has witnessed an unprecedented demand on product quantity and quality, along with the expectations to meet the stringent environmental and safety regulations. To this end, experimental efforts in process development have been reduced with the application of model-based process technology (Bogusch et al., 2001). However, given the large and diverse nature of chemical process units and their complex physical phenomena, the efforts to develop and establish a detailed mathematical model for the process industry remains high. In this study, we developed a dynamic simulation model 20

15

Bitumen in lumps 10 (mass %)

Pipe 1 in Pipe 2 in Pipe 2 out

5

4.5. Effect of slurry density

0 L = 1600

The oil sands slurries produced are generally non-homogenous (Masliyah et al., 2004). These typically consist of two distinct phases: (i) a water carrier phase, including bitumen and fines which are less

L = 2400

L = 3200

Pipe Length (m) Fig. 7. Ore ablation as a function of hydrotransport pipe length. 74

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Density (kg/m3)

5.2

1560 4.4

1520

3.6

1480

Slurry and Deposition Velocity (m/s)

1600

2.8 0

1000

2000

3000

4000

5000

6000

7000

8000

Time (Seconds) Density Slurry Velocity Deposition Velocity Fig. 8. Slurry Velocity inside the HT lines as a function of slurry density.

representing the bitumen mining and recovery process based on first principles using the IDEAS interactive platform. The entire model is divided into three distinct networks: the dry OPP, wet OPP and PSC. A detailed framework for each of the networks, along with its major unit operations and modelling philosophy, is presented. The results from the simulated model are compared to the data from a reference plant, and the results show good agreement, yielding satisfactory model validation. To evaluate the dynamic response of the model, a few scenarios are developed and analyzed to understand the essential process variable–response relationship in the bitumen extraction process from oil sands. These include (i) the influence of ore grade on final bitumen recovery in the PSC unit (ii) the effect of ore ablation rates inside the mix-box on bitumen recovery (iii) the role of HT lengths on ablation of the ore and (iv) the effect of slurry velocity inside the HT lines. In each of the cases, the model is able to predict the response dynamically and as per the physics of the unit operations. Also, the simulated results for the scenarios are at par with the industry norms and in alignment with published reports from various authors. Computer-aided process design can be used in the chemical process industry to facilitate process analysis, evaluation and optimization with a good degree of success (Petrides et al., 1998; Bogusch et al., 2001). Dynamic process models provide an excellent training environment for plant personnel and operators, thereby, boosting competency, confidence and accuracy, while working in normal and abnormal conditions; thus, imparting great operational skills. Such models can also play a great role in engineering education, particularly, the design and operation of chemical processes given real-world constraints.

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Acknowledgments The authors would like to thank Mr. Aashay Jain, Mr. Chandrasekhar Naidu and Mr. Sangeeth Kannoly, ANDRITZ Technologies Pvt. Ltd., for their valuable technical expertise in process modelling and with respect to the oil sands industry.

Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sector.

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