Troubleshooting refinery equipment with multiphase CFD modelling
Computational fluid dynamics is a useful and increasingly practical tool for improving the design of and increasing the understanding of common process equipment
Grant Niccum and Steve White
Process Consulting Services
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Much of the common process equipment in refineries today was designed and built according to traditional empirical design methodologies that were developed decades ago. Without an intimate understanding of the complex flow patterns present within a given system, designers had to rely on conservative assumptions and trial and error to ensure that equipment met design requirements. Modern computational fluid dynamics (CFD) tools allow designers to pull back the veil on complex internal flows, but their use has been limited by available computing power. As computing power continues to increase, CFD is becoming a practical tool for industrial scale problems. Through a deeper understanding of standard process equipment, it is now possible to identify opportunities to improve both the function and the capacity of installed systems.
In many cases, small modifications can eliminate the need to design and fabricate new equipment, resulting in significant cost savings without compromising performance. This article will discuss several cases demonstrating the application of CFD to ‘traditional’ process equipment. Each case presented will discuss the motivation for the use of CFD, the assumptions required to yield a practical and robust CFD simulation, some details pertaining to the CFD modelling itself, and — most importantly — the practical outcome of the simulation exercise.
Central to the growing popularity of CFD for industrial scale problems is an ability to simplify a simulation. An extremely detailed simulation incorporating all of the relevant physical minutiae is of little value if the results cannot be interpreted and applied to solve a real world problem. Furthermore, the additional complexity and computational expense required to perform an extremely high fidelity simulation is often unjustifiable or unattainable for many industrial problems. In most cases, a simplified modelling approach specifically developed to examine the variable(s) of interest is the most efficient practice. By carefully considering all of the independent and dependent variables relevant to the design question at hand, the pain and expense of a CFD project can be greatly reduced. Just because an engineer can solve for every possible variable throughout an entire domain doesn’t mean that â€¨he or she should. Unnecessary physics complicates the setup â€¨of a simulation, significantly increases the computational time required, and may decrease the stability of the simulation to the point where a converged solution is impossible.
Case 1: liquid knockout drum
After performing a detailed dynamic process simulation study of a particular unit, it was discovered that, given the right circumstances, vapour/liquid rates could be far above the design capacity of an existing liquid knockout drum. In addition to incurring significant expense, replacement of the new drum would have been difficult due to space restrictions. It was hypothesised that internals could be added to the drum to adequately increase the vapour-liquid separation. For verification, CFD could be used to confirm the effectiveness of any design changes that would see the drum operate while significantly under-sized according to traditional sizing methods. Particle sizes of a certain critical diameter were considered the break point for effective operation of the separator. Therefore, the CFD analysis was used to develop and test modifications to allow the drum to effectively trap particles with larger than acceptable diameters within the drum.
A ‘brute force’ CFD approach to this design problem would have been to model all of the relevant physical phenomena at the same time: multiphase vapour/liquid flow at the inlet, breakup/coalescence of the liquid droplets, formation of a liquid film on the walls of the drum, collection/movement of free liquid in the bottom of the drum, and so on. The modelling task was greatly simplified, however, by carefully considering the variables of interest. The functions of the liquid knockout drum are to separate and collect liquid particles, and to prevent re-entrainment of the free liquid phase that has collected in the bottom of the drum. The variables that needed to be evaluated to verify that the drum would perform as required are the fates of liquid particles entrained with the gas at the inlet to the drum and the shape/size of the stable liquid area in the bottom of the drum. Variables such as liquid wall film thickness are not significant to the overall function of the drum and were not modelled, as their omission did not significantly affect the variables of interest.
Modelling was further simplified by segregating the variables of interest, as they are independent of one another. Each design option was evaluated using one model for particle tracking and a second for monitoring the free liquid phase. Although it may seem counterintuitive that two models would be more efficient than one, this arrangement allowed the designer to perform several design iterations using the less computationally intense particle tracking model before running the more complex gas/liquid interface tracking model. Furthermore, the separation allowed the two models to be set up quite differently to give the best answers for the variables that each was tasked with solving.
The first of the two simulations was used to track particles entrained with the vapour at the drum inlet. The discrete phase model (DPM) was chosen in this situation for its ability to track particles through the domain and because the volume fraction of liquid entrained within the vapour flow was low. Small, light particles follow vapour flow streamlines more closely than larger particles because they have less momentum relative to the drag caused by the bulk vapour flow (lower Stokes number). With this principle in mind, a conservative particle size should be smaller than the maximum allowable droplet size. These conservatively sized particles were injected with the vapour at the separator inlet and tracked throughout the domain as the vapour travelled from the separator inlet to the outlet. A simplifying assumption was that the particles underwent partially elastic collisions (some energy lost) when they encountered walls within the vessel. Thus, wall collisions tended to slow the liquid droplets down until they separated from the vapour flow and settled in the bottom of the drum. In reality, some of these collisions would have splashed to create multiple smaller droplets, but this phenomenon was ignored because escape of smaller droplets was acceptable and therefore not consequential to the design. The design was modified and simulated iteratively until no particles escaped through the drum outlet.
The second simulation was designed to model the stability of the free liquid phase in the bottom of the drum. This simulation employed the volume of fluid (VOF) model to track a well defined vapour-liquid interface. Liquid volumetric flow rates were significantly lower than gas volumetric flow rates, and liquid entering and exiting the drum was not significant to the problem of maintaining a stable liquid layer. The model was therefore built with no liquid flow in/out, and a mass of liquid was manually placed within the drum at the start of the simulation and allowed to ‘slosh’ around due to interaction with the vapour flow. If the flow agitated the liquid layer to the point where liquid mass escaped through the outlet, the design was modified. Stabilising the free liquid layer with high vapour flow rates proved more difficult than trapping the initially entrained particles. The high velocity vapour flow tended to re-entrain significant quantities of liquid. However, a design was developed that could satisfy both requirements.
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