CFD Application in

Fixed Bed Reactor Internals

Subhasish Mitra

M.Tech Scholar

Department of Chemical Engineering

IIT Kanpur

Computational Fluid Dynamics:

A reliable tool when modeling and simulating flow and heat transfer phenomena for designing process equipment without performing an actual experiment.

In the last decade, CFD has been considered as a powerful tool to help chemical engineering

development [1, 2].

1. Trambouze, P., 1996. CFD applied to process engineering. Revue de l'Institut Français duPétrole 51 2, pp. 199–203

2. Kuipers, J.A.M. and van Swaaij, W.P.M., 1997. Application of computational fluid dynamics to chemical reaction engineering. Review in Chemical Engineering 13 3, pp.1–118

Brief Background:

� Multi-bed down-flow catalytic reactors are used in petroleum & petrochemical industries for hydrotreating, hydrodesulfurisation, hydrofinishing and hydrocarckingpurposes.

� Process liquid is mixed with gas and passed through the packed catalyst beds.

� Hot spots may generate inside the reactor if uniform flow pattern is not ensured.

� Reactor internals are provided for collecting and mixing liquid & gas exiting from one bed before distribution to the next bed.

CFD by FVM – basics:

Finite volume domain

Cell

FaceSteady state transport equation of scalar quantity Ф:

FVM discretized linear form

Basic Governing Equations: [5]

Gas phase continuity equation

Liquid phase continuity equation

Volume fraction conservation

Gas phase momentum equation

Liquid phase momentum equation

5. CFD simulation of hydrodynamics of valve tray, Chemical Engineering and Processing 48 (2009) 145–151,Xin Gang Li, De Xin Liu, Shi Min Xu, Hong Li

Equations (Contd) [5]:

M: Interphase momentum exchange term

Cd: Drag coefficient

d: gas bubble diameter

V: Velocity

ρ: Density

g: Gravity constant

α:volume fraction

µ:viscosity

Rep: Particle Reynolds Number

G & L refers to gas & liquid phase respectively.

5. CFD simulation of hydrodynamics of valve tray, Chemical Engineering and Processing 48 (2009) 145–151,Xin Gang Li, De Xin Liu, Shi Min Xu, Hong Li

Schiller Noumann Correlation for drag coefficient

Cd = 24/[Rep(1+0.15Rep0.687)]

Schematic sketch of the fixed bed reactor & the distributor: [3]

3. United States Patent No US 7,473,405 B2, Kemoun et al, Jan 6, 2009.

Row of Nozzles

Eulerian simulation details:

CFD simulation has been carried out to observe the flow pattern of gas and liquid flow inside the nozzle using Euler-Eulerapproach.

In the Euler-Euler approach, the different phases are treated as interpenetrating continua.

Since the volume of a phase cannot be occupied by the other phases, the concept of phasic volume fraction is introduced.

Mesh:Rectangular grid. Nozzle geometry is decomposed into three distinct fluid zones i.e. gas, liquid and mixture to track each zone distinctly.

Standard k-ε turbulence model: (Launder and Spalding, 1972) [4]

•This two equation model includes two extra transport equations to represent the turbulent properties of the flow.

•The first transported variable is turbulent kinetic energy (k) whichdetermines the energy in turbulence.

• The second transported variable is the turbulent dissipation (ε) which determines the scale of the turbulence.

4. Introductory Fluent notes, Fluent v6.1, Feb 2003.

[Ref 4]

Standard k-ε turbulence model (Contd):

ρ:density, µ:viscosity, µt: turbulent viscosity, k: turbulent kinetic energy, ε: turbulent dissipation rate, Gk: turbulence generation term

Simulation parameters (E-E):

Gas Inlet: velocity : 1m/sec, Gas vol frac:1Turbulence Intensity : 5%Liquid Inlet:velocity : 0.25 m/sec, Liq vol frac:1TI : 2%Outlet : Pr : 0 barg, Turbulence Intensity : 5%Wall : No slip

Boundary conditions

First order upwind, SIMPLEDiscretization scheme & Pressure Velocity coupling

Air, WaterMaterial

k-ε (standard), Schiller NaumannTurbulence model, Drag model

Eulerian-2 phaseMultiphase

2D, Unsteady stateSolver

Outer dia: 20 mm, Inner dia: 10 mm, Gas & Liquid inlet slot dia: 5 mm, Throat dia:8 mm, Nozzle outlet: 12 mm

Type 2 nozzle geometry

Outer dia: 8.12 mm, Inner dia: 4.8 mm, Gas & Liquid inlet slot dia: 2.5 mm, Throat dia:5.4 mm

Type 1 nozzle geometry

CFD simulation of Nozzle [3](Fig 2):-Schematic & CFD (air/water) volume fraction contour

3. United States Patent No US 7,473,405 B2, Kemoun et al, Jan 6, 2009.

CFD simulation of Nozzle [3](Fig 2):-Schematic & CFD (air/water) velocity magnitude contour

3. United States Patent No US 7,473,405 B2, Kemoun et al, Jan 6, 2009.

CFD simulation of Nozzle [3](Fig 3): -Schematic & CFD (air/water) volume fraction contour

3. United States Patent No US 7,473,405 B2, Kemoun et al, Jan 6, 2009.

CFD simulation of Nozzle [3](Fig 3):-Schematic & CFD (air/water) velocity magnitude contour

3. United States Patent No US 7,473,405 B2, Kemoun et al, Jan 6, 2009.

Discrete Phase simulation basics:

• Used to simulate a second discrete phase consists of spherical particles (represents droplets or bubbles).

• Calculates discrete phase trajectory using a Lagrangianformulation that includes the discrete phase inertia, hydrodynamic drag, and the force of gravity.

•dispersed (volume fraction <10%) in the continuous phase.

•The model is used to understand the flow characteristic at nozzle tip, which follows a spray pattern in the inter catalyst bed space.

Simulation parameters (E-L):

Interaction with continuous phase: EnabledUpdate DPM sources every flow iterations: EnabledUnsteady particle tracking: Enabled

Particle time step size : 0.001Max steps: 500, Step length factor : 5Drag model: dynamic drag

Spray model : droplet collision & break-upBreak up model : TABBreak up constants : y0 = 0, parcels:2

Discrete phase model

Diesel and air mixtureSpecies transport model

5%, 0.225 mTurbulent intensity, hydraulic diameter

15 m/secAir velocity

k-ε (standard) 2 equationsTurbulence model

AirContinuous field

Atomization principle:

Liquid is accelerated through narrow pathwayinside the nozzle. It then emerges from the orificeas a thinning sheet, which is unstable and breaksup into ligaments and subsequently into droplets.

Taylor Analogy Breakup Model:

• Taylor’s analogy between a oscillating & distorting droplet and spring mass system.

• Restoring force of spring� surface tension force

• External force� droplet drag force

• Damping force� droplet viscosity force

• Parent droplet breaks up into number of child droplets

when oscillation grow to a critical value (Wecrit).

Stochastic tracking – DRW Model:

• Predicts turbulent dispersion of particles by integrating the trajectory equation for individual particles using instantaneous velocity.

• Includes random effect of turbulence of particle dispersion by comparing trajectory for sufficient number of particles.

• Velocity components are discrete piecewise constant functions of time.

• Random value of velocity components is kept constant over an time interval by the characteristics life time of eddies.

Spray pattern at nozzle tip:-CFD simulation of particle residence time (Diesel droplet/Air mixture)

Thanks for your attention!