Plug flow reactor modeled as a chain of well stirred reactors

This example solves a plug flow reactor problem, where the chemistry is surface chemistry. The specific problem simulated is the partial oxidation of methane over a platinum catalyst in a packed bed reactor. To avoid needing to solve a DAE system, the PFR is approximated as a chain of successive WSRs. See surf_pfr.py for a more advanced implementation that solves the DAE system directly.

Requires: cantera >= 3.0

Tags: Python catalysis reactor network surface chemistry plug flow reactor packed bed reactor

import csv

import cantera as ct

# unit conversion factors to SI
cm = 0.01
minute = 60.0

Input Parameters

tc = 800.0  # Temperature in Celsius
length = 0.3 * cm  # Catalyst bed length
area = 1.0 * cm**2  # Catalyst bed area
cat_area_per_vol = 1000.0 / cm  # Catalyst particle surface area per unit volume
velocity = 40.0 * cm / minute  # gas velocity
porosity = 0.3  # Catalyst bed porosity

# input file containing the surface reaction mechanism
yaml_file = 'methane_pox_on_pt.yaml'

output_filename = 'surf_pfr_output.csv'

# The PFR will be simulated by a chain of 'NReactors' stirred reactors.
NReactors = 201
dt = 1.0

t = tc + 273.15  # convert to Kelvin

# import the gas model and set the initial conditions
gas = ct.Solution(yaml_file, 'gas')
gas.TPX = t, ct.one_atm, 'CH4:1, O2:1.5, AR:0.1'

# import the surface model
surf = ct.Interface(yaml_file, 'Pt_surf', [gas])
surf.TP = t, ct.one_atm

rlen = length/(NReactors-1)
rvol = area * rlen * porosity

# catalyst area in one reactor
cat_area = cat_area_per_vol * rvol

mass_flow_rate = velocity * gas.density * area * porosity

# The plug flow reactor is represented by a linear chain of zero-dimensional
# reactors. The gas at the inlet to the first one has the specified inlet
# composition, and for all others the inlet composition is fixed at the
# composition of the reactor immediately upstream. Since in a PFR model there
# is no diffusion, the upstream reactors are not affected by any downstream
# reactors, and therefore the problem may be solved by simply marching from
# the first to last reactor, integrating each one to steady state.

TDY = gas.TDY
cov = surf.coverages

print('    distance       X_CH4        X_H2        X_CO')

# create a new reactor
gas.TDY = TDY
r = ct.IdealGasReactor(gas, energy='off')
r.volume = rvol

# create a reservoir to represent the reactor immediately upstream. Note
# that the gas object is set already to the state of the upstream reactor
upstream = ct.Reservoir(gas, name='upstream')

# create a reservoir for the reactor to exhaust into. The composition of
# this reservoir is irrelevant.
downstream = ct.Reservoir(gas, name='downstream')

# Add the reacting surface to the reactor. The area is set to the desired
# catalyst area in the reactor.
rsurf = ct.ReactorSurface(surf, r, A=cat_area)

# The mass flow rate into the reactor will be fixed by using a
# MassFlowController object.
m = ct.MassFlowController(upstream, r, mdot=mass_flow_rate)

# We need an outlet to the downstream reservoir. This will determine the
# pressure in the reactor. The value of K will only affect the transient
# pressure difference.
v = ct.PressureController(r, downstream, primary=m, K=1e-5)

sim = ct.ReactorNet([r])

output_data = []

for n in range(NReactors):
    # Set the state of the reservoir to match that of the previous reactor
    gas.TDY = r.thermo.TDY
    upstream.syncState()
    sim.reinitialize()
    sim.advance_to_steady_state()
    dist = n * rlen * 1.0e3  # distance in mm

    if n % 10 == 0:
        print('  {0:10f}  {1:10f}  {2:10f}  {3:10f}'.format(
            dist, *r.thermo['CH4', 'H2', 'CO'].X))

    # write the gas mole fractions and surface coverages vs. distance
    output_data.append(
        [dist, r.T - 273.15, r.thermo.P / ct.one_atm]
        + list(r.thermo.X)  # use r.thermo.X not gas.X
        + list(rsurf.kinetics.coverages)  # use rsurf.kinetics.coverages not surf.coverages
    )

with open(output_filename, 'w', newline="") as outfile:
    writer = csv.writer(outfile)
    writer.writerow(['Distance (mm)', 'T (C)', 'P (atm)'] +
                    gas.species_names + surf.species_names)
    writer.writerows(output_data)

print("Results saved to '{0}'".format(output_filename))

    distance       X_CH4        X_H2        X_CO
    0.000000    0.093002    0.001365    0.010247
    0.150000    0.043947    0.145965    0.029179
    0.300000    0.033928    0.173629    0.035129
    0.450000    0.028437    0.188531    0.038647
    0.600000    0.024724    0.198522    0.041114
    0.750000    0.021957    0.205922    0.042996
    0.900000    0.019778    0.211726    0.044503
    1.050000    0.017997    0.216453    0.045751
    1.200000    0.016503    0.220406    0.046809
    1.350000    0.015227    0.223777    0.047721
    1.500000    0.014119    0.226696    0.048517
    1.650000    0.013147    0.229253    0.049221
    1.800000    0.012286    0.231516    0.049848
    1.950000    0.011516    0.233534    0.050411
    2.100000    0.010825    0.235347    0.050919
    2.250000    0.010199    0.236984    0.051380
    2.400000    0.009630    0.238471    0.051801
    2.550000    0.009112    0.239826    0.052186
    2.700000    0.008636    0.241066    0.052540
    2.850000    0.008200    0.242206    0.052866
    3.000000    0.007797    0.243255    0.053167
Results saved to 'surf_pfr_output.csv'