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Migrate CSTR example from Jupyter
Co-authored-by: Santosh Shanbhogue <santosh.shanbhogue@gmail.com>
This commit is contained in:
Ray Speth
committed by
Ingmar Schoegl
Ingmar Schoegl
parent
84b9c9c3a7
commit
b39c64aee8
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doc/sphinx/_static/images/samples/stirred-reactor-network.svg
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After Width: | Height: | Size: 4.4 KiB |
295
samples/python/reactors/continuous_reactor.py
Normal file
295
samples/python/reactors/continuous_reactor.py
Normal file
@@ -0,0 +1,295 @@
|
||||
r"""
|
||||
Continuously Stirred Tank Reactor
|
||||
=================================
|
||||
|
||||
In this example we will illustrate how Cantera can be used to simulate a
|
||||
`continuously stirred tank reactor <https://en.wikipedia.org/wiki/Continuous_stirred-tank_reactor>`__
|
||||
(CSTR), also interchangeably referred to as a perfectly stirred reactor (PSR), a well
|
||||
stirred reactor (WSR), a jet stirred reactor (JSR), or a
|
||||
`Longwell reactor <https://nap.nationalacademies.org/read/13160/chapter/48>`__ (and
|
||||
there may well be more "aliases").
|
||||
|
||||
Requires: cantera >= 3.0, matplotlib >= 2.0, pandas
|
||||
|
||||
.. tags:: Python, combustion, reactor network, well-stirred reactor
|
||||
|
||||
Simulation of a CSTR/PSR/WSR
|
||||
----------------------------
|
||||
|
||||
A diagram of a CSTR is shown below:
|
||||
|
||||
.. image:: /_static/images/samples/stirred-reactor-solo.svg
|
||||
:width: 50%
|
||||
:alt: A continuously stirred tank reactor with inflow and outflow
|
||||
:align: center
|
||||
|
||||
As the figure illustrates, this is an open system (unlike a batch reactor, which is
|
||||
isolated). *P*, *V* and *T* are the reactor's pressure, volume and temperature
|
||||
respectively. The mass flow rate at which reactants come in is the same as that of the
|
||||
products which exit, and on average this mass stays in the reactor for a characteristic
|
||||
time :math:`\tau`, called the *residence time*. This is a key quantity in sizing the
|
||||
reactor and is defined as follows:
|
||||
|
||||
.. math::
|
||||
|
||||
\tau = \frac{m}{\dot{m}}
|
||||
|
||||
where :math:`m` is the mass of the gas.
|
||||
"""
|
||||
|
||||
# %%
|
||||
# Import modules and set plotting defaults
|
||||
# ----------------------------------------
|
||||
|
||||
import time
|
||||
from io import StringIO
|
||||
import matplotlib.pyplot as plt
|
||||
import pandas as pd
|
||||
import cantera as ct
|
||||
|
||||
print(f"Running Cantera version: {ct.__version__}")
|
||||
|
||||
|
||||
# %%
|
||||
# Define the gas
|
||||
# --------------
|
||||
#
|
||||
# In this example, we will work with n-C₇H₁₆/O₂/He mixtures, for which experimental data
|
||||
# can be found in the paper by Zhang et al. [1]_ We will use the same mechanism reported
|
||||
# in the paper. It consists of 1268 species and 5336 reactions.
|
||||
|
||||
gas = ct.Solution("example_data/n-hexane-NUIG-2015.yaml")
|
||||
|
||||
# %%
|
||||
# Define initial conditions
|
||||
# -------------------------
|
||||
#
|
||||
# Inlet conditions for the gas and reactor parameters
|
||||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
reactor_temperature = 925 # Kelvin
|
||||
reactor_pressure = 1.046138 * ct.one_atm # in atm. This equals 1.06 bars
|
||||
inlet_concentrations = {"NC7H16": 0.005, "O2": 0.0275, "HE": 0.9675}
|
||||
gas.TPX = reactor_temperature, reactor_pressure, inlet_concentrations
|
||||
|
||||
residence_time = 2 # s
|
||||
reactor_volume = 30.5 * (1e-2) ** 3 # m3
|
||||
|
||||
# %%
|
||||
# Simulation parameters
|
||||
# ^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
# Simulation termination criterion
|
||||
max_simulation_time = 50 # seconds
|
||||
|
||||
# %%
|
||||
# Reactor arrangement
|
||||
# -------------------
|
||||
#
|
||||
# We showed a cartoon of the reactor in the first figure in this notebook, but to
|
||||
# actually simulate that, we need a few peripherals. A mass-flow controller upstream of
|
||||
# the stirred reactor will allow us to flow gases in, and in-turn, a "reservoir" which
|
||||
# simulates a gas tank is required to supply gases to the mass flow controller.
|
||||
# Downstream of the reactor, we install a pressure regulator which allows the reactor
|
||||
# pressure to stay within. Downstream of the regulator we will need another reservoir
|
||||
# which acts like a "sink" or capture tank to capture all exhaust gases (even our
|
||||
# simulations are environmentally friendly!). This arrangement is illustrated below:
|
||||
#
|
||||
# .. image:: /_static/images/samples/stirred-reactor-network.svg
|
||||
# :width: 80%
|
||||
# :alt: A complete reactor network representing a continuously stirred tank reactor
|
||||
# :align: center
|
||||
#
|
||||
# Initialize the stirred reactor and connect all peripherals
|
||||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
fuel_air_mixture_tank = ct.Reservoir(gas)
|
||||
exhaust = ct.Reservoir(gas)
|
||||
|
||||
stirred_reactor = ct.IdealGasMoleReactor(gas, energy="off", volume=reactor_volume)
|
||||
|
||||
mass_flow_controller = ct.MassFlowController(
|
||||
upstream=fuel_air_mixture_tank,
|
||||
downstream=stirred_reactor,
|
||||
mdot=stirred_reactor.mass / residence_time,
|
||||
)
|
||||
|
||||
pressure_regulator = ct.PressureController(
|
||||
upstream=stirred_reactor,
|
||||
downstream=exhaust,
|
||||
primary=mass_flow_controller,
|
||||
K=1e-3,
|
||||
)
|
||||
|
||||
reactor_network = ct.ReactorNet([stirred_reactor])
|
||||
|
||||
# Create a SolutionArray to store the data
|
||||
time_history = ct.SolutionArray(gas, extra=["t"])
|
||||
|
||||
# Set the maximum simulation time
|
||||
max_simulation_time = 50 # seconds
|
||||
|
||||
# Start the stopwatch
|
||||
tic = time.time()
|
||||
|
||||
# Set simulation start time to zero
|
||||
t = 0
|
||||
counter = 1
|
||||
while t < max_simulation_time:
|
||||
t = reactor_network.step()
|
||||
|
||||
# We will store only every 10th value. Remember, we have 1200+ species, so there
|
||||
# will be 1200+ columns for us to work with
|
||||
if counter % 10 == 0:
|
||||
# Extract the state of the reactor
|
||||
time_history.append(stirred_reactor.thermo.state, t=t)
|
||||
|
||||
counter += 1
|
||||
|
||||
# Stop the stopwatch
|
||||
toc = time.time()
|
||||
|
||||
print(f"Simulation Took {toc-tic:3.2f}s to compute, with {counter} steps")
|
||||
|
||||
# %%
|
||||
# Plot the results
|
||||
# ^^^^^^^^^^^^^^^^
|
||||
#
|
||||
# As a test, we plot the mole fraction of CO and see if the simulation has converged. If
|
||||
# not, go back and adjust max. number of steps and/or simulation time.
|
||||
|
||||
plt.figure()
|
||||
plt.semilogx(time_history.t, time_history("CO").X, "-o")
|
||||
plt.xlabel("Time (s)")
|
||||
plt.ylabel("Mole Fraction : $X_{CO}$")
|
||||
|
||||
# %%
|
||||
# Illustration : Modeling experimental data
|
||||
# -----------------------------------------
|
||||
#
|
||||
# Let us see if the reactor can reproduce actual experimental measurements.
|
||||
#
|
||||
# We first load the data. This is also supplied in the paper by Zhang et al. [1]_ as an
|
||||
# excel sheet
|
||||
|
||||
experimental_data_csv = """
|
||||
T,NC7H16,O2,CO,CO2
|
||||
500,5.07E-03,2.93E-02,0.00E+00,0.00E+00
|
||||
525,4.92E-03,2.86E-02,0.00E+00,0.00E+00
|
||||
550,4.66E-03,2.85E-02,0.00E+00,0.00E+00
|
||||
575,4.16E-03,2.63E-02,2.43E-04,1.01E-04
|
||||
600,3.55E-03,2.33E-02,9.68E-04,2.51E-04
|
||||
625,3.36E-03,2.31E-02,1.42E-03,2.67E-04
|
||||
650,3.67E-03,2.45E-02,9.16E-04,1.46E-04
|
||||
675,4.38E-03,2.77E-02,2.25E-04,0.00E+00
|
||||
700,4.79E-03,2.87E-02,0.00E+00,0.00E+00
|
||||
725,4.89E-03,2.93E-02,0.00E+00,0.00E+00
|
||||
750,4.91E-03,2.84E-02,0.00E+00,0.00E+00
|
||||
775,4.93E-03,2.80E-02,0.00E+00,0.00E+00
|
||||
800,4.78E-03,2.82E-02,0.00E+00,0.00E+00
|
||||
825,4.41E-03,2.80E-02,1.49E-05,0.00E+00
|
||||
850,3.68E-03,2.80E-02,4.18E-04,1.66E-04
|
||||
875,2.13E-03,2.45E-02,1.65E-03,2.22E-04
|
||||
900,1.03E-03,2.05E-02,5.51E-03,3.69E-04
|
||||
925,5.82E-04,1.79E-02,8.59E-03,6.78E-04
|
||||
950,3.88E-04,1.47E-02,1.05E-02,1.07E-03
|
||||
975,2.35E-04,1.28E-02,1.19E-02,1.36E-03
|
||||
1000,1.14E-04,1.16E-02,1.34E-02,1.82E-03
|
||||
1025,4.83E-05,9.88E-03,1.52E-02,2.41E-03
|
||||
1050,1.64E-05,8.16E-03,1.83E-02,2.97E-03
|
||||
1075,1.22E-06,5.48E-03,1.95E-02,3.67E-03
|
||||
1100,0.00E+00,3.24E-03,2.14E-02,4.38E-03
|
||||
"""
|
||||
|
||||
experimental_data = pd.read_csv(StringIO(experimental_data_csv))
|
||||
experimental_data.head()
|
||||
|
||||
# %%
|
||||
|
||||
# Define all the temperatures at which we will run simulations. These should overlap
|
||||
# with the values reported in the paper as much as possible
|
||||
T = [650, 700, 750, 775, 825, 850, 875, 925, 950, 1075, 1100]
|
||||
|
||||
# Create a SolutionArray to store values for the above points
|
||||
temp_dependence = ct.SolutionArray(gas)
|
||||
|
||||
# %%
|
||||
# Now we simply run the reactor code we used above for each temperature
|
||||
|
||||
concentrations = inlet_concentrations
|
||||
|
||||
for reactor_temperature in T:
|
||||
# Use concentrations from the previous iteration to speed up convergence
|
||||
gas.TPX = reactor_temperature, reactor_pressure, concentrations
|
||||
|
||||
stirred_reactor = ct.IdealGasReactor(gas, energy="off", volume=reactor_volume)
|
||||
fuel_air_mixture_tank = ct.Reservoir(gas)
|
||||
mass_flow_controller = ct.MassFlowController(
|
||||
upstream=fuel_air_mixture_tank,
|
||||
downstream=stirred_reactor,
|
||||
mdot=stirred_reactor.mass / residence_time,
|
||||
)
|
||||
pressure_regulator = ct.PressureController(
|
||||
upstream=stirred_reactor, downstream=exhaust, primary=mass_flow_controller
|
||||
)
|
||||
reactor_network = ct.ReactorNet([stirred_reactor])
|
||||
|
||||
# Re-run the isothermal simulations
|
||||
tic = time.time()
|
||||
reactor_network.advance(max_simulation_time)
|
||||
toc = time.time()
|
||||
print(f"Simulation at T={reactor_temperature}K took {toc-tic:3.2f}s to compute")
|
||||
|
||||
concentrations = stirred_reactor.thermo.X
|
||||
temp_dependence.append(stirred_reactor.thermo.state)
|
||||
|
||||
# %%
|
||||
# Compare the model results with experimental data
|
||||
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||||
|
||||
plt.figure()
|
||||
plt.plot(
|
||||
temp_dependence.T, temp_dependence("NC7H16").X, color="C0", label="$nC_{7}H_{16}$"
|
||||
)
|
||||
plt.plot(temp_dependence.T, temp_dependence("CO").X, color="C1", label="CO")
|
||||
plt.plot(temp_dependence.T, temp_dependence("O2").X, color="C2", label="O$_{2}$")
|
||||
|
||||
plt.plot(
|
||||
experimental_data["T"],
|
||||
experimental_data["NC7H16"],
|
||||
color="C0",
|
||||
marker="o",
|
||||
label="$nC_{7}H_{16}$ (exp)",
|
||||
)
|
||||
plt.plot(
|
||||
experimental_data["T"],
|
||||
experimental_data["CO"],
|
||||
color="C1",
|
||||
marker="^",
|
||||
linestyle="none",
|
||||
label="CO (exp)",
|
||||
)
|
||||
plt.plot(
|
||||
experimental_data["T"],
|
||||
experimental_data["O2"],
|
||||
color="C2",
|
||||
marker="s",
|
||||
linestyle="none",
|
||||
label="O$_2$ (exp)",
|
||||
)
|
||||
|
||||
plt.xlabel("Temperature (K)")
|
||||
plt.ylabel(r"Mole Fractions")
|
||||
|
||||
plt.xlim([650, 1100])
|
||||
plt.legend(loc=1)
|
||||
|
||||
# %%
|
||||
# References
|
||||
# ----------
|
||||
#
|
||||
# .. [1] K. Zhang, C. Banyon, C. Togbé, P. Dagaut, J. Bugler, H. J. Curran (2015). "An
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# experimental and kinetic modeling study of n-hexane oxidation," *Combustion and
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# Flame* 162:11, 4194-4207, https://doi.org/10.1016/j.combustflame.2015.08.001.
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# sphinx_gallery_thumbnail_number = -1
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