def ConstructInterpolatorIsothermAtTnew(df,
T_0,
T_new,
loading_key=None,
pressure_key=None,
hoa_key=None,
fill_value=None):
"""
Returns InterpolatorIsotherm from pure-component isotherm data measured at
temperature T0 extrapolated to temperature Tnew using the heat of adsorption.
:param df: DataFrame Pandas DataFrame with pure-component isotherm tabular
data measured at temperature T0
:param T_0: Float temperature at which isotherm in df was measured (T0)
:param T_new: Float temperature at which we desire to extrapolate the isotherm
in df
:param loading_key: String key for loading column in df
:param pressure_key: String key for pressure column in df
:param hoa_key: String key for heat of adsorption column in df
:param fill_value: Float value of loading to assume when an attempt is made
to interpolate at a pressure greater than the largest
pressure observed in the data
HOA needs to be in units kJ/mol.
"""
R = 8.3144621 / 1000.0 # kJ/mol/K
n = df.shape[0] # number of values in the isotherm
df_new = pd.DataFrame()
df_new[pressure_key] = np.zeros((n, ))
df_new[loading_key] = df[loading_key].values
# for every point, shift pressures according to Classius-Clapyeron eqn
for i in range(n):
p_0 = df[pressure_key].iloc[i] # Pa
HoA = df[hoa_key].iloc[i] # kJ/mol (negative)
p_new = p_0 * np.exp(HoA / R * (1.0 / T_new - 1.0 / T_0))
df_new[pressure_key].iloc[i] = p_new
# return a pyiast.isotherm object
return pyiast.InterpolatorIsotherm(df_new,
loading_key=loading_key,
pressure_key=pressure_key,
fill_value=fill_value), df_new
plt.show()
# ## Use IAST to predict mixture data, compare to dual component GCMC
# Construct isotherm objects. Use the interpolator isotherm here, as Langmuir and Quadratic isotherms do not fit well.
#
# We use fill_value = largest loading so that, when the linear interpolation routine calls a pressure beyond our data, it will yield this value. Essentially, the assumption is that the saturation loading = the highest loading observed in the data.
# ### Interpolator isotherm for Methane
# In[7]:
ch4_isotherm = pyiast.InterpolatorIsotherm(df_ch4, loading_key="Loading(mmol/g)",
pressure_key="Pressure(bar)",
fill_value=df_ch4['Loading(mmol/g)'].max())
pyiast.plot_isotherm(ch4_isotherm)
# ### Interpolator isotherm for ethane
# In[8]:
ch3ch3_isotherm = pyiast.InterpolatorIsotherm(df_ch3ch3, loading_key="Loading(mmol/g)",
pressure_key="Pressure(bar)",
fill_value=df_ch3ch3["Loading(mmol/g)"].max())
pyiast.plot_isotherm(ch3ch3_isotherm)
# ## Perform IAST at same mixture conditions as binary GCMC simulations
# use pyIAST to fit model to data
isotherm = pyiast.ModelIsotherm(
df, pressure_key='P', loading_key='L', model=model)
isotherm.print_params()
# plot fit
P_plot = np.linspace(0, 1, 100)
fig = plt.figure()
plt.scatter(df['P'], df['L'], label='Synthetic data', clip_on=False)
plt.plot(P_plot, isotherm.loading(P_plot), label='pyIAST fit')
plt.xlim([0, 1])
plt.ylim(ymin=0)
plt.xlabel('Pressure')
plt.ylabel('Uptake')
plt.title(model)
plt.legend(loc='lower right')
plt.show()
# assert parameters are equal
for param in isotherm.params.keys():
np.testing.assert_almost_equal(
isotherm.params[param], model_params[model][param], decimal=3)
# ### Quick visual test on the Interpolator isotherm
# In[7]:
isotherm = pyiast.InterpolatorIsotherm(df, pressure_key='P', loading_key='L')
pyiast.plot_isotherm(isotherm)