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This notebook shows how to visualize how CO2 density relates to pressure and temperature using an EOS

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import DiadFit as pf
import CoolProp.CoolProp as CP
pf.__version__

'0.0.91'

Install DiadFit if you havent already! You might also have to install CoolProp if you want to use Span and Wagner EOS - the error message will give you instructions, else reach out

#!pip install --upgrade DiadFit

Calculations of CO2 density as a function of pressure and temperature

# Make a linearly spaced vector of pressures,

# we do 2 here to get a smoother curve and combine
P=np.linspace(100, 2000*10**6, 100)
Psmall=np.linspace(0, 100, 100)
P_combo=np.concatenate((Psmall, P))

# calculating for different temperatures 1300-1000 celcius
CalcD_P_1300=CP.PropsSI('D', 'P', P_combo, 'T', 1300+273.15, 'CO2')
CalcD_P_1200=CP.PropsSI('D', 'P', P_combo, 'T', 1200+273.15, 'CO2')
CalcD_P_1100=CP.PropsSI('D', 'P', P_combo, 'T', 1100+273.15, 'CO2')
CalcD_P_1000=CP.PropsSI('D', 'P', P_combo, 'T', 1000+273.15, 'CO2')

Some things to allow us to plot in pressure vs. depth space

rho=2700 # Crustal density
P_axis_min=0 # Min pressure to show
P_axis_max=20 # Max pressure to show
# Calculating how they relate to each other
D_axis_min=100000*P_axis_min/(9.8*rho)
D_axis_max=100000*P_axis_max/(9.8*rho)

Now lets make a pretty plot showing how CO2 density, pressure and temperature are related

fig, (ax4) = plt.subplots(1, 1, figsize=(6,5))



ax5 = ax4.twiny()
ax4.plot(P_combo*10**(-8), CalcD_P_1000/1000, '-', color='black', label='1000 C')
ax4.plot(P_combo*10**(-8), CalcD_P_1100/1000, '-', color='darkred', label='1100 C')
ax4.plot(P_combo*10**(-8), CalcD_P_1200/1000, '-', color='red',  label='1200 C')
ax4.plot(P_combo*10**(-8), CalcD_P_1300/1000, '-', color='orange',  label='1300 C')



ax4.set_xlim([P_axis_min, P_axis_max])
ax4.set_ylim([0, 1.5])

ax5.set_xlim([D_axis_min, D_axis_max])
ax4.set_ylabel('Density (g/cm$^{3}$)')
ax5.set_xlabel('Depth (km)')
ax4.set_xlabel('Pressure (kbar)')

ax4.legend()

<matplotlib.legend.Legend at 0x1fdc5c8f110>

# Lets do it for just one temperature (e.g. perhaps for a conference talk)
fig, (ax1) = plt.subplots(1, 1, figsize=(7,5))
ax1.plot(100*P_combo*10**(-8), CalcD_P_1200/1000, '-', color='red')
ax5 = ax1.twiny()
ax1.set_xlabel('Pressure (MPa)')
ax1.set_ylabel('Density (g/cm$^{3}$)')
ax5.set_xlim([D_axis_min, D_axis_max])
ax1.set_xlim([P_axis_min*100, P_axis_max*100])
ax5.set_xlabel('Depth (km)')
fig.savefig('Pressure_Depth.png')