e5bf977ca5
Rename module directories (module 6 → module_6, module 7 → module_7) to fix Google Colab "Open in Colab" link compatibility. Update Colab badge URL in Peng_Robinson_EOS.ipynb accordingly. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
561 lines
101 KiB
Text
561 lines
101 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "7af16f2a",
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"metadata": {},
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"source": [
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"# Phase diagram calculations for carbon dioxide"
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]
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},
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{
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"cell_type": "markdown",
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"id": "32d5dd49",
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"metadata": {},
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"source": [
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"Contruct a P-T phase diagram for carbon dioxide."
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]
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},
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{
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"cell_type": "markdown",
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"id": "bb1887c3",
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"metadata": {},
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"source": [
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"## Antoine equation\n",
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"\n",
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"The Antoine equation is an emprical equation of the form\n",
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"\n",
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"$$ \\log P^\\mathrm{sub}(T) = A - \\frac{B}{T + C} $$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "83c296bf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Antoine equation given parameters A, B, and C\n",
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"# Pressure is returned in Pa\n",
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"def P_antoine(A, B, C, T):\n",
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" return 10**(A - B/(T + C))*101325"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed693003",
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"metadata": {},
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"source": [
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"## Clausius-Clapeyron equation\n",
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"\n",
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"Given $\\Delta^\\mathrm{sat}\\underline{H}$, $T_1$, $P_1$, and $T_2$, return $P_2$ by the equation\n",
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"\n",
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"$$ \\ln \\frac{P^\\mathrm{sat}(T_2)}{P^\\mathrm{sat}(T_1)} = \\frac{-\\Delta^\\mathrm{sat}\\underline{H}}{R} \\left ( \\frac{1}{T_2} - \\frac{1}{T_1} \\right ) $$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "4a95c76a",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Clausius-Clapeyron equation\n",
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"# Pressures in Pa, Temperatues in K\n",
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"# Enthalpy in J / mol\n",
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"\n",
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"import numpy as np\n",
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"from scipy import constants\n",
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"R = constants.R\n",
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"\n",
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"def P_CC(delta_sat_H, T1, P1, T2):\n",
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" return P1*np.exp(-delta_sat_H/R*(1/T2-1/T1))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "93580680",
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"metadata": {},
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"source": [
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"## $P-T$ plot for CO2"
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]
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},
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{
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"cell_type": "markdown",
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"id": "87fec321",
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"metadata": {},
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"source": [
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"### Data for CO2 -- sublimation, VLE, critical point, and triple point\n",
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"\n",
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"NIST Webbook data is included below:\n",
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"\n",
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"https://webbook.nist.gov/cgi/cbook.cgi?ID=C124389&Units=SI&Mask=4#Thermo-Phase\n",
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"\n",
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"Full vapor-liquid properties can also be calculated in the NIST Webbook:\n",
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"\n",
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"https://webbook.nist.gov/cgi/fluid.cgi?TLow=217&THigh=304&TInc=&Digits=5&ID=C124389&Action=Load&Type=SatP&TUnit=K&PUnit=MPa&DUnit=mol%2Fm3&HUnit=kJ%2Fmol&WUnit=m%2Fs&VisUnit=uPa*s&STUnit=N%2Fm&RefState=DEF#Auxiliary\n",
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"\n",
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"We can find other thermodynamic data in:\n",
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"\n",
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"1. A. Jäger and R. Span, Equation of State for Solid Carbon Dioxide Based on the Gibbs Free Energy, J. Chem. Eng. Data 57, 590 (2012). https://doi.org/10.1021/je2011677\n",
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"1. R. Span and W. Wagner, A New Equation of State for Carbon Dioxide Covering the Fluid Region from the Triple-Point Temperature to 1100 K at Pressures up to 800 MPa, Journal of Physical and Chemical Reference Data 25, 1509 (1996). https://doi.org/10.1063/1.555991\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "2b4bdcfe",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Data for CO2\n",
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"# Molecular weight\n",
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"MW = 44.0095 # g/mol\n",
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"\n",
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"# Critical parameters and acentricity\n",
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"Pc = 73.8*101325 # Critical pressure in Pa\n",
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"Tc = 304.18 # Critical temp in K\n",
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"Vc = 0.0000919 # Critical volume in m^3/mol\n",
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"omega = 0.225 # acentric factor\n",
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"\n",
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"# Triple point\n",
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"T_triple = 216.58 # temp in K\n",
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"P_triple = 5.185*101325 # pressure in Pa\n",
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"\n",
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"# Antoine equation parameters\n",
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"# Valid temperature range in K\n",
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"T_Antoine_min = 154.26 \n",
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"T_Antoine_max = 195.89\n",
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"\n",
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"A = 6.81228\n",
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"B = 1301.679\n",
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"C = -3.494\n",
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"\n",
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"# Enthalpy of sublimation \n",
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"H_sub_1 = 26.1*1000 # J / mol, 198 to 216 K\n",
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"H_sub_2 = 25.2*1000 # J / mol, 154 to 196 K\n",
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"\n",
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"# Enthalpy of vaporization\n",
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"H_vap_1 = 16.7*1000 # J / mol, 273 to 304 K\n",
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"H_vap_2 = 16.4*1000 # J / mol, 216 to 273 K\n",
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"H_vap_3 = 16.7*1000 # J / mol, 267 to 303 K"
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]
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},
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{
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"cell_type": "markdown",
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"id": "72d6aac4",
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"metadata": {},
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"source": [
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"### Solid-liquid equilibrium\n",
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"\n",
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"Finding data for the solid-liquid equilibrium line is a little more challenging. Data for CO2 lists \n",
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"\n",
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"1562 kg/m3 solid at 1 atm (100 kPa) and −78.5 C \\\n",
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"1101 kg/m3 liquid at saturation −37 C \\\n",
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"1.977 kg/m3 gas at 1 atm (100 kPa) and 0 C\n",
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"\n",
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"Assuming that the solid and liquid specific (molar) volumes don't change much, we could estimate the fusion (melting) line with the Classius equation. But can we estimate the heat of fusion?\n",
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"\n",
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"J\\\"ager and Span (J. Chemical Eng. Data, 2006) cite the following value:\n",
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"\n",
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"$$\\Delta^\\mathrm{fus}\\underline{H} = 8875 \\text{ J/mol}$$\n",
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"\n",
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"By the Clapeyron equation, we can then find\n",
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"\n",
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"$$ \\left ( \\frac{\\partial P^\\mathrm{sat}}{\\partial T} \\right )_{\\underline{G}^I = \\underline{G}^{II}} = \\frac{\\Delta \\underline{H}}{T \\Delta \\underline{V} }$$\n",
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"\n",
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"As expected, this line should be quite steep. We can integrate the function to give\n",
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"\n",
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"$$ P^\\mathrm{sat}(T_2) = P^\\mathrm{sat}(T_1) + \\frac{\\Delta \\underline{H}}{\\Delta \\underline{V} } \\ln \\left ( \\frac{T_2}{T_1} \\right )$$"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "cd670958",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Integrated Clapeyron equation\n",
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"def P_clapeyron(T1, P1, T2, delta_H, delta_V):\n",
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" return P1 + delta_H/delta_V*np.log(T2/T1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "1f0615ef",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"9.546529284164859e-05\n",
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"92965723.30973995\n"
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]
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}
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],
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"source": [
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"# Calculate Delta V\n",
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"delta_fus_V = 1/((1562-1101)/(MW/1000)) # Change in molar volume\n",
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"\n",
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"# Other data\n",
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"delta_fus_H = 8875 # Heat of fusion, J/mol\n",
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"\n",
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"print(delta_fus_V)\n",
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"print(delta_fus_H/delta_fus_V)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3289cdae",
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"metadata": {},
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"source": [
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"### Building the PT diagram plot"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "69d95f54",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Text(0.5, 1.0, 'P-T diagram for carbon dioxide')"
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]
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},
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"execution_count": 17,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# Make a blank plot\n",
|
||
"fig, ax = plt.subplots()\n",
|
||
"\n",
|
||
"# Plot the triple point\n",
|
||
"plt.plot(T_triple, P_triple, \"bo\", label=\"triple point\")\n",
|
||
"\n",
|
||
"# Plot the critical point\n",
|
||
"plt.plot(Tc, Pc, \"ro\", label=\"critical point\")\n",
|
||
"\n",
|
||
"# Plot the Antoine equation\n",
|
||
"T = np.arange(T_Antoine_min, T_Antoine_max, 1)\n",
|
||
"ax.plot(T, P_antoine(A, B, C, T), label=r\"$P^\\mathrm{sub}$ (Antoine)\")\n",
|
||
"\n",
|
||
"# Use Clausius-Clapeyron to plot the sublimation curve\n",
|
||
"T = np.linspace(T_Antoine_max, T_triple, 300)\n",
|
||
"P = P_CC(H_sub_1,T_triple, P_triple, T)\n",
|
||
"ax.plot(T, P, label=r\"$P^\\mathrm{sub}$ (Clausius-Clapeyron)\")\n",
|
||
"\n",
|
||
"# Use Clausius-Clapeyron to plot the vaporization curve\n",
|
||
"T = np.linspace(T_triple, Tc, 300)\n",
|
||
"P = P_CC(H_vap_1,T_triple, P_triple, T)\n",
|
||
"ax.plot(T, P, label=r\"$P^\\mathrm{vap}$ (Clausius-Clapeyron)\")\n",
|
||
"\n",
|
||
"# Use the integrated Clapeyron equation to plot the melting line\n",
|
||
"T = np.linspace(T_triple, T_triple+18, 100)\n",
|
||
"P = P_clapeyron(T_triple, P_triple, T, delta_fus_H, delta_fus_V)\n",
|
||
"ax.plot(T, P, label=r\"$P^\\mathrm{fus}$ (Clausius)\")\n",
|
||
"\n",
|
||
"# Label and axes\n",
|
||
"#plt.ylim([0,1e6])\n",
|
||
"#ax.set_yscale(\"log\")\n",
|
||
"ax.legend(frameon=True, fancybox=True, edgecolor='none')\n",
|
||
"plt.xlabel('T (K)')\n",
|
||
"plt.ylabel('P (Pa)')\n",
|
||
"plt.title('P-T diagram for carbon dioxide')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "aa885660",
|
||
"metadata": {},
|
||
"source": [
|
||
"This seems reasonable. You can see that the vaporization curve calculated by the Clausius-Clapeyron equation is senstive to the heat of vaporization. The value we use overestimates the critical point slightly. A slightly lower value would bring the two into agreement.\n",
|
||
"\n",
|
||
"Let's rebuild the plot, but focus on the lower part of the phase diagram by plotting it on a log-linear scale."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 18,
|
||
"id": "0abc0fe5",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"Text(0.5, 1.0, 'P-T diagram for carbon dioxide')"
|
||
]
|
||
},
|
||
"execution_count": 18,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 640x480 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"import matplotlib.pyplot as plt\n",
|
||
"import numpy as np\n",
|
||
"\n",
|
||
"# Make a blank plot\n",
|
||
"fig, ax = plt.subplots()\n",
|
||
"\n",
|
||
"# Plot the triple point\n",
|
||
"plt.plot(T_triple, P_triple, \"bo\", label=\"triple point\")\n",
|
||
"\n",
|
||
"# Plot the critical point\n",
|
||
"plt.plot(Tc, Pc, \"ro\", label=\"critical point\")\n",
|
||
"\n",
|
||
"# Plot the Antoine equation\n",
|
||
"T = np.arange(T_Antoine_min, T_Antoine_max, 1)\n",
|
||
"ax.plot(T, P_antoine(A, B, C, T), label=r\"$P^\\mathrm{sub}$ (Antoine)\")\n",
|
||
"\n",
|
||
"# Use Clausius-Clapeyron to plot the sublimation curve\n",
|
||
"T = np.linspace(T_Antoine_max, T_triple, 300)\n",
|
||
"P = P_CC(H_sub_1,T_triple, P_triple, T)\n",
|
||
"ax.plot(T, P, label=r\"$P^\\mathrm{sub}$ (Clausius-Clapeyron)\")\n",
|
||
"\n",
|
||
"# Use Clausius-Clapeyron to plot the vaporization curve\n",
|
||
"T = np.linspace(T_triple, Tc, 300)\n",
|
||
"P = P_CC(H_vap_1,T_triple, P_triple, T)\n",
|
||
"ax.plot(T, P, label=r\"$P^\\mathrm{vap}$ (Clausius-Clapeyron)\")\n",
|
||
"\n",
|
||
"# Use the integrated Clapeyron equation to plot the melting line\n",
|
||
"T = np.linspace(T_triple, T_triple+18, 100)\n",
|
||
"P = P_clapeyron(T_triple, P_triple, T, delta_fus_H, delta_fus_V)\n",
|
||
"ax.plot(T, P, label=r\"$P^\\mathrm{fus}$ (Clausius)\")\n",
|
||
"\n",
|
||
"# Label and axes\n",
|
||
"ax.set_yscale(\"log\")\n",
|
||
"#plt.ylim([0,4e6])\n",
|
||
"#plt.xlim([T_Antoine_min,240])\n",
|
||
"ax.legend(frameon=True, fancybox=True, edgecolor='none')\n",
|
||
"plt.xlabel('T (K)')\n",
|
||
"plt.ylabel('P (Pa)')\n",
|
||
"plt.title('P-T diagram for carbon dioxide')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b26ddb75",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Answer for part C of homework problem\n",
|
||
"\n",
|
||
"In our exerpience with carbon dioxide, it is often in its gaseous state and sometimes in its solid form. The reason is clear: at atmospheric pressure, we are well below the triple point, the lowest temperature where we would observe a liquid carbon dioxide phase. \n",
|
||
"\n",
|
||
"When the gas phase is cooled (for instance, by a Joule-Thompson expansion) we expect to form the solid phase. Likewise, when handling dry ice, we observe that it sublimates directly to the gas phase without melting into a liquid first."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5ce26d98",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Answer for part B\n",
|
||
"\n",
|
||
"Part B asks us what the state of a full gas cylinder for a home carbonation system is at room temperature (298 K) given that the cylinder volume is 0.64 L and initially contains 410 g of\n",
|
||
"carbon dioxide.\n",
|
||
"\n",
|
||
"In this case, we have the temperature and molar volume:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "f5a1a461",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Molar volume: 6.87e-05 m^3/mol\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"T = 298\n",
|
||
"V_molar = 0.64/1000/(410/MW)\n",
|
||
"\n",
|
||
"print(f\"Molar volume: {V_molar:.2e} m^3/mol\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a3d34536",
|
||
"metadata": {},
|
||
"source": [
|
||
"From the temperature, we know that we are below the critical temperature, and the molar volume is much less than critical volume. It's reasonable to assume that the carbon dioxide is in a liquid phase at this temperature and molar volume. Looking at the PV diagram we calculated in problem 1, this is close to the saturated liquid. \n",
|
||
"\n",
|
||
"The maximum pressure on the cylinder is 250 bar, which is 25 MPa, but this is likely the limit of the cylinder pressure limit. The CO2 would be supercritical in this case.\n",
|
||
"\n",
|
||
"We can calculate the pressure using the Peng-Robinson equation of state."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "c3e0db15",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"Generalized Peng-Robinson EOS \n",
|
||
"PR_pressure returns the pressure given V, T, Pc, Tc, omega\n",
|
||
"PR_volume returns all molar volumes (real roots of EOS) given P, T, Pc, Tc, omega\n",
|
||
"\"\"\"\n",
|
||
"import numpy as np\n",
|
||
"from scipy import constants\n",
|
||
"from numpy.polynomial import Polynomial\n",
|
||
"\n",
|
||
"R = constants.R # Set the gas constant to R\n",
|
||
"\n",
|
||
"def calc_b(Pc,Tc):\n",
|
||
" return 0.07780*R*Tc/Pc\n",
|
||
"\n",
|
||
"def calc_a(T,Pc,Tc,omega):\n",
|
||
" kappa = 0.37464 + 1.54226*omega - 0.26992*omega**2\n",
|
||
" sqrtalpha = 1 + kappa*(1-np.sqrt(T/Tc))\n",
|
||
" return 0.45724*R**2*Tc**2/Pc*sqrtalpha**2\n",
|
||
"\n",
|
||
"# Calculate the presure given V, T for PR EOS\n",
|
||
"def PR_pressure(V,T,Pc,Tc,omega):\n",
|
||
" a = calc_a(T,Pc,Tc,omega)\n",
|
||
" b = calc_b(Pc,Tc)\n",
|
||
"\n",
|
||
" P = R*T/(V-b) - a/(V*(V+b)+b*(V-b))\n",
|
||
" return P\n",
|
||
"\n",
|
||
"# Calculate the molar volume given P, T for PR EOS in m^3/mol\n",
|
||
"# Note that we can return multiple real roots (up to three)\n",
|
||
"# The largest and smallest will be the vapor and liquid, respectively\n",
|
||
"def PR_volume(P, T, Pc, Tc, omega):\n",
|
||
" # Calculate a, b, A, and B\n",
|
||
" a = calc_a(T, Pc, Tc, omega)\n",
|
||
" b = calc_b(Pc, Tc)\n",
|
||
" A = a*P/R**2/T**2\n",
|
||
" B = b*P/R/T\n",
|
||
"\n",
|
||
" # Definitions of alpha, beta, gamma in SIS Table 6.4-3 for PR EOS\n",
|
||
" alpha = -1 + B\n",
|
||
" beta = A - 3*B**2 -2*B\n",
|
||
" gamma = -A*B + B**2 + B**3\n",
|
||
"\n",
|
||
" # polynomial with coefficients in increasing order: c0 + c1 x + c2 x**2 + ...\n",
|
||
" p = Polynomial([ gamma, beta, alpha, 1 ]) \n",
|
||
"\n",
|
||
" roots = p.roots() # returns all (possibly complex) roots of Z\n",
|
||
" real_roots = roots.real[abs(roots.imag) < 1e-12] # filter real ones\n",
|
||
"\n",
|
||
" # Convert real values of Z to molar volume\n",
|
||
" V = real_roots*R*T/P\n",
|
||
"\n",
|
||
" return V\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "5e07a484",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"The pressure is: 6.54e+06 Pa\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f\"The pressure is: {PR_pressure(V_molar,298,Pc,Tc,omega):.2e} Pa\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bc8959a7",
|
||
"metadata": {},
|
||
"source": [
|
||
"The calculated pressure is very close to the vaporization curve.\n",
|
||
"\n",
|
||
"The pressure from the Clausius-Clapeyron equation at this temperature is a little higher:"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 22,
|
||
"id": "eecd9901",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"P^vap(298K) = 6.62e+06 Pa\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(f\"P^vap(298K) = {P_CC(H_vap_1,T_triple, P_triple, 298):.2e} Pa\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6eba2f5f",
|
||
"metadata": {},
|
||
"source": [
|
||
"But we noted that the Clausius-Clapeyron equation might overestimate the vapor pressure curve. A more accurate calculation is necessary."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.12.11"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|