examples/pcsaft_phase_diagram.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pure substance phase diagrams\n",
    "\n",
    "## Goal of this notebook\n",
    "\n",
    "- Learn how to generate and work with phase diagrams.\n",
    "- Learn what `PhaseEquilibrium` objects are and how to use them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feos.si import *\n",
    "from feos.pcsaft import *\n",
    "from feos.eos import *\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_context('talk')\n",
    "sns.set_palette('Dark2')\n",
    "sns.set_style('ticks')\n",
    "colors = sns.palettes.color_palette('Dark2', 8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read parameters from json for a single substance and generate `PcSaft` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|component|molarweight|$m$|$\\sigma$|$\\varepsilon$|$\\mu$|$Q$|$\\kappa_{AB}$|$\\varepsilon_{AB}$|$N_A$|$N_B$|\n",
       "|-|-|-|-|-|-|-|-|-|-|-|\n",
       "|hexane|86.177|3.0576|3.7983|236.77|0|0|0|0|1|1|"
      ],
      "text/plain": [
       "PcSaftParameters(\n",
       "\tmolarweight=[86.177]\n",
       "\tm=[3.0576]\n",
       "\tsigma=[3.7983]\n",
       "\tepsilon_k=[236.77]\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = PcSaftParameters.from_json(\n",
    "    ['hexane'], \n",
    "    '../parameters/pcsaft/gross2001.json'\n",
    ")\n",
    "parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pcsaft = EquationOfState.pcsaft(parameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## The `PhaseDiagram` object\n",
    "\n",
    "A `PhaseDiagram` object contains multiple thermodyanmic states at phase equilibrium.\n",
    "**For a single substance** it can be constructed via the `PhaseDiagram.pure` method by providing \n",
    "\n",
    "- an equation of state,\n",
    "- the minimum temperature, and\n",
    "- the number of points to compute.\n",
    "\n",
    "The points are evenly distributed between the defined minimum temperature and the critical temperature which is either computed (default) or can be provided via the `critical_temperature` argument.\n",
    "\n",
    "Furthermore, we can define options for the numerics:\n",
    "- `max_iter` to define the maximum number of iterations for the phase equilibrium calculations, and\n",
    "- `tol` to set the tolerance used for deterimation of phase equilibria.\n",
    "\n",
    "A `Verbosity` object can be provided via the `verbosity` argument to print intermediate calculations to the screen.\n",
    "\n",
    "Starting from the minimum temperature, phase equilibria are computed in sequence using prior results (i.e. at prior temperature) as input for the next iteration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "phase_diagram = PhaseDiagram.pure(pcsaft, min_temperature=200*KELVIN, npoints=501)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|temperature|density|\n",
       "|-|-|\n",
       "|200.00000 K|12.21572 mmol/m³|"
      ],
      "text/plain": [
       "T = 200.00000 K, ρ = 12.21572 mmol/m³"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phase_diagram.vapor[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stored information\n",
    "\n",
    "A `PhaseDiagram` object contains the following *fields*:\n",
    "\n",
    "- `states`: a list (with length of `npoints`) of `PhaseEquilibrium` objects at the different temperatures,\n",
    "- `vapor` and `liquid`: so-called `StateVec` objects that can be used to compute properties for the vapor and liquid phase, respectively.\n",
    "\n",
    "The `to_dict` *method* can be used to conveniently generate a `pandas.DataFrame` object (see below)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building a `pandas.DataFrame`: the `to_dict` method\n",
    "\n",
    "\n",
    "The `PhaseDiagramPure` object contains some physical properties (such as densities, temperatures and pressures) as well as the `PhaseEquilibrium` objects at each temperature.\n",
    "\n",
    "Before we take a look at these objects, a useful tool when working with phase diagrams is the `to_dict` method. It generates a Python dictionary of some properties (with hard-coded units, see the docstring of `to_dict`). This dictionary can readily be used to generate a `pandas.DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>molar enthalpy vapor</th>\n",
       "      <th>temperature</th>\n",
       "      <th>density liquid</th>\n",
       "      <th>density vapor</th>\n",
       "      <th>molar enthalpy liquid</th>\n",
       "      <th>pressure</th>\n",
       "      <th>molar entropy vapor</th>\n",
       "      <th>molar entropy liquid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-16.735400</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>8539.589591</td>\n",
       "      <td>0.012216</td>\n",
       "      <td>-54.057307</td>\n",
       "      <td>20.312763</td>\n",
       "      <td>0.003135</td>\n",
       "      <td>-0.183475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-16.639176</td>\n",
       "      <td>200.638669</td>\n",
       "      <td>8532.663964</td>\n",
       "      <td>0.013078</td>\n",
       "      <td>-53.920803</td>\n",
       "      <td>21.816282</td>\n",
       "      <td>0.003021</td>\n",
       "      <td>-0.182794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-16.542786</td>\n",
       "      <td>201.277337</td>\n",
       "      <td>8525.749142</td>\n",
       "      <td>0.013994</td>\n",
       "      <td>-53.784188</td>\n",
       "      <td>23.418684</td>\n",
       "      <td>0.002912</td>\n",
       "      <td>-0.182114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-16.446230</td>\n",
       "      <td>201.916006</td>\n",
       "      <td>8518.845002</td>\n",
       "      <td>0.014967</td>\n",
       "      <td>-53.647463</td>\n",
       "      <td>25.125608</td>\n",
       "      <td>0.002806</td>\n",
       "      <td>-0.181436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-16.349508</td>\n",
       "      <td>202.554674</td>\n",
       "      <td>8511.951422</td>\n",
       "      <td>0.015999</td>\n",
       "      <td>-53.510626</td>\n",
       "      <td>26.942961</td>\n",
       "      <td>0.002703</td>\n",
       "      <td>-0.180759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   molar enthalpy vapor  temperature  density liquid  density vapor  \\\n",
       "0            -16.735400   200.000000     8539.589591       0.012216   \n",
       "1            -16.639176   200.638669     8532.663964       0.013078   \n",
       "2            -16.542786   201.277337     8525.749142       0.013994   \n",
       "3            -16.446230   201.916006     8518.845002       0.014967   \n",
       "4            -16.349508   202.554674     8511.951422       0.015999   \n",
       "\n",
       "   molar enthalpy liquid   pressure  molar entropy vapor  molar entropy liquid  \n",
       "0             -54.057307  20.312763             0.003135             -0.183475  \n",
       "1             -53.920803  21.816282             0.003021             -0.182794  \n",
       "2             -53.784188  23.418684             0.002912             -0.182114  \n",
       "3             -53.647463  25.125608             0.002806             -0.181436  \n",
       "4             -53.510626  26.942961             0.002703             -0.180759  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(phase_diagram.to_dict())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 6))\n",
    "ax[0].set_title(f\"saturation pressure of {parameters.pure_records[0].identifier.name}\")\n",
    "sns.lineplot(y=df.pressure, x=1.0/df.temperature, ax=ax[0])\n",
    "\n",
    "# axis and styling \n",
    "ax[0].set_yscale('log')\n",
    "ax[0].set_xlabel(r'$\\frac{1}{T}$ / K$^{-1}$');\n",
    "ax[0].set_ylabel(r'$p$ / Pa');\n",
    "ax[0].set_xlim(0.002, 0.005)\n",
    "ax[0].set_ylim(1e1, 1e7)\n",
    "\n",
    "ax[1].set_title(r\"$T$-$\\rho$-diagram of {}\".format(parameters.pure_records[0].identifier.name))\n",
    "sns.lineplot(y=df.temperature, x=df['density vapor'], ax=ax[1], color=colors[0])\n",
    "sns.lineplot(y=df.temperature, x=df['density liquid'], ax=ax[1], color=colors[0])\n",
    "\n",
    "# axis and styling \n",
    "ax[1].set_ylabel(r'$T$ / K');\n",
    "ax[1].set_xlabel(r'$\\rho$ / mol/m³');\n",
    "ax[1].set_ylim(200, 600)\n",
    "ax[1].set_xlim(0, 9000)\n",
    "\n",
    "sns.despine(offset=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `PhaseEquilibrium` object\n",
    "\n",
    "A `PhaseEquilibrium` object contains two thermodynamic states (`State` objects) that are in thermal, mechanical and chemical equilibrium.\n",
    "The `PhaseEquilibrium.pure` constructor can be used to compute a phase equilibrium for a single substance. We have to provide the equation of state and either temperature or pressure. Optionally, we can also provide `PhaseEquilibrium` object which can be used as starting point for the calculation which possibly speeds up the computation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "||temperature|density|\n",
       "|-|-|-|\n",
       "|phase 1|300.00000 K|8.86860  mol/m³|\n",
       "|phase 2|300.00000 K|7.51850 kmol/m³|\n"
      ],
      "text/plain": [
       "phase 0: T = 300.00000 K, ρ = 8.86860  mol/m³\n",
       "phase 1: T = 300.00000 K, ρ = 7.51850 kmol/m³"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vle = PhaseEquilibrium.pure(\n",
    "    pcsaft, \n",
    "    temperature_or_pressure=300.0*KELVIN\n",
    ")\n",
    "vle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two equilibrium states can be extracted via the `liquid` and `vapor` getters, respectively. Returned are `State` objects, for which we can now compute any property that is available for the `State` object and the given equation of state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saturation pressure      p_sat(T = 300 K) =   0.22 bar\n",
      "enthalpy of vaporization h_lv (T = 300 K) = 365.83 kJ/kg\n"
     ]
    }
   ],
   "source": [
    "liquid = vle.liquid\n",
    "vapor = vle.vapor\n",
    "\n",
    "assert(abs((liquid.pressure() - vapor.pressure()) / BAR) < 1e-10)\n",
    "print(f'saturation pressure      p_sat(T = {liquid.temperature}) = {liquid.pressure() / BAR:6.2f} bar')\n",
    "print(f'enthalpy of vaporization h_lv (T = {liquid.temperature}) = {(vapor.specific_enthalpy() - liquid.specific_enthalpy()) / (KILO*JOULE/KILOGRAM):6.2f} kJ/kg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to compute a boiling temperature or saturation pressure without needing the `PhaseEquilibrium` object you can use the\n",
    "\n",
    "- `PhaseEquilibrium.boiling_temperature` and\n",
    "- `PhaseEquilibrium.vapor_pressure`\n",
    "\n",
    "methods. Note that these methods return lists (even for pure substance systems) where each entry contains the pure substance property."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$300\\,\\mathrm{K}$"
      ],
      "text/plain": [
       "299.99999999998977 K"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PhaseEquilibrium.boiling_temperature(pcsaft, liquid.pressure())[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The `states` method returns all `PhaseEquilibrium` objects from `PhaseDiagram`\n",
    "\n",
    "Once a `PhaseDiagram` object is created, we can access all underlying `PhaseEquilibrium` objects via the `states` field.\n",
    "In the following cell, we compute the enthalpy of vaporization by iterating through all states and calling the `specific_enthalpy` method on the vapor and liquid states of the `PhaseEquilibrium` object, respectively.\n",
    "\n",
    "Note that this is merely an example to show how to compute any property of the states. The total value of enthalpy of vaporization may not be correct, depending on the ideal gas model used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add enthalpy of vaporization to dataframe\n",
    "df['hlv'] = [\n",
    "    (vle.vapor.specific_enthalpy() - vle.liquid.specific_enthalpy()) \n",
    "    / (KILO * JOULE / KILOGRAM) \n",
    "    for vle in phase_diagram.states\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 6))\n",
    "sns.lineplot(y=df.hlv, x=df.temperature, ax=ax)\n",
    "\n",
    "# axis and styling \n",
    "ax.set_xlabel(r'$T$ / K');\n",
    "ax.set_ylabel(r'$\\Delta_{LV} h$ / kJ / kg');\n",
    "ax.set_xlim(200, 600)\n",
    "ax.set_ylim(0, 500)\n",
    "\n",
    "sns.despine(offset=10);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}