{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entropy scaling of pure substances\n",
    "\n",
    "## Goal\n",
    "\n",
    "- Learn how to compute dynamic properties (viscosity in this example)\n",
    "- Compare substance specific parameters against homo-segmented group contribution\n",
    "- Compare viscosity to NIST data (generated in NIST's [webapp](https://webbook.nist.gov/chemistry/fluid/))\n",
    "\n",
    "## Import needed packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import feos\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import si_units as si\n",
    "\n",
    "sns.set_context(\"talk\")\n",
    "sns.set_palette(\"Dark2\")\n",
    "sns.set_style(\"ticks\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PC-SAFT (individual component parameters)\n",
    "\n",
    "First, we read parameters adjusted to hexane saturation pressure and liquid densities (for the regular SAFT parameters) and to viscosity (for correlation). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "|component|molarweight|m|sigma|epsilon_k|viscosity|\n",
       "|-|-|-|-|-|-|\n",
       "|hexane|86.177|3.0576|3.7983|236.77|[-1.2035,-2.5958,-0.4816,-0.0865]|"
      ],
      "text/plain": [
       "[\n",
       "  {\n",
       "    \"identifier\": {\n",
       "      \"cas\": \"110-54-3\",\n",
       "      \"name\": \"hexane\",\n",
       "      \"iupac_name\": \"hexane\",\n",
       "      \"smiles\": \"CCCCCC\",\n",
       "      \"inchi\": \"InChI=1S/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
       "      \"formula\": \"C6H14\"\n",
       "    },\n",
       "    \"molarweight\": 86.177,\n",
       "    \"m\": 3.0576,\n",
       "    \"sigma\": 3.7983,\n",
       "    \"epsilon_k\": 236.77,\n",
       "    \"viscosity\": [\n",
       "      -1.2035,\n",
       "      -2.5958,\n",
       "      -0.4816,\n",
       "      -0.0865\n",
       "    ]\n",
       "  }\n",
       "]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = feos.Parameters.from_json(\n",
    "    [\"hexane\"], \"../../../parameters/pcsaft/loetgeringlin2018.json\"\n",
    ")\n",
    "parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PC-SAFT homo-GC\n",
    "\n",
    "For transparency, we build parameters by hand. You can read a detailed explanation about PC-SAFT parameters in the \"working with parameters\" tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hexane = feos.ChemicalRecord(\n",
    "    identifier=feos.Identifier(\n",
    "        cas=\"110-54-3\",\n",
    "        name=\"hexane\",\n",
    "        iupac_name=\"hexane\",\n",
    "        smiles=\"CCCCCC\",\n",
    "        inchi=\"InChI=1/C6H14/c1-3-5-6-4-2/h3-6H2,1-2H3\",\n",
    "        formula=\"C6H14\"\n",
    "    ),\n",
    "    segments=['CH3', 'CH2', 'CH2', 'CH2', 'CH2', 'CH3']\n",
    ")\n",
    "\n",
    "ch3 = feos.SegmentRecord(\n",
    "    'CH3', \n",
    "    molarweight=15.0345, \n",
    "    m=0.61198,\n",
    "    sigma=3.7202,\n",
    "    epsilon_k=229.90,\n",
    "    viscosity=[-8.6878e-3, -1.7951e-1, -12.2359e-2, -0.01245]\n",
    ")\n",
    "\n",
    "ch2 = feos.SegmentRecord(\n",
    "    'CH2', \n",
    "    molarweight=14.02658, \n",
    "    m=0.45606,\n",
    "    sigma=3.8900,\n",
    "    epsilon_k=239.01,\n",
    "    viscosity=[-0.9194e-3, -1.3316e-1, -4.2657e-2, -0.01245]\n",
    ")\n",
    "\n",
    "segment_records = {r.identifier: r for r in [ch3, ch2]}\n",
    "\n",
    "def from_segments(chemical_record, segment_records):\n",
    "    m = 0\n",
    "    s3 = 0\n",
    "    eps = 0\n",
    "    mw = 0\n",
    "    viscosity = np.zeros(4)\n",
    "    for s in chemical_record.segments:\n",
    "        segment = segment_records[s]\n",
    "        mw += segment.molarweight\n",
    "        m += segment.model_record[\"m\"]\n",
    "        s3 += segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3\n",
    "        eps += segment.model_record[\"m\"] * segment.model_record[\"epsilon_k\"]\n",
    "        v = segment.model_record[\"viscosity\"]\n",
    "        viscosity += np.array([\n",
    "            v[0] * segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3, \n",
    "            v[1] * segment.model_record[\"m\"] * segment.model_record[\"sigma\"]**3, \n",
    "            v[2], \n",
    "            v[3]\n",
    "        ])\n",
    "    viscosity[1] /= s3**0.45\n",
    "    \n",
    "    # We have to shift the \"A\" parameter because the implemented reference\n",
    "    # is eta_CE according to eq. 3 of Loetgerin-Lin (2018)\n",
    "    # A = A(GC) + log(sqrt(1/m)) = -log(m)/2\n",
    "    viscosity[0] += np.log(np.sqrt(1/m))\n",
    "    return feos.PureRecord(chemical_record.identifier, mw, m=m, sigma=np.cbrt(s3 / m), epsilon_k=eps / m, viscosity=list(viscosity))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build equations of state\n",
    "\n",
    "We instantiate an equation of state for each parameter set. `saft` uses substance specific parameters while `saft_gc` uses homo GC parameters both for SAFT as well as correlation parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_gc = feos.Parameters.new_pure(from_segments(hexane, segment_records))\n",
    "saft_gc = feos.EquationOfState.pcsaft(parameters_gc)\n",
    "saft = feos.EquationOfState.pcsaft(parameters)\n",
    "\n",
    "m_gc = parameters_gc.pure_records[0].model_record[\"m\"]\n",
    "m = parameters.pure_records[0].model_record[\"m\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Substance specific:  [-1.2035, -2.5958, -0.4816, -0.0865]\n",
      "Segments          :  [-1.2034921145837285, -2.536713016411593, -0.415346, -0.0747]\n"
     ]
    }
   ],
   "source": [
    "print(\"Substance specific: \", parameters.pure_records[0].model_record[\"viscosity\"])\n",
    "print(\"Segments          : \", parameters_gc.pure_records[0].model_record[\"viscosity\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare methods to NIST data (T = 450 K)\n",
    "\n",
    "We will compute the residual entropy, viscosity and logarithmic reduced viscosity and compare to literature data (for which the entropy is computed with parameters fitted to the component, not GC)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Temperature (K)</th>\n",
       "      <th>Pressure (MPa)</th>\n",
       "      <th>Density (mol/m3)</th>\n",
       "      <th>Volume (m3/mol)</th>\n",
       "      <th>Internal Energy (kJ/mol)</th>\n",
       "      <th>Enthalpy (kJ/mol)</th>\n",
       "      <th>Entropy (J/mol*K)</th>\n",
       "      <th>Cv (J/mol*K)</th>\n",
       "      <th>Cp (J/mol*K)</th>\n",
       "      <th>Sound Spd. (m/s)</th>\n",
       "      <th>Joule-Thomson (K/MPa)</th>\n",
       "      <th>Viscosity (Pa*s)</th>\n",
       "      <th>Therm. Cond. (W/m*K)</th>\n",
       "      <th>Phase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>450.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>2.6774</td>\n",
       "      <td>0.373500</td>\n",
       "      <td>45.229</td>\n",
       "      <td>48.964</td>\n",
       "      <td>154.33</td>\n",
       "      <td>193.37</td>\n",
       "      <td>201.76</td>\n",
       "      <td>212.47</td>\n",
       "      <td>11.204</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.029374</td>\n",
       "      <td>vapor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>450.0</td>\n",
       "      <td>0.11</td>\n",
       "      <td>29.9840</td>\n",
       "      <td>0.033351</td>\n",
       "      <td>45.065</td>\n",
       "      <td>48.734</td>\n",
       "      <td>134.03</td>\n",
       "      <td>193.94</td>\n",
       "      <td>203.09</td>\n",
       "      <td>209.04</td>\n",
       "      <td>11.510</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.029276</td>\n",
       "      <td>vapor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>450.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>58.3290</td>\n",
       "      <td>0.017144</td>\n",
       "      <td>44.896</td>\n",
       "      <td>48.496</td>\n",
       "      <td>128.27</td>\n",
       "      <td>194.53</td>\n",
       "      <td>204.55</td>\n",
       "      <td>205.48</td>\n",
       "      <td>11.842</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.029196</td>\n",
       "      <td>vapor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>450.0</td>\n",
       "      <td>0.31</td>\n",
       "      <td>87.8260</td>\n",
       "      <td>0.011386</td>\n",
       "      <td>44.720</td>\n",
       "      <td>48.249</td>\n",
       "      <td>124.64</td>\n",
       "      <td>195.15</td>\n",
       "      <td>206.15</td>\n",
       "      <td>201.76</td>\n",
       "      <td>12.207</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>0.029136</td>\n",
       "      <td>vapor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>450.0</td>\n",
       "      <td>0.41</td>\n",
       "      <td>118.6100</td>\n",
       "      <td>0.008431</td>\n",
       "      <td>44.536</td>\n",
       "      <td>47.992</td>\n",
       "      <td>121.90</td>\n",
       "      <td>195.80</td>\n",
       "      <td>207.93</td>\n",
       "      <td>197.87</td>\n",
       "      <td>12.608</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>0.029098</td>\n",
       "      <td>vapor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Temperature (K)  Pressure (MPa)  Density (mol/m3)  Volume (m3/mol)  \\\n",
       "0            450.0            0.01            2.6774         0.373500   \n",
       "1            450.0            0.11           29.9840         0.033351   \n",
       "2            450.0            0.21           58.3290         0.017144   \n",
       "3            450.0            0.31           87.8260         0.011386   \n",
       "4            450.0            0.41          118.6100         0.008431   \n",
       "\n",
       "   Internal Energy (kJ/mol)  Enthalpy (kJ/mol)  Entropy (J/mol*K)  \\\n",
       "0                    45.229             48.964             154.33   \n",
       "1                    45.065             48.734             134.03   \n",
       "2                    44.896             48.496             128.27   \n",
       "3                    44.720             48.249             124.64   \n",
       "4                    44.536             47.992             121.90   \n",
       "\n",
       "   Cv (J/mol*K)  Cp (J/mol*K)  Sound Spd. (m/s)  Joule-Thomson (K/MPa)  \\\n",
       "0        193.37        201.76            212.47                 11.204   \n",
       "1        193.94        203.09            209.04                 11.510   \n",
       "2        194.53        204.55            205.48                 11.842   \n",
       "3        195.15        206.15            201.76                 12.207   \n",
       "4        195.80        207.93            197.87                 12.608   \n",
       "\n",
       "   Viscosity (Pa*s)  Therm. Cond. (W/m*K)  Phase  \n",
       "0          0.000009              0.029374  vapor  \n",
       "1          0.000009              0.029276  vapor  \n",
       "2          0.000009              0.029196  vapor  \n",
       "3          0.000009              0.029136  vapor  \n",
       "4          0.000010              0.029098  vapor  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "literature = pd.read_csv(\"../../../examples/data/hexane_nist.csv\", delimiter=\"\\t\")\n",
    "literature.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We loop through experimental data, read temperature, pressure and the phase (liquid or vapor) and generate `State` objects for the experimental conditions. Then, we compute the residual molar entropy and the logarithmic reduced viscosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>pressure</th>\n",
       "      <th>s*_res/m</th>\n",
       "      <th>viscosity</th>\n",
       "      <th>ln_viscosity_reduced</th>\n",
       "      <th>source</th>\n",
       "      <th>rel.dev.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.000526</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-1.170829</td>\n",
       "      <td>literature</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.000526</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-1.202136</td>\n",
       "      <td>saft</td>\n",
       "      <td>-3.082130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.01</td>\n",
       "      <td>-0.000531</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-1.202146</td>\n",
       "      <td>homo-GC</td>\n",
       "      <td>-4.154342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.005862</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-1.172124</td>\n",
       "      <td>literature</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.005862</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>-1.188299</td>\n",
       "      <td>saft</td>\n",
       "      <td>-1.604523</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pressure  s*_res/m  viscosity  ln_viscosity_reduced      source  rel.dev.\n",
       "0      0.01 -0.000526   0.000009             -1.170829  literature  0.000000\n",
       "1      0.01 -0.000526   0.000009             -1.202136        saft -3.082130\n",
       "2      0.01 -0.000531   0.000009             -1.202146     homo-GC -4.154342\n",
       "3      0.11 -0.005862   0.000009             -1.172124  literature  0.000000\n",
       "4      0.11 -0.005862   0.000009             -1.188299        saft -1.604523"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = []\n",
    "for i, row in literature.iterrows():\n",
    "    t = row['Temperature (K)'] * si.KELVIN\n",
    "    p = row['Pressure (MPa)'] * si.MEGA * si.PASCAL\n",
    "    viscosity_lit = row['Viscosity (Pa*s)'] * si.PASCAL * si.SECOND\n",
    "    \n",
    "    # literature\n",
    "    state = feos.State(saft, temperature=t, pressure=p, total_moles=si.MOL, density_initialization=row.Phase)\n",
    "    s = state.molar_entropy(feos.Contributions.Residual)\n",
    "    results.append(\n",
    "        {\n",
    "            \"pressure\": p / si.MEGA / si.PASCAL,\n",
    "            \"s*_res/m\": s / si.RGAS / m,\n",
    "            \"viscosity\": viscosity_lit / (si.PASCAL * si.SECOND),\n",
    "            \"ln_viscosity_reduced\": np.log(viscosity_lit/ state.viscosity_reference()),\n",
    "            \"source\": \"literature\",\n",
    "            \"rel.dev.\": 0.0\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    # individual parameters\n",
    "    viscosity = state.viscosity()\n",
    "    ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
    "    results.append(\n",
    "        {\n",
    "            \"pressure\": p / si.MEGA / si.PASCAL,\n",
    "            \"s*_res/m\": s / si.RGAS / m,\n",
    "            \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
    "            \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
    "            \"source\": \"saft\",\n",
    "            \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
    "        }\n",
    "    )\n",
    "    \n",
    "    # homo GC\n",
    "    state = feos.State(saft_gc, temperature=t, pressure=p, total_moles=si.MOL)\n",
    "    s = state.molar_entropy(feos.Contributions.Residual)\n",
    "    viscosity = state.viscosity()\n",
    "    ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
    "    results.append(\n",
    "        {\n",
    "            \"pressure\": p / si.MEGA / si.PASCAL,\n",
    "            \"s*_res/m\": s / si.RGAS / m_gc,\n",
    "            \"viscosity\": viscosity / (si.PASCAL * si.SECOND),\n",
    "            \"ln_viscosity_reduced\": ln_viscosity_reduced,\n",
    "            \"source\": \"homo-GC\",\n",
    "            \"rel.dev.\": (viscosity - viscosity_lit) / viscosity_lit * 100\n",
    "        }\n",
    "    )\n",
    "\n",
    "# gather everything in a data frame\n",
    "data = pd.DataFrame(results)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 2, figsize=(15, 4), gridspec_kw={'wspace': 0.35})\n",
    "sns.scatterplot(data=data, x='s*_res/m', y='ln_viscosity_reduced', hue='source', ax=ax[0])\n",
    "ax[0].set_xlabel(r\"$\\frac{s_{res}}{R \\cdot m}$\", fontsize=22)\n",
    "ax[0].set_ylabel(r\"$\\ln\\left(\\frac{\\eta}{\\eta^{CE}}\\right)$\", fontsize=22)\n",
    "ax[0].legend(frameon=False)\n",
    "\n",
    "sns.lineplot(data=data, x='pressure', y='viscosity', hue='source', ax=ax[1])\n",
    "ax[1].set_xlabel(r\"$p$ / MPa\", fontsize=22)\n",
    "ax[1].set_ylabel(r\"$\\eta$ / Pa*s\", fontsize=22)\n",
    "ax[1].legend(frameon=False)\n",
    "sns.despine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Viscosity hexane compared to NIST data at T = 450 K\n",
      "MARD (individual): 0.81 %\n",
      "MARD (homo-GC)   : 3.70 %\n"
     ]
    }
   ],
   "source": [
    "# check mean absolute relative deviation in percent\n",
    "mard = data.groupby('source')['rel.dev.'].apply(lambda x: np.mean(np.abs(x)))\n",
    "print('Viscosity hexane compared to NIST data at T = 450 K')\n",
    "print(f'MARD (individual): {mard.saft:.2f} %')\n",
    "print(f'MARD (homo-GC)   : {mard[\"homo-GC\"]:.2f} %')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "feos",
   "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": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}