{
 "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": [
    "from feos.si import *\n",
    "from feos.pcsaft import *\n",
    "from feos.eos import *\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import pandas as pd\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|$m$|$\\sigma$|$\\varepsilon$|$\\mu$|$Q$|$\\kappa_{AB}$|$\\varepsilon_{AB}$|$N_A$|$N_B$|\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameters = PcSaftParameters.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 [following the official tutorial](https://feos-org.github.io/feos/examples/eos/pcsaft/pcsaft_working_with_parameters.html#From-Python)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "hexane = ChemicalRecord(\n",
    "    identifier=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 = SegmentRecord(\n",
    "    'CH3', \n",
    "    molarweight=15.0345, \n",
    "    model_record=PcSaftRecord(\n",
    "        m=0.61198, sigma=3.7202, epsilon_k=229.90,\n",
    "        viscosity=[-8.6878e-3, -1.7951e-1, -12.2359e-2, -0.01245]\n",
    "    )\n",
    ")\n",
    "\n",
    "ch2 = SegmentRecord(\n",
    "    'CH2', \n",
    "    molarweight=14.02658, \n",
    "    model_record=PcSaftRecord(\n",
    "        m=0.45606, sigma=3.8900, epsilon_k=239.01,\n",
    "        viscosity=[-0.9194e-3, -1.3316e-1, -4.2657e-2, -0.01245]\n",
    "    )\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",
    "    saft_record = PcSaftRecord(m, np.cbrt(s3 / m), eps / m, viscosity=viscosity)\n",
    "    return PureRecord(chemical_record.identifier, mw, saft_record)"
   ]
  },
  {
   "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 = PcSaftParameters.new_pure(from_segments(hexane, segment_records))\n",
    "saft_gc = EquationOfState.pcsaft(parameters_gc)\n",
    "saft = 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(\"hexane_nist.csv\", delimiter=\"\\t\")\n",
    "literature.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\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)'] * KELVIN\n",
    "    p = row['Pressure (MPa)'] * MEGA * PASCAL\n",
    "    viscosity_lit = row['Viscosity (Pa*s)'] * PASCAL * SECOND\n",
    "    \n",
    "    # literature\n",
    "    state = State(saft, temperature=t, pressure=p, total_moles=MOL, density_initialization=row.Phase)\n",
    "    s = state.molar_entropy(Contributions.ResidualNvt)\n",
    "    results.append(\n",
    "        {\n",
    "            \"pressure\": p / MEGA / PASCAL,\n",
    "            \"s*_res/m\": s / RGAS / m,\n",
    "            \"viscosity\": viscosity_lit / (PASCAL * 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 / MEGA / PASCAL,\n",
    "            \"s*_res/m\": s / RGAS / m,\n",
    "            \"viscosity\": viscosity / (PASCAL * 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 = State(saft_gc, temperature=t, pressure=p, total_moles=MOL)\n",
    "    s = state.molar_entropy(Contributions.ResidualNvt)\n",
    "    viscosity = state.viscosity()\n",
    "    ln_viscosity_reduced = state.ln_viscosity_reduced()\n",
    "    results.append(\n",
    "        {\n",
    "            \"pressure\": p / MEGA / PASCAL,\n",
    "            \"s*_res/m\": s / RGAS / m_gc,\n",
    "            \"viscosity\": viscosity / (PASCAL * 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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\n",
      "text/plain": [
       "<Figure size 1080x288 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": "feos"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}