notebook/chloroform_methanol.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "7bbf254d",
   "metadata": {},
   "source": [
    "# Excess properties for the chloroform/methanol mixture\n",
    "\n",
    "In this document we compare the excess properties calculated by `F-SAC` with experimental data for the chloforom/methanol mixture.\n",
    "\n",
    "We will use the JCOSMO implementation, make sure you download it at the [LVPP website](https://ufrgs.br/lvpp) and have it running on your machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f57911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math, os, sys\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "parent_directory = os.path.dirname(os.path.abspath('.'))\n",
    "sys.path.append(parent_directory)\n",
    "\n",
    "from pysac import SAC, RGAS, RGAS_SI, DEFAULT_Q_EFF\n",
    "\n",
    "from py4j.java_gateway import JavaGateway\n",
    "gateway = JavaGateway(auto_field=True)\n",
    "JCOSMO = gateway.entry_point\n",
    "\n",
    "# List the available models\n",
    "try:\n",
    "\tmodel = JCOSMO.newModel('F-SAC2')\n",
    "except:\n",
    "\tprint('Could not connect to JCOSMO, make sure it is running.')\n",
    "\tquit()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2c848bfd",
   "metadata": {},
   "source": [
    "## Compounds and sigma-profiles with F-SAC\n",
    "\n",
    "In F-SAC, compounds are defined with a few surface areas and their respective charge densities.\n",
    "\n",
    "The basic literature is:\n",
    " - [Functional-Segment Activity Coefficient model. 1. Model formulation](http://dx.doi.org/10.1021/ie400170a). Industrial & Engineering Chemistry Research, v. 52, p. 11159-11171, 2013.\n",
    " - [Functional-Segment Activity Coefficient model. 2. Associating mixtures](http://dx.doi.org/10.1021/ie4013979). Industrial & Engineering Chemistry Research, v. 52, p. 11172-11181, 2013.\n",
    "\n",
    "Our first compound, **chloroform** and the second is **acetone**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ef2e3471",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncomps = 2\n",
    "comps = gateway.new_array(gateway.jvm.java.lang.String, ncomps)\n",
    "comps[0] = 'CHLOROFORM'\n",
    "comps[1] = 'METHANOL'\n",
    "\n",
    "model.setCompounds(comps)\n",
    "comps = model.getComps()\n",
    "\n",
    "Q = []\n",
    "Qdon = []\n",
    "Qacc = []\n",
    "sigma = comps[0].sigmaAvg\n",
    "for m in range(len(sigma)) :\n",
    "    if comps[0].type[m] == 0:\n",
    "        Q.append(comps[0].area[m])\n",
    "        Qacc.append(0.0)\n",
    "        Qdon.append(0.0)\n",
    "    else :\n",
    "        Q.append(0.0)\n",
    "        if sigma[m] < 0:\n",
    "            Qdon.append(comps[0].area[m])\n",
    "            Qacc.append(0.0)\n",
    "        else:\n",
    "            Qacc.append(comps[0].area[m])\n",
    "            Qdon.append(0.0)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.bar(sigma, Q, color='k', fill=False, width = 0.002)\n",
    "plt.bar(sigma, Qdon, bottom=Q, color='b', width = 0.002)\n",
    "plt.bar(sigma, Qacc, bottom=Q, color='r', width = 0.002)\n",
    "plt.title(r'chloroform')\n",
    "plt.xlabel(r'sigma [e/$\\AA^2$]')\n",
    "plt.ylabel(r'area [$\\AA^2$]')\n",
    "plt.ylim([0, 60])\n",
    "plt.xlim([-0.025, 0.025])\n",
    "\n",
    "Q = []\n",
    "Qdon = []\n",
    "Qacc = []\n",
    "sigma = comps[1].sigmaAvg\n",
    "for m in range(len(sigma)) :\n",
    "    if comps[1].type[m] == 0:\n",
    "        Q.append(comps[1].area[m])\n",
    "        Qacc.append(0.0)\n",
    "        Qdon.append(0.0)\n",
    "    else :\n",
    "        Q.append(0.0)\n",
    "        if sigma[m] < 0:\n",
    "            Qdon.append(comps[1].area[m])\n",
    "            Qacc.append(0.0)\n",
    "        else:\n",
    "            Qacc.append(comps[1].area[m])\n",
    "            Qdon.append(0.0)\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.bar(sigma, Q, color='k', fill=False, width = 0.002)\n",
    "plt.bar(sigma, Qdon, bottom=Q, color='b', width = 0.002)\n",
    "plt.bar(sigma, Qacc, bottom=Q, color='r', width = 0.002)\n",
    "plt.title('methanol')\n",
    "plt.xlabel(r'sigma [e/$\\AA^2$]')\n",
    "plt.xlim([-0.025, 0.025])\n",
    "plt.ylim([0, 60])\n",
    "\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.tight_layout()\n",
    "plt.savefig('pdf/f-sac-profiles-chloroform-methanol.pdf')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b1d62130",
   "metadata": {},
   "source": [
    "## Excess properties\n",
    "\n",
    "Here we calculate the excess properties on multiple compositions and compare the F-SAC results with experimental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d008cdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = 308.13\n",
    "N = 51\n",
    "ue = []\n",
    "ae = []\n",
    "tse = []\n",
    "\n",
    "# Experimental data from NIST TDE, Gibbs excess was smothed from VLE assuming valid the modified Raout's law.\n",
    "x1_exp = [0.051, 0.117, 0.208, 0.331, 0.493, 0.65, 0.808, 0.809, 0.919, 0.965]\n",
    "ge_exp = [95.76, 223.73, 399.23, 613.81, 807.56, 836.22, 639.67, 637.57, 330.34, 154.28]\n",
    "he_exp = [-213, -414, -548, -511, -213, 147, 440, 440, 440, 264]\n",
    "tse_exp = []\n",
    "for i in range(len(ge_exp)):\n",
    "    tse_exp.append(he_exp[i] - ge_exp[i])\n",
    "\n",
    "x1 = []\n",
    "\n",
    "x = gateway.new_array(gateway.jvm.double,ncomps)\n",
    "model.setTemperature(T)\n",
    "\n",
    "for l in range(N):\n",
    "    x1_ = l/(N-1)\n",
    "    x1.append(x1_)\n",
    "\n",
    "    x[0] = x1_\n",
    "    x[1] = 1 - x1_\n",
    "    model.setComposition(x)\n",
    "    model.activityCoefficientLn()\n",
    "\n",
    "    he_ = model.excessEnthalpy()\n",
    "    ge_ = model.excessGibbs()\n",
    "    tse_ = he_ - ge_\n",
    "\n",
    "    ae.append(ge_ * RGAS_SI * T)\n",
    "    ue.append(he_ * RGAS_SI * T)\n",
    "    tse.append(tse_ * RGAS_SI * T)\n",
    "\n",
    "plt.plot(x1_exp, ge_exp, 'ks')\n",
    "plt.plot(x1_exp, he_exp, 'rs')\n",
    "plt.plot(x1_exp, tse_exp, 'bs')\n",
    "plt.plot(x1, ae, 'k-', label=r'$g^E$')\n",
    "plt.plot(x1, ue, 'r-', label=r'$h^E$')\n",
    "plt.plot(x1, tse, 'b-', label=r'$Ts^E$')\n",
    "\n",
    "plt.legend(loc='lower right')\n",
    "plt.xlabel(r'$x_1$')\n",
    "plt.ylabel(r'excess property [J/mol]')\n",
    "plt.xlim([0, 1])\n",
    "#plt.ylim([-0.6, 0.6])\n",
    "\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "plt.tight_layout()\n",
    "plt.plot()\n",
    "plt.savefig('pdf/chloroform-methanol-excess.pdf')"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  },
  "vscode": {
   "interpreter": {
    "hash": "916dbcbb3f70747c44a77c7bcd40155683ae19c65e1c03b4aa3499c5328201f1"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}