examples/14. Fitting Pure Component Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fitting Pure component data\n",
    "\n",
    "The purpose of this notebook is to show how fit to vapor pressure data of pure component, volume translation for a cubic EoS and also influence paramaters used in square gradient theory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from phasepy import component"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Experimental Saturation Data of water obtained from NIST\n",
    "#Saturation Temperature in Kelvin\n",
    "Tsat = np.array([290., 300., 310., 320., 330., 340., 350., 360., 370., 380.]) \n",
    "\n",
    "#Saturation Pressure in bar\n",
    "Psat = np.array([0.0192  , 0.035368, 0.062311, 0.10546 , 0.17213 , 0.27188 ,\n",
    "       0.41682 , 0.62194 , 0.90535 , 1.2885  ])\n",
    "\n",
    "#Saturated Liquid density in mol/cm3\n",
    "rhol = np.array([0.05544 , 0.055315, 0.055139, 0.054919, 0.054662, 0.054371,\n",
    "       0.054049, 0.053698, 0.053321, 0.052918]) \n",
    "\n",
    "#Interfacial Tension in mN/m\n",
    "tension = np.array([73.21 , 71.686, 70.106, 68.47 , 66.781, 65.04 , 63.248, 61.406,\n",
    "       59.517, 57.581]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Antoine Parameters can be fitted using Temperature and Saturation pressure experimental data, as the objective function can have several local minimun an initial guess can be provided (optional)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2.423780448316938e-07, [11.6573823, 3800.11357063, -46.77260501])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting Antoine Coefficients\n",
    "from phasepy.fit import fit_ant\n",
    "\n",
    "Ant = fit_ant(Tsat, Psat)\n",
    "#Objection function value, Antoine Parameters\n",
    "5.1205342479858257e-05, [1.34826650e+01, 5.02634690e+03, 9.07664231e-04]\n",
    "\n",
    "#Optionally an initial guess for the parameters can be passed to the function\n",
    "\n",
    "Ant = fit_ant(Tsat, Psat, x0 = [11, 3800, -44])\n",
    "#Objection function value, Antoine Parameters\n",
    "2.423780448316938e-07,[ 11.6573823 , 3800.11357063,  -46.77260501]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using Peng-Robinson-Stryjek-Vera EoS, the parameters for the $\\alpha$ function can optimized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1.5233471126821199e-10, [0.87185176, -0.06621339])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fitting ksv for PRSV EoS\n",
    "from phasepy.fit import fit_ksv\n",
    "\n",
    "#parameters of pure component obtained from DIPPR\n",
    "name = 'water'\n",
    "Tc = 647.13 #K\n",
    "Pc = 220.55 #bar\n",
    "Zc = 0.229\n",
    "Vc = 55.948 #cm3/mol\n",
    "w = 0.344861\n",
    "pure = component(name = name, Tc = Tc, Pc = Pc, Zc = Zc, Vc = Vc, w = w)\n",
    "\n",
    "ksv = fit_ksv(pure, Tsat, Psat)\n",
    "#Objection function value, ksv Parameters\n",
    "1.5233471126821199e-10, [ 0.87185176, -0.06621339]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When using a cubic EoS the volume can be traslated as suggested by Péneloux. In phase it is possible to optimize this parameter with liquid density experimental data as can be seen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.001270834833817397, 3.46862174)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from phasepy import prsveos\n",
    "from phasepy.fit import fit_vt\n",
    "\n",
    "#Defining the component with the optimized alpha parameters\n",
    "pure = component(name = name, Tc = Tc, Pc = Pc, Zc = Zc, Vc = Vc, w = w,\n",
    "                ksv = [ 0.87185176, -0.06621339] )\n",
    "\n",
    "vt = fit_vt(pure, prsveos, Tsat, Psat, rhol)\n",
    "#Objetive function and volume translation\n",
    "0.001270834833817397, 3.46862174"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So far saturation pressure data and liquid density has been fitted, when using a cubic equation state it is possible to fit the influence parameter used in square gradient theory for interfacial description. In phasepy the influece parameter can be fitted using a Polynomial respect the temperature.\n",
    "\n",
    "Beware that the value of the influence parameter depends of the value of densities predicted by the EoS so a volume traslation will modify the value of this parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2.74008872e-26, 1.23088986e-23, 3.05681188e-21]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from phasepy.fit import fit_cii\n",
    "#Defining the component with the volume traslation parameter.\n",
    "pure = component(name = name, Tc = Tc, Pc = Pc, Zc = Zc, Vc = Vc, w = w,\n",
    "                ksv = [ 0.87185176, -0.06621339], c =  3.46862174)\n",
    "\n",
    "eos = prsveos(pure, volume_translation = False)\n",
    "cii = fit_cii(tension, Tsat, eos, order = 2)\n",
    "#fitted influence parameter polynomial\n",
    "[2.06553362e-26, 2.64204784e-23, 4.10320513e-21]\n",
    "eos = prsveos(pure, volume_translation = True)\n",
    "cii = fit_cii(tension, Tsat, eos, order = 2)\n",
    "#fitted influence parameter polynomial with volume translation\n",
    "[2.74008872e-26, 1.23088986e-23, 3.05681188e-21]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally you can compare the fitted values against the exeprimental data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from phasepy.sgt import sgt_pure\n",
    "\n",
    "water =  component(name = name, Tc = Tc, Pc = Pc, Zc = Zc, Vc = Vc, w = w,\n",
    "                ksv = [ 0.87185176, -0.06621339], c =  3.46862174,\n",
    "                   cii = [2.74008872e-26, 1.23088986e-23, 3.05681188e-21])\n",
    "\n",
    "eos = prsveos(water, volume_translation = True)\n",
    "\n",
    "P = np.zeros_like(Psat)\n",
    "vl = np.zeros_like(rhol)\n",
    "vv = np.zeros_like(rhol)\n",
    "ten = np.zeros_like(tension)\n",
    "\n",
    "n = len(Tsat)\n",
    "#computing the saturation pressure and the volumes of the phases\n",
    "for i in range(n):\n",
    "    P[i], vl[i], vv[i] = eos.psat(Tsat[i])\n",
    "\n",
    "rho_l = 1/vl\n",
    "rho_v = 1/vv\n",
    "#computing the interfacial tension\n",
    "for i in range(n):\n",
    "    ten[i] = sgt_pure(rho_v[i], rho_l[i], Tsat[i], P[i], eos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'IFT / mN m$^{-1}$')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (14, 4))\n",
    "fig.subplots_adjust(wspace = 0.5)\n",
    "ax1 = fig.add_subplot(131)\n",
    "ax1.plot(Tsat, Psat, '.')\n",
    "ax1.plot(Tsat, P)\n",
    "ax1.set_xlabel('T/K')\n",
    "ax1.set_ylabel('Psat / bar')\n",
    "\n",
    "ax2 = fig.add_subplot(132)\n",
    "ax2.plot(Tsat, rhol, '.')\n",
    "ax2.plot(Tsat, rho_l)\n",
    "ax2.set_xlabel('T/K')\n",
    "ax2.set_ylabel(r'Liquid density / cm$^3$ mol$^{-1}$')\n",
    "\n",
    "ax3 = fig.add_subplot(133)\n",
    "ax3.plot(Tsat, tension, '.')\n",
    "ax3.plot(Tsat, ten)\n",
    "ax3.set_xlabel('T/K')\n",
    "ax3.set_ylabel('IFT / mN m$^{-1}$')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For further information please also check [official documentation](https://phasepy.readthedocs.io/), or just try:\n",
    "\n",
    "```function?```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "thermo",
   "language": "python",
   "name": "thermo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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