{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b824cdef",
   "metadata": {},
   "source": [
    " # Détermination de 5 inconnus avec 5 équations par bilan matière  \n",
    "\n",
    "Nous considérerons un séparateur avec 1 entrée (numérotée 1) et deux sorties (numérotée 2 et 3) fonctionnant avec un mélange binaire de constituants A et B dont les compositions sont données par des fractions, $x_A$ et $x_B$. Les débits globaux sont notés $F_T$ et les partiels, $F_A$ et $F_B$. \n",
    "\n",
    ">Le bloc de code ci-dessous permet de dessiner le flowsheet et de faire apparaître les débits globaux et partiels ainsi que les compositions des trois courants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec990393",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "xs, ys = 40, 40       #position du separateur\n",
    "lx, ly = 40, 20       #taille du separateur\n",
    "la, hla, hwa=25, 5,5  #taille des fleches\n",
    "\n",
    "#tracé du flowsheet\n",
    "def plot_flowsheet_sd():\n",
    "    fig, ax = plt.subplots(figsize=(5, 5)) \n",
    "    ax.set_aspect( 1 )\n",
    "    plt.axis('off')\n",
    "    plt.plot([xs,xs+lx,xs+lx,xs,xs],[ys,ys,ys+ly,ys+ly,ys])\n",
    "    #plt.plot([xs,xs+lx],[ys,ys+ly],'b--')\n",
    "    plt.arrow(xs-la-hla,ys+ly/2,la,0, head_width=hwa, head_length=hla, fc='k', ec='k')\n",
    "    plt.arrow(xs+lx/2,ys,0,-la, head_width=hwa, head_length=hla, fc='k', ec='k')\n",
    "    plt.arrow(xs+lx/2,ys+ly,0,la, head_width=hwa, head_length=hla, fc='k', ec='k')\n",
    "    #plt.text(45,48,'Séparateur',c='b', size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/2-6, r\"$F^1$\", size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/2-14, r\"$x^1_A$\", size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/2-22, r\"$x^1_B$\", size=15)\n",
    "    plt.text(xs+lx/2+5,ys-6, r\"$F^2$\", size=15)\n",
    "    plt.text(xs+lx/2+5,ys-14, r\"$x^2_A$\", size=15)\n",
    "    plt.text(xs+lx/2+5,ys-22, r\"$x^2_B$\", size=15)\n",
    "    plt.text(xs+lx/2+5,ys+ly+25-6, r\"$F^3$\", size=15)\n",
    "    plt.text(xs+lx/2+5,ys+ly+25-14, r\"$x^3_A$\", size=15)\n",
    "    plt.text(xs+lx/2+5,ys+ly+25-22, r\"$x^3_B$\", size=15)\n",
    "\n",
    "#Les 4 paramètres fixés\n",
    "FT1=300\n",
    "XB1=0.5\n",
    "XB2=0.7\n",
    "FT3=100\n",
    "\n",
    "#Analyse de la cohérence du jeu de données\n",
    "if ((FT1*XB1)-(FT1-FT3)*XB2)/FT3 >1:\n",
    "    print ('Attention le jeu de données ne correspond pas à une solution physique')\n",
    "    print ('XB3>1, il y a soit :')\n",
    "    print ('      i)   trop de B en entrée (essayez de réduire XB1 ou FT1)' )\n",
    "    print ('      ii)  pas assez de B en sortie 2 (essayez d augmenter XB2 ou FT2=FT1-FT3)' )\n",
    "if ((FT1*XB1)-(FT1-FT3)*XB2)/FT3 <0:\n",
    "    print ('Attention le jeu de données ne correspond pas à une solution physique')\n",
    "    print ('XB3<0, il y a soit :')\n",
    "    print ('      i)   pas assez de B en entrée (essayez d augmenter XB1 ou FT1)' )\n",
    "    print ('      ii)  trop de B en sortie 2 (essayez de réduire XB2 ou FT2=FT1-FT3)' )    \n",
    "#Tracé des 4 paramètres fixés sur le flowsheet\n",
    "def plot_flowsheet_data():\n",
    "    plt.text(xs-la-hla,ys+ly/2-6, r\"$F^1$\"+'={}'.format(FT1), size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/2-22, r\"$x^1_B$\"+'={:.2f}'.format(XB1), size=15)\n",
    "    plt.text(xs+lx/2+5,ys-22, r\"$x^2_B$\"+'={:.2f}'.format(XB2), size=15)\n",
    "    plt.text(xs+lx/2+5,ys+ly+25-6, r\"$F^3$\"+'={}'.format(FT3), size=15)\n",
    "def plot_flowsheet():\n",
    "    plot_flowsheet_sd()\n",
    "    plot_flowsheet_data()  \n",
    "plot_flowsheet()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "85b16940",
   "metadata": {},
   "source": [
    "1- Qu’est ce qui vous semble manquer sur ce schéma ? Faites des hypothèses qui vous semblent cohérentes pour le compléter. \n",
    "\n",
    "> Il manque les unités. Si les fractions sont molaires alors les débits devront être exprimés en mole par unité de temps. Si les fractions sont massiques alors les débits devront être exprimés en masse par unité de temps. \n",
    ">\n",
    "> Considérons par exemple que les fractions sont molaires (nous utilisons dans ce cours x pour les fractions molaires) et que les débits sont en moles par heure.\n",
    "\n",
    "2- Ecrire 5 équations reliant les inconnues.\n",
    "\n",
    ">Les équations, permettant de décrire les bilans de matière, sont les suivantes :\n",
    ">\n",
    ">**Bilan sur le débit global**\n",
    ">1. $F^1_T=F^2_T+F^3_T$\n",
    ">\n",
    ">**Bilan sur les débits partiels de A et de B**\n",
    ">\n",
    ">2. $F^1_A=F^2_A+F^3_A$ soit $F^1_T x^1_A=F^2_T x^2_A+F^3_T x^3_A$\n",
    ">3. $F^1_B=F^2_B+F^3_B$ soit $F^1_T x^1_B=F^2_T x^2_B+F^3_T x^3_B$\n",
    ">\n",
    ">**Lien entre les différentes fractions molaires**\n",
    ">\n",
    ">4. $x^1_A+x^1_B=1$\n",
    ">5. $x^2_A+x^2_B=1$\n",
    ">6. $x^3_A+x^3_B=1$\n",
    ">\n",
    ">Il y a donc 6 équations pour 9 inconnues \n",
    ">\n",
    ">$F^1_T,F^2_T,F^3_T,x^1_A,x^2_A,x^3_A,x^1_B,x^2_B,x^3_B$. \n",
    ">\n",
    ">Il faut cependant noter que ces équations ne sont pas indépendantes puisque la première est la somme >des équations 2 et 3 en considérant les équations 4, 5 et 6.\n",
    ">\n",
    ">Il y a donc seulement **5 équations indépendantes** qui forment un système comportant **9 inconnues**. Il faut donc **fixer 4 variables** pour obtenir une solution unique et déterminer les 5 inconnues à l'aide de 5 équations.\n",
    ">\n",
    ">On a fixé dans cet exercice les 4 paramètres suivants : $F^1_T,F^3_T,x^1_B,x^2_B$. Les 5 équations nous permettront alors de déterminer les 5 inconnues : $F^2_T,x^1_A,x^2_A,x^3_A,x^3_B$\n",
    ">\n",
    ">Les lignes de codes ci-dessous permettent de visualiser les 4 données fixées et de vérifier si une solution \"physique\" existe. Si ce n'est pas le cas, un message s'affiche pour vous dire dans quel sens modifier les 4 données pour retomber sur un problème ayant une solution physique. Vous pouvez donc vous exercer en changeant le jeu des 4 paramètres fixés et refaire les calculs. \n",
    ">\n",
    ">A noter que si vous choisissez $x^1_B=x^2_B$, il s'agit d'un simple diviseur de courant qui ne modifie pas la composition des courants de sortie."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e3c7a587",
   "metadata": {},
   "source": [
    "3- Déterminer les inconnues.\n",
    "\n",
    "> Les blocs suivant permettent d'appliquer les différents bilans (équations par équations) pour déterminer l'ensemble des inconnues. Vous pouvez donc exécuter les blocs successivement pour comprendre comment on arrive à déterminer l'ensemble des inconnues. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "63c8e5a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_flowsheet()\n",
    "XA1=1-XB1\n",
    "plt.text(xs-la-hla,ys+ly/2-14, r\"$x^1_A$\"+'={:.2f}'.format(XA1), color='r',size=15)\n",
    "plt.text(0,95, r\"$x^1_A+x^1_B=1$\", color='r',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bb48b385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_flowsheet()\n",
    "XA1=1-XB1\n",
    "plt.text(xs-la-hla,ys+ly/2-14, r\"$x^1_A$\"+'={:.2f}'.format(XA1), color='r',size=15)\n",
    "plt.text(0,95, r\"$x^1_A+x^1_B=1$\", color='r',size=12)\n",
    "XA2=1-XB2\n",
    "plt.text(xs+lx/2+5,ys-14, r\"$x^2_A$\"+'={:.2f}'.format(XA2), color='b',size=15)\n",
    "plt.text(0,90, r\"$x^2_A+x^2_B=1$\", color='b',size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "25c0e51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_flowsheet()\n",
    "XA1=1-XB1\n",
    "plt.text(xs-la-hla,ys+ly/2-14, r\"$x^1_A$\"+'={:.2f}'.format(XA1), color='r',size=15)\n",
    "plt.text(0,95, r\"$x^1_A+x^1_B=1$\", color='r',size=12)\n",
    "XA2=1-XB2\n",
    "plt.text(xs+lx/2+5,ys-14, r\"$x^2_A$\"+'={:.2f}'.format(XA2), color='b',size=15)\n",
    "plt.text(0,90, r\"$x^2_A+x^2_B=1$\", color='b',size=12)\n",
    "FT2=FT1-FT3\n",
    "plt.text(xs+lx/2+5,ys-6, r\"$F^2_T$\"+'={}'.format(FT2), color='g',size=15)\n",
    "plt.text(0,85, r\"$F^1_T=F^2_T+F^3_T$\", color='g',size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "77b1bb53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_flowsheet()\n",
    "XA1=1-XB1\n",
    "plt.text(xs-la-hla,ys+ly/2-14, r\"$x^1_A$\"+'={:.2f}'.format(XA1), color='r',size=15)\n",
    "plt.text(0,95, r\"$x^1_A+x^1_B=1$\", color='r',size=12)\n",
    "XA2=1-XB2\n",
    "plt.text(xs+lx/2+5,ys-14, r\"$x^2_A$\"+'={:.2f}'.format(XA2), color='b',size=15)\n",
    "plt.text(0,90, r\"$x^2_A+x^2_B=1$\", color='b',size=12)\n",
    "FT2=FT1-FT3\n",
    "plt.text(xs+lx/2+5,ys-6, r\"$F^2_T$\"+'={}'.format(FT2), color='g',size=15)\n",
    "plt.text(0,85, r\"$F^1_T=F^2_T+F^3_T$\", color='g',size=12)\n",
    "XA3=(FT1*XA1-FT2*XA2)/FT3\n",
    "plt.text(xs+lx/2+5,ys+ly+25-14, r\"$x^3_A$\"+'={:.2f}'.format(XA3), color='c',size=15)\n",
    "plt.text(0,80, r\"$F^1_T x^1_A=F^2_T x^2_A+F^3_T x^3_A$\", color='c',size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "0dd407ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_flowsheet()\n",
    "XA1=1-XB1\n",
    "plt.text(xs-la-hla,ys+ly/2-14, r\"$x^1_A$\"+'={:.2f}'.format(XA1), color='r',size=15)\n",
    "plt.text(0,95, r\"$x^1_A+x^1_B=1$\", color='r',size=12)\n",
    "XA2=1-XB2\n",
    "plt.text(xs+lx/2+5,ys-14, r\"$x^2_A$\"+'={:.2f}'.format(XA2), color='b',size=15)\n",
    "plt.text(0,90, r\"$x^2_A+x^2_B=1$\", color='b',size=12)\n",
    "FT2=FT1-FT3\n",
    "plt.text(xs+lx/2+5,ys-6, r\"$F^2_T$\"+'={}'.format(FT2), color='g',size=15)\n",
    "plt.text(0,85, r\"$F^1_T=F^2_T+F^3_T$\", color='g',size=12)\n",
    "XA3=(FT1*XA1-FT2*XA2)/FT3\n",
    "plt.text(xs+lx/2+5,ys+ly+25-14, r\"$x^3_A$\"+'={:.2f}'.format(XA3), color='c',size=15)\n",
    "plt.text(0,80, r\"$F^1_T x^1_A=F^2_T x^2_A+F^3_T x^3_A$\", color='c',size=12)\n",
    "XB3=(FT1*XB1-FT2*XB2)/FT3\n",
    "plt.text(xs+lx/2+5,ys+ly+25-22, r\"$x^3_B$\"+'={:.2f}'.format(XB3), color='m',size=15)\n",
    "plt.text(0,75, r\"$F^1_T x^1_B=F^2_T x^2_B+F^3_T x^3_B$\", color='m',size=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b0bf706",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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