{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "700c9139",
   "metadata": {},
   "source": [
    "# Débits globaux et partiels\n",
    "\n",
    "\n",
    "On étudie le mélange du lait (35g/kg de matière grasse) avec de la crème fraiche 35% (350 g/kg de matière grasse) pour la préparation d’un dessert. \n",
    "\n",
    "1. Calculer la masse volumique du lait non écrémé et de la crème fraiche. On considère que ces deux fluides sont un mélange de lait écrémé (masse volumique 1035,5 kg/m3) et de matière grasse (masse volumique 1035,5 kg/m3).\n",
    "\n",
    "> La masse volumique d'un mélange (ici du lait écrémé mélangé à de la matière grasse)\n",
    "$$\\frac{1}{\\rho_T} = \\frac{x_{mA}}{\\rho_A}+\\frac{x_{mB}}{\\rho_B}$$\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "add0f91b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La masse volumique de la crème fraiche est de 1004.89 kg/m3 ou de 1.00489 kg/L\n",
      "La masse volumique de la crème fraiche est de 1032.36 kg/m3 ou de 1.03236 kg/L\n",
      "Les résultats sont cohérents si la masse volumique diminue quand il y a de la matière grasse (car la masse volumique de la matière grasse est plus faible\n"
     ]
    }
   ],
   "source": [
    "rho_lait_ecreme=1035.5     #kg.m-3 Masse volumique du lait écrémé\n",
    "rho_MG  =952.6             #kg.m-3 Masse volumique de la matière grasse (MG)\n",
    "x_MG_creme=0.35\n",
    "x_MG_lait=0.035\n",
    "\n",
    "#Calcul de la masse volumique de la crème à 35 % de MG\n",
    "rho_creme=1/((1-x_MG_creme)/rho_lait_ecreme+x_MG_creme/rho_MG)\n",
    "print('La masse volumique de la crème fraiche est de {:.2f} kg/m3 ou de {:.5f} kg/L'.format(rho_creme, rho_creme/1000))\n",
    "\n",
    "#Calcul de la masse volumique du lait à 3.5 % de MG\n",
    "rho_lait=1/((1-x_MG_lait)/rho_lait_ecreme+x_MG_lait/rho_MG)\n",
    "print('La masse volumique de la crème fraiche est de {:.2f} kg/m3 ou de {:.5f} kg/L'.format(rho_lait, rho_lait/1000))\n",
    "\n",
    "print ('Les résultats sont cohérents si la masse volumique diminue quand il y a de la matière grasse (car la masse volumique de la matière grasse est plus faible')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dbea99d",
   "metadata": {},
   "source": [
    "Dans le procédé agroalimentaire, on mélange une cuve de lait et une cuve de crême fraiche en soutirant un débit de ces deux cuves que l'on mélange ensuite dans un mélangeur. Pour déterminer les débits on mesure la hauteur des cuves qui font 1,5 m de diamètre. Les hauteurs dans les cuves diminuent de 10 cm et de 2 cm toutes les 5 minutes pour la cuve de lait et de crème fraiche respectivement.\n",
    "\n",
    "2. Calculer les débits massiques et volumiques du lait et de la crème fraiche avant le mélangeur.\n",
    "\n",
    "> A l'aide de la variation de hauteur, $\\Delta H$, pendant le temps, $\\Delta t$, on peut en déduire le volume perdu dans la cuve par unité $\\frac{\\Delta V}{\\Delta t}$ qui correspond à un débit volumique, $F_V$ :\n",
    "$$F_V=S\\frac{\\Delta H}{\\Delta t}=\\frac{\\Delta V}{\\Delta t}$$\n",
    ">où $S$ est la section de la cuve (il vous faut connaître le périmètre, la surface et le volume des principales géométries) \n",
    "> Le débit massique, $F_M$, est alors :\n",
    "$$F_M=\\rho~F_V$$\n",
    ">où $\\rho$ est la masse volumique du fluide qui s'écoule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "a1d2567b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le débit volumique du lait est de 5.89e-04 m3/s ou de 2120.58 L/h\n",
      "Le débit volumique de la crème est de 1.18e-04 m3/s ou de 424.12 L/h\n",
      "\n",
      "Le débit massique du lait est de 6.08e-01 kg/s ou de 2189.19 kg/h\n",
      "Le débit massique de la crème est de 1.18e-01 kg/s ou de 426.19 kg/h\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "DH_lait=0.1   #m\n",
    "DH_creme=0.02 #m\n",
    "DT=5*60       #s\n",
    "D_cuve=1.5    #m\n",
    "S=np.pi*(D_cuve/2)**2 #m2 section de la cuve\n",
    "\n",
    "#Calcul du débit volumique et massique\n",
    "FV_lait=S*DH_lait/DT #m3/s\n",
    "FV_creme=S*DH_creme/DT #m3/s\n",
    "print('Le débit volumique du lait est de {:.2e} m3/s ou de {:.2f} L/h'.format(FV_lait, FV_lait*1000*3600))\n",
    "print('Le débit volumique de la crème est de {:.2e} m3/s ou de {:.2f} L/h'.format(FV_creme, FV_creme*1000*3600))\n",
    "\n",
    "FM_lait=rho_lait*FV_lait    #kg/s\n",
    "FM_creme=rho_creme*FV_creme #kg/s\n",
    "print('')\n",
    "print('Le débit massique du lait est de {:.2e} kg/s ou de {:.2f} kg/h'.format(FM_lait, FM_lait*3600))\n",
    "print('Le débit massique de la crème est de {:.2e} kg/s ou de {:.2f} kg/h'.format(FM_creme, FM_creme*3600))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57fad3ff",
   "metadata": {},
   "source": [
    "3. Calculer le débits massique et volumique du lait et de la crème fraiche après le mélangeur.\n",
    "\n",
    ">Lorsqu'on fonctionne en continu (le mélangeur est donc plein de liquide), le débit global en sortie est égale à la somme des débits entrants soit :\n",
    "$$\\sum{F_{sortant}}=\\sum{F_{entrant}}$$\n",
    "> Dans le cas présent, il y a une sortie et deux entrées : le lait et la crème. Le bilan s'écrit donc :\n",
    "$$F_{mélange}=F_{lait}+F_{crème}$$\n",
    "> On peut écrire ce bilan sur les débits volumiques (si on néglige les volumes de mélange) et sur les débits massiques.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "7a63be76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le débit volumique en sortie du mélangeur est de 7.07e-04 m3/s ou de 2544.69 L/h\n",
      "Le débit massique en sortie du mélangeur est de 7.26e-01 m3/s ou de 2615377.35 L/h\n"
     ]
    }
   ],
   "source": [
    "FV_mel=FV_lait+FV_creme\n",
    "FM_mel=FM_lait+FM_creme\n",
    "print('Le débit volumique en sortie du mélangeur est de {:.2e} m3/s ou de {:.2f} L/h'.format(FV_mel, FV_mel*1000*3600))\n",
    "print('Le débit massique en sortie du mélangeur est de {:.2e} m3/s ou de {:.2f} L/h'.format(FM_mel, FM_mel*1000*3600))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37462339",
   "metadata": {},
   "source": [
    "4. Quel est le temps de séjour moyen dans le mélangeur d’un volume de 10 L ?\n",
    "\n",
    ">Le temps de séjour dans le mélangeur est définit comme le rapport du volume de l'appareil sur le débit volumique qui s'écoule dans l'appareil :\n",
    "$$t_s=V/FV_{mélange}$$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "4187b01e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le temps de séjour d une molécule dans le mélangeur est de 14.15 s\n"
     ]
    }
   ],
   "source": [
    "V=10      #L\n",
    "V=V/1000 #m3\n",
    "\n",
    "ts=V/FV_mel\n",
    "print('Le temps de séjour d une molécule dans le mélangeur est de {:.2f} s'.format(ts))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b5436f",
   "metadata": {},
   "source": [
    "5. Calculer le pourcentage massique en matière grasse en sortie du mélangeur.\n",
    "\n",
    "> Il est nécessaire de faire un petit schéma pour réprésenter tout celà !\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "9ea6ea0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "xs, ys = 40, 40       #position du melangeur\n",
    "lx, ly = 40, 20       #taille du melangeur\n",
    "la, hla, hwa=25, 5,5  #taille des fleches\n",
    "\n",
    "#tracé du flowsheet\n",
    "def plot_flowsheet_melangeur():\n",
    "    fig, ax = plt.subplots(figsize=(8, 8)) \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([60,60],[75,54],'b',linewidth=4)\n",
    "    plt.text(xs+10,ys+6,r\"$\\infty$\",size=92,color='b')\n",
    "    plt.arrow(xs-la-hla,ys+ly/3,la,0, head_width=hwa, head_length=hla, fc='k', ec='k')\n",
    "    plt.arrow(xs-la-hla,ys+2*ly/3,la,0, head_width=hwa, head_length=hla, fc='k', ec='k')\n",
    "    plt.arrow(xs+lx,ys+ly/2,la,0, head_width=hwa, head_length=hla, fc='k', ec='k')\n",
    "    plt.text(50,42,'Mélangeur',c='b', size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/3-12, r\"$F_{L}$\"+'={:.3f} kg/s'.format(FM_lait), size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/3-18, r\"$x_{MG~L}$\"+'={:.1f} %'.format(x_MG_lait*100), size=15)\n",
    "    plt.text(xs-la-hla,ys+ly/3-6, 'Lait', size=15)\n",
    "    plt.text(xs-la-hla,ys+ly+8, r\"$F_{C}$\"+'={:.3f} kg/s'.format(FM_creme), size=15)\n",
    "    plt.text(xs-la-hla,ys+ly+2, r\"$x_{MG~C}$\"+'={:.0f} %'.format(x_MG_creme*100), size=15)\n",
    "    plt.text(xs-la-hla,ys+ly-4, 'Crème', size=15)\n",
    "    plt.text(xs+lx+5,ys+ly/2-12, r\"$F_{M}$\"+'={:.3f} kg/s'.format(FM_mel), size=15)\n",
    "    plt.text(xs+lx+5,ys+ly/2-6, 'Mélange', size=15)\n",
    "plot_flowsheet_melangeur()\n",
    "plt.text(xs+lx+5,ys+ly/2-18, r\"$x_{MG~M}$\"+'= ? %', size=15, color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11bc454d",
   "metadata": {},
   "source": [
    "> IL faut se dire : la quantité de matière grasse qui sort doit être égale à la quantité de crème qui rentre avec le lait à 3,5 % et avec la crème à 35%. La quantité qui sort est le débit partiel est matière grasse c'est à dire $F_{MG3}=F_{3}~x_{MG3}$. On peut de la même façon écrire le débit partiel en matière grasse qui rentre avec le lait $F_{MG1}=F_{1}~x_{MG1}$ et le débit partiel en matière grasse qui rentre avec la crème $F_{MG2}=F_{2}~x_{MG2}$. \n",
    ">\n",
    ">On peut alors écrire :\n",
    "$$F_{MG3}=F_{MG3}+F_{MG1}$$\n",
    ">et ainsi en déduire $x_{MG3}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "9b842ed6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La fraction massique en matière grasse est de 0.0863 dans le mélange\n"
     ]
    }
   ],
   "source": [
    "FM_MG_lait=FM_lait*x_MG_lait     #kg de MG/s\n",
    "FM_MG_creme=FM_creme*x_MG_creme  #kg de MG/s\n",
    "FM_MG_mel=FM_MG_lait+FM_MG_creme #kg de MG/s\n",
    "x_MG_mel=FM_MG_mel/FM_mel\n",
    "\n",
    "print('La fraction massique en matière grasse est de {:.4f} dans le mélange'.format(x_MG_mel))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b8541647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_flowsheet_melangeur()\n",
    "plt.text(xs+lx+5,ys+ly/2-18, r\"$x_{MG~M}$\"+'= {:.2f} %'.format(x_MG_mel*100), size=15, color='r')\n",
    "plt.text(xs-la-hla,ys+ly+14, r\"$F_{MG~C}$\"+'={:.4f} kg MG/s'.format(FM_MG_creme), size=15, color='g')\n",
    "plt.text(xs-la-hla,ys+ly/3-24, r\"$F_{MG~L}$\"+'={:.4f} kg MG/s'.format(FM_MG_lait), size=15, color='g')\n",
    "plt.text(xs+lx+5,ys+ly/2-24, r\"$F_{MG~M}$\"+'= {:.4f} kg MG/s'.format(FM_MG_mel), size=15, color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a93f20d4",
   "metadata": {},
   "source": [
    "> Un dernier coup d oeil permet de vérifier que tout semble correct ! Il n'y a plus qu'à rajouter les oeufs !"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c028617",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}