{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# L3PPC Ultrafiltration (version à compléter)\n", "\n", "**Commencer par enregistrer cette feuille avec votre nom avant de commencer les modifications du code. Vous devrez ensuite sauvegarder régulièrement.**\n", "\n", "Ce code permet de traiter les résultats du TP d'ultrafiltration de L3 PPC que vous pouvez trouver en vidéo et sous forme de résultats bruts (dans la section Travail pratique virtuel) :\n", "- Détermination de la perméabilité à l'eau\n", "- Détermination de la concentration de gel et du coefficient de transfert de matière\n", "- Détermination de la concentration à la membrane et du coefficient de transfert de matière\n", "- A partir de vos données, modélisation du procédé pour d'autres vitesses tangentielles\n", "\n", "Le code n'est pas complet. Vous devrez rajouter ou modifier les lignes de code après les commentaires apparaissant en MAJUSCULES afin de rajouter les données expérimentales et certaines lignes de code manquantes XXXXX. Vous devrez aussi compléter les zones de commentaires avec une analyse des résultats. \n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Détermination de la perméabilité à l'eau\n", "\n", "La loi de filtration issue de la loi de Darcy decrivant la mécanique des fluides dans un milieu poreux permet de déterminer la perméabilité de la membrane, $L_p$, ou la resistance hydraulique de la membrane, $R_m$ :\n", "$$J=L_p TMP=\\frac{TMP}{\\mu R_m}$$\n", "où $J$ est le flux de perméation $TMP$ est la pression transmembranaire et $\\mu$ est la viscosité de l'eau (le fluide qui circule dans les pores de la membrane).\n", "\n", "> le code permet de :\n", "- tracer les données de flux en fonction de la pression transmembranaire\n", "- par regression linéaire de calculer la perméabilité et la résistance hydraulique" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (, line 16)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m16\u001b[0m\n\u001b[1;33m Q= #m3/s\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n" ] } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "#ENTRER dans les tableaux les valeurs de pression transmembranaire\n", "PTM_bar_alim=np.array([0,2])\n", "PTM_bar_ret=np.array([0,2])\n", "\n", "PTM = 1e5*(PTM_bar_alim + PTM_bar_ret)/2 #calcul de la PTM en Pa\n", "\n", "#ENTRER dans le tableaux vos valeurs de volume en ml \n", "V=np.array([0,2])\n", "#ENTRER dans le tableau les valeurs de temps en s correspondantes \n", "t=np.array([1,105])\n", "\n", "#ECRIRE les équations permettant de calculer le débit de permeation, Q, puis le flux de permeation, J\n", "Q= #m3/s\n", "S= #cm2\n", "J= #m/s\n", "\n", "#Vérification des valeurs \n", "print('Pression transmembranaire en Pa')\n", "print(PTM)\n", "print('Flux de permation en m/s')\n", "print(J)\n", "\n", "plt.plot(PTM, J, marker='x')\n", "plt.title('Flux de permeation, J, en fonction de la pression transmembranaire, PTM')\n", "plt.xlabel('PTM (Pa)')\n", "plt.ylabel('J (m/s)')\n", "\n", "#regression lineaire pour la determination de la resistance hydraulique\n", "reglin_perm= np.polyfit(PTM,J,1)\n", "pente_perm=reglin_perm[0]\n", "oo_perm=reglin_perm[1]\n", "plt.plot(PTM, pente_perm*PTM+oo_perm, 'g--')\n", "\n", "mu=0.001 #Pa.s ou kg m-1 s-1\n", "Rm=1/(mu*pente_perm) #ENTRER ICI LA RELATION POUR CALCULER Rm\n", "print('Permeabilité de la membrane, Lp=', pente_perm*1000*3600*1e5,' L/(h.m2.bar)')\n", "print('Resistance hydraulique de la membrane, Rm=', Rm,'DONNER L UNITE')\n", "\n", "#CALCULER ET FAIRE ECRIRE LA VALEUR DU FLUX DE PERMEATION POUR UNE PRESSION DE 2 BAR POUR DE L'EAU LIQUIDE A 0°C (données wikipedia)\n", "#VOUS POURRIEZ EGALEMENT FAIRE UNE PARTIE DE CODE PERMETTANT DE CALCULER LE FLUX POUR (données sur Techniques de l'Ingénieur J 2 790v2 – 3)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse du colmatage \n", "\n", "Cette partie permet de rentrer les mesures expérimentales de la filtration des solutions de PVP concentrées à 2, 5 et 10 g/L puis de réaliser des calculs pour au final déterminer la concentration à la membrane ainsi que le coefficient de transfert de matière.\n", "\n", "Trois équations sont utilisées pour décrire l'impact de l'accumulation de matière à la membrane, $c_m$, sur la productivité du procédé, $J$\n", "\n", "1) Une relation issue de l'analyse du transfert de matière dans la couche limite près de la membrane d'épaisseur , $\\delta$, qui permet d'estimer la concentration en PVP à la surface de la membrane en fonction des conditions opératoires :\n", "$$\\frac{c_m-c_p}{c_0-c_p}=e^{\\frac{J\\delta}{D}}$$\n", "où $c_0$ est la concentration de la solution, $c_p$ est la concentration dans le perméat, $J$ est le flux de perméation et $D$ est le coefficient de diffusion. A noter que $D/\\delta$ est aussi le coefficient de transfert de matière, $k$ et vous reconnaîtrez peut-être le Péclet sous le visage de $\\frac{J\\delta}{D}$ !\n", "\n", "2) La loi de filtration issue de la loi de Darcy decrivant la mécanique des fluides dans un milieu poreux et modifiée pour prendre en compte le phénomène d'osmose induit par la différence de concentration entre les deux côtés de la membrane\n", "$$J=XX$$ A MODIFIER\n", "où $\\Delta\\Pi$ est la différence de pression osmotique $\\Pi(c_m)-\\Pi(c_p)$\n", "\n", "3) L'équation d'état pour la PVP qui se traduit par la relation donnant la pression osmotique de la PVP en fonction de la concentration\n", "\n", "ECRIRE ICI L'EQUATION ET LA DESCRIPTION DU PARAMETRE (EN SUIVANT LES EXEMPLES CI-DESSUS)\n", "\n", "> le code permet de :\n", "- tracer les données\n", "- calculer la pression osmotique par l'équation 2\n", "- calculer la concentration à la membrane par l'équation 3\n", "- calculer le coefficient de transfert de matière" ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "J 2gl [4.71698113e-07 1.63280116e-06 3.03983229e-06 3.51819035e-06\n", " 3.64849369e-06]\n", "J 5gl [4.71698113e-07 1.57232704e-06 2.71030960e-06 2.90275762e-06\n", " 2.96422312e-06]\n", "J 10gl [4.71698113e-07 1.52840729e-06 2.35849057e-06 2.42364224e-06\n", " 2.46216048e-06]\n" ] }, { "data": { "image/png": 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\n", 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tiMdCEARBEATbkGRPSYy8efNqDw+PxDZDEAQhWeHn53dJa50vse0QRFgkOTw8PPD19U1sMwRBEJIVSqn/Yl5LSAikKUQQBEEQBNsQYSEIgiAIgm2IsBAEQRAEwTakj0UyIDg4GH9/f+7evZvYpiQqmTJlonDhwqRPnz6xTREEQRCiQIRFMsDf359s2bLh4eHBo0n5Ug9aa4KCgvD396d48eKJbY4gCIIQBdIUkgy4e/cuefLkSbWiAkApRZ48eVK910YQUhLDhsGmTY/O27TJzBeSLyIskgmpWVSEI8dAEFIWXl7QqNFDcbFpk5n28kpcuwTnkKYQQRAEIVHw9gYfH6hfH0qVguPHzbS3d2JbJjiDeCyEGDlz5gze3t6UKlWK559/njFjxsSrnNq1a3P27FmqVasmQcAEIZUTEmJExNdfw9WrsGMHNG0qoiIlIMIipeGCRst06dIxYsQIjhw5ws6dOxk/fjyHDx+OUxl37tzh8uXLuLu7x9sOQRCSP1evwvDh8NRT0LgxnD4NWbPCl1/CvHmPP76E5IcIi5SGCxotCxUqRIUKFQDIli0bpUqV4uzZs4+td+LECSpVqoSXlxf9+vUja9asD5Zt3ryZatWqxdsGQRCSNydOQOfOULgw9OgBTz4JAwbA/fuwbBkMHWo8GI6PLyF5In0skhuffw779kW/jpsb1KoFhQrB+fOm8fLbb80vMsqVg9GjY1X9qVOn2Lt3LxUrVnxsWZcuXejSpQtNmzZl0qRJjyxbtWoV7777bqzqEAQhZaA1/PEHjBplxEO6dKa54/PPoXx540h17FMR3udizx5pEknOiMciJZIrlxEVp0+bv7ly2VLszZs3adCgAaNHjyZ79uyPLd+xYwcNGzYEoFmzZo8s27ZtG5UrV7bFDkEQkjb378Ps2fDii1CtGmzdCr17w3//wcyZRlQA9Oz5uIDw9jbzheSLeCySG7HxLIQ3f/TtCxMnQv/+Tsv/4OBgGjRoQPPmzalfv36ctv33338pUqQIGTJkcMoGQRCSNpcuweTJMH68cZY+9xxMmQIffABPPJHY1gkJhXgsUhrhosLHB777zpZGS601rVu3plSpUnTr1i3K9SpVqsSiRYsAmD9//oP5q1atonbt2vGuXxCEpM2RI/Dpp1CkCPTpA2XKwOrVcPAgtGkjoiK1IcIipbFnT9SNlvFk27ZtzJ49m40bN1KuXDnKlSvHypUrH1tv9OjRjBw5kpdeeonz58+TI0cOAFavXv2YsKhbty6FCxemcOHCD5pPBEFIPmgNa9dCnTrGMzFrFnz4IRw6ZERFrVogMe1SJ9IUktKIrHHS29upppDKlSujtY5xPXd3d3bu3IlSivnz5+Pp6cm9e/c4f/48Hh4eD9bbvHlzvG0RBCFxuXMH5swxrbKHDkHBgmZ0x6efQr580W87bNswvNy88C7+8Hm06eQm9pzbQ89XpWNFSkGEhWAbfn5+dOrUCa01OXPmZPr06WTMmFGCYQlCCuDCBZgwwXTbunTJDCabOdPEosiYMXZleLl50WhhI3ze98G7uDebTm56MC2kHERYCLZRpUoV9u/fn9hmCIJgI/v3m+Gi8+ZBcDC8/TZ07QqvvRb3pg7v4t7MazCPevPrUSRHEQJuBTwQGULKQfpYxAGl1HSlVIBS6mCE+Z8ppY4qpQ4ppYY5zO+llPrHWlYr4S0WBEGIO2Fh8L//QfXqxjOxcCG0bQtHj8LSpWYIaVxFRXBoMD/v+5mOKzty4/4NDgcepvkLzUVUpEBEWMSNn4FHeiEqpbyBekAZrfXzwHBr/nNAE+B5a5sJSqm0CWqtIAhCHLh1ywwVffZZeOcd+OcfE8TqzBkYNw5Klox7mXdD7jJxz0RKjitJy6UtCQsLI3vG7PSp0oc5B+aw6aSE2UxpiLCIA1rrP4DLEWa3B4Zqre9Z6wRY8+sB87XW97TWJ4F/gJcSzFhBEIRYcuaMydVRuDB06gS5c8P8+SYMd48e8Yuxd+v+LUbuGMmTY56kw8oOuGVzY3CNwVy9d5XfGv/GgOoD8Hnfh0YLG4m4SGGIsHCep4EqSqldSqnflVLhSTncgTMO6/lb8wRBEJIEu3ebENvFi5vEYDVrwvbtsHOn6ZSZPn3cy7x29xqDtwzGY4wHX6z9glL5SrHxo41sa7WNtCrtI30qvIt74/O+D3vOxX84vJD0kM6bzpMOyAVUArwAH6XUk0BkLZCRjtlUSrUF2gIULVrURWY6h4eHB9myZSNt2rSkS5cuXiM9ateuzbRp02jevDnDhw/H09PTBZYKghAdISHw22+mQ+b27ZA9u8nd8dlnUKxY/Mu9dPsSY3aOYdzucVy7d426JevSu0pvXi7y8oN1IhtS6l3cW/pZpDBEWDiPP7BYm0APu5VSYUBea34Rh/UKA+ciK0BrPQWYAuDp6RlzwIhocOU48U2bNpE3b954bStp0wUhcbl2DaZNg7FjTc6OJ5+EMWOgZUvIli3+5Z6/cZ4RO0YwyXcSt4Nv0+C5Bnxd+WvKFypvn/FCskKaQpznN6A6gFLqaSADcAlYBjRRSmVUShUHSgK7XW1M+Djx8DbL8HHiXm7xT5seWyRtuiAkPf7913gkihSBL74wXoklS+DYMZPGPL6i4vS103Ra2YniY4ozeudo6peqz6EOh1jQcIGIilSOeCzigFJqHlANyKuU8gf6A9OB6dYQ1PtAC8t7cUgp5QMcBkKAjlrrUGdt+Hz15+y7EH3adLdsbtT6pRaFshXi/I3zlMpXim9//5Zvf488bXq5guUYXTv65GZKKd544w2UUnz66ae0bdv2sXUkbbogJA20NhlFR40yw0PTpIEmTYzAePFF58o+HnScoVuHMuuvWSgUH5f7mC9f/ZKncj9lj/FCskeERRzQWjeNYtEHUaw/CBjkOosiJ1emXBTKVojT105TNEdRcmVyPm36tm3bcHNzIyAggJo1a/Lss89StWrVR9bZsWMHv/32G2DSpnfv3v2R7YcPH+60HYIgRM39+7BggREUfn5mdMdXX0HHjuDm5lzZBwMOMnjLYH499CsZ0magg2cHur/SnSI5isS8sZCqEGGRzIjJswAPmz/6Vu3LRN+J9H+tv9Odo9ysp1L+/Pl577332L1792PCIiokbboguJbLl0268h9/hHPn4JlnYNIkkxQsc2bnyvY958ugLYP47e/fyJohK91f7k63l7tRIGsB5w0fNgy8vB7NZbRpk0maGFneIyFZIH0sUhiOsfe/8/7OlnHit27d4saNGw/+X7t2LaVLl35sPUmbLggJy9Gj0L69iT/x9dcmy+iKFXD4sEkK5oyo2Hp6K3Xm1MHrJy82n9pM/9f689/n//F9ze/tERVgREWjRkZMgPnbqJGZLyRbxGORwthzbk+U48Tj67W4ePEi7733HgAhISE0a9YsUqEwevRoPvjgA0aMGEHdunUfSZs+bty4R9atW7cu6a1B8i+//DILFiyIl22CkNrQGjZsMM0dK1eaBGDNm5v+Ey+84GzZmg0nNzDwj4H8/t/v5Mucj6E1htLeqz3ZM2a3Zwcc8fYGHx947z0TTMPf30w7kY1ZSHxEWKQwXDFO/Mknn4xVcjFJmy4IruPuXZg716QrP3AA8ueHb74xHov8+Z0rW2vN8mPLGbhlILvP7sY9mzuja42mzYttyJzeybaU6Ni71zSHXLsG+/aZYBoiKpI9IiwE25C06YJgPxcvmlTlEydCQIDxSkyfbiJmZsrkXNmhYaEsOrKIwVsGs//ifornLM7ktybTomwLMqaLZS70+PDPP9C3r4kbnjUrZMliRMXUqcZ7IeIiWSPCQrANSZsuCPZx4IBp7pgzx4z2qFvXpCuvXj3umUUjEhwazLyD8xi8ZTBHg47ybN5nmfXuLJq+0JR0aVz4Wjh3DgYMMAIiQwZo1gzWrDFDWby94Y03TB8LaQ5J1kjnTUEQhCRCWJjpfPn661CmDPz6K7RuDX//DcuXQ40azomKeyH3mOw7mad/fJoWv7UgU7pM+Lzvw8H2B/mw7IeuExVXr0KvXlCihBEVbdsar0XZsg9FBTzsc7FHcockZ8RjIQiCkMjcugWzZpkQ20ePgrs7DBli3r+5cztf/u3g20zxm8IP23/g3I1zVHSvyLg646hbsi7KWfdHtBXfNmNghw6FK1eMh+K77+ApK5hWZENKvb3FW5HMEWEhCIKQSJw9a967kyeb966np2n6aNgwfplFI3L93nUm7JnAyB0jCbwdSDWPasx6dxbVi1d3raAIDoYZM+Dbb03zx5tvwqBBUK6c6+oUkgwiLARBEBIYX1/Tf8LHxzR/vPuu6T/x6qvO958ACLodxNhdYxm7eyxX716lTok69K7Sm1eLvup84dERFgYLF0KfPnD8OLzyCsybB7EMpiekDERYCLGiVatWLF++nPz583Pw4MEH8y9fvkzjxo05deoUHh4e+Pj4kCtX3EKIDxkyhKJFi3L8+HGyZs36SChwQUgphIaavB2jRpk8HtmyQadOJhFY8eL21HHh5gVG7hjJRN+J3Lx/k/eefY/eVXrzopuTCUJiQmtYt870o/jzT3j+eVi2DN56yx6lJCQrpPNmCmPYsIdB7MLZtMnMd4aPP/6Y1atXPzZ/6NCh1KhRg+PHj1OjRg2GDh0a57LXrl3LG2+84ZyBgpBEuX7dxJ4oWRIaNDAxoEaONH9HjbJHVJy5dobOqzpTfExxRuwYwTvPvMOB9gdY3Hix60XFrl2mV2mtWhAUBDNnwv798PbbIipSKSIsUhiuipBbtWpVckfSi2zp0qW0aNECgBYtWjxIQubI7du3adSoEWXKlKFx48ZUrFjxQWyL69evc//+ffLly+ecgYKQxDh1Crp1M+nKu3Y1ScAWLjSDIbp2hew2BLI8cfkEbZa14amxTzHRdyLNSjfj745/M6f+HErnfzzsvq0cOQL160OlSnDw4MOepx99BGnTurZuIUkjTSHJjM8/NwHqosPNzXw8FCoE589DqVKmD9W3kWdNp1w580UVHy5evEihQoUAKFSoEAEBAY+tM2HCBHLlysVff/3FwYMHKefQgWv9+vXUqFEjfpULQhJDa9i+3Xgiliwx6cobNjRCws70F4cDDzNk6xDmHphL+jTpaftiW3q80oNiOYvZV0lUnD5tQn7OnGkCW337rdnBbNlcX7eQLBBhkQLJlcuIitOnoWhRM52YbN26lS5dugBQunRpypQp82DZ6tWradmyZWKZJgi2EBxsvBGjRpkQDLlyQY8epg9F4cL21bP3/F4GbRnE4iOLyZw+M90qdaPby90olK2QfZVExaVLMHgwjB9vprt0MX0qxNsoRECERTIjNp6F8OaPvn1NGOD+/V03LLxAgQKcP3+eQoUKcf78efJHkrRAax3l9rt372bixImuMU4QbCSyDN/Llpn05AcOmD4TJUua926LFuZj3i52nNnBwC0DWXl8JTky5qB3ld50qdSFvJnz2ldJVNy8aRTTDz+YgBstWhiPRdGirq9bSJZIH4sURrio8PExcWh8fB7tc2E377zzDjNnzgRg5syZ1KtX77F1KleujI+PDwCHDx/mwIEDABw6dIhnn32WtNIeKyQDHPsvHTtmhojWqwerVsHTT8P//mciZHboYI+o0Fqz8eRGqs+szivTX2GX/y4GVR/Ef5//x4DqA1wvKu7dg3Hj4MknoV8/Ew70wAGTqEREhRAdWmv5xfIHTAcCgIORLOsOaCCvNa2AscA/wF9AhdjU8eKLL+qIHD58+LF5UfH991pv3PjovI0bzXxnaNKkiS5YsKBOly6ddnd311OnTtVaa33p0iVdvXp1XaJECV29enUdFBT02LY3b97UDRo00C+88IL+6KOPdIUKFfSxY8f0Dz/8oGfMmPFgvf79++scOXJod3f3B7+IxOVYCILdLFqkdcaMWpveFFrXqqX1vn321hEWFqaXH12uX576suYbdKHhhfTI7SP1zXs37a0oKkJCtJ41S2sPD7OT1appvWNHwtTtBICvTgLvCflpERZxOlhQFagQUVgARYA1wH8OwuJNYJUlMCoBu2JTh7PCIikSEhKi79y5o7XW+p9//tHFihXT9+7d06+//ro+d+5cnHF8a4sAACAASURBVMpK7sdCSJ6EhWk9f77WefJonSaNeXJ27WpvHaFhoXrhoYW6/KTymm/QxUYV0xP3TNR3gu/YW1FUhIVpvWyZ1qVLmx0sX17rNWvM/GSACIuk85M+FnFAa/2HUsojkkWjgJ7AUod59YBZ1gW/UymVUylVSGt93vWWJi1u376Nt7c3wcHBaK2ZOHEiGTJkYN26dYltmiDESECAad5YtAieecYEl+zUyfRfevtt5/svhYSFMP/gfAZvGcyRS0d4Os/TzKg3g+YvNCd9WhvieseGLVvgq6/MkJYSJUw684YNzbAWQYgjIiycRCn1DnBWa70/Qux9d+CMw7S/Ne8xYaGUagu0BSiaAtsus2XL9iBuhSAkJ3x8jKi4cQPatDFDSBctepgny5kM3/dC7jFr/yyGbhvKv1f+5YX8LzC/wXzef+590qZJoH5H+/fD11/DypVmKNmkSdCqlT2JSoRUi8hRJ1BKZQZ6A/0iWxzJvEiHR2itp2itPbXWnlEFijKOj9SNHAMhoQgIMB/sjRubvot795oPeUcREd8M33eC7zBu1zhKjCtB2+VtyfNEHpY2Wcq+dvtoXLpxwoiKEyegeXMoX954KYYONZG7Pv1URIXgNOKxcI6ngOJAuLeiMPCnUuoljIeiiMO6hYFz8akkU6ZMBAUFkSdPHtdmJEzCaK0JCgoiU6ZMiW2KkMLx8YGOHU0o7iFDoHt3SJcOnnvu8XXjkuH7xr0bTPSdyIgdIwi4FUDVYlWZ9s40aj5ZM+Hu6wsXYMAAmDLFCIgvvzSpyxM72I2QohBh4QRa6wPAg8ANSqlTgKfW+pJSahnQSSk1H6gIXItv/4rChQvj7+9PYGCgHWYnWzJlykRhO6MNCYIDAQFGUCxcaIaWzphhcmk5y5U7Vxi7ayxjdo3hyt0rvPHUG/Su0puqxRIw4+e1ayYOxahRZhhpmzYm0I2bW8LZIKQaRFjEAaXUPKAakFcp5Q/011pPi2L1lZiRIf8At4F4h5dMnz49xe1KfygIwmNE5aVwhoBbAYzaMYrxe8Zz4/4N6j1Tj95VeuPlbmNs75i4c8dE7BoyBC5fhiZNTICbkiUTzgYh1SHCIg5orZvGsNzD4X8NdHS1TYIgxB9XeCn8r/szfPtwpvhN4W7IXRo934ivq3xNmQJlYt7YLkJC4OefTYTMs2dN8qDBg6FChYSzQUi1iLAQBCFVsmCBGfFhl5fi3yv/8v3W7/l5/8+EhoXyYdkP+erVr3gm7zP2GR0TWpthK336mEyjFSvC7Nmui+kvCJEgwkIQhFRFYKARFAsXgqen+bB3xktxJPDIg0yjadOkpXX51vR8tSceOT3sMjl2rF9vkoL5+pqUxkuWmJjjqbTDt5B4iLAQBCHV4OilGDzYZCCNjZdi2LZheLl54V384Zf/T34/MW73OA4GHOSJ9E/QuWJnur/SHbdsCdwhcs8eE4ti/XooUsS053z4IUgOHiGREGEhCEKKJzDQ9KVYsCB+XgovNy8aLWyEz/s+PJH+Cbqu6cpO/51kTp+ZXpV78Xmlz8mXJYHTh//9t2nyWLQI8uY1Iz7atQMZki0kMiIsBEFI0cTXS+GId3Fvpr8znTpz6nAv9B4KRctyLRlZayQ5M+V0jeFR4e9vOmXOmAGZM0P//tCtG2TPnrB2CEIUiLAQBCFFEtFLMWMGlC4dv7J2nNnBZ6s+437ofQB6vtqToa8PtdHaWBAUZCJkjhtnEpZ89plpAsmfP+ZtBSEBkZDegiCkOBYuNE0dS5caL8WOHfETFaFhoQzeMpgqM6pwN+QuOTLloG/VvkzbO41NJzfZb3hk3LoFgwaZ2OIjRphYFMeOwejRIiqEJIkIC0EQUgyBgSa/R8OGUKwY+PmZgRLxGUZ6/sZ5av1Si94be1OlWBVCwkJY3Ggx33l/h8/7PjRa2Mi14uL+fRPc6qmnTF+KatXgr79MBxEPD9fVKwhOIsJCEIQUQbiXYskS84EfXy8FwKrjqyg7qSzbz2xn6ttTqf1UbRY0XPBgVIh3cW983vdhz7k4ZiCLDWFhMGcOPPusyc/+9NOwbZtxv8R3hwQhAZE+FoIgJGsCA83718cHXnwRNm6M//v3fuh9eq3vxcidIylToAzzG8ynVL5Ska7rXdz7keGnTqO1SV/+9dfGM1G2rJmuXVtiUQjJChEWgiAkWxYtgvbt4epV46Xo2TP+0TOPBx2n6aKm+J33o6NXR4a/MZxM6RJo6Oa2babNZssW05di7lzTppNGnMpC8kOEhSAIyQ47vRQAv/z1C+1XtCd9mvQsbrSY90q9Z5+x0XHgAPTuDf/7HxQsCBMmQOvWkCFDwtQvCC5AhIUgCMkKO70UN+/fpOPKjszaP4vKRSszp/4ciuYoaq/BkXHypIk/8csvJv7E4MHQuTNkyeL6ugXBxYiwEAQhWXDpkvFS/PqrPV6KP8//SZOFTThx5QT9qvaj72t9SZfGxY/EixeNGpo0yYTc7tEDvvwScud2bb2CkICIsBAEIcnj6KUYONB4KdKnj19ZWmvG7BpDz3U9yZ8lPxs/2shrHq/Za3BErl+H4cNh5Ei4exdatYJ+/aBwYdfWKwiJgAgLQRCSLHZ7KQJvBdJyaUtWHF/B20+/zYx6M8iTOY99Bkfk7l3Tb2LwYBM5s2FDGDAAnknAVOqCkMBIl+M4oJSarpQKUEoddJj3g1Lqb6XUX0qpJUqpnA7Leiml/lFKHVVK1UocqwUhebJoETz3HCxebLwUzsSlANh0chNlJ5Vl3b/rGFt7LEubLHWdqAgJgenTTQyKL76AChVMFlIfHxEVQopHhEXc+BmoHWHeOqC01roMcAzoBaCUeg5oAjxvbTNBKSV5jAUhBi5dgqZN4f33TRZwPz8zcCK+TR8hYSH02diHGrNqkD1jdnZ9sovPKn6GckVsCK2NEnrhBTO6o2BBk8587VqTsEQQUgEiLOKA1voP4HKEeWu11iHW5E4gvNG0HjBfa31Pa30S+Ad4KcGMFYRkyOLFJnrmokXGS7Fzp3lHx5f/rv7Haz+/xqAtg/i43Mf4tfWjXMFy9hnsyKZNUKkSNGhgBMaiRbBrF9So4Zr6BCGJIsLCXloBq6z/3YEzDsv8rXmPoZRqq5TyVUr5BgYGuthEQUh6hHspGjQw/Rmd9VIALDq8iHKTy3Hg4gHm1p/L9HrTyZLBhuGcw4YZERGOnx94eUH16nDuHEybBgcPQv36EjFTSJWIsLAJpVRvIASYEz4rktV0ZNtqradorT211p758uVzlYmCkCRZsuShl2LAAOe9FHeC79BueTveX/A+JXOXZO+ne2n6QlP7DPbygkaNYNYsEx3T09OIi3bt4PhxM+IjvoE1BCEFIFe/DSilWgBvATW01uHiwR8o4rBaYeBcQtsmCEmVS5fgs89g/nzTt3HdOihTxrkyDwUcovHCxhwKPESPV3owsPpAMqS1OYpltWpmdEeLFkZAZM5sduLtt+2tRxCSKeKxcBKlVG3gS+AdrfVth0XLgCZKqYxKqeJASWB3YtgoCEmNyLwUzogKrTWTfSfj+ZMngbcDWd18NcNqDrNfVFy/brwVEyeaER8hIWbUh4gKQXiACIs4oJSaB+wAnlFK+SulWgM/AtmAdUqpfUqpSQBa60OAD3AYWA101FqHJpLpgpAkCAoyfSnq1zd9KXx9oU8f5/pSXL17lUYLG9FuRTuqFK3C/nb7qVXCBaO7DxwwzSCLF0PbtnD5MvTta0SGY58LQUjlSFNIHNBaR9ZQOy2a9QcBg1xnkSAkH5YsMd0QrlwxXoovv3ROUABsP7OdZouacfbGWb5//Xu6v9KdNMoF30uzZ8Onn5q8HiNGmLDcPj7g7W1+jRo9nBaEVI54LARBcClBQdCsmfFSuLvb46UIDQtl8JbBVJ1RlTQqDVtbbqXnqz3tFxV37xo19NFHxluxdy/cv/+oiPD2NtN79thbtyAkU9TDvoZCUsDT01P7+vomthmCYAuOXoq+feGrr5z3Upy7cY4Pl3zIxpMbafx8Yya/NZkcmXLYY7Ajp06ZKF1+fiZZ2ODBMtojCaOU8tNaSxSyJIDcJYIg2E5QkMkCPnculC9vz4gPgJXHV9Litxbcun+LqW9PpVX5Vq6JoLlyJXzwAYSGGnX07rv21yEIKRRpChEEwVZ++82M+FiwAL77zgSfdFZU3Au5R7c13ag7ty5u2dzwa+tH6wqt7RcVoaHGtVK37sN44iIqBCFOiMdCEARbiOilWLvWHi/F8aDjNFnUhD/P/0lHr44Mf2M4mdJlcr7giAQGms4g69dDy5Ywfjw88YT99QhCCkeEhSAITvPbb6YvxeXLxkthR18KgNn7Z9NhZQfSp0nPksZLePdZF3kPduwwQa8uXYKpU00CMUEQ4oUIC0EQ4o2jl6JcOfu8FDfu3aDjyo7M/ms2VYpWYU79ORTJUSTmDeOK1jB2LHTvbpo+tm83YUAFQYg3IiwEQYgXS5ea0A5BQfDtt9Crlz1eCr9zfjRd1JQTV07Q/7X+9Knah3RpXPCounEDPvnEDBV9+22YORNy5bK/HkFIZYiwEAQhTkT0UqxZA2XLOl+u1prRO0fz5fovyZ8lPxs/2shrHq85X3BkHDpkUqkePw5DhkDPnpBG+rILgh2IsBAEIda4yksReCuQj5d+zMrjK3nnmXeY/s508mTO43zBkTFnjgnJnTWr6agp0TIFwVZSpbBQSuUHXgXcgDvAQcBXax2WqIYJQhLl8mXjpZgzx14vBcDGkxv5YPEHBN0JYlydcXT06uia2BT37kG3bjBhAlSuDL/+Cm5u9tcjCKmcVOX7U0p5K6XWACuAOkAh4DmgD3BAKfWtUip7YtooCEmNpUvhuefMe/ibb2D3bntERUhYCL039Ob1Wa+TPWN2dn+ym04vdXKNqPjvP6hSxYiKL76AjRtFVAiCi0htHos3gTZa69MRFyil0gFvATWBRQltmCAkNVzppTh19RTNFjVjh/8OWpVrxdg6Y8mSIYs9hUdk9Wpo3hyCg02e9vr1XVOPIAhAKhMWWuse0Syup7UWQSEIwLJlpi/FpUvGS/H11/b0pQBYeHghnyz7hDAdxtz6c2n6QmRJg20gNNQE1RgwAEqXNqKiZEnX1CUIwgNSVVNIDIxKbAMEIbG5fBk+/BDq1YOCBU0m0v797REVd4Lv0G55OxouaMjTeZ5m76d7XScqLl2COnWMsPjwQ9i5U0SFICQQqcpjEQMuaNgVhORDRC9Fr16QIYM9ZR8MOEiThU04FHiInq/0ZED1AWRIa1PhEdm500TRDAyEKVNMrApX9NsQBCFSxGPxkBjzxyulpiulApRSBx3m5VZKrVNKHbf+5rLmK6XUWKXUP0qpv5RSEs5PSJI4eikKFIA9e4yXwg5RobVmsu9kvH7yIvB2IKubr+b7mt+7RlRoDePGQdWqJr35tm3Qpo2ICkFIYFKVsFBKHbBe8hF/B4ACsSjiZ6B2hHlfARu01iWBDdY0mFEnJa1fW2CiLTshCPFk2DDYtOnReYMGQbFiMH/+wxEf5crZU9+VO1douKAh7Va0o0rRKuxvt59aJWrZU3hEbt40CcQ6d4ZateDPP+HFF11TlyAI0ZLamkLecmZjrfUfSimPCLPrAdWs/2cCm4EvrfmztNYa2KmUyqmUKqS1Pu+MDYIQX7y8oFEjE8G6bFlo3NjEh3rqKVi40D5BAbD9zHaaLmrKuRvn+P717+n+SnfSKBd9xxw+bKJoHjsGgwfDl19KFE1BSERSm7CYAqwGVmmt/7apzALhYkFrfd4KvgXgDpxxWM/fmveYsFBKtcV4NShatKhNZgnCo3h7G1Hx7rtmwMStW/DRR/DTT/b1pQgNC2Xo1qH039yfojmKsrXlVioWrmhP4ZExb55p7sicGdatg+rVXVeXIAixIrXJ+hbAFeAbpdSfSqmJSql6SqmsLqgrsobdSPtxaK2naK09tdae+fLlc4EpgmDCcE+bBtevG1HRpo3Ju2WXqDh34xw1Z9ekz6Y+NHy+IXs/3es6UXH/Pnz2mWn+KFcO9u4VUSEISYRUJSy01he01j9rrZsAnsAs4EVgjVJqvVKqZzyKvaiUKgRg/Q2w5vsDjnmeCwPn4m+9IMSfJUvg+efNB37mzCYuxZIlj/e5iC8rjq2g7KSy7Dq7i2nvTGNu/bnkyJTDnsIjcvq06aD544/QtavZCXd319QlCEKcSVXCwhGtdZjWeofWup/W+lWgCXA2HkUtw3hCsP4udZj/kTU6pBJwTfpXCAlNYCA0aWKCTWbLBjlywPLlptOmj4/pc+GMuLgXco+uq7vy1ry3cMvmhm8bX1qVb+WasNwAa9dChQqmX8WCBTBypH2RuwRBsIXU1scCAKXUOB5vlrgG+Maw3TxMR828Sil/oD8wFPBRSrUGTgMNrdVXYkKI/wPcBlraZb8gxIYFC6BjR7h6FQYONP0ZK1V6mMwzvM/Fnj3xS/B5LOgYTRc15c/zf9LJqxM/vPEDmdJlsncnwgkLMxE0v/3WuF4WLYKnn3ZNXYIgOIUygxZSF0qpKcCzwAJrVgPgEKbp4l+t9eeJZZunp6f29Y1W3whCtAQEGEGxcCF4esKMGSaitZ3M2j+LDis6kDFdRqa/M516z9aztwJHLl2CDz4wyUo+/BAmToQsLsorIiRblFJ+WmvPxLZDSKUeC6AEUF1rHQKglJoIrMUkIDuQmIYJQnzR2mQg7dQJbtyAoUNNIs90Nt7lN+7doMPKDvzy1y9ULVaVX977hSI5isS8YXzZvRvefx8uXoRJk6BtWwl4JQhJnNTax8IdcPzkyQK4aa1DgXuJY5IgxJ8LF0w/iqZNoUQJ2LfPhHOwU1T4nfOjwpQKzD0wl29e+4aNH210najQGsaPh8qVTRvOtm0m3riICkFI8qRWj8UwYJ9SajNmWGhVYLBSKguwPjENE4S4oLVJa965M9y5Az/8YAZKpE1rXx1hOozRO0fz1fqvyJ8lP5tabKJqsar2VRCRmzeNZ2LePHjzTZg9G3Lndl19giDYSqoUFlrraUqplcBLGGHxtdY6fChodKnVBSHJcO6c+YhfvhxeeQWmT4dnnrG3joBbAbRc2pKVx1dS75l6THtnGnky57G3EkeOHDFNH0eOmB6nvXpJFE1BSGakKmGhlPLQWp8CEyWTh0NDw5crwF1r7Z8I5glCrNAaZs2Czz+He/dg1CgTK8pOLwXAhn838MGSD7hy5wo/1vmRDl4dXDeMFEwHkdatTaCNtWvh9dddV5cgCC4jVQkL4AelVBqMoPADAoFMmM6c3kANzBBSERZCksTf37QSrFoFVaoYL0WJEvbWERwazDebv2HI1iE8nedpVjdfTdmCZe2txJH796FHDxg7Fl5+2YyBLVzYdfUJguBSUpWw0Fo3VEo9BzQHWgGFMDEmjmDiTgzSWt9NRBMFIVK0NiKiWzcICTHZwTt0sL+V4NTVUzRb1Iwd/jtoVa4VY+uMJUsGFw7t9PeHhg1h507o0sWkYLUrxrggCIlCqhIWAFrrw0DvxLZDEGLL6dMmr8fatVCtmsn38eST9tez4NAC2vyvDWE6jHkN5tGkdBP7K3Fk3TqT6+PuXdMM0qiRa+sTBCFBkF5RgpBE0RomTzaBJrdvhwkTYMMG+0XF7eDbfPq/T2m0sBHP5H2Gfe32uVZUhEfRrFULChQwoT9FVAhCiiHVeSwEITlw6hR88okREjVqwNSp4OFhfz0HAw7SZGETDgUeoucrPRlQfQAZ0rqwKSIoyETPXLUKmjc3ykmiaApCikI8FoKQhAgLM3GhSpc2QScnTzYtBs6KimHbhrHp5MNsY1pruq7uSvnJ5Qm8HciaD9bwfc3vXSsq9uwxCcQ2bDDul9mzRVQIQgok1XoslFJlAA8cjoHWenGiGSSkek6cMKMtf//dtBJMmQJFi9pTtpebF40WNsLnfR/KFSxHvfn12HJ6C55unixvupwCWQvYU1FkaG3CcX/+ORQsCFu3gpeX6+oTBCFRSZXCQik1HSiDSTwWZs3WgAgLIcEJC4MffzSxoNKnN50zW7a0N3q1d3FvfN734d1f3wXg+r3rtK3QlolvTSSNcqHj8tYtE8VrzhyoXRt++QXyuDDAliAIiU6qFBZAJa31c4lthCAcPw6tWpmP+DffNE0frgjhcObaGcbvGc/1e9cBaFW+FZPfnmx/RY4cPQoNGsDhw/Ddd9C7t0TRFIRUQGq9y3dY8SwEIVEIDYWRI6FMGTh4EGbONKG57RYVwaHBDN8+nFLjS7Hs6DIyp89Mr8q9WHZ02SN9LmxnwQKTs/3CBZPuvG9fERWCkEpIrR6LmRhxcQGTzVQBWmtdJnHNElIDf/9tvBQ7dsA778DEieDmZn89f/z3Bx1WdOBQ4CEqFa7EsUvHWNhoId7Fvan5ZM0HfS68i3vbV2lwMPTsCaNHQ6VKJopmERemVRcEIcmRWoXFdOBD4AAP+1g4hVKqK/AJpq/GAaAlJrLnfCA38Cfwodb6vh31CcmPcC9F375mMMScOSbNud3pNy7evEjP9T2ZtX8WxXIUY2mTpfx96W+83LweiIjwPhd7zu2xT1j4+0PjxiboRufOJtWqRNEUhFSH0lontg0JjlJqo9a6uo3luQNbgee01neUUj6YEOFvAou11vOVUpOA/VrridGV5enpqX19fe0yTUgiHD5sOmTu3g3vvWdGWxYsaG8doWGhTPabzNcbvuZ28G16vNKD3lV7kzl9ZnsriowNG4xKun3b9D5t3Nj1dQqCA0opP621Z2LbIaRej8XfSqm5wP8wTSGA08NN0wFPKKWCgczAeaA60MxaPhP4BohWWAgpi5AQ8+H+zTeQPTvMn2+CTNrtpdhzdg/tV7TH77wf1YtXZ/yb43k277P2VhIZYWEwZAj062dyti9aBKVKub5eQRCSLKlVWDyBERRvOMyL93BTrfVZpdRw4DRwB1iLyZ56VWsdYq3mD7hHtr1Sqi3QFqCoXYELhETnwAHjpfDzM3m2fvwR8ue3t47Ldy7Te0NvJvtNpkDWAsxrMI/Gzzd2bXrzB5Vfho8+ghUrjLdiyhTImtX19QqCkKRJlcJCa93SzvKUUrmAekBx4CqwAKgTWdVR2DMFmAKmKcRO24SEJzgYhg416TBy5jQDJN5/3946wnQYs/bPose6Hly+c5nOFTvzbbVvyZEph70VRYWvr1FLZ88axdShg/1uGEEQkiWpcvyXUmqmUiqnw3QuK2hWfHkdOKm1DtRaB2M8H68AOZVS4eKtMHDOiTqEZMD+/VCxomkZCA/hYLeoOHDxAK/9/Botl7akZO6S+LX1Y3Tt0QkjKsIzo736qumNumULdOwookIQhAekSmEBlNFaXw2f0FpfAco7Ud5poJJSKrMyPugawGFgExD+WmkBLHWiDiEJc/++6Ufh6QnnzsHixTBvHuTNa18dN+7d4Is1X1B+cnmOBB5h2jvT2NpqK+UKlrOvkui4fRtatIB27cDbG/7806goQRAEB1JlUwiQRimVyxIUKKVy48Sx0FrvUkotxAwpDQH2Ypo2VgDzlVIDrXnTnLZcSHL8+afpS/HXX/DBByaEg51Rq7XWLDi8gK5runLuxjnaVGjDkBpDyJM5AUNjHztmXDCHDhkF1acPpE2bcPULgpBsSK3CYgSw3RIDGmgEDHKmQK11f6B/hNn/Ai85U66QdLl3z/SjGDrUdMpctgzeftveOo4FHaPTyk6s+3cd5QuWZ1GjRVQqXMneSmJi0SKjnDJkMOnOa9VK2PoFQUhWpEphobWepZTyxQwHVUB9rfXhRDZLSEbs2WPetYcOwccfm8BXuXLZV/6d4DsM3jKYYduHkSldJsbWHkt7r/akS5OAt2xwMHz5JYwaZZo8fHzsS7cqCEKKJVUKCwBLSIiYEOLE3bumJeCHH6BQIVi5EupENv7HCZYfW07nVZ05efUkzV9ozvA3hlMwq83RtGLi7FkT5GrbNujUCUaMkCiagiDEilQrLAQhruzcabwUf/8NrVubd20OGwdi/Hf1P7qs7sLSo0splbcUGz/aaG8ej9iyaRM0aWJSns+da2JUCIIgxJLUOipEEGLNnTvQo4cZYXnrFqxeDVOn2icq7ofeZ+jWoZQaX4p1/65jaI2h7Gu3L+FFRXgUzddfh9y5TfxxERWCIMQR8VgIQjRs22YykR47Bp9+CsOGmdDcdrHx5EY6ruzI35f+5r1n32N07dEUzZEI/RiuXDFRNJcvN00gP/0E2bIlvB2CICR7xGMhCJFw+zZ07QpVqpjRH+vXw6RJ9omK8zfO03xxc2rMqsG9kHssb7qcxY0XJ46o+PNPePFFWLMGxo41AThEVAiCEE/EYyEIEfjjD+OlOHHCRKoeOtS+92xIWAgT9kyg76a+3A25S7+q/fiq8lc8kf4JeyqIC1qbNp3PPoN8+cyOV0rgoayCIKQ4RFgIgsXNm9Crl0l9Uby46cNYrZp95e/030n7Fe3Zd2Efbzz1Bj/W+ZGSeUraV0FcuH3bqKaZM6FmTZgzx4gLQRAEJxFhIQgYEdG6NZw8CZ07w+DBkCWLPWUH3Q7iq/VfMXXvVNyzubOg4QIalGqQMBlIwxk2DLy8TCju48dNApO//oIaNUzQK4miKQiCTYiwEFI1N26YGFATJ0KJEqY1oEoVe8oO02FM3zudL9d/ybW71+j+cnf6vdaPbBkTof+Clxc0amQSho0aZZpBsmeH3r1FVAiCYCsiLIRUy/r18MkncPq06ag5cCBkzmxP2XvP76XDyg7s9N9JlaJVmFB3AqXzl7an8Lhy/boJvpEtG3z7Lbi5mUhfCxcaD4YgCIKNyKgQIdVx/Tq0bWu6FmTMCFu3mpDcdoiKa3ev0WVVFzx/8uTE5RPMfHcmv3/8e+KIin37TCZSd3fTnyJnTqhb16Rf7dhRRIUgCC5Bbg2fbgAAHYhJREFUPBZCqmLNGmjTxkSs7tHDfMA/YcOADK018w7O44u1X3Dx5kXae7ZnYPWB5HrCxgQiseHOHViwwLTt7NwJmTKZKJrt25veqY0bQ9++Zrm3t4gLQRBsR4SFkCq4ehW6dYMZM6BUKdi+3eTVsoMjgUfouLIjm05twtPNk/81/R+ebp72FB5bjh2DyZPh55/h8mV45hnTl+Kjj0wUzU2bjKjw8XkoKBo1ejgtCIJgEyIshBTPihWm6ePCBTOctF8/8yHvLLfu32LgHwMZsWMEWTJkYWLdibSp0Ia0aRKoM2RwMCxdaiJ3bdgA6dLBe+8Z70S1auA46mTPnkdFhLe3md6zR4SFIAi2orTWiW2D4ICnp6f29fVNbDNSBFeuwOefw6xZULq08VZ42uBI0Fqz9OhSuqzuwulrp2lRtgXDag4jf5b8zhceG86cMSG3p06F8+dNKvO2bc142YIJnAVVEJIISik/rXUCuwqFyBCPhU0opXICU4HSgAZaAUeBXwEP4BTQSGt9JZFMTFUsW2ZyewQGQp8+5pcxo/Pl/nvlXzqv6syK4ysonb80f3z8B1WK2TQ+NTrCwkwHkUmTTD4PrU2+9ilTzF8ZMioIQhJBhIV9jAFWa63fV0plADIDXwMbtNZDlVJfAV8BXyamkSkNx7hPAEFBpivBhg1QpoxpBqlQwfl67oXcY9i2YQzeOph0adIxvOZwOlfsTPq06Z0vPDoCAmD6dCMgTp6E/PlN4I22bcHDw7V1C4IgxAMRFjaglMoOVAU+BtBa3wfuK6XqAdWs1WYCmxFhYSvhcZ98fEzTR+vWpqNmixbmXZwhg/N1rD2xlk4rO3H88nEaPteQkbVGUjh7YecLjgqtYcsW451YuND0pXjtNZPS/L337NkpQRAEFyHCwh6eBAKBGUqpsoAf0AUooLU+D6C1Pq+UirQRXinVFmgLULRoImS3TMZ4e5uRk3XqmCyk6dKZ7geffOJ82Wevn6Xrmq4sOLyAErlLsOaDNbzx1BvOFxwV166ZDiGTJsHhw5Ajh+mI2a6dGcoiCIKQDBBhYQ/pgArAZ1rrXUqpMZhmj1ihtZ4CTAHTedM1JqY87t+HMWPgu+/MRz1Az57Oi4rg0GDG7R5H/839CQkL4btq39Hj1R5kSmfDUJLI8PMz6mjePJMczMsLpk0z8SfsCgUqCIKQQIiwsAd/wF9rvcuaXogRFheVUoUsb0UhICDRLExhrFkDXbrA0aMm0/fRo9Cpk3k/v/56/EdQbj29lfYr2nMw4CBvlnyTcXXG8WSuJ+01HoyAmD/feCf27DFRupo1Mx6KF1+0vz5BEIQEQkJ624DW+gJwRin1jDWrBnAYWAa0sOa1AJYmgnkpin//hXr1oHZtCA01WUj/+QcWLTKeCx8f0+di06a4lRtwK4CWS1tSZUYVrt+7zpLGS1jedLn9ouLIETMG1t3ddAi5dQvGjjVhtqdOFVEhCEKyRzwW9vEZMMcaEfIv0BIj3HyUUq2B00DDRLQvWXP7tum7+MMPph/FkCEmcdiYMc7FfQoNC+WnP3+i14Ze3Lp/i69e/Yo+VfuQJYNNOdPBtNksWWK8E5s3Q/r00KCB8U5UqfJoICtBEIRkjgTISmJIgKxH0dqkvuje3cSFatbMDDF1d3e+bN9zvrRf0R7fc754e3gz/s3xlMpnYyfJU6fM0JRp08ywUQ8PE1yjVSszbFQQBNuQAFlJB/FYCEmWgwehc2fTrFG2LMyZYz7wneXKnSv03tibSb6TKJC1AHPqz6Fp6aYoOzwHoaGwapXxTqxcabwRb71lRnbUqgVppPVREISUjQgLIclx9arJ5zFhghlxOWGCiQflbHBJrTWz/5pN97XdCboTxGcvfcZ33t+RI1MO542+cMF4JqZMgdOnTWjt3r1NKlUZQiwIQipChIWQZAgLM0Eme/UyCTo//RQGDIA8eZwv+2DAQTqs6MCW01v+397dx8lY738cf32IqCSkDpXQiZJCWZHqnHFT0g1OWt3XUb/qKCepR3SkU+4Tp6R+ku6227Wk6FeOxIpu5CaEKOumFEWUkLu1398f32s1Nrs7Mzu7s8b7+XjMY2euua7vfOaa2ZnPfK/v9fnS/MTmTGk/hSY1mhStUef8mIlRo/wYiuxsaN0ahg/3I0zLFXNVThGRUkiJhZQKs2dD9+4wbx60bAkjR0KTIn7vA2zbvY1HZjzC47Mfp3KFyoy5fAxdm3SljBXhkMTPP0Namj/c8dVXUKWKP2Zz221+unIRkUOYEgtJqB9+gN69/fd0jRrw6qt+gGZRhzs453hz2Zv0+G8Pvt/6Pbc2uZXBbQZz7BHHxtqgP9Vk1Chff2LnTl9A46WX/PmtFSsWLWARkSShxEISYs8e3yvx8MP+O7pXLz8koVKlore9YtMKuk/uzpSVU2h0fCPGXTWOFie1iK2x7dvh9dd9QrFgARx5pJ+I5Pbb49OlIiKSZJRYSImbOtVXzVy2zM/x8cQTUK9e0dvdsWcHQz4awpCPh3B42cMZ0W4E3VK6cViZGN7mS5b4Qx2vvAK//goNG8LTT8P118PRRxc9WBGRJKXEQkrMmjXQs6cf51i3Lkya5M/EjMdZnu+teI/uk7uz6udVXNPwGoZfNJwalWpE18iuXb6E56hR8NFHfhbR1FR/quh556mQlYhIBJRYSLH77Td49FFf2KpMGRg40CcYFaKc02vox0NJqZlCqM7vJTXTl6QzYOYAlm5cSv1q9Zl24zRa1WkVXcOrVsHo0f6UlJ9+glNO8cH+/e9wbIxjMkREDlFKLKTYOAcTJvgk4ttvoUsXX5L7pJNiay+lZgqp41PJ6JxBy1otuevduxizYAzly5RnUKtB3HvevZQvWz6yxrKz4d13fe/ElCm+SMYVV/jeiTZtVMhKRCRGSiykWHz5pT8Dc9o0OPNMX+7hL38pWpuhOiEyOmfQaWwnypUpx087fuK8k87jtb+9Ru1jakfWSO5kX2PGwHffQc2afgTprbfGp064iMghTomFxNWWLf57euRIf4bHyJG+E+CwIr7TnHNkrsmk/8z+bNm1BYAuZ3QhvXN64Rvn5MD06b53YuJEX3b7oov8rKKXX1704EREZB99okpc5OT4WhS9e8PGjb6S9cCBRR+i4Jzjv1n/ZcCsAXyy9hOqVqjKkeWOpHuz7jy34DkyV2fuN+ZiP5s2+ToTo0fDihW+hGfPnr6Q1Z//XLTARETkgHQgWYpszhxo0cJP2nnKKb6O1OjRRUsqnHNMXD6RZs81o/3r7Vm7ZS13n3s3ZcqU4Z1r3mFwm8FkdM4gdXwqmaszwzeETz6BG2/0hzbuu8/PJPrqq/7Qx9ChSipERIqREguJ2YYNcMstcO65fnDmyy/7szTPOSf2Nvfm7GXc0nE0Ht2YjmM7snnHZsZcPoasf2ZRs1JNMrL/RmiNXzd3zMXcWel+UpFnnoHGjX1N8Lff9sEtWuSDuu666E9DERGRqOlQiERtzx4/4+i//+0LU953H/TtW7S6Udk52aQvSWfgrIEs/2k59avVJ61jGteeee2+Alf3t7wfdmf62hIZGRAKEZq2klD3l30jO3f6xGL0aLjmmviU8RQRkagosYgjMysLzAO+d85dZmZ1gHSgKvA5cINzbnciYyyq6dP92R5Ll8LFF/uqmaedFnt7u/fu5tUvXmXQrEGs/HklDY9rSPqV6XRu0JmyZQ4wT3ooBG+84WcPPfpo+P57P4votdfCP/4BzZqpkJWISAIpsYivu4FlQO5v90eBx51z6Wb2DHALMCpRwRXFN9/4nonx46FOHX+k4YorYv8O35W9ixcWvMCQj4fw7ZZvafKnJkxInUCH0zoUPPPo9Olwzz2wdau/tG3rJwWrWjW2QEREJK40xiJOzOxE4FLgueC2Aa2A8cEqaUDHxEQXux07oF8/OP10X0+qXz/fW9GhQ2xJxW97fmPE7BHUfbIu3d7rRs1KNXn32neZf9t8Op3eKf+kYvVquPJKaN3an3ZSqRI8+KCfGGzRoqI9SRERiRv1WMTPE8D9QO6B/WrAL8657OD2d8ABKzCZ2W3AbQC1atUq5jAj45wv+XDPPX6Oj6uugmHDINbwtu7ayqh5oxj+6XA2bN/AhSdfSFrHNFrXaY0VlKFs2waDB8Pw4b46ZteufpKRiRP9YZFWrfYbcyEiIomlHos4MLPLgA3Oufnhiw+wqjvQ9s65Z51zTZ1zTatXr14sMUZj+XJo1w46dfKzhE+b5r+3Y0kqtuzcwoCZA6g9oja9PuhFo+Mb8eHNH/LhzR/Spm6b/JOKnBw/s2i9ejBokM9svv4a6tffP4kIhfztuXNjf8IiIhI36rGIj5bAFWbWHqiAH2PxBHCMmR0W9FqcCKxLYIyF+vVXf6hjxAifUIwYAd26xVaYctNvm3hi9hOMnDOSLbu2cOmpl/LghQ/S/MTmhW88Z46fV332bEhJ8TOOtmjh77v//j+uHwqpt0JEpJRQYhEHzrkHgAcAzOyvwH3OuevMbBzQGX9myE3AxIQFWYDczoFevXxtiq5dfSfBccdF39aG7RsY/slw/nfe/7Jt9zY6ndaJBy98kLNrnF34xuvXwwMP+BKef/qTr5p5ww2aEExE5CCixKJ49QLSzWwAsAB4PsHx/MH8+dC9O3z6qS909c47vpMgWuu2ruOxjx9j9PzR7MzeSZeGXehzQR8aHtew8I137vTnrQ4cCLt3+wynTx/VoRAROQgpsYgz59wMYEZwfRXQLJHx5GfjRv/d/dxzUL06vPiir4IdbefAN798w6MfP8rzC55nb85erj/reh44/wHqH1u/8I2d8wMxe/aEVav8qSbDhqnktojIQUyJxSEmO9tP8vnQQ/6Ei3vu8dcrV46unazNWQz5aAhpi9IwjJsb30zv83tTt0rdyBpYssQ/+AcfQIMG8P77viaFiIgc1JRYHEJmzPCHPZYsgTZt/Kzhp58eXRvLf1rOwFkDeX3x65QrU47bz7md+1veT63KEZ4ysnmzrwU+apQ/1PHkk75ipqYuFxFJCvo0PwSsXeurZmZkwMkn+5MsOnWKrsDV4h8XM2DWAMYtHUfFchXpcW4P7jvvPmpUqhFZA9nZfg6Phx6CX36BO+6ARx4p+rzqIiJSqiixSCJDh/qBl7lnXu7cCXfe6c/4KFsWHn7Yn61ZsWLkbc5fN58Bswbw9vK3Oar8UfRq2YueLXpS/cgo6m1MmwY9eviuklDIn8d65plRPTcRETk4KLFIIikpvgjl2LF+/MQdd/gzOC+4wE9pXrt25G19uvZT+s/sz+SsyRxT4Rj+/Zd/889z/0nVilHMybFqle8qeestP8HIhAnQsaMmCRMRSWJKLJJIbhHK9u19b0XZsvDYY/67PRLOOT785kP6z+zP9NXTqVaxGgNbDeTOlDupXCGK0Z3btvlCGMOH+5lHBw3yAzUrVIjtiYmIyEFDiUWSCYV89evcgleRJBXOOaaumkr/mf356NuPOP7I4xnWdhi3N72do8ofFfmD5+TAq69C796+q+SGG/w8HycccIoUERFJQkoskkxmJkyeDH37+hMv2rTJv9q1c453V7xL/5n9mfP9HE6odAJPtnuSW8++lYrlohiIAfDZZ74M92efQbNm/rBH8wjKd4uISFJRYpFEMjP3n+gzFDrwxJ85Loe3lr3FgFkDWPjDQmofU5vRl43mpkY3cfhhh0f3oOvW+TLcL7/sy3CnpcH116sMt4jIIUqJRRKZOzf/iT9DIdibs5eMpRkMnDWQpRuXcmrVU3mxw4tcd+Z1lCtbLroH27kTHn/cl+Hes8cf/vjXv1SGW0TkEGfOHXAmb0mQpk2bunnz5sW1zT179/Da4tcYNGsQKzavoEH1BvS5oA9dzuhC2TJlo2vMOXj7bbj3Xli92p/lMWwYnHJKXGMWEYmGmc13zjVNdByiHoukMfTjoaTUTCFU5/djHlOypvDs/Gf5/IfPWfPLGhod34jxV42n0+mdKGMxHKpYssTXo5g2Dc44A6ZO9YM4REREAjoQniRSaqaQOj6VzNWZ7Nizg+7vdaf96+2ZsHwC1Y+ozqSrJ7Hg9gVc2eDK6JOKTZvgrrugUSP4/HN46ilYuFBJhYiI/IF6LJJEqE6ItI5pXP7G5QBs37Odhsc1ZPhFw2lbty0WS1Gq7Gx45hlfhnvLFj+nxyOPQLVqcY5eRESShRKLJFL9iOps37MdgBvOuoG0jmmxJRTgZx3t0QOWLoVWreCJJ1SGW0RECqVDIUlk2+5tVKlQhb4X9mVy1mRmrJkRfSMrV/oBmW3bwm+/+XLcH3ygpEJERCKixCIOzOwkM8s0s2VmttTM7g6WVzWzqWa2IvhbpbhiyFydSer4VN5MfZN+oX5kdM7YN+YiIlu3+noUDRr4RGLwYPjyS83tISIiUVFiER/ZwL3OudOB5sCdZtYA6A1Mc86dCkwLbheLuevmktE5Y99ZIaE6ITI6ZzB33dyCN8zJ8UWt6tWDIUPg6qvh6699XQrN7SEiIlFSHYtiYGYTgaeCy1+dc+vNrAYwwzlXv6Bti6OORb5mz/ZluOfM8WW4n3wSzj23ZB5bRCSOVMei9FCPRZyZWW2gCfAZcLxzbj1A8Pe4fLa5zczmmdm8jRs3Fn+Q69bBjTdCixawdq0vx/3pp0oqRESkyJRYxJGZHQW8CfRwzv0a6XbOuWedc02dc02rV69efAHu3OmnMK9XD8aO9SW4v/7az0KquT1ERCQOdLppnJhZOXxS8ZpzbkKw+EczqxF2KGRDQoJzzp/dce+9sGYNdOrky3DXrZuQcEREJHnpZ2ocmC8W8TywzDn3n7C7JgE3BddvAiaWdGwsXuwrZF55JRx1lD/jY8IEJRUiIlIslFjER0vgBqCVmS0MLu2BIUBbM1sBtA1ul4xNm+DOO6FxY19+++mnYcECaN26xEIQEZFDjw6FxIFz7iMgv2IPxftNPnQopKT8Plf6nj3Qsyc8/zzs3g3duvky3FWrFmsYIiIioMTi4JeSAqmpkJEBe/fCrbfCN99Akyb+bI+GDRMdoYiIHEKUWBzsQiFIT4d27XwPRZky0L8/9OmjipkiIlLiNMYiGbRuDU2DujC9esGDDyqpEBGRhFBikQwyM309ir59YcwYf1tERCQBlFgc7DIzfx9j0a+f/5uaquRCREQSQonFwW7uXJ9M5J4VEgr523MLmXxMRESkGGgSslKmRCchExFJEpqErPRQj4WIiIjEjRILERERiRslFiIiIhI3SixEREQkbpRYiIiISNzorJBSxsw2At8kOIxjgZ8SHENBFF/RlPb4oPTHqPiKpjjiO9k5Vz3ObUoMlFjIH5jZvNJ82pbiK5rSHh+U/hgVX9GU9vikaHQoREREROJGiYWIiIjEjRILOZBnEx1AIRRf0ZT2+KD0x6j4iqa0xydFoDEWIiIiEjfqsRAREZG4UWIhIiIicaPEIkmZ2Ulmlmlmy8xsqZndHSx/2My+N7OFwaV92DYPmFmWmX1lZheHLW8XLMsys95hy+uY2WdmtsLMxppZ+ShjXGNmi4M45gXLqprZ1KDNqWZWJVhuZvZkEMMXZnZ2WDs3BeuvMLObwpafE7SfFWxrUcRWP2wfLTSzX82sR6L3n5m9YGYbzGxJ2LJi32f5PUaE8T1mZsuDGN4ys2OC5bXNbEfYvnwm1jgKeq4RxFfsr6mZHR7czgrurx1FfGPDYltjZgsTuP/y+1wpNe9BKQWcc7ok4QWoAZwdXK8EfA00AB4G7jvA+g2ARcDhQB1gJVA2uKwE6gLlg3UaBNtkAFcH158B/hFljGuAY/MsGwr0Dq73Bh4NrrcHJgMGNAc+C5ZXBVYFf6sE16sE980BWgTbTAYuiXFflgV+AE5O9P4DLgTOBpaU5D7L7zEijO8i4LDg+qNh8dUOXy9PO1HFkd9zjTC+Yn9NgW7AM8H1q4GxkcaX5/7hwEMJ3H/5fa6UmvegLom/qMciSTnn1jvnPg+ubwWWAScUsEkHIN05t8s5txrIApoFlyzn3Crn3G4gHegQ/IpoBYwPtk8DOsYh9A5BW3nb7AC87LzZwDFmVgO4GJjqnNvsnPsZmAq0C+472jn3qfOfRC8XIb7WwErnXEEVUUtk/znnZgKbD/DYxb3P8nuMQuNzzr3vnMsObs4GTizoOcYYR37PtdD4ChDP1zQ87vFA69xf4pHGF6yfCrxRUNDFvP/y+1wpNe9BSTwlFoeAoNu1CfBZsOiuoFvyhbDuxBOAtWGbfRcsy295NeCXsC+M3OXRcMD7ZjbfzG4Llh3vnFsP/kMMOC7G+E4IruddHour2f/DvLTsv1wlsc/ye4xodcX/Cs1Vx8wWmNmHZnZBWNzRxpHfc4pUcb+m+7YJ7t8SrB+NC4AfnXMrwpYlbP/l+Vw5mN6DUsyUWCQ5MzsKeBPo4Zz7FRgFnAI0Btbju1bBdzvm5WJYHo2WzrmzgUuAO83swgLWTUR8BMfIrwDGBYtK0/4rTKmKycz6ANnAa8Gi9UAt51wToCfwupkdHWMcRYm9JF7TeOzba9g/wU3Y/jvA50q07Sby/0KKmRKLJGZm5fD//K855yYAOOd+dM7tdc7lAGPw3brgfxmcFLb5icC6Apb/hO/WPCzP8og559YFfzcAbwWx/JjbBRv83RBjfN+xf5d71PEFLgE+d879GMRaavZfmJLYZ/k9RkSCwXmXAdcFXdwEhxg2Bdfn48ct1IsxjvyeU6FK6DXdt01wf2UiPySTu83fgLFhcSdk/x3ocyWGdkv8PSglR4lFkgqOxz4PLHPO/Sdsefhx005A7ujzScDV5kev1wFOxQ+imgucan60e3n8YYFJwZdDJtA52P4mYGIU8R1pZpVyr+MH+C0J4sgdIR7e5iTgxmCUeXNgS9AdOgW4yMyqBF3YFwFTgvu2mlnzYF/cGE18Yfb7lVha9l8eJbHP8nuMQplZO6AXcIVz7rew5dXNrGxwvS5+n62KMY78nmsk8ZXEaxoed2dgem6CFaE2wHLn3L7DBInYf/l9rsTQbom+B6WEuVIwglSX+F+A8/FdiF8AC4NLe+AVYHGwfBJQI2ybPvhfPV8RdgZFsN3XwX19wpbXxX/QZuEPFRweRXx18aPpFwFLc9vFH3eeBqwI/lYNlhvwdBDDYqBpWFtdgxiygL+HLW+K/5JYCTxFUGk2ihiPADYBlcOWJXT/4ZOc9cAe/K+7W0pin+X3GBHGl4U/np77Psw9O+LK4LVfBHwOXB5rHAU91wjiK/bXFKgQ3M4K7q8baXzB8peAO/Ksm4j9l9/nSql5D+qS+ItKeouIiEjc6FCIiIiIxI0SCxEREYkbJRYiIiISN0osREREJG6UWIiIiEjcKLEQSUJmttf8jJdLzGycmZ1gv8+C+YPtP5tneTNzZvZK2PaHmdlGM/u/fNpvYmbPBddvDtZdaGZfmtn/FBLbmWb2UlyfsIiUGkosRJLTDudcY+dcQ2A30CW43Rg/6+bjubedn0hrO9DQzCoG27cFvi+g/X8BI8Nujw3a/iswyMyOz29D59xi4EQzqxXzsxORUkuJhUjymwX8OYL1JgOXBtfzzkuxT1Ax9Szn3KK89zlfnn0lcLKZNTOzT8xPkvWJmdUPW/UdfMVKEUkySixEklgwx8Ql+KqHhUnHl7CuAJzF77Ph5pVbGfFAj1cXX30yC1gOXOj8JFkPAYPCVp2Hn61TRJLMYYWvIiIHoYpmtjC4Pgs/v0OBnHNfmJ8K+xrgvQJWrQFszLOsi5mdD+wCbnfObTazk4A0MzsVXwa6XNj6G4CakTwRETm4KLEQSU47gjEP0ZoEDMOPlaiWX9v4uS/CjXXO3ZVnWX8g0znXKUhYZoTdVyFoR0SSjA6FiEi4F4B+wQDL/CwjsjEblfl9AOjNee6rRz6HU0Tk4KbEQkT2cc5955wbUcg6y4HKudPeF2AoMNjMPgbK5rkvBLwbe6QiUlppdlMRiZqZ3QNsdc49F8O2hwMfAuc757LjHpyIJJR6LEQkFqPwAzVjUQvoraRCJDmpx0JERETiRj0WIiIiEjdKLERERCRulFiIiIhI3CixEBERkbhRYiEiIiJx8/+nGgQF0r/E2QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "k_2gl [2.42741050e-07 5.38706256e-07 8.66211800e-07 8.63329785e-07\n", " 8.40967388e-07]\n", "k_5gl [3.00978093e-07 7.07352215e-07 9.57504094e-07 8.88471452e-07\n", " 8.51052997e-07]\n", "k_10gl [4.19115735e-07 8.59914639e-07 1.02849215e-06 9.14855511e-07\n", " 8.66447726e-07]\n", "Coefficient de transfert de matière moyen pour 2g/L 8.63329785069308e-07\n", "Coefficient de transfert de matière moyen pour 5g/L 9.229877733933571e-07\n", "Coefficient de transfert de matière moyen pour 10g/L 9.716738288471353e-07\n" ] } ], "source": [ "from scipy.optimize import fsolve\n", "## Détermination de la pression osmotique et du coefficient de transfert de matière \n", "\n", "#ENTRER dans le tableaux vos valeurs de PTM\n", "PTM_bar_2gl=np.array()\n", "PTM_bar_5gl=np.array()\n", "PTM_bar_10gl=np.array()\n", "#ENTRER dans le tableaux vos valeurs de volume en ml \n", "V_2gl=np.array\n", "V_5gl=\n", "V_10gl=\n", "#ENTRER dans le tableau les valeurs de temps en s correspondantes \n", "t_2gl=\n", "t_5gl=\n", "t_10gl=\n", "\n", "#ENTRER LES FORMULES POUR CALCULER LES PTM en Pa\n", "PTM_2gl=\n", "\n", "\n", "#ENTRER LES FORMULES POUR CALCULER LES FLUX DE PERMEATION\n", "J_2gl= #m/s\n", "J_5gl= #m/s\n", "J_10gl= #m/s\n", "print('J 2gl',J_2gl)\n", "print('J 5gl',J_5gl)\n", "print('J 10gl',J_10gl)\n", "\n", "#Tracé des flux de permeation\n", "plt.plot(PTM, J, marker='x', label='Eau')\n", "plt.plot(PTM_2gl, J_2gl, marker='x', color='r', label='2 g/L')\n", "plt.plot(PTM_5gl, J_5gl, marker='x', color='g', label='5 g/L')\n", "plt.plot(PTM_10gl, J_10gl, marker='x', color='b', label='10 g/L')\n", "plt.legend(loc='best')\n", "plt.title('Flux de permeation, J, en fonction de la pression transmembranaire, PTM')\n", "plt.xlabel('PTM (Pa)')\n", "plt.ylabel('J (m/s)')\n", "plt.show()\n", "\n", "#ENTRER lES EQUATIONS POUR LE CALCUL DE LA PRESSION OSMOTIQUE (SELON eq. 2)\n", "Pi_cm_2gl=\n", "Pi_cm_5gl=\n", "Pi_cm_10gl=\n", "#vous pouvez imprimer les valeurs pour vérifier vos calculs\n", "#print ('Pi_2gl',Pi_cm_2gl)\n", "\n", "#Fonction devant être égale à zero pour trouver la concentration en PVP, c, correspondant à une pression osmotique, pi\n", "def pic(c,pi):\n", " c=c/1000. #concentration en g/cm3\n", " pic=0.210*c+16.05*c*c+163.8*c**3 #ENTRER la formule de pression osmotique en bar\n", " return pic*1e5-pi #pression osmotique en Pa\n", "\n", "#Calcul des concentrations à la membrane, cm, à partir de la pression osmotique (equation 3)\n", "#Définition du tableau de valeur pour cm à 2g/L\n", "cm_2gl=np.zeros(len(PTM_2gl))\n", "#Résolution de l'équation 3 pour définir cm à partir de pi(cm) pour chaques valeurs de pi(cm)\n", "for i in range(len(PTM_2gl)):\n", " cm_2gl[i]=fsolve(pic,2, args=Pi_cm_2gl[i])\n", "print ('cm_2gl',cm_2gl)\n", "#idem pour les valeurs à 5g/L\n", "cm_5gl=np.zeros(len(PTM_5gl))\n", "for i in range(len(PTM_5gl)):\n", " cm_5gl[i]=fsolve(pic,2, args=Pi_cm_5gl[i])\n", "print ('cm_5gl',cm_5gl)\n", "#idem pour les valeurs à 10g/L\n", "cm_10gl=np.zeros(len(PTM_10gl))\n", "for i in range(len(PTM_10gl)):\n", " cm_10gl[i]=fsolve(pic,2, args=Pi_cm_10gl[i])\n", "print ('cm_2gl',cm_10gl)\n", "\n", "#Tracé des concentrations à la membrane\n", "plt.plot(PTM_2gl, cm_2gl, marker='x', color='r', label='2 g/L')\n", "plt.plot(PTM_5gl, cm_5gl, marker='x', color='g', label='5 g/L')\n", "plt.plot(PTM_10gl, cm_10gl, marker='x', color='b', label='10 g/L')\n", "plt.title('Concentration à la membrane, cm, en fonction de la pression transmembranaire, PTM')\n", "plt.xlabel('PTM (Pa)')\n", "plt.ylabel('cm (g/L)')\n", "plt.legend(loc='best')\n", "plt.show()\n", "\n", "#ENTRER L'EQUATION POUR LE CALCUL DU COEFFICIENT DE MATIERE (selon eq.1)\n", "k_2gl=\n", "k_5gl=\n", "k_10gl=\n", "print ('k_2gl',k_2gl)\n", "print ('k_5gl',k_5gl)\n", "print ('k_10gl',k_10gl)\n", "\n", "\n", "print ('Coefficient de transfert de matière moyen pour 2g/L', np.mean (k_2gl[3:4]))\n", "print ('Coefficient de transfert de matière moyen pour 5g/L', np.mean (k_5gl[2:4]))\n", "print ('Coefficient de transfert de matière moyen pour 10g/L', np.mean (k_10gl[2:4]))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Commentaires :**\n", "\n", "hlhflqsdflqkh\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modèle du gel \n", "\n", "On peut noter sur les expériences précédentes que les valeurs de flux de perméation semblent atteindre un plateau quand la pression est élevée. Ce flux, qu'on appelle flux limite, $J_{lim}$, est atteind car la concentration en PVP atteind une concentration tellement élevée qu'un gel est formé : la concentration de gel, $c_g$. La loi décrivant l'accumulation de matière à la membrane s'écrit alors :\n", "$$\\frac{c_g-c_p}{c_0-c_p}=e^{\\frac{J_{lim}\\delta}{D}}$$\n", "\n", "> le code ci-dessous permet de :\n", "- tracer $J_{lim}$ en fonction du $ln(c_0)$\n", "- par regression linéaire de trouver la concentration de gel $c_g$ et le coefficient de transfert de matière $k$" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficient de transfert de matière, k= 7.321298781538278e-07 m/s\n", "La concentration de gel est de 287.9867027594029 g/L\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "#Rentrer dans le tableaux vos valeurs de concentration, C0\n", "c0=np.array([2,5,10]) #valeurs en g/L\n", "lnc0=np.log(c0)\n", "\n", "#Rentrer dans le tableaux vos valeurs de pression transmembranaire\n", "Jlim=np.array([3.64e-06,2.96422312e-06,2.46216048e-06]) #valeurs en m/s\n", "\n", "#regression lineaire pour la determination de la resistance hydraulique\n", "\n", "reglin_Jlim= np.polyfit(lnc0,Jlim,1)\n", "pente_Jlim=reglin_Jlim[0]\n", "oo_Jlim=reglin_Jlim[1]\n", "plt.plot(lnc0, Jlim, 'x')\n", "plt.plot(lnc0, pente_Jlim*lnc0+oo_Jlim, 'r--')\n", "plt.title('Flux limite, Jlim, en fonction du log de la concentration')\n", "plt.xlabel('PTM (Pa)')\n", "plt.ylabel('ln(c0) (-)')\n", "\n", "#INSERER L'EQUATION POUR CALCULER LA CONCENTRATION DE GEL\n", "print('Coefficient de transfert de matière, k=',-pente_Jlim, 'm/s')\n", "cg=np.exp(-oo_Jlim/pente_Jlim)\n", "print('La concentration de gel est de ', cg, 'g/L')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Commentaires :**\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#Bonus +++\n", "#vous pourriez ici calculer les flux de permeations que nous devrions obtenir si le débit de circulation était multiplié par 2\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 2 }