{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Etude de l'évolution d'un système chimique (version professeur)" ] }, { "cell_type": "raw", "metadata": { "raw_mimetype": "text/restructuredtext" }, "source": [ ":download:`Télécharger le pdf <./suivi_avancement_professeur.pdf>`\n", "\n", ":download:`Télécharger le notebook <./suivi_avancement_professeur.ipynb>`\n", "\n", ":download:`Lancer le notebook sur binder (lent) `" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ce programme permet d'étudier l'évolution des quantités de matière des réactifs et produits d'une réaction dont l'équation est du type :\n", "aA + bB --> cC + dD\n", "où a, b, c et d sont les nombres stoechiométriques respectifs des espèces chimiques A, B, C et D.\n", "\n", "Le programme doit tout d'abord demander les valeurs des nombres stoechiométriques, pour ensuite demander les quantités de matière initiales des réactifs A et B et des produits C et D." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# rattachement des librairies gérant les tracés \n", "# de courbes et certains outils mathématiques \n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Entrée des nombres stoechiométriques**\n", "\n", "Sur le modèle de la ligne de code 2, ajouter les lignes de code nécessaires (lignes 3, 4 et 5) pour entrer les valeurs des nombres stoechiométriques b, c et d." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#Nombres stoechiométriques\n", "a=2 #par exemple !! Donc à adapter...\n", "b=1\n", "c=1\n", "d=3" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l'équation étudiée est du type : 2 A + 1 B --> 1 C + 3 D\n" ] } ], "source": [ "# Affichage de l'équation de la réaction\n", "print(\"l'équation étudiée est du type : \",\n", " a,\" A + \",b,\" B --> \",\n", " c,\" C + \",d,\" D\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Entrée des valeurs de quantités de matière initiales**\n", "\n", "Les valeurs des quantités de matière initiales des réactifs et des produits (exprimées en mole) seront stockées dans des variables notées n_0\n", "\n", "(ex : nA_0 pour l'espèce chimique A).\n", "\n", "Sur le modèle de la ligne de code 2, ajouter les lignes de code nécessaires (lignes 3, 4 et 5) pour entrer les quantités de matière initiales des autres espèces chimiques en jeu.\n", "Attention de bien entrer les valeurs en mol! Vous pourrez par exemple taper 2.5e-3 pour 2,5 mmol" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Quantités de matières initiales\n", "nA_0 = 2.5e-3 #par exemple !!\n", "nB_0 = 5e-3\n", "nC_0 = 0\n", "nD_0 = 0" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "#Initialisation des variables\n", "\n", "# Initialisation de la chaine de caractère correspondant \n", "# au réactif limitant\n", "Rlimitant ='' \n", "\n", "# Avancement initial\n", "x=0 \n", "\n", "# Pas d'avancement (on augmentera progressivement x de la valeur dx)\n", "dx=0.001 \n", "\n", "# Création des listes contenant les quantités de matière \n", "# et initialisation de ces listes avec la valeur initiale \n", "nA=[nA_0] \n", "nB=[nB_0] \n", "nC=[nC_0]\n", "nD=[nD_0]\n", "\n", "# Création et nitialisation de la liste contenant l'avancement\n", "X=[x] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Calculs des quantités de matière en cours d'avancement**\n", "\n", "Sur le modèle de la ligne 5, écrire les lignes de code 6, 7 et 8 permettant de calculer les quantités de matière du\n", " réactif B, ainsi que des produits C et D.\n", " \n", " NOTE CODAGE : l'instruction \"nA.append(nA_0-a*x)\" permet d'ajouter la valeur indiquée entre parenthèses à la fin de la liste nA.\n", " \n", " **Détermination du réactif limitant**\n", "\n", "Compléter les tests des lignes de code 11 et 12 en choisissant parmi : <0, <=0, >0 et >=0.\n", "\n", "Compléter la ligne de code 13 en choisissant l'opérateur logique adéquat parmi : and (ET logique) et or (OU logique).\n", "\n", "NOTE CODAGE : l'indice -1 permet d'avoir accès à la dernière valeur de la liste.\n", "\n", "**Affichage du nom du réactif limitant et de l'avancement maximal**\n", "\n", "Il sera intéressant de modifier en ligne 18, le nombre de chiffres après la virgule afin de respecter le nombre de chiffres significatifs pour l'avancement x." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le réactif limitant est A \n", " Avancement maximum : 0.0020 mol\n" ] } ], "source": [ "# Calculs des quantités de matière en cours d'avancement\n", "while nA[-1] > 0 and nB[-1] > 0 :\n", " x=x+dx\n", " X.append(x)\n", " nA.append(nA_0-a*x)\n", " nB.append(nB_0-b*x)\n", " nC.append(nC_0+c*x) \n", " nD.append(nD_0+d*x) \n", "\n", "#Détermination du réactif limitant \n", "if nA[-1] <=0 : Rlimitant = 'A'\n", "if nB[-1] <=0 : Rlimitant = 'B'\n", "if nA[-1] <=0 and nB[-1]<=0 : \n", " Rlimitant='A et B : le mélange est stoechiometrique'\n", " \n", "#Affichage des résultats\n", "print('Le réactif limitant est ',Rlimitant,\n", " '\\n Avancement maximum : ','{0:.4f}'.format(x),\n", " 'mol' )\n", "#{0:.4f} permet d’afficher un nombre arrondi à \n", "# 4 chiffres après la virgule (ici)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Affichage des courbes permettant de suivre l'évolution des quantités de matière**\n", "\n", "La ligne de code 2 ci-dessous permet d'afficher le graphe de l'évolution de la quantité de matière de A en fonction de l'avancement X. \n", "\n", "Compléter les lignes 3, 4 et 5 pour afficher les courbes correspondant aux évolutions des quantités de matière des espèces chimiques B, C et D en fonction de l'avancement.\n", "\n", "NOTE CODAGE : la commande plt.plot peut être enrichie de divers arguments (comme ici avec r- = r pour red et - pour ligne) :\n", " \n", "•\tCouleur : r (red), k (black), b (blue), y (yellow), g (green)\n", "\n", "•\tMarqueur : o (gros point), - (ligne), . (pointillé), x (croix), s (square), v (triangle)\n", "\n", "• lw signifie linewidth (largeur de la ligne)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,10))\n", "plt.plot(X,nA,'r-',lw=1,label='nA')\n", "plt.plot(X,nB,'g-',lw=1,label='nB') \n", "plt.plot(X,nC,'b-',lw=1,label='nC') \n", "plt.plot(X,nD,'k-',lw=1,label='nD') \n", "plt.grid()\n", "plt.xlabel('x (mol)')\n", "plt.ylabel('n (mol)')\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Modélisation des droites obtenues**\n", "\n", "Les lignes de code suivantes vont permettre de modéliser chacune des 4 droites obtenues sur le graphe ci-dessus.\n", "Ces droites sont de type linéaire ou affine et peuvent être modélisées avec un polynôme de degré 1 : mx+p (où x est à la puissance 1).\n", "Les résultats des quatre modélisations sont ensuite affichés pour analyse.\n", "\n", "Compléter les lignes de code 2, 3 et 4 (sur le modèle de la ligne 1) pour modéliser les courbes concernant l'évolution des quantités de matière des espèces chimiques B, C et D.\n", "\n", "NOTE CODAGE : .0f en lignes 8, 13, 18 et 23 signifie qu'il y aura 1 seul chiffre significatif (pas de chiffre après la virgule), alors que .3f en lignes 9, 14, 19 et 24 signifie qu'il y aura 3 chiffres après la virgule." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "la droite représentant l'évolution de nA en fonction de x a pour équation : nA = -2 x + 0.002\n", "la droite représentant l'évolution de nB en fonction de x a pour équation : nB = -1 x + 0.005\n", "la droite représentant l'évolution de nC en fonction de x a pour équation : nC = 1 x + 0.000\n", "la droite représentant l'évolution de nD en fonction de x a pour équation : nD = 3 x + 0.000\n" ] } ], "source": [ "Amodel=np.polyfit(X, nA,1)\n", "Bmodel=np.polyfit(X, nB,1)\n", "Cmodel=np.polyfit(X, nC,1)\n", "Dmodel=np.polyfit(X, nD,1)\n", "\n", "print (\"la droite représentant l'évolution de nA\" \n", " \" en fonction de x a pour équation : nA = \",\n", " '{0:.0f}'.format(Amodel[0]),'{:5}'.format(\"x +\"),\n", " \"{0:.3f}\".format(Amodel[1]) )\n", "\n", "print (\"la droite représentant l'évolution de nB\"\n", " \" en fonction de x a pour équation : nB = \",\n", " \"{0:.0f}\".format(Bmodel[0]),'{:5}'.format(\"x +\"),\n", " \"{0:.3f}\".format(Bmodel[1]) )\n", "\n", "print (\"la droite représentant l'évolution de nC\"\n", " \" en fonction de x a pour équation : nC = \",\n", " \"{0:.0f}\".format(Cmodel[0]),'{:5}'.format(\"x +\"),\n", " \"{0:.3f}\".format(Cmodel[1]) )\n", "\n", "print (\"la droite représentant l'évolution de nD\"\n", " \" en fonction de x a pour équation : nD = \",\n", " \"{0:.0f}\".format(Dmodel[0]),'{:5}'.format(\"x +\"),\n", " \"{0:.3f}\".format(Dmodel[1])) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Commenter les équations des courbes modélisées...." ] } ], "metadata": { "celltoolbar": "Format de la Cellule Texte Brut", "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": "3.6.9" } }, "nbformat": 4, "nbformat_minor": 4 }