{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyN2QZshBvkiXCFbtc9+9k6b"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"mpgaFvrX0KNZ"},"source":["Résolution de l'équation pour trouver l'avancement volumique.\n","\n"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":467},"id":"gQgWE6ucjK-J","executionInfo":{"status":"ok","timestamp":1758885251339,"user_tz":-120,"elapsed":170,"user":{"displayName":"Lionnel Malara","userId":"16342666118884215780"}},"outputId":"f81f4374-c2bf-4809-9f7f-e6a79dc35e3b"},"source":["import matplotlib.pyplot as plt #Pour tracer des graphiques\n","import numpy as np #Pour faire divers calculs\n","import scipy.optimize as op #Importe la bibliothèque pour faire la résolution d'équation\n","\n","\n","## Constantes d'equilibre réaction\n","\n","K=10**(-1) # valeur de K\n","C_0=10**(-2) # valeur de C0 en mol/L\n","\n","## Quotient reactionnel réaction\n","\n","def Qr(ksi):\n","    # Renvoie la valeur du quotient reactionnel de la reaction pour l'avancements ksi\n","\n","    return (ksi**2)/(C_0-ksi)\n","\n","## Définition de la fonction Qr - K\n","\n","def f(ksi): # chercher l'avancement a l'equilibre revient a chercher le zero de Qr - K\n","\n","    return Qr(ksi)-K\n","\n","## Tracé de f(ksi)\n","n=100 #nombre de points de l'intervalle 0;C0\n","ksi_i=C_0/n #premier point\n","ksi_f=C_0*(n-1)/n #dernier point\n","ksi = np.linspace(ksi_i,ksi_f,n) # liste des avancements entre 0 et C0\n","\n","y = []# initialisation du np.array des y\n","\n","for x in ksi: #boucle de calcul des y sur l'intervale de ksi\n","\n","    y.append(f(x))# calcul et stockage de la fonction Qr-K dans y\n","\n","plt.figure(1)\n","plt.xlabel('ksi') #Légende de l’axe des abscisses\n","plt.ylabel('f') #Légende de l’axe des ordonnées\n","plt.plot(ksi,y,'b-',label='Qr(ksi)-K°') #Représente y en fonction de x en bleu de manière continue avec une étiquette pour la courbe\n","plt.legend() #Affiche l’étiquette de la courbe\n","plt.grid() #Affiche le quadrillage\n","plt.show() #Affiche le graphique\n","\n","## Determination de l'avancements à l'equilibre : resolution de l'equation\n","\n","def f(ksi):# chercher l'avancement a l'equilibre revient a chercher le zero de Qr - K\n","\n","    return Qr(ksi)-K\n","\n","ksi_eq=op.fsolve(f,C_0*0.8) #Résolution de la fonction f=0 avec la valeur initiale C0*0.8\n","\n","print('ksi_eq = ',ksi_eq[0]) #Affiche le résultat\n"],"execution_count":2,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["ksi_eq =  0.00916079783099616\n"]}]},{"cell_type":"markdown","metadata":{"id":"sUGwdWjN0thQ"},"source":["Tracé de l'évolution de l'avancement avec la concentration initiale"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":544},"id":"gDJf5X8i0s0n","executionInfo":{"status":"ok","timestamp":1758606279452,"user_tz":-120,"elapsed":180,"user":{"displayName":"Lionnel Malara","userId":"16342666118884215780"}},"outputId":"207881c3-cfba-4a4b-8af1-c927ca8de66c"},"source":["import matplotlib.pyplot as plt #Pour tracer des graphiques\n","import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import scipy.optimize as op #Importe la bibliothèque pour faire la résolution d'équation\n","\n","## Constantes d'equilibre réaction\n","\n","K=10**(-3) # valeur de K\n","\n","## Quotient reactionnel réaction\n","\n","def Qr(ksi):\n","    # Renvoie la valeur du quotient reactionnel de la reaction pour l'avancements ksi\n","\n","    return (ksi**2)/(C_0-ksi)\n","\n","## Définition de la fonction Qr - K\n","\n","def f(ksi): # chercher l'avancement a l'equilibre revient a chercher le zero de Qr - K\n","\n","    return Qr(ksi)-K\n","\n","## Trace de ksi=f(C_0)\n","n=[-1,-2,-3,-4,-5]\n","x=[] # initialisation de la liste de valeur de C0\n","y=[] # initialisation de la liste de valeur de tau_eq\n","z=[] # initialisation de la liste de valeur de ksi_eq\n","\n","for k in n: #boucle pour les différentes valeurs de n\n","\n","    C_0=10**k# calcul de C0\n","    x.append(C_0)# stockage des C0\n","    ksi_eq=op.fsolve(f,C_0*.99)# calcul de ksi_eq\n","    z.append(ksi_eq[0])# stockage des ksi_eq\n","    y.append(ksi_eq[0]/C_0*100)# stockage des tau_eq\n","\n","plt.figure(1)\n","plt.xlabel('n de $C_0=10^n$') #Légende de l’axe des abscisses\n","plt.ylabel('tau (%)') #Légende de l’axe des ordonnées\n","plt.plot(n,y,'b+',label='tau') #Représente y en fonction de x avec des croix bleues avec une étiquette pour la courbe\n","plt.legend() #Affiche l’étiquette de la courbe\n","plt.grid() #Affiche le quadrillage\n","plt.show() #Affiche le graphique\n","\n","for k in n: #boucle pour les différentes valeurs de n\n","    print('C0 = ',format(x[k],\"#.2e\"),'mol/L ; ','ksi_eq = ',format(z[k],\"#.2e\"),'mol/L ; ','tau_eq = ',format(y[k],\"#.1f\"), '%') #Affiche C0, ksi_eq et tau_eq\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["C0 =  1.00e-05 mol/L ;  ksi_eq =  9.90e-06 mol/L ;  tau_eq =  99.0 %\n","C0 =  1.00e-04 mol/L ;  ksi_eq =  9.16e-05 mol/L ;  tau_eq =  91.6 %\n","C0 =  1.00e-03 mol/L ;  ksi_eq =  6.18e-04 mol/L ;  tau_eq =  61.8 %\n","C0 =  1.00e-02 mol/L ;  ksi_eq =  2.70e-03 mol/L ;  tau_eq =  27.0 %\n","C0 =  1.00e-01 mol/L ;  ksi_eq =  9.51e-03 mol/L ;  tau_eq =  9.5 %\n"]}]},{"cell_type":"markdown","metadata":{"id":"BFcEe9jI8-vx"},"source":["Tracé de l'évolution de l'avancement en fonction de la constante d'équilibre"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":540},"id":"aVC1MuMy9DKR","executionInfo":{"status":"ok","timestamp":1758885314892,"user_tz":-120,"elapsed":354,"user":{"displayName":"Lionnel Malara","userId":"16342666118884215780"}},"outputId":"f5cdbe31-23b9-43b8-953f-877573c67f56"},"source":["import matplotlib.pyplot as plt #Pour tracer des graphiques\n","import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import scipy.optimize as op #Importe la bibliothèque pour faire la résolution d'équation\n","\n","\n","## Concentration initiale\n","\n","C_0=10**(-2) # valeur de C0 en mol/L\n","\n","## Quotient reactionnel réaction\n","\n","def Qr(ksi):\n","    # Renvoie la valeur du quotient reactionnel de la reaction pour l'avancements ksi\n","\n","    return (ksi**2)/(C_0-ksi)\n","\n","## Définition de la fonction Qr - K\n","\n","def f(ksi): # chercher l'avancement a l'equilibre revient a chercher le zero de Qr - K\n","\n","    return Qr(ksi)-K\n","\n","## Trace de ksi=f(K)\n","n=[-1,-2,-3,-4,-5]\n","x=[] # initialisation de la liste de valeur de K\n","y=[] # initialisation de la liste de valeur de tau_eq\n","z=[] # initialisation de la liste de valeur de ksi_eq\n","\n","for k in n: #boucle pour les différentes valeurs de n\n","\n","    K=10**k# calcul de K\n","    x.append(K)# stockage des K\n","    ksi_eq=op.fsolve(f,C_0*.99)# calcul de ksi_eq\n","    z.append(ksi_eq[0])# stockage des ksi_eq\n","    y.append(ksi_eq[0]/C_0*100)# stockage des tau_eq\n","\n","plt.figure(1)\n","plt.xlabel('n de $K=10^n$')#Légende de l’axe des abscisses\n","plt.ylabel('tau (%)') #Légende de l’axe des ordonnées\n","plt.plot(n,y,'b+',label='tau') #Représente y en fonction de x avec des croix bleues avec une étiquette pour la courbe\n","plt.legend() #Affiche l’étiquette de la courbe\n","plt.grid() #Affiche le quadrillage\n","plt.show() #Affiche le graphique\n","\n","for k in n: #boucle pour les différentes valeurs de n\n","   print('K = ',format(x[k],\"#.2e\"),'; ksi_eq = ',format(z[k],\"#.2e\"),'mol/L ; ','tau_eq = ',format(y[k],\"#.1f\"), '%') #Affiche C0, ksi_eq et tau_eq\n"],"execution_count":3,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 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\n"},"metadata":{}},{"output_type":"stream","name":"stdout","text":["K =  1.00e-05 ; ksi_eq =  3.11e-04 mol/L ;  tau_eq =  3.1 %\n","K =  1.00e-04 ; ksi_eq =  9.51e-04 mol/L ;  tau_eq =  9.5 %\n","K =  1.00e-03 ; ksi_eq =  2.70e-03 mol/L ;  tau_eq =  27.0 %\n","K =  1.00e-02 ; ksi_eq =  6.18e-03 mol/L ;  tau_eq =  61.8 %\n","K =  1.00e-01 ; ksi_eq =  9.16e-03 mol/L ;  tau_eq =  91.6 %\n"]}]},{"cell_type":"markdown","metadata":{"id":"3r_lYB9SJUdA"},"source":["Tracé de l'évolution de l'avancement avec la concentration initiale (fonction)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":284},"id":"7TFO2n_yJZ0p","executionInfo":{"status":"ok","timestamp":1631086173099,"user_tz":-120,"elapsed":292,"user":{"displayName":"Lionnel Malara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg9uzWMkTid1qlQikWp7IMi29R7n5oSh3ECmY5O=s64","userId":"16342666118884215780"}},"outputId":"a984c2c7-0fdc-49fd-84eb-03ec663cb430"},"source":["import matplotlib.pyplot as plt #Pour tracer des graphiques\n","import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import scipy.optimize as op #Importe la bibliothèque pour faire la résolution d'équation\n","\n","\n","## Constantes d'equilibre réaction\n","\n","K=10**(-3) # valeur de K\n","\n","## Quotient reactionnel réaction\n","\n","def Qr(ksi):\n","    # Renvoie la valeur du quotient reactionnel de la reaction pour l'avancements ksi\n","\n","    return (ksi**2)/(C_0-ksi)\n","\n","## Définition de la fonction Qr - K\n","\n","def f(ksi): # chercher l'avancement a l'equilibre revient a chercher le zero de Qr - K\n","\n","    return Qr(ksi)-K\n","\n","## Trace de ksi=f(C_0)\n","x=[] # initialisation de la liste de valeur de C0\n","y=[] # initialisation de la liste de valeur de tau_eq\n","z=[] # initialisation de la liste de valeur de ksi_eq\n","\n","n=list(np.arange(0,-6,-.3))\n","\n","for k in n: #boucle pour les différentes valeurs de n\n","    C_0=10**k# calcul de C0\n","    x.append(C_0)# stockage des C0\n","    ksi_eq=op.fsolve(f,C_0*.99)# calcul de ksi_eq\n","    z.append(ksi_eq[0])# stockage des ksi_eq\n","    y.append(ksi_eq[0]/C_0*100)# stockage des tau_eq\n","\n","plt.figure(1)\n","plt.xlabel('n de $C_0=10^n$')#Légende de l’axe des abscisses\n","plt.ylabel('tau (%)') #Légende de l’axe des ordonnées\n","plt.plot(n,y,'b+-',label='tau') #Représente y en fonction de x avec des crois bleues de manière continue avec une étiquette pour la courbe\n","plt.legend() #Affiche l’étiquette de la courbe\n","plt.grid() #Affiche le quadrillage\n","plt.show() #Affiche le graphique\n"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7tDC6AliMpBI"},"source":["Tracé de l'évolution de l'avancement en fonction de la constante d'équilibre (fonction)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":282},"id":"E_IJs57gMtGI","executionInfo":{"status":"ok","timestamp":1631086200464,"user_tz":-120,"elapsed":255,"user":{"displayName":"Lionnel Malara","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Gg9uzWMkTid1qlQikWp7IMi29R7n5oSh3ECmY5O=s64","userId":"16342666118884215780"}},"outputId":"c56aebb2-03a0-4566-8c66-57d721a9b661"},"source":["import matplotlib.pyplot as plt #Pour tracer des graphiques\n","import numpy as np\t#Importe la bibliothèque pour faire divers calculs\n","import scipy.optimize as op #Importe la bibliothèque pour faire la résolution d'équation\n","\n","\n","## Concentration initiale\n","\n","C_0=10**(-2) # valeur de C0 en mol/L\n","\n","## Quotient reactionnel réaction\n","\n","def Qr(ksi):\n","    # Renvoie la valeur du quotient reactionnel de la reaction pour l'avancements ksi\n","\n","    return (ksi**2)/(C_0-ksi)\n","\n","## Définition de la fonction Qr - K\n","\n","def f(ksi): # chercher l'avancement a l'equilibre revient a chercher le zero de Qr - K\n","\n","    return Qr(ksi)-K\n","\n","## Trace de ksi=f(K)\n","x=[] # initialisation de la liste de valeur de K\n","y=[] # initialisation de la liste de valeur de tau_eq\n","z=[] # initialisation de la liste de valeur de ksi_eq\n","\n","n=list(np.arange(0,-5,-.3))\n","\n","for k in n: #boucle pour les différentes valeurs de n\n","    K=10**k# calcul de K\n","    x.append(K)# stockage des K\n","    ksi_eq=op.fsolve(f,C_0*.99)# calcul de ksi_eq\n","    z.append(ksi_eq[0])# stockage des ksi_eq\n","    y.append(ksi_eq[0]/C_0*100)# stockage des tau_eq\n","\n","plt.figure(1)\n","plt.xlabel('n de $K=10^n$')#Légende de l’axe des abscisses\n","plt.ylabel('tau (%)') #Légende de l’axe des ordonnées\n","plt.plot(n,y,'b+-',label='tau') #Représente y en fonction de x avec des crois bleues de manière continue avec une étiquette pour la courbe\n","plt.legend() #Affiche l’étiquette de la courbe\n","plt.grid() #Affiche le quadrillage\n","plt.show() #Affiche le graphique"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]}]}