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  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Interactive Langmuir Adsorption Model (with Python!)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Daniel Domene ( [CAChemE](http://cacheme.org))\n",
      "- CC By 4.0"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "*English version is on the way* :)\n",
      "\n",
      "Un Widget interactivo es una herramienta que proporcionan muchos softwares de programaci\u00f3n y que tiene como finalidad facilitar la observaci\u00f3n de resultados. \n",
      "Uno de los m\u00e1s destacados y sencillos de implementar son los de tipo slider, como por ejemplo:"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "![img](langmuir-two-components.gif)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Interesante, \u00bfno?\n",
      "\n",
      "Pues este ejemplo est\u00e1 hecho con c\u00f3digo que aparece al final de este Notebook ;)\n",
      "\n",
      "### \u00bfPor qu\u00e9 widgets?\n",
      "\n",
      "Imaginemos que necesitamos que un cierto proceso viene definido por una funci\u00f3n cualquiera, y que esta funci\u00f3n depende de uno o m\u00e1s par\u00e1metros. En muchos casos estaremos interesados en ver qu\u00e9 ocurre con la funci\u00f3n si modificamos esos par\u00e1metros, algo que en muchas ocasiones no es trivial. \n",
      "\n",
      "Lo m\u00e1s com\u00fan es representar la funci\u00f3n e ir variando en nuestro c\u00f3digo los valores de los par\u00e1metros para observar que ocurre, pero esta puede llegar a ser una tarea muy tediosa y aburrida, y generalmente se tienen que repetir las representaciones para llegar a establecer conclusiones. En este sentido, todo ser\u00eda m\u00e1s f\u00e1cil si introdujeramos widget que permitiria, tan solo moviendo una barrita, variar el valor de los par\u00e1metros y observar las variaciones de manera simult\u00e1nea.\n",
      "\n",
      "Si opinas lo mismo est\u00e1s en el lugar adecuado, pues vamos a ense\u00f1ar c\u00f3mo se hace y aplicarlo a alg\u00fan ejemplo como el de arriba."
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Comenzamos..."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "En primer lugar tenemos que cargar las bibliotecas que vamos a necesitar:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline \n",
      "\n",
      "import numpy as np \n",
      "import matplotlib.pyplot as plt \n",
      "from IPython.html.widgets import interact"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u00bfQu\u00e9 acabamos de hacer aqu\u00ed arriba? Primero, hemos configurado las gr\u00e1ficas para que aparezcan dentro de este Notebook. Con los dos comandos siguientes hemos importado la bibliotecas NumPy y Matplotlib. Finalmente importamos tambi\u00e9n la biblioteca que nos permitir\u00e1 utilizar los widgets interactivos. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a ver en qu\u00e9 consiste el widget que vamos a implementar con el comando interact. Primero, creamos una funci\u00f3n muy sencilla que imprime la variable de entrada por pantalla:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# En Python los comentarios se escriben con una almohadilla\n",
      "\n",
      "def funcion_prueba(x):\n",
      "    print(x) \n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Ahora s\u00f3lo tenemos hacer uso de `interact` para que nos cree un slider y as\u00ed poder cambiar el valor de x con el rat\u00f3n con el intervalo que especifiquemos."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(funcion_prueba, x=(1,10,1))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "5\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<function __main__.funcion_prueba>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "<div class=\"alert alert-info\">Nota: Para poder ver los sliders debes de ejecutar este notebook en tu ordenador o en la nube. Si es la primera vez que utilizas Python, dale un vistazo a nuestro [curso online grautito](http://cacheme.org/curso-online-python-cientifico-ingenieros/).<div>"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Con este ejemplo sencillo queda m\u00e1s claro c\u00f3mo se ha de implementar el comando interact. En primer lugar se ha de definir una funci\u00f3n, que podr\u00e1 tener diferentes par\u00e1metros, en este caso 'x', y se establece lo que la funci\u00f3n tiene que hacer, en este caso solo mostrar el valor de la variable. \n",
      "Cuando llamamos al comando interact le indicamos que funci\u00f3n queremos que haga referencia y le damos el rango de valores que pueden adoptar los par\u00e1metros, en este caso, x var\u00eda de 1 a 10 de uno en uno dependiendo d\u00f3nde coloquemos la barrita. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Ejemplos interactivos "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Generalmente, cuando se est\u00e1 realizando alg\u00fan tipo de trabajo cient\u00edfico lo que m\u00e1s interesa es observar los datos, y la mejor manera es en gr\u00e1ficas. Si implementamos este tipo de widgets a nuestras representaciones, la dotaremos de una vistosidad y versatilidad como nunca. Vamos con unos ejemplos. "
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Isoterma de adsorci\u00f3n ( [Wikipedia](http://http://es.wikipedia.org/wiki/Isoterma_de_adsorci%C3%B3n))"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Una isoterma de adsorci\u00f3n (tambi\u00e9n llamada isoterma de sorci\u00f3n) describe el equilibrio de la adsorci\u00f3n de un material en una superficie (de modo m\u00e1s general sobre una superficie l\u00edmite) a temperatura constante. Representa la cantidad de material unido a la superficie [...] como una funci\u00f3n del material presente en la fase gas o en la disoluci\u00f3n. Las isotermas de adsorci\u00f3n se usan con frecuencia como modelos experimentales,1 que no hacen afirmaciones sobre los mecanismos subyacentes y las variables medidas. Se obtienen a partir de datos de medida por medio de an\u00e1lisis de regresi\u00f3n."
     ]
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Isoterma de Langmuir"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Esta Isoterma tiene como par\u00e1metros:\n",
      "* $\\theta_a$: Como la fracci\u00f3n de huecos ocupada por A.\n",
      "* K: Como el coeficiente de reacci\u00f3n\n",
      "* Pa: Como la presi\u00f3n parcial del componete A. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Presenta la siguiente forma: "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ \\theta_a = \\frac{Kp_a}{(1+Kp_a)} $$"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Vamos a implementar la ecuaci\u00f3n anterior dentro de una funci\u00f3n que llamaremos Isoterma_1, y tan solo variaremos el el coeficiente de reacci\u00f3n para estudiar su influencia en la adsorci\u00f3n. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Isoterma_1(K):\n",
      "\n",
      "    #Hacemos un vector para la presi\u00f3n parcial de A. Este rango corresponde con el eje de adcisas. \n",
      "    Pa = np.linspace(0,1,1000)\n",
      "    #Definimos la ecuaci\u00f3n de la Isoterma.\n",
      "    Theta_a = (K*Pa) / (1 + K*Pa)           \n",
      "    #Escribimos los comandos necesarios para represetar los resultados. \n",
      "    #Con este primer comando delimitamos el tama\u00f1o de la figura. \n",
      "    fig = plt.figure(figsize=(10, 8))\n",
      "    #Representa el vector de theta frente al de presiones parciales. \n",
      "    plt.plot(Pa,Theta_a) \n",
      "    #Establece los l\u00edmites de la figura. \n",
      "    plt.ylim([0,1])\n",
      "    plt.xlim([0,0.1])\n",
      "    #Coloca el t\u00edtulo de los ejes y a la figura. \n",
      "    plt.ylabel('theta')\n",
      "    plt.xlabel(u'Presi\u00f3n parcial de A')\n",
      "    plt.title(u'REPRESENTACI\u00d3N DE LA ISOTERMA DE LANGMUIR')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(Isoterma_1, K = (0,150,0.5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<function __main__.Isoterma_1>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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GNDWEWbPgP/4DJk6Ee++FtdcuuyJJkmrHLk7VvQkTYPvtYcUV4c47DWeSpOZn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\n0CRJkuqMAU2SJKnOGNAkSZLqzP8HiBT5+KRNxIIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6b2b320>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Si quieres cambiar el estilo de las figuras\n",
      "# con este comando (opcional) puedes hacerlo\n",
      "\n",
      "plt.style.use('ggplot')\n",
      "\n",
      "# Para ver otros estilos disponibles puedes usar:\n",
      "# print plt.style.available"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pero, si es una isoterma, \u00bfd\u00f3nde aparece la temperatura? El coeficiente de reacci\u00f3n depende de otros factores que no considera directamente la expresi\u00f3n implementada. Este coeficiente se rige por la ecuaci\u00f3n de Arrenious: \n",
      "$$\\ K = k_0e^{\\frac{-E_a}{RT}} $$\n",
      "\n",
      "\u00a1Implement\u00e9moslo!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Isoterma_1(T,Ea):\n",
      "\n",
      "    #Hacemos un vector para la presi\u00f3n parcial de A. Este rango corresponde con el eje de adcisas. \n",
      "    Pa = np.linspace(0,1,1000)\n",
      "    #Coeficiente R\n",
      "    R = 8.314 \n",
      "    #Coeficiente preexponencial\n",
      "    ko = 10**(-8) \n",
      "    #Definimos K\n",
      "    K = ko*np.exp(-(Ea*1000)/(R*T))\n",
      "    #Definimos la ecuaci\u00f3n de la Isoterma.\n",
      "    Theta_a = (K*Pa) / (1 + K*Pa)           \n",
      "    #Escribimos los comandos necesarios para represetar los resultados. \n",
      "    #Con este primer comando delimitamos el tama\u00f1o de la figura. \n",
      "    fig = plt.figure(figsize=(10, 8))\n",
      "    #Representa el vector de theta frente al de presiones parciales. \n",
      "    plt.plot(Pa,Theta_a) \n",
      "    #Establece los l\u00edmites de la figura. \n",
      "    plt.ylim([0,1.5])\n",
      "    plt.xlim([0,0.1])\n",
      "    \n",
      "    #Coloca el t\u00edtulo de los ejes y a la figura. \n",
      "    plt.ylabel('theta')\n",
      "    plt.xlabel(u'Presi\u00f3n parcial de A')\n",
      "    plt.title(u'REPRESENTACI\u00d3N DE LA ISOTERMA DE LANGMUIR')\n",
      "    \n",
      "    # Cambiamos el estilo por defecto (opcional)\n",
      "    plt.style.use('ggplot')\n",
      "    \n",
      "interact(Isoterma_1, T = (0,500,0.5), Ea = (-100,0,0.5) ) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<function __main__.Isoterma_1>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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nxzuckZGhjIyMFi3fbsKby+VScXGxd7y4uFgul8uvXUMfsry8vM3rQ9twOp30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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6bc7780>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Isoterma para adsorci\u00f3n de dos componentes"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Puede darse el caso que necesitemos variar m\u00e1s de dos par\u00e1metros, en ese caso simplemente tenemos que ir a\u00f1adiendolos al c\u00f3digo. \n",
      "Vamos a ver otro tipo de isoterma, cuando la adsorci\u00f3n se da para dos componentes. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ \\theta_a = \\frac{K_ap_a}{(1 + K_ap_a + K_bp_b)} $$ "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Isoterma_2(T,Ea_a,Ea_b):\n",
      "\n",
      "    #Coeficiente R (gases ideales) \n",
      "    R = 8.314\n",
      "    #Coeficiente prexponencial\n",
      "    ko = 10**(-8) \n",
      "    #Definimos el coeficiente para el componente A\n",
      "    Ka = ko*np.exp(-(Ea_a*1000)/(R*T))\n",
      "    #Definimos el coeficiente para el componente B \n",
      "    Kb = ko*np.exp(-(Ea_b*1000)/(R*T))\n",
      "    #Vectores de presiones parciales y presi\u00f3n total\n",
      "    Pa = np.linspace(0,.25,1000)\n",
      "    Pb = np.linspace(0,.25,1000)\n",
      "    P = Pa + Pb\n",
      "    \n",
      "    #Funci\u00f3n para representar la adsorci\u00f3n de A \n",
      "    Ocupacion_a = (Ka*Pa) / (1 + Ka*Pa + Kb*Pb )\n",
      "    #Funci\u00f3n para representar la adsorci\u00f3n de B\n",
      "    Ocupacion_b = (Kb*Pb) / (1 + Ka*Pa + Kb*Pb )\n",
      "    \n",
      "    \n",
      "    plt.figure(figsize=(8, 5))\n",
      "    \n",
      "    plt.plot(P,Ocupacion_a, label='A component')\n",
      "    plt.plot(P,Ocupacion_b, label='B component')\n",
      "    \n",
      "    plt.ylim([0,1])\n",
      "    plt.xlim([0,0.1])\n",
      "    \n",
      "    plt.ylabel(r'$\\theta$ / fractional-occupancy')\n",
      "    plt.xlabel(u'$ p_A $ / Partial pressure ')\n",
      "    plt.title(u'Langmuir adsorption model for two components')\n",
      "    plt.legend()\n",
      "    \n",
      "    # Cambiamos el estilo por defecto (opcional)\n",
      "    plt.style.use('ggplot')\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$ \\theta_a = \\frac{K_ap_a}{(1 + K_ap_a + K_bp_b)} $ ; $ K = k_0e^{\\frac{-E_a}{RT}} $"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "interact(Isoterma_2, T = (0.5,500,0.5), Ea_a = (-100,0,0.5), Ea_b = (-100,0,0.5) )  "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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E7HWEEJgyZQrs7OxQUFAALy8vbN68uca6aWlpOHDgABISEuDm5gbAeA4CAOzY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\nRGBBQERERAD+DxwkbBXuet+EAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x6b42e48>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 4,
     "metadata": {},
     "source": [
      "Adsorci\u00f3n disociativa "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Con este ejemplo se ve c\u00f3mo de \u00fatil puede ser este tipo de widgets, ya que permite comparar funciones y ver de qu\u00e9 manera afectan los factores a las mismas. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "$$ \\theta_a = \\frac{(Kp_a)^{1/2}}{(1+(Kp_a)^{1/2})} $$"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def Isoterma_3(K_1 , K_2 , K_3):\n",
      "\n",
      "    #Coeficiente R (gases ideales) en kJ/molK\n",
      "    R = 8.31*(10**(-3))\n",
      "\n",
      "    #Coeficiente de equilibrio de adsorci\u00f3n de A\n",
      "    #K_a = k_0*np.exp(E_a/(R*T))\n",
      "    #K_1 = 100\n",
      "    #K_2 = 10\n",
      "    #K_3 = 1\n",
      "    \n",
      "    #Hacemos un vector para la presi\u00f3n de A \n",
      "    P = np.linspace(0,1,1000)\n",
      "\n",
      "    Ocupacion_1 = ((K_1*P)**(0.5)) / (1 + ((K_1*P)**(0.5)))\n",
      "    Ocupacion_2 = ((K_2*P)**(0.5)) / (1 + ((K_2*P)**(0.5)))\n",
      "    Ocupacion_3 = (K_3*P) / (1 + K_3*P)\n",
      "    \n",
      "    fig = plt.figure(figsize=(10, 6))\n",
      "    plt.plot(P,Ocupacion_1)\n",
      "    plt.plot(P,Ocupacion_2)\n",
      "    plt.plot(P,Ocupacion_3)\n",
      "    plt.ylim([0,1])\n",
      "    plt.xlim([0,0.1])\n",
      "    \n",
      "    plt.ylabel(r'$\\theta$ / fractional-occupancy')\n",
      "    plt.xlabel(u'$ p_A $ / Partial pressure $')\n",
      "    \n",
      "    # Cambiamos el estilo por defecto (opcional)\n",
      "    plt.style.use('ggplot')\n",
      "    \n",
      "interact(Isoterma_3, K_1 = (0,150,0.5) , K_2 = (0,150,0.5), K_3 = (0,150,0.5))\n",
      "   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<function __main__.Isoterma_3>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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qA5atssLpFrnuGxFRllLJFGKRCGKROGLROOLxJGIJCbFkGnFJRiylIJ7WEFM0xFUBcVVA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\nTWhu60VLfxwtCR1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eOm7FPMh44qYSTJ25NN/lERER0TCMOMx96UtfymUdNM40TcOxQ3H0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\nGcHBwQBaw9KwYcM6LBsREYGoqChs3Lixw/MxMTFYsmQJgNZ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       "text": [
        "<matplotlib.figure.Figure at 0x6d106d8>"
       ]
      }
     ],
     "prompt_number": 10
    }
   ],
   "metadata": {}
  }
 ]
}