{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beispiel 4.2: Berechnung von Gleichgewichtskonstanten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_(Dieses Beispiel finden Sie im Lehrbuch auf Seite 42)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In diesem Berechnungsbeispiel sollen exemplarisch die thermodynamische Gleichgewichtskonstante K$°$ sowie die spezielle Gleichgewichtskonstante K$_{x}$ für die Ammoniaksynthese berechnet werden. Die erforderlichen Gleichungen (4.1) bis (4.15) sind auf den Seiten 37 bis 43 beschrieben."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Laden der benötigten Pakete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Innerhalb dieses Beispiels werden die Pakete _numpy_ und _matplotlib.pyplot_ benötigt. Während _numpy_ wichtige Rechenoperationen und Funktionalitäten bereitstellt, werden mit Hilfe des Pakets _matplotlib.pyplot_ die Ergebnisse grafisch dargestellt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition der Arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zuerst müssen die jeweiligen Arrays initialisiert werden.\n",
    "Die Komponenten Stickstoff (N<sub>2</sub>), Wasserstoff (H<sub>2</sub>) und Ammoniak (NH<sub>3</sub>) werden mit den Indizes 0, 1 und 2 beschrieben."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "comps = np.linspace(0,2,3) # 0: N_2, 1: H_2, 2: NH_3\n",
    "nu = np.array((comps))\n",
    "cp = np.array((comps))\n",
    "H_f = np.array((comps))\n",
    "S_g = np.array((comps))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition der Konstanten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stöchiometrische Koeffizienten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die stöchiometrischen Koeffizienten $\\nu_i$ können in der Reaktionsgleichung (Tabelle 4.1, Seite 38) abgelesen werden:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "N<sub>2</sub> + 3 H<sub>2</sub> ⇌ 2 NH<sub>3</sub>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Während die stöchiometrischen Koeffizienten der Edukte ein negatives Vorzeichen erhalten, ist das Vorzeichen der Produkte positiv."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "nu[0] = -1 # N_2\n",
    "nu[1] = -3 # H_2\n",
    "nu[2] = 2 # NH_3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Thermodynamische Daten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Außerdem werden für die Berechnung einige thermodynamische Daten benötigt, welche folgender Quelle entnommen wurden: https://webbook.nist.gov/chemistry (2017)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Wärmekapazität $c_\\mathrm{p}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cp[0] = 29.1 # J / (mol * K)\n",
    "cp[1] = 28.8 # J / (mol * K)\n",
    "cp[2] = 35.1 # J / (mol * K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Bildungsenthalpie $H_\\mathrm{f}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "H_f[0] = 0 # J / (mol)\n",
    "H_f[1] = 0 # J / (mol)\n",
    "H_f[2] = -45.9e3 # J / (mol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Entropie $S$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "S_g[0] = 191.61 # J / (mol * K)\n",
    "S_g[1] = 130.68 # J / (mol * K)\n",
    "S_g[2] =  192.77 # J / (mol * K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Referenztemperatur T$°$, Referenzdruck p$°$ sowie universelle Gaskonstante R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_S = 298.15 # K\n",
    "p_S = 1e5 # Pa\n",
    "R = 8.314 # J (mol * K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Berechnung der Reaktionsdaten"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Wärmekapazität $\\Delta_\\mathrm{R}c_\\mathrm{p}$ (Gleichung 4.5), die Reaktionsenthalpie $\\Delta_\\mathrm{R}H$ (Gleichung 4.1) und die Reaktionsentropie $\\Delta_\\mathrm{R}S$ (Gleichung 4.11) unter Standardbedingungen sind das Skalarprodukt (_numpy_-Funktion _np.dot()_) aus den thermodynamischen Daten und den stöchiometrischen Koeffizienten."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Delta_R_cp_S = np.dot(nu,cp) # J / (mol * K)\n",
    "Delta_R_H_S = np.dot(nu,H_f) # J / (mol * K)\n",
    "Delta_R_S_S = np.dot(nu,S_g) # J / (mol * K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " ---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition der temperaturabhängigen Funktionen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im weiteren Verlauf müssen die temperaturabhängigen Funktionen für die Reaktionsenthalpie $\\Delta_\\mathrm{R}H(T)$ (Gleichung 4.4), die Reaktionsentropie $\\Delta_\\mathrm{R}S(T)$ (Gleichung 4.10) sowie für die freie Reaktionsenthalpie $\\Delta_\\mathrm{R}G(T)$ (Gleichung 4.9) definiert werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Delta_R_H(T):\n",
    "    return (Delta_R_H_S + Delta_R_cp_S * (T - T_S))\n",
    "\n",
    "def Delta_R_S(T):\n",
    "    return (Delta_R_S_S + Delta_R_cp_S * np.log(T/T_S))\n",
    "\n",
    "def Delta_R_G(T):\n",
    "    return( Delta_R_H(T) - T * Delta_R_S(T))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definition der Gleichgewichtskonstanten "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die thermodynamische Gleichgewichtskonstante K$°$ berechnet sich nach Gleichung (4.12)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def K_S(T):\n",
    "    return( np.exp( -Delta_R_G(T) / (R * T) ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für die spezielle Gleichgewichtskonstante K$_{x}$ kann Gleichung (4.14) herangezogen werden."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def K_x(T,p):\n",
    "    return (K_S(T) * (p/p_S)**2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Darstellung der Ergebnisse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Für die Darstellung der Ergebnisse muss der zu betrachtende Temperaturbereich $T_\\mathrm{range}$ angegeben werden. In diesem Fall ist dies der Bereich von 400 °C bis 500 °C in Schritten von 1 K."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "T_range = np.linspace(273.15 + 400, 273.15 + 500, 101) # K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Anschließend werden die Funktionen über die Temperatur mit Hilfe des Pakets _matplotlib.pyplot_ dargestellt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12,5))\n",
    "\n",
    "ax1.plot(T_range-273.15, K_S(T_range)*1e4, color='black')\n",
    "ax1.set_xlabel(\"T / °C\")\n",
    "ax1.set_ylabel(\"K$°$ $\\cdot 10^{4}$ / 1\")\n",
    "ax1.set_xlim(400,500)\n",
    "ax1.set_ylim(0,1.5)\n",
    "\n",
    "ax2.plot(T_range-273.15, K_x(T_range, 100e5), color='black', linestyle='dotted', label=\"100 bar\")\n",
    "ax2.plot(T_range-273.15, K_x(T_range, 200e5), color='black', linestyle='dashed', label=\"200 bar\")\n",
    "ax2.plot(T_range-273.15, K_x(T_range, 300e5), color='black', linestyle='solid', label=\"300 bar\")\n",
    "ax2.set_xlabel(\"T / °C\")\n",
    "ax2.set_ylabel(\"K$_{x}$ / 1\")\n",
    "ax2.set_xlim(400,500)\n",
    "ax2.set_ylim(0,14)\n",
    "ax2.legend(loc='best')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die Berechnungsergebnisse sind ebenfalls im Lehrbuch in Abbildung 4.1 auf Seite 43 dargestellt."
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}