MITES_2022_Final_Project_Thermodynamics_&_Climate_Change.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0dM54caI5eOC",
        "outputId": "ad7f70f6-335d-4dd0-fb8d-21ed5b77741f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: coolprop in /usr/local/lib/python3.7/dist-packages (6.4.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.3.5)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2022.1)\n",
            "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (1.21.6)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/ptgodart/nasaPoly.git\n",
            "  Cloning https://github.com/ptgodart/nasaPoly.git to /tmp/pip-req-build-vguebc92\n",
            "  Running command git clone -q https://github.com/ptgodart/nasaPoly.git /tmp/pip-req-build-vguebc92\n"
          ]
        }
      ],
      "source": [
        "!pip3 install coolprop\n",
        "!pip3 install pandas\n",
        "!pip3 install --upgrade git+https://github.com/ptgodart/nasaPoly.git\n",
        "from CoolProp.constants import PT_INPUTS\n",
        "import CoolProp as cp\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import nasaPoly\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Anaerobic Sequencing Batch Reactor (ASBR) Cycle\n",
        "Modelling electrical energy and biogas yield"
      ],
      "metadata": {
        "id": "twHE9Y9wZSzV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7GNBjIzoZVEL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Efficiency Evaluation of Biogas Applications as Fuel\n",
        "A side-by-side comparison with existing nonrenewable fuel sources"
      ],
      "metadata": {
        "id": "Vl4kR5wdhmFD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Parameterization & Initialization\n",
        "\n",
        "Temperature = 298.15 # K\n",
        "\n",
        "Coal = nasaPoly.Species('C')\n",
        "O2 = nasaPoly.Species('O2')\n",
        "CO2 = nasaPoly.Species('CO2')\n",
        "C8H18 = nasaPoly.Species('C8H18(L),isooct')\n",
        "H2O_g = nasaPoly.Species('H2O')\n",
        "C3H8 = nasaPoly.Species('C3H8')\n",
        "CH4 = nasaPoly.Species('CH4')\n",
        "\n",
        "molar_energies = list()\n",
        "carbon_intensities = list()"
      ],
      "metadata": {
        "id": "NkSNKhxLoXQo"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Biogas combustion efficiency\n",
        "# combustion energy yield per one mole of biogas under constant pressure\n",
        "# ---------------------------------------------------\n",
        "# Based on a separation of species in biogas to 98% CH4, 2.0% CO2\n",
        "# 9.8 * CH4 + 0.2 * CO2 + 19.6 * O2 -> (9.8 + 0.2) * CO2 + 19.6 * H2O(g)\n",
        "h_products = 10 * CO2.h_0(Temperature) + 19.6 * H2O_g.h_0(Temperature) # J/mol\n",
        "h_reactants = 9.8 * CH4.h_0(Temperature) + 0.2 * CO2.h_0(Temperature) + 19.6 * 0 # J/mol\n",
        "q_p_CH4 = abs(h_products - h_reactants) / (9.8 * 1000) # kJ/mol --> LHV as gaseous H2O is \n",
        "print(f\"The energy released by the combustion of a 98% CH4 biogas sample is {q_p_CH4:.3f} kJ/mol.\")\n",
        "\n",
        "# Carbon intensity of C3H8 combustion\n",
        "# ---------------------------------------------------\n",
        "# I_CO2 = m_CO2 / q_p \n",
        "I_CO2_CH4 = 10 * CO2.molecular_wt / (q_p_CH4) # g_CO2 / k\n",
        "print(f\"The carbon intensity of a 98% CH4 biogas sample's combustion is {I_CO2_CH4:.3f} g of CO2/kJ.\\n\")\n",
        "\n",
        "# Coal combustion efficiency\n",
        "# combustion energy yield per one mole of coal under constant pressure\n",
        "# ---------------------------------------------------\n",
        "# C(s) + O2 -> CO2\n",
        "h_products = CO2.h_0(Temperature) # J/mol\n",
        "h_reactants = 0 + 0 # J/mol\n",
        "q_p_coal = abs(h_products - h_reactants) / 1000 # kJ/mol\n",
        "print(f\"The energy released by the combustion of coal is {q_p_coal:.3f} kJ/mol.\")\n",
        "\n",
        "# Carbon intensity of coal combustion\n",
        "# ---------------------------------------------------\n",
        "# I_CO2_coal = m_CO2 / q_p_coal \n",
        "I_CO2_coal = 1 * CO2.molecular_wt / q_p_coal # g_CO2 / kJ\n",
        "print(f\"The carbon intensity of coal combustion is {I_CO2_coal:.3f} g of CO2/kJ.\\n\")\n",
        "\n",
        "# Petrol (Octane) combustion efficiency\n",
        "# combustion energy yield per one mole of C8H18 under constant pressure\n",
        "# ---------------------------------------------------\n",
        "# 2 * C8H18 + 25 * O2 -> 16 * CO2 + 18 * H2O(g)\n",
        "h_products = 16 * CO2.h_0(Temperature) + 18 * H2O_g.h_0(Temperature) # J/mol\n",
        "h_reactants = 2 * C8H18.h_0(Temperature) + 25 * 0 # J/mol\n",
        "q_p_C8H18 = abs(h_products - h_reactants) / (2 * 1000) # kJ/mol --> LHV as gaseous H2O is produced\n",
        "print(f\"The energy released by the combustion of C8H18 is {q_p_C8H18:.3f} kJ/mol.\")\n",
        "\n",
        "# Carbon intensity of C8H18 combustion\n",
        "# ---------------------------------------------------\n",
        "# I_CO2 = m_CO2 / q_p \n",
        "I_CO2_C8H18 = 16 * CO2.molecular_wt / (q_p_C8H18) # g_CO2 / k\n",
        "print(f\"The carbon intensity of C8H18 combustion is {I_CO2_C8H18:.3f} g of CO2/kJ.\\n\")\n",
        "\n",
        "# Propane combustion efficiency\n",
        "# combustion energy yield per one mole of C3H8 under constant pressure\n",
        "# ---------------------------------------------------\n",
        "# C3H8(g) + 5 * O2 -> 3 * CO2 + 4 * H2O(g)\n",
        "h_products = 3 * CO2.h_0(Temperature) + 4 * H2O_g.h_0(Temperature) # J/mol\n",
        "h_reactants = C3H8.h_0(Temperature) + 5 * 0 # J/mol\n",
        "q_p_C3H8 = abs(h_products - h_reactants) / 1000 # kJ/mol --> LHV as gaseous H2O is produced\n",
        "print(f\"The energy released by the combustion of C8H18 is {q_p_C3H8:.3f} kJ/mol.\")\n",
        "\n",
        "# Carbon intensity of C3H8 combustion\n",
        "# ---------------------------------------------------\n",
        "# I_CO2 = m_CO2 / q_p \n",
        "I_CO2_C3H8 = 3 * CO2.molecular_wt / (q_p_C3H8) # g_CO2 / k\n",
        "print(f\"The carbon intensity of biogas combustion is {I_CO2_C3H8:.3f} g of CO2/kJ.\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LQmVV25ulydn",
        "outputId": "ec0854cc-7033-4eef-e865-a13cfb5c6025"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The energy released by the combustion of a 98% CH4 biogas sample is 802.561 kJ/mol.\n",
            "The carbon intensity of a 98% CH4 biogas sample's combustion is 0.548 g of CO2/kJ.\n",
            "\n",
            "The energy released by the combustion of coal is 393.510 kJ/mol.\n",
            "The carbon intensity of coal combustion is 0.112 g of CO2/kJ.\n",
            "\n",
            "The energy released by the combustion of C8H18 is 5065.348 kJ/mol.\n",
            "The carbon intensity of C8H18 combustion is 0.139 g of CO2/kJ.\n",
            "\n",
            "The energy released by the combustion of C8H18 is 2043.152 kJ/mol.\n",
            "The carbon intensity of biogas combustion is 0.065 g of CO2/kJ.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# graphing hehe\n",
        "import pandas as pd\n",
        "from io import StringIO\n",
        "\n",
        "energies = [q_p_coal, q_p_C8H18, q_p_C3H8, q_p_CH4]\n",
        "intensities = [I_CO2_coal, I_CO2_C8H18, I_CO2_C3H8, I_CO2_CH4]\n",
        "data_blob = StringIO(f'''    energies    intensities\n",
        "Coal    {energies[0]}    {intensities[0]}\n",
        "Petrol    {energies[1]}    {intensities[1]}\n",
        "Propane    {energies[2]}    {intensities[2]}\n",
        "Biogas    {energies[3]}    {intensities[3]}\n",
        "''')\n",
        "df = pd.read_csv(data_blob, index_col=0, delimiter=' ', skipinitialspace=True)\n",
        "\n",
        "fig = plt.figure() # Create matplotlib figure\n",
        "\n",
        "ax = fig.add_subplot(111) # Create matplotlib axes\n",
        "ax2 = ax.twinx() # Create another axes that shares the same x-axis as ax.\n",
        "\n",
        "width = 0.3\n",
        "\n",
        "df.energies.plot(kind='bar', color='red', ax=ax, width=width, position=1)\n",
        "df.intensities.plot(kind='bar', color='blue', ax=ax2, width=width, position=0)\n",
        "bars = [\"Molar energy release\", \"Carbon intensity\"]\n",
        "ax.set_ylabel('$q_p$ [kJ/mol]')\n",
        "ax2.set_ylabel('$I_{CO_2}$ [$g_{CO_2}$/kJ]')\n",
        "ax.legend([\"Molar energy release\"], bbox_to_anchor=(1.58, 0.85))\n",
        "ax2.legend([\"Carbon intensity\"], bbox_to_anchor=(1.2, 1))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "CFveTBy_x6ut",
        "outputId": "f5bb1e38-6315-4733-e349-0d7b94be1e03"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "MITES 2022 Final Project_Thermodynamics & Climate Change.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}