{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Supercritical water oxidation (SCWO)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import PFAS_SAT as ps\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Image\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_colwidth', 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model document\n",
    "\n",
    "SCWO systems use supercritical water (i.e., water above 373.946 C and 22.064 MPa) to facilitate the oxidation of PFAS or other hazardous substances in aqueous streams. SCWO systems are already in operation in Japan and Korea to manage PCBs and halogenated wastes, and the Chematur Engineering facility in the UK uses SCWO to recover metals from catalysts. Research into the use the SCWO for PFAS destruction is ongoing, and there are several different systems being developed. SCWO produces steam, water, and slurry outputs. The mineralized fluoride remains in the slurry. The destruction of PFAS is modeled using a destruction and removal efficiency (DRE). Any PFAS that is not destroyed or removed remains in the water. There may also be small amounts of PFAS volatilized in the steam and remaining in the slurry. The process model estimates the fraction of incoming water that goes to each stream, and the PFAS remaining in each. \n",
    "\n",
    "<img src=\"../Images/ProcessModels/SCWO.png\" alt=\"Drawing\" style=\"width: 700px;\"/>\n",
    "\n",
    "\n",
    "### Assumptions and Limitations:\n",
    "\n",
    "1.\tIt assumes that the destruction and removal efficiency remains constant for each PFAS.\n",
    "2.\tBy default, the model assumes that all the remaining PFAS is in the water stream. However, this is a user input, and the user can send PFAS to the steam or slurry streams as well. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Input Parameters for SCWO model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Dictonary_Name</th>\n",
       "      <th>Parameter Name</th>\n",
       "      <th>Parameter Description</th>\n",
       "      <th>amount</th>\n",
       "      <th>unit</th>\n",
       "      <th>Reference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFOA</td>\n",
       "      <td>DRE of PFOA</td>\n",
       "      <td>0.99260</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFOS</td>\n",
       "      <td>DRE of PFOS</td>\n",
       "      <td>0.99997</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFBA</td>\n",
       "      <td>DRE of PFBA</td>\n",
       "      <td>0.95043</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFPeA</td>\n",
       "      <td>DRE of PFPeA</td>\n",
       "      <td>0.94257</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFHxA</td>\n",
       "      <td>DRE of PFHxA</td>\n",
       "      <td>0.99843</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFHpA</td>\n",
       "      <td>DRE of PFHpA</td>\n",
       "      <td>0.96303</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFNA</td>\n",
       "      <td>DRE of PFNA</td>\n",
       "      <td>0.99260</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFDA</td>\n",
       "      <td>DRE of PFDA</td>\n",
       "      <td>0.99970</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFBS</td>\n",
       "      <td>DRE of PFBS</td>\n",
       "      <td>0.99950</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Total destruction and removal efficiency</td>\n",
       "      <td>DRE</td>\n",
       "      <td>PFHxS</td>\n",
       "      <td>DRE of PFHxS</td>\n",
       "      <td>0.99993</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>SCWO parameters</td>\n",
       "      <td>SCWO</td>\n",
       "      <td>frac_water_to_steam</td>\n",
       "      <td>Fraction of incoming water to steam</td>\n",
       "      <td>0.01000</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SCWO parameters</td>\n",
       "      <td>SCWO</td>\n",
       "      <td>frac_water_to_slurry</td>\n",
       "      <td>Fraction of incoming water to slurry</td>\n",
       "      <td>0.10000</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SCWO parameters</td>\n",
       "      <td>SCWO</td>\n",
       "      <td>frac_PFAS_to_steam</td>\n",
       "      <td>Fraction of destroyed and removed that remains in steam</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SCWO parameters</td>\n",
       "      <td>SCWO</td>\n",
       "      <td>frac_PFAS_to_slurry</td>\n",
       "      <td>Fraction of destroyed and removed that remains in slurry</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>fraction</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    Category Dictonary_Name  \\\n",
       "0   Total destruction and removal efficiency  DRE             \n",
       "1   Total destruction and removal efficiency  DRE             \n",
       "2   Total destruction and removal efficiency  DRE             \n",
       "3   Total destruction and removal efficiency  DRE             \n",
       "4   Total destruction and removal efficiency  DRE             \n",
       "5   Total destruction and removal efficiency  DRE             \n",
       "6   Total destruction and removal efficiency  DRE             \n",
       "7   Total destruction and removal efficiency  DRE             \n",
       "8   Total destruction and removal efficiency  DRE             \n",
       "9   Total destruction and removal efficiency  DRE             \n",
       "10  SCWO parameters                           SCWO            \n",
       "11  SCWO parameters                           SCWO            \n",
       "12  SCWO parameters                           SCWO            \n",
       "13  SCWO parameters                           SCWO            \n",
       "\n",
       "          Parameter Name  \\\n",
       "0   PFOA                   \n",
       "1   PFOS                   \n",
       "2   PFBA                   \n",
       "3   PFPeA                  \n",
       "4   PFHxA                  \n",
       "5   PFHpA                  \n",
       "6   PFNA                   \n",
       "7   PFDA                   \n",
       "8   PFBS                   \n",
       "9   PFHxS                  \n",
       "10  frac_water_to_steam    \n",
       "11  frac_water_to_slurry   \n",
       "12  frac_PFAS_to_steam     \n",
       "13  frac_PFAS_to_slurry    \n",
       "\n",
       "                                       Parameter Description   amount  \\\n",
       "0   DRE of PFOA                                               0.99260   \n",
       "1   DRE of PFOS                                               0.99997   \n",
       "2   DRE of PFBA                                               0.95043   \n",
       "3   DRE of PFPeA                                              0.94257   \n",
       "4   DRE of PFHxA                                              0.99843   \n",
       "5   DRE of PFHpA                                              0.96303   \n",
       "6   DRE of PFNA                                               0.99260   \n",
       "7   DRE of PFDA                                               0.99970   \n",
       "8   DRE of PFBS                                               0.99950   \n",
       "9   DRE of PFHxS                                              0.99993   \n",
       "10  Fraction of incoming water to steam                       0.01000   \n",
       "11  Fraction of incoming water to slurry                      0.10000   \n",
       "12  Fraction of destroyed and removed that remains in steam   0.00000   \n",
       "13  Fraction of destroyed and removed that remains in slurry  0.00000   \n",
       "\n",
       "        unit  Reference  \n",
       "0   fraction NaN         \n",
       "1   fraction NaN         \n",
       "2   fraction NaN         \n",
       "3   fraction NaN         \n",
       "4   fraction NaN         \n",
       "5   fraction NaN         \n",
       "6   fraction NaN         \n",
       "7   fraction NaN         \n",
       "8   fraction NaN         \n",
       "9   fraction NaN         \n",
       "10  fraction NaN         \n",
       "11  fraction NaN         \n",
       "12  fraction NaN         \n",
       "13  fraction NaN         "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SCWO = ps.SCWO()\n",
    "SCWO.InputData.Data[['Category','Dictonary_Name','Parameter Name', 'Parameter Description', 'amount', 'unit','Reference']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incoming Contaminated Water to SCWO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Parameter</th>\n",
       "      <th>Unit</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mass flow</td>\n",
       "      <td>kg</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Solids flow</td>\n",
       "      <td>kg</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Moisture flow</td>\n",
       "      <td>kg</td>\n",
       "      <td>995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Volume flow</td>\n",
       "      <td>L</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carbon flow</td>\n",
       "      <td>kg</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PFOA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PFOS</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PFBA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PFPeA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>PFHxA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PFHpA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>PFNA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>PFDA</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PFBS</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>PFHxS</td>\n",
       "      <td>μg</td>\n",
       "      <td>100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Parameter Unit  Amount\n",
       "0   Mass flow      kg   1000  \n",
       "1   Solids flow    kg   5     \n",
       "2   Moisture flow  kg   995   \n",
       "3   Volume flow    L    1000  \n",
       "4   Carbon flow    kg   2.5   \n",
       "5   PFOA           μg   100000\n",
       "6   PFOS           μg   100000\n",
       "7   PFBA           μg   100000\n",
       "8   PFPeA          μg   100000\n",
       "9   PFHxA          μg   100000\n",
       "10  PFHpA          μg   100000\n",
       "11  PFNA           μg   100000\n",
       "12  PFDA           μg   100000\n",
       "13  PFBS           μg   100000\n",
       "14  PFHxS          μg   100000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IncominWaste = ps.IncomFlow()\n",
    "IncominWaste.set_flow('ContaminatedWater', 1000)\n",
    "IncominWaste.calc()\n",
    "ContaminatedWater = IncominWaste.Inc_flow\n",
    "ContaminatedWater.report()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PFAS balance in SCWO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Effluent</th>\n",
       "      <th>Slurry</th>\n",
       "      <th>Steam</th>\n",
       "      <th>Destroyed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PFOA</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFOS</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>100.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFBA</th>\n",
       "      <td>4.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFPeA</th>\n",
       "      <td>5.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>94.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFHxA</th>\n",
       "      <td>0.16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFHpA</th>\n",
       "      <td>3.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>96.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFNA</th>\n",
       "      <td>0.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFDA</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFBS</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PFHxS</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Effluent  Slurry  Steam  Destroyed\n",
       "PFOA   0.74      0.0     0.0    99.26    \n",
       "PFOS   0.00      0.0     0.0    100.00   \n",
       "PFBA   4.96      0.0     0.0    95.04    \n",
       "PFPeA  5.74      0.0     0.0    94.26    \n",
       "PFHxA  0.16      0.0     0.0    99.84    \n",
       "PFHpA  3.70      0.0     0.0    96.30    \n",
       "PFNA   0.74      0.0     0.0    99.26    \n",
       "PFDA   0.03      0.0     0.0    99.97    \n",
       "PFBS   0.05      0.0     0.0    99.95    \n",
       "PFHxS  0.01      0.0     0.0    99.99    "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SCWO.calc(Inc_flow=ContaminatedWater)\n",
    "SCWO.report(normalized=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SCWO.plot_sankey()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SCWO.plot_sankey_report(margin=.5)"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
