{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ADM1F SRT: single tank and two-phase anaerobic dynamic membrane bioreactor\n",
    "\n",
    "This script is used to simulate a single tank suspended anaerobic dynamic membrane digester and a novel two-phase anaerobic dynamic membrane bioreator with separated SRT and HRT. In the two-phase reactor the effluent (model output) from the first phase dynamic membrane bioreactor is converted to the influent (model input) for the second-phase anaerobic dynamic membrane bioreactor as shown in the figure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Before running the ADM1F SRT simulations see intructions on how to compile `adm1f_srt.cxx` in the User Guide.\n",
    "\n",
    "\n",
    "**Mass balance equation with SRT:**\n",
    "\n",
    "The ADM1 and ADM1F solve the mass balance equation (i.e. mass_change = mass_in – mass_out + reaction). ADM1F_SRT version of the model includes solid retention time  ($t_{res,X}$) as shown in the equation below. \n",
    "\n",
    "$$\n",
    "\\frac{d\\, S_{\\text{liq,i}}}{dt} = \\frac{qS_{\\text{in,i}}}{V_{\\text{liq}}} - \\frac{qS_{\\text{liq,i}} }{V_{\\text{liq}}}+ \\sum_{j=1}^{12}\\rho_{j} v_{i,j}\n",
    "$$\n",
    "\n",
    "$$\n",
    "\\frac{d\\, X_{\\text{liq,i}}}{dt} = \\frac{qX_{\\text{in,i}}}{V_{\\text{liq}}} - \\frac{X_{\\text{liq,i}}}{t_{\\text{res,X}} + V_{\\text{liq}}/q} + \\sum_{j=13}^{24}\\rho_{j} v_{i,j}\n",
    "$$\n",
    "\n",
    "Authors: Wenjuan Zhang, Elchin Jafarov, Kuang Zhu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:41:31.196813Z",
     "start_time": "2020-09-22T22:41:29.366849Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Load packages\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import seaborn as sns\n",
    "import adm1f_utils as adm1fu\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# navigate to simulations folder\n",
    "os.chdir('../simulations')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:41:42.100135Z",
     "start_time": "2020-09-22T22:41:31.231830Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Grab the names and unit of all the outputs\n",
    "(output_name,output_unit)=adm1fu.get_output_names()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/elchin/project/ADM1F_WM/build/adm1f\r\n"
     ]
    }
   ],
   "source": [
    "#check the path to the executable\n",
    "!echo $ADM1F_EXE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Single tank anaerobic dynamic membrane bioreactor (AnDMBR)\n",
    "\n",
    "To simulate the single tank AnDMBR that has separated SRT and HRT, We can call the function`reactor1` with different Q (flow-rate), Vliq (reactor volume), t_resx (SRT-HRT) values, the function will return the corresponding output\n",
    "\n",
    "Usage example: reactor1(Q=100, t_resx=30, Vliq=300) \n",
    "\n",
    "**(Unit)** Q: [m3/d], t_resx: [day], Vliq: [m3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:43:01.349288Z",
     "start_time": "2020-09-22T22:43:01.343864Z"
    },
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 0 -influent_file influent_cur.dat\n",
      "indicator-228.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 1 -influent_file influent_cur.dat\n",
      "indicator-307.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 2 -influent_file influent_cur.dat\n",
      "indicator-437.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 3 -influent_file influent_cur.dat\n",
      "indicator-563.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 4 -influent_file influent_cur.dat\n",
      "indicator-521.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 5 -influent_file influent_cur.dat\n",
      "indicator-516.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 6 -influent_file influent_cur.dat\n",
      "indicator-534.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 7 -influent_file influent_cur.dat\n",
      "indicator-542.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 8 -influent_file influent_cur.dat\n",
      "indicator-556.out\n",
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE -ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady -Vliq 300 -t_resx 9 -influent_file influent_cur.dat\n",
      "indicator-576.out\n"
     ]
    }
   ],
   "source": [
    "# testing different SRTs=[0,1,2,...9] on the one-phase reactor\n",
    "resx_list = [i for i in range(10)]\n",
    "# setup the matrix with columns correspoding SRTs and rows to outputs\n",
    "output1_resx = np.zeros((len(resx_list), 67))\n",
    "# here we utilize back euler solver and adataptive time step\n",
    "# for more command options see \"User Guide/Running ADM1F/step 5\"\n",
    "options='-ts_type beuler -ts_adapt_type basic -ts_max_snes_failures -1 -steady'\n",
    "\n",
    "for i in range(len(resx_list)):\n",
    "    output1_resx[i] = adm1fu.reactor1(opt=options, Vliq=300, Q=600, t_resx=resx_list[i])\n",
    "    \n",
    "np.savetxt('output_1phase.csv',output1_resx,delimiter=',',fmt='%1.4e')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:43:13.547934Z",
     "start_time": "2020-09-22T22:43:13.481493Z"
    }
   },
   "outputs": [],
   "source": [
    "df1_resx = pd.read_csv('output_1phase.csv', sep=',', header=None)\n",
    "df1_resx.columns = output_name  \n",
    "df1_resx.insert(0,\"T_resx\",resx_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Relation between t_resx and output when Vliq=300m3, Q=600m3/d**</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:43:41.970938Z",
     "start_time": "2020-09-22T22:43:41.906442Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T_resx</th>\n",
       "      <th>Ssu</th>\n",
       "      <th>Saa</th>\n",
       "      <th>Sfa</th>\n",
       "      <th>Sva</th>\n",
       "      <th>Sbu</th>\n",
       "      <th>Spro</th>\n",
       "      <th>Sac</th>\n",
       "      <th>Sh2</th>\n",
       "      <th>Sch4</th>\n",
       "      <th>...</th>\n",
       "      <th>Alk</th>\n",
       "      <th>NH3</th>\n",
       "      <th>NH4</th>\n",
       "      <th>LCFA</th>\n",
       "      <th>percentch4</th>\n",
       "      <th>energych4</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>VFA/ALK</th>\n",
       "      <th>ACN</th>\n",
       "      <th>sampleT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2152.800</td>\n",
       "      <td>324.2700</td>\n",
       "      <td>7382.70</td>\n",
       "      <td>2381.700</td>\n",
       "      <td>3667.300</td>\n",
       "      <td>2542.700</td>\n",
       "      <td>8263.5</td>\n",
       "      <td>25.643000</td>\n",
       "      <td>-8.963700e-82</td>\n",
       "      <td>...</td>\n",
       "      <td>44957.0</td>\n",
       "      <td>0.053067</td>\n",
       "      <td>1074.00</td>\n",
       "      <td>7382.70</td>\n",
       "      <td>-1.133000e-81</td>\n",
       "      <td>83.808</td>\n",
       "      <td>3.9774</td>\n",
       "      <td>0.351490</td>\n",
       "      <td>-3.203400e-72</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>123.060</td>\n",
       "      <td>49.1670</td>\n",
       "      <td>7751.20</td>\n",
       "      <td>2574.400</td>\n",
       "      <td>4202.900</td>\n",
       "      <td>3244.500</td>\n",
       "      <td>9600.7</td>\n",
       "      <td>30.352000</td>\n",
       "      <td>3.859800e-12</td>\n",
       "      <td>...</td>\n",
       "      <td>59182.0</td>\n",
       "      <td>0.047701</td>\n",
       "      <td>1135.60</td>\n",
       "      <td>7751.20</td>\n",
       "      <td>1.599000e-12</td>\n",
       "      <td>100.630</td>\n",
       "      <td>-76.4800</td>\n",
       "      <td>0.310840</td>\n",
       "      <td>1.255000e-08</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>63.057</td>\n",
       "      <td>26.6380</td>\n",
       "      <td>7839.00</td>\n",
       "      <td>2610.000</td>\n",
       "      <td>4268.600</td>\n",
       "      <td>3305.000</td>\n",
       "      <td>9742.9</td>\n",
       "      <td>30.772000</td>\n",
       "      <td>1.085400e-09</td>\n",
       "      <td>...</td>\n",
       "      <td>61421.0</td>\n",
       "      <td>0.048208</td>\n",
       "      <td>1150.90</td>\n",
       "      <td>7839.00</td>\n",
       "      <td>4.422100e-10</td>\n",
       "      <td>102.210</td>\n",
       "      <td>-155.0200</td>\n",
       "      <td>0.304150</td>\n",
       "      <td>2.777100e-07</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>39.313</td>\n",
       "      <td>17.3450</td>\n",
       "      <td>908.19</td>\n",
       "      <td>41.129</td>\n",
       "      <td>54.118</td>\n",
       "      <td>53.411</td>\n",
       "      <td>3339.7</td>\n",
       "      <td>0.000662</td>\n",
       "      <td>2.574900e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>16289.0</td>\n",
       "      <td>2.513100</td>\n",
       "      <td>963.03</td>\n",
       "      <td>908.19</td>\n",
       "      <td>5.579800e+01</td>\n",
       "      <td>209.370</td>\n",
       "      <td>-218.4400</td>\n",
       "      <td>0.200760</td>\n",
       "      <td>9.097200e+00</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>30.634</td>\n",
       "      <td>13.5750</td>\n",
       "      <td>457.20</td>\n",
       "      <td>30.592</td>\n",
       "      <td>39.989</td>\n",
       "      <td>35.895</td>\n",
       "      <td>3066.6</td>\n",
       "      <td>0.000515</td>\n",
       "      <td>2.653300e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>18846.0</td>\n",
       "      <td>3.157300</td>\n",
       "      <td>963.70</td>\n",
       "      <td>457.20</td>\n",
       "      <td>5.623900e+01</td>\n",
       "      <td>214.510</td>\n",
       "      <td>-304.2100</td>\n",
       "      <td>0.157850</td>\n",
       "      <td>1.324200e+01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>25.261</td>\n",
       "      <td>11.2240</td>\n",
       "      <td>309.54</td>\n",
       "      <td>24.543</td>\n",
       "      <td>31.958</td>\n",
       "      <td>27.254</td>\n",
       "      <td>2856.4</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>2.695900e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>21319.0</td>\n",
       "      <td>3.693200</td>\n",
       "      <td>966.73</td>\n",
       "      <td>309.54</td>\n",
       "      <td>5.648800e+01</td>\n",
       "      <td>217.230</td>\n",
       "      <td>-390.0200</td>\n",
       "      <td>0.129290</td>\n",
       "      <td>1.778600e+01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>21.606</td>\n",
       "      <td>9.6181</td>\n",
       "      <td>236.08</td>\n",
       "      <td>20.615</td>\n",
       "      <td>26.775</td>\n",
       "      <td>22.111</td>\n",
       "      <td>2685.8</td>\n",
       "      <td>0.000362</td>\n",
       "      <td>2.726300e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>23721.0</td>\n",
       "      <td>4.147900</td>\n",
       "      <td>970.17</td>\n",
       "      <td>236.08</td>\n",
       "      <td>5.666900e+01</td>\n",
       "      <td>219.140</td>\n",
       "      <td>-475.6200</td>\n",
       "      <td>0.108900</td>\n",
       "      <td>2.279000e+01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>18.960</td>\n",
       "      <td>8.4513</td>\n",
       "      <td>192.14</td>\n",
       "      <td>17.859</td>\n",
       "      <td>23.153</td>\n",
       "      <td>18.702</td>\n",
       "      <td>2543.7</td>\n",
       "      <td>0.000317</td>\n",
       "      <td>2.750200e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>26050.0</td>\n",
       "      <td>4.540600</td>\n",
       "      <td>973.69</td>\n",
       "      <td>192.14</td>\n",
       "      <td>5.681200e+01</td>\n",
       "      <td>220.630</td>\n",
       "      <td>-560.8900</td>\n",
       "      <td>0.093693</td>\n",
       "      <td>2.825000e+01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>16.955</td>\n",
       "      <td>7.5652</td>\n",
       "      <td>162.87</td>\n",
       "      <td>15.818</td>\n",
       "      <td>20.479</td>\n",
       "      <td>16.276</td>\n",
       "      <td>2423.2</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>2.770000e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>28311.0</td>\n",
       "      <td>4.885500</td>\n",
       "      <td>977.21</td>\n",
       "      <td>162.87</td>\n",
       "      <td>5.692900e+01</td>\n",
       "      <td>221.870</td>\n",
       "      <td>-645.8400</td>\n",
       "      <td>0.081984</td>\n",
       "      <td>3.416600e+01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>15.383</td>\n",
       "      <td>6.8691</td>\n",
       "      <td>141.96</td>\n",
       "      <td>14.244</td>\n",
       "      <td>18.422</td>\n",
       "      <td>14.462</td>\n",
       "      <td>2319.3</td>\n",
       "      <td>0.000257</td>\n",
       "      <td>2.786900e+02</td>\n",
       "      <td>...</td>\n",
       "      <td>30505.0</td>\n",
       "      <td>5.192300</td>\n",
       "      <td>980.68</td>\n",
       "      <td>141.96</td>\n",
       "      <td>5.702800e+01</td>\n",
       "      <td>222.920</td>\n",
       "      <td>-730.4900</td>\n",
       "      <td>0.072728</td>\n",
       "      <td>4.053400e+01</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   T_resx      Ssu       Saa      Sfa       Sva       Sbu      Spro     Sac   \\\n",
       "0       0  2152.800  324.2700  7382.70  2381.700  3667.300  2542.700  8263.5   \n",
       "1       1   123.060   49.1670  7751.20  2574.400  4202.900  3244.500  9600.7   \n",
       "2       2    63.057   26.6380  7839.00  2610.000  4268.600  3305.000  9742.9   \n",
       "3       3    39.313   17.3450   908.19    41.129    54.118    53.411  3339.7   \n",
       "4       4    30.634   13.5750   457.20    30.592    39.989    35.895  3066.6   \n",
       "5       5    25.261   11.2240   309.54    24.543    31.958    27.254  2856.4   \n",
       "6       6    21.606    9.6181   236.08    20.615    26.775    22.111  2685.8   \n",
       "7       7    18.960    8.4513   192.14    17.859    23.153    18.702  2543.7   \n",
       "8       8    16.955    7.5652   162.87    15.818    20.479    16.276  2423.2   \n",
       "9       9    15.383    6.8691   141.96    14.244    18.422    14.462  2319.3   \n",
       "\n",
       "        Sh2          Sch4   ...     Alk       NH3      NH4     LCFA   \\\n",
       "0  25.643000 -8.963700e-82  ...  44957.0  0.053067  1074.00  7382.70   \n",
       "1  30.352000  3.859800e-12  ...  59182.0  0.047701  1135.60  7751.20   \n",
       "2  30.772000  1.085400e-09  ...  61421.0  0.048208  1150.90  7839.00   \n",
       "3   0.000662  2.574900e+02  ...  16289.0  2.513100   963.03   908.19   \n",
       "4   0.000515  2.653300e+02  ...  18846.0  3.157300   963.70   457.20   \n",
       "5   0.000424  2.695900e+02  ...  21319.0  3.693200   966.73   309.54   \n",
       "6   0.000362  2.726300e+02  ...  23721.0  4.147900   970.17   236.08   \n",
       "7   0.000317  2.750200e+02  ...  26050.0  4.540600   973.69   192.14   \n",
       "8   0.000284  2.770000e+02  ...  28311.0  4.885500   977.21   162.87   \n",
       "9   0.000257  2.786900e+02  ...  30505.0  5.192300   980.68   141.96   \n",
       "\n",
       "    percentch4    energych4   efficiency   VFA/ALK           ACN  sampleT  \n",
       "0 -1.133000e-81       83.808      3.9774  0.351490 -3.203400e-72      0.5  \n",
       "1  1.599000e-12      100.630    -76.4800  0.310840  1.255000e-08      0.5  \n",
       "2  4.422100e-10      102.210   -155.0200  0.304150  2.777100e-07      0.5  \n",
       "3  5.579800e+01      209.370   -218.4400  0.200760  9.097200e+00      0.5  \n",
       "4  5.623900e+01      214.510   -304.2100  0.157850  1.324200e+01      0.5  \n",
       "5  5.648800e+01      217.230   -390.0200  0.129290  1.778600e+01      0.5  \n",
       "6  5.666900e+01      219.140   -475.6200  0.108900  2.279000e+01      0.5  \n",
       "7  5.681200e+01      220.630   -560.8900  0.093693  2.825000e+01      0.5  \n",
       "8  5.692900e+01      221.870   -645.8400  0.081984  3.416600e+01      0.5  \n",
       "9  5.702800e+01      222.920   -730.4900  0.072728  4.053400e+01      0.5  \n",
       "\n",
       "[10 rows x 68 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the results with increasing T_resx, we should expect decrease in Ssu\n",
    "df1_resx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:43:49.427644Z",
     "start_time": "2020-09-22T22:43:48.218696Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x72 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# correlation between SRT and output\n",
    "plt.figure(figsize=(24,1))\n",
    "corr1=df1_resx.corr()\n",
    "sns.heatmap(corr1.iloc[0:1,-67:], xticklabels=df1_resx.columns[-67:], yticklabels=df1_resx.columns[0:1], cmap=sns.diverging_palette(220, 10, as_cmap=True))\n",
    "plt.title('Correlation Matrix [t_resx/results]',fontsize=20);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Configurations**\n",
    "\n",
    "| Configuration |  Vliq (m$^3$) | t\\_resx (d) | Q (m$^3$/d)|  \n",
    "| ------ | ------ | ------ | ------|\n",
    "Default | 3400 | 0 | 134 |\n",
    "Phase 1 | 340 | 1.5 | 618 |\n",
    "Phase 2 | 3400 | 700 | 618/--- |\n",
    "\n",
    "where t\\_resx = SRT - HRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:44:08.135447Z",
     "start_time": "2020-09-22T22:44:08.128013Z"
    }
   },
   "outputs": [],
   "source": [
    "config_default = {'Vliq':3400, 't_resx':0, 'Q':134}\n",
    "config1 = {'Vliq':340, 't_resx':1.5, 'Q':618}\n",
    "config2 = {'Vliq':3400, 't_resx':700, 'Q':618}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Default Configuration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:44:10.150873Z",
     "start_time": "2020-09-22T22:44:09.462343Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE  -Vliq 3400 -t_resx 0 -influent_file influent_cur.dat\n",
      "indicator-034.out\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ssu</th>\n",
       "      <th>Saa</th>\n",
       "      <th>Sfa</th>\n",
       "      <th>Sva</th>\n",
       "      <th>Sbu</th>\n",
       "      <th>Spro</th>\n",
       "      <th>Sac</th>\n",
       "      <th>Sh2</th>\n",
       "      <th>Sch4</th>\n",
       "      <th>Sic</th>\n",
       "      <th>...</th>\n",
       "      <th>Alk</th>\n",
       "      <th>NH3</th>\n",
       "      <th>NH4</th>\n",
       "      <th>LCFA</th>\n",
       "      <th>percentch4</th>\n",
       "      <th>energych4</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>VFA/ALK</th>\n",
       "      <th>ACN</th>\n",
       "      <th>sampleT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Default Configuration</th>\n",
       "      <td>7.17674</td>\n",
       "      <td>3.21796</td>\n",
       "      <td>54.9446</td>\n",
       "      <td>6.39778</td>\n",
       "      <td>8.23228</td>\n",
       "      <td>6.08339</td>\n",
       "      <td>1963.6</td>\n",
       "      <td>0.00012</td>\n",
       "      <td>48.3303</td>\n",
       "      <td>639.764</td>\n",
       "      <td>...</td>\n",
       "      <td>8438.52</td>\n",
       "      <td>9.32395</td>\n",
       "      <td>1026.69</td>\n",
       "      <td>54.9446</td>\n",
       "      <td>56.905</td>\n",
       "      <td>65.228</td>\n",
       "      <td>53.3712</td>\n",
       "      <td>0.220453</td>\n",
       "      <td>109.164</td>\n",
       "      <td>25.3731</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Ssu      Saa      Sfa      Sva      Sbu     Spro   \\\n",
       "Default Configuration  7.17674  3.21796  54.9446  6.39778  8.23228  6.08339   \n",
       "\n",
       "                         Sac      Sh2     Sch4      Sic   ...     Alk   \\\n",
       "Default Configuration  1963.6  0.00012  48.3303  639.764  ...  8438.52   \n",
       "\n",
       "                          NH3      NH4     LCFA    percentch4    energych4   \\\n",
       "Default Configuration  9.32395  1026.69  54.9446        56.905       65.228   \n",
       "\n",
       "                       efficiency   VFA/ALK      ACN  sampleT  \n",
       "Default Configuration     53.3712  0.220453  109.164  25.3731  \n",
       "\n",
       "[1 rows x 67 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output using default configuration\n",
    "ls_default = adm1fu.reactor1(**config_default).tolist()\n",
    "df_default = pd.DataFrame(data = [ls_default],columns=output_name, index=['Default Configuration'])\n",
    "df_default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configuration 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:44:14.308216Z",
     "start_time": "2020-09-22T22:44:13.579557Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE  -Vliq 340 -t_resx 1.5 -influent_file influent_cur.dat\n",
      "indicator-051.out\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ssu</th>\n",
       "      <th>Saa</th>\n",
       "      <th>Sfa</th>\n",
       "      <th>Sva</th>\n",
       "      <th>Sbu</th>\n",
       "      <th>Spro</th>\n",
       "      <th>Sac</th>\n",
       "      <th>Sh2</th>\n",
       "      <th>Sch4</th>\n",
       "      <th>Sic</th>\n",
       "      <th>...</th>\n",
       "      <th>Alk</th>\n",
       "      <th>NH3</th>\n",
       "      <th>NH4</th>\n",
       "      <th>LCFA</th>\n",
       "      <th>percentch4</th>\n",
       "      <th>energych4</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>VFA/ALK</th>\n",
       "      <th>ACN</th>\n",
       "      <th>sampleT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phase 1 Configuration</th>\n",
       "      <td>80.5616</td>\n",
       "      <td>33.4704</td>\n",
       "      <td>7808.75</td>\n",
       "      <td>2598.14</td>\n",
       "      <td>4246.87</td>\n",
       "      <td>3285.25</td>\n",
       "      <td>9696.05</td>\n",
       "      <td>28.5069</td>\n",
       "      <td>7.737870e-30</td>\n",
       "      <td>147.214</td>\n",
       "      <td>...</td>\n",
       "      <td>60316.4</td>\n",
       "      <td>0.048011</td>\n",
       "      <td>1145.6</td>\n",
       "      <td>7808.75</td>\n",
       "      <td>3.164870e-30</td>\n",
       "      <td>104.203</td>\n",
       "      <td>-104.737</td>\n",
       "      <td>0.308161</td>\n",
       "      <td>1.202840e-17</td>\n",
       "      <td>0.550162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Ssu      Saa      Sfa      Sva      Sbu     Spro   \\\n",
       "Phase 1 Configuration  80.5616  33.4704  7808.75  2598.14  4246.87  3285.25   \n",
       "\n",
       "                          Sac      Sh2          Sch4      Sic   ...     Alk   \\\n",
       "Phase 1 Configuration  9696.05  28.5069  7.737870e-30  147.214  ...  60316.4   \n",
       "\n",
       "                           NH3     NH4     LCFA    percentch4    energych4   \\\n",
       "Phase 1 Configuration  0.048011  1145.6  7808.75  3.164870e-30      104.203   \n",
       "\n",
       "                       efficiency   VFA/ALK           ACN   sampleT  \n",
       "Phase 1 Configuration    -104.737  0.308161  1.202840e-17  0.550162  \n",
       "\n",
       "[1 rows x 67 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output using configuration 1\n",
    "ls_config1 = adm1fu.reactor1(**config1).tolist()\n",
    "df_config1 = pd.DataFrame(data = [ls_config1],columns=output_name, index=['Phase 1 Configuration'])\n",
    "df_config1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Configuration 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:44:18.309478Z",
     "start_time": "2020-09-22T22:44:17.238137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE  -Vliq 3400 -t_resx 700 -influent_file influent_cur.dat\n",
      "indicator-025.out\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ssu</th>\n",
       "      <th>Saa</th>\n",
       "      <th>Sfa</th>\n",
       "      <th>Sva</th>\n",
       "      <th>Sbu</th>\n",
       "      <th>Spro</th>\n",
       "      <th>Sac</th>\n",
       "      <th>Sh2</th>\n",
       "      <th>Sch4</th>\n",
       "      <th>Sic</th>\n",
       "      <th>...</th>\n",
       "      <th>Alk</th>\n",
       "      <th>NH3</th>\n",
       "      <th>NH4</th>\n",
       "      <th>LCFA</th>\n",
       "      <th>percentch4</th>\n",
       "      <th>energych4</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>VFA/ALK</th>\n",
       "      <th>ACN</th>\n",
       "      <th>sampleT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phase 2 Configuration</th>\n",
       "      <td>2.70068</td>\n",
       "      <td>1.20723</td>\n",
       "      <td>19.0168</td>\n",
       "      <td>2.34856</td>\n",
       "      <td>3.01652</td>\n",
       "      <td>2.16597</td>\n",
       "      <td>1125.18</td>\n",
       "      <td>0.000045</td>\n",
       "      <td>75.1774</td>\n",
       "      <td>952.376</td>\n",
       "      <td>...</td>\n",
       "      <td>21574.7</td>\n",
       "      <td>14.7549</td>\n",
       "      <td>1186.64</td>\n",
       "      <td>19.0168</td>\n",
       "      <td>58.194</td>\n",
       "      <td>255.569</td>\n",
       "      <td>-899.833</td>\n",
       "      <td>0.049221</td>\n",
       "      <td>3302.96</td>\n",
       "      <td>5.50162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Ssu      Saa      Sfa      Sva      Sbu     Spro   \\\n",
       "Phase 2 Configuration  2.70068  1.20723  19.0168  2.34856  3.01652  2.16597   \n",
       "\n",
       "                          Sac       Sh2     Sch4      Sic   ...     Alk   \\\n",
       "Phase 2 Configuration  1125.18  0.000045  75.1774  952.376  ...  21574.7   \n",
       "\n",
       "                          NH3      NH4     LCFA    percentch4    energych4   \\\n",
       "Phase 2 Configuration  14.7549  1186.64  19.0168        58.194      255.569   \n",
       "\n",
       "                       efficiency   VFA/ALK      ACN  sampleT  \n",
       "Phase 2 Configuration    -899.833  0.049221  3302.96  5.50162  \n",
       "\n",
       "[1 rows x 67 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output using configuration 2\n",
    "ls_config2 = adm1fu.reactor1(**config2).tolist()\n",
    "df_config2 = pd.DataFrame(data = [ls_config2],columns=output_name, index=['Phase 2 Configuration'])\n",
    "df_config2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Two-Phase anaerobic dynamic membrane bioreactor\n",
    "\n",
    "To simulate a novel two-phase AnDMBR, We can call the function `reactor2` with different Q1 (phase 1 flow rate), Q2 (phase 2 flow rate, normally equal to Q1), Vliq1 (phase 1 volume), Vliq2 (phase 2 volume), t_resx1 (SRT-HRT for phase 1), and t_resx2 (SRT-HRT for phase 1) values. In this function, the model output from phase 1 will be extracted and converted to the model input for phase 2. The function will return phase1 output and phase2 output\n",
    "\n",
    "Usage example: reactor2(Q1=100,  t_resx1=30, t_resx2=100, Vliq1=300, Vliq2=3000) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Schematics of the two-phase reactor\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "print ('Schematics of the two-phase reactor')\n",
    "Image(filename='../notebooks/2phase_reactor.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Config12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Configurations**\n",
    "\n",
    "| Configuration |  Vliq (m$^3$) | t\\_resx (d) | Q (m$^3$/d)|  \n",
    "| ------ | ------ | ------ | ------|\n",
    "Default | 3400 | 0 | 134 |\n",
    "Phase 1 | 340 | 1.5 | 618 |\n",
    "Phase 2 | 3400 | 700 | 618/--- |\n",
    "\n",
    "where t\\_resx = SRT - HRT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:45:08.804996Z",
     "start_time": "2020-09-22T22:45:08.799762Z"
    }
   },
   "outputs": [],
   "source": [
    "# config12 = {\"Vliq1\":340, \"Vliq2\":3400, \"t_resx1\":1.5, \"t_resx2\":700, \"Q1\":618, \"Q2\":618}\n",
    "config12 = {\"Vliq1\":340, \"Vliq2\":3400, \"t_resx1\":1.5, \"t_resx2\":700, \"Q1\":618}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE  -Vliq 340 -t_resx 1.5 -influent_file influent_cur.dat\n",
      "indicator-051.out\n",
      "Reactor run, phase-two:\n",
      "$ADM1F_EXE -Vliq 3400 -t_resx 700 -influent_file influent_cur.dat\n",
      "indicator-024.out\n"
     ]
    }
   ],
   "source": [
    "result_config12 = adm1fu.reactor2(**config12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-09-22T22:45:19.563151Z",
     "start_time": "2020-09-22T22:45:17.365242Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reactor run, phase-one:\n",
      "$ADM1F_EXE  -Vliq 340 -t_resx 1.5 -influent_file influent_cur.dat\n",
      "indicator-051.out\n",
      "Reactor run, phase-two:\n",
      "$ADM1F_EXE -Vliq 3400 -t_resx 700 -influent_file influent_cur.dat\n",
      "indicator-024.out\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ssu</th>\n",
       "      <th>Saa</th>\n",
       "      <th>Sfa</th>\n",
       "      <th>Sva</th>\n",
       "      <th>Sbu</th>\n",
       "      <th>Spro</th>\n",
       "      <th>Sac</th>\n",
       "      <th>Sh2</th>\n",
       "      <th>Sch4</th>\n",
       "      <th>Sic</th>\n",
       "      <th>...</th>\n",
       "      <th>Alk</th>\n",
       "      <th>NH3</th>\n",
       "      <th>NH4</th>\n",
       "      <th>LCFA</th>\n",
       "      <th>percentch4</th>\n",
       "      <th>energych4</th>\n",
       "      <th>efficiency</th>\n",
       "      <th>VFA/ALK</th>\n",
       "      <th>ACN</th>\n",
       "      <th>sampleT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Phase1</th>\n",
       "      <td>80.561600</td>\n",
       "      <td>33.470400</td>\n",
       "      <td>7808.7500</td>\n",
       "      <td>2598.14000</td>\n",
       "      <td>4246.87000</td>\n",
       "      <td>3285.25000</td>\n",
       "      <td>9696.05</td>\n",
       "      <td>28.506900</td>\n",
       "      <td>7.771710e-32</td>\n",
       "      <td>147.214</td>\n",
       "      <td>...</td>\n",
       "      <td>60316.4</td>\n",
       "      <td>0.048011</td>\n",
       "      <td>1145.6</td>\n",
       "      <td>7808.7500</td>\n",
       "      <td>3.068330e-32</td>\n",
       "      <td>104.203</td>\n",
       "      <td>-104.737</td>\n",
       "      <td>0.308161</td>\n",
       "      <td>1.216580e-18</td>\n",
       "      <td>0.550162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Phase2</th>\n",
       "      <td>0.355681</td>\n",
       "      <td>0.132504</td>\n",
       "      <td>19.2757</td>\n",
       "      <td>2.37729</td>\n",
       "      <td>3.05196</td>\n",
       "      <td>2.19399</td>\n",
       "      <td>1248.96</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>7.114040e+01</td>\n",
       "      <td>867.356</td>\n",
       "      <td>...</td>\n",
       "      <td>17101.8</td>\n",
       "      <td>15.289200</td>\n",
       "      <td>1139.4</td>\n",
       "      <td>19.2757</td>\n",
       "      <td>6.198330e+01</td>\n",
       "      <td>192.277</td>\n",
       "      <td>-590.307</td>\n",
       "      <td>0.068884</td>\n",
       "      <td>-2.807000e+01</td>\n",
       "      <td>6.241160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Ssu        Saa        Sfa         Sva         Sbu        Spro   \\\n",
       "Phase1  80.561600  33.470400  7808.7500  2598.14000  4246.87000  3285.25000   \n",
       "Phase2   0.355681   0.132504    19.2757     2.37729     3.05196     2.19399   \n",
       "\n",
       "           Sac        Sh2          Sch4      Sic   ...     Alk        NH3   \\\n",
       "Phase1  9696.05  28.506900  7.771710e-32  147.214  ...  60316.4   0.048011   \n",
       "Phase2  1248.96   0.000046  7.114040e+01  867.356  ...  17101.8  15.289200   \n",
       "\n",
       "          NH4       LCFA    percentch4    energych4   efficiency   VFA/ALK  \\\n",
       "Phase1  1145.6  7808.7500  3.068330e-32      104.203    -104.737  0.308161   \n",
       "Phase2  1139.4    19.2757  6.198330e+01      192.277    -590.307  0.068884   \n",
       "\n",
       "                 ACN   sampleT  \n",
       "Phase1  1.216580e-18  0.550162  \n",
       "Phase2 -2.807000e+01  6.241160  \n",
       "\n",
       "[2 rows x 67 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# output using config12\n",
    "result_config12 = adm1fu.reactor2(**config12)\n",
    "ls1_config12 = result_config12[0].tolist()\n",
    "ls2_config12 = result_config12[1].tolist()\n",
    "\n",
    "df_config12 = pd.DataFrame(data = [ls1_config12],columns=output_name,index=['Phase1'])\n",
    "df_config12.loc['Phase2'] = ls2_config12\n",
    "df_config12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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