NIST-ISODB-ColumnSimulator.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description and Software Disclaimer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Title: NIST Adsorption Isotherm Database Column Simulator\n",
    "\n",
    "Software Description: This notebook utilizes single-component isotherms in the NIST/ARPA-E Database of Novel Adsorbent Materials (https://adsorption.nist.gov/isodb) and Ideal Adsorbed Solution Theory to model an adsorption column, to estimate column breakthrough time.\n",
    "\n",
    "Authors:  \n",
    "Tom R. C. Van Assche, PhD  \n",
    "Daniel W. Siderius, PhD\n",
    "\n",
    "------------VERSION HISTORY---------------- \n",
    "\n",
    "2018-07-31: v 1.0 (initial release)<br>\n",
    "2018-10-24: v 1.0.1 (slight refactor, fix bug for countdown bar)<br>\n",
    "2021-04-29: v. 1.0.2 (update tqdm import statement)\n",
    "\n",
    "\n",
    "------------SOFTWARE DISCLAIMER AND REDISTRIBUTION CONDITIONS----------------\n",
    "\n",
    "This software was developed at the National Institute of Standards and Technology by employees of the Federal Government in the course of their official duties. Pursuant to Title 17 Section 105 of the United States Code this software is not subject to copyright protection and is in the public domain. This software is an experimental system. NIST assumes no responsibility whatsoever for its use by other parties, and makes no guarantees, expressed or implied, about its quality, reliability, or any other characteristic. We would appreciate acknowledgement if the software is used.\n",
    "\n",
    "This software can be redistributed and/or modified freely provided that any derivative works bear some notice that they are derived from it, and any modified versions bear some notice that they have been modified.\n",
    "\n",
    "------------DECLARATION OF CONFLICTING INTERESTS----------------\n",
    "\n",
    "Certain commercially available items may be identified in this paper. This identification does not imply recommendation by NIST, nor does it imply that it is the best available for the purposes described."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# User Guide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preface\n",
    "The purpose of this notebook is to perform a crude simulation of a plug-flow adsorption column, which may be used to estimate the break-through time of various components in an inlet stream that is flowed through a column containing adsorbent that entrains some or all of the input components. The engineering approach and solution algorithm is based on work published by Tom Van Assche, Joeri Denayer, and Gino Baron of the Department of Chemical Engieering at Vrije Universiteit Brussel in Brussels, Belgium. The particular code used here is based on a rigid-adsorbent version of the algorithm disclosed in:\n",
    "\n",
    "Van Assche, T.R.C., Baron, G., Denayer, J.F.M., Molecular separations with breathing metal-organic frameworks: modelling packed bed adsorbers, Dalton Trans., 2016, 45, pp. 4416-4430. (https://dx.doi.org/10.1039/C6DT00258G)\n",
    "\n",
    "For full discussion of the approach and algorithm, please consult the reference above. A short summary and list of assumptions is as follows:\n",
    "1. Gas flows through the column at fixed velocity; this implies an excess of inert (non-adsorbing) carrier.\n",
    "2. No pressure drop in the column is considered.\n",
    "3. The column is operated isothermally (hence, no heat balance).\n",
    "4. The column is assumed to be plug-flow, hence there is no radial variation in the composition. As a result, the column cross-sectional area is unimportant and is undefined here.\n",
    "5. This approach ignores mass-transfer and adsorption kinetics; at each time and column position the approach imposes instantaneous thermodynamic equilibrium (equivalent to infinitely fast kinetics) between the mobile (gas) phase and the stationary (adsorbed) phase.\n",
    "\n",
    "### Multicomponent Adsorption Thermodynamics\n",
    "\n",
    "This column simulator uses \"Ideal Adsorbed Solution Theory\" (IAST) to model multicomponent adsorption equilibrium. Additionally, it uses the pyIASTpackage to apply IAST. End-users should consult the pyIAST documentation (https://github.com/CorySimon/pyIAST, http://pyiast.readthedocs.io/en/latest/) to understand its syntax and warning codes. A Python implementation of IAST in conjunction with the NIST Adsorption Isotherm (NIST-ISODB) is provided at https://github.com/dwsideriusNIST/adsorption_tools/blob/master/NIST-ISODB_IAST.ipynb\n",
    "\n",
    "As of 2017-07-31, pyIAST requires Python 3.x. The end user is responsible for installing Python 3.x and then confirming that pyIAST is loaded properly. Also, since it is not possible to automatically select an isotherm model for fitting an experimental isotherm; the end user must ensure that the selected model or isotherm fit adequately represents the input isotherm data. The different isotherm models available in pyIAST may be accessed by running \n",
    "\n",
    "pyiast._MODELS\n",
    "\n",
    "### Usage\n",
    "This notebook is intended as an example of how to simulate an adsorption column in which the multicomponent adsorption equilibrium is determined via IAST and for which the single-component isotherms are provided by the NIST-ISODB application programming interface (API); actual use may require extensive modification of the Python code that follows as certain assumptions have been built-in to the code, though those assumptions will be pointed out. Use of this notebook follows the general sequence that follows, with each element corresponding to a major heading of the notebook below:\n",
    "1. Import Required Python Packages\n",
    "   This step simply imports the supporting packages required by the notebook\n",
    "   \n",
    "2. Define Custom Class and Import Custom Package\n",
    "   This step:\n",
    "\n",
    "    - defines a custom class that is used to abstract the isotherm data from the NIST-ISODB API\n",
    "    - imports a custom package that provides the function that solves the non-linear mass balance equation\n",
    "\n",
    "\n",
    "3. User Selection of Pure-Component Isotherms\n",
    "   In this step, the user must identify the single-component isotherms that will be used for IAST. The input isotherms must be single-component, or the isotherm instantiation step will fail via a Python exception. \n",
    "   \n",
    "4. Instantiate Isotherms and Conduct Basic Error Checks\n",
    "   This step create the isotherm objects based on the class definition in Step 2, and then performs basic tests to ensure thermodynamic consistency needed for the IAST calculations.\n",
    "\n",
    "5. Common pyIAST Operations\n",
    "   This step creates the Pandas dataframe objects based on the single-component isotherms defined in Step 3 and imported in Step 4.\n",
    " \n",
    "6. Set Relevant Thermophysical Constants\n",
    "   This step defines the universal gas constant, identifies the temperature (from the input isotherms), and pre-calculates \"RT\" to save computations later. Additionally, this block is where unit conversions would be defined. The default units for the simulator are:  \n",
    "   Temperature: Kelvin  \n",
    "   Pressure: bar  \n",
    "   Volume: meters^3 (m^3)  \n",
    "   Mass: kg\n",
    "   Adsorption Isotherms: mol/kg  (identical to mmol/g)  \n",
    "   Density: kg/m^3  \n",
    "   Porosity: Dimensionless  \n",
    "   Time: seconds  \n",
    "   \n",
    "7. Column Initialization\n",
    "   This step sets the essential parameters of the column, including the adsorbent density, the (external) sorbent porosity, the inlet partial pressures, initial partial pressures, the column length, interstitial velocity, number of time steps, and number of divisions (axial slices) of the column.\n",
    "   \n",
    "8. Simulate the Plug-flow Adsorption Column\n",
    "   This steps simulates the adsorption column, by first initializing the data structures that will store both the instantaneous partial pressure of the mobile phase and the adsorbed amounts in the stationary phase at each time and axial position. At each time and position, a set of mass balance equations is imposed, which is solved via the \"solverloading\" function provided by NISTPFR.py; this function, in turn, uses a non-linear solver function provided by the SciPy package.\n",
    "   \n",
    "9. Plot Partial Pressures at exit, and as function of time and position\n",
    "   This block simply displays 1) the outlet partial pressures and 2) partial pressure as a function of time and position for each component.\n",
    "\n",
    "### Warnings\n",
    "1. This notebook conducts some basic checks:\n",
    "   <br>1) Compatible pressure units across the source single-component isotherms\n",
    "   <br>2) Compatible adsorption units across the source single-component isotherms\n",
    "   <br>3) Same adsorbent across the source single-component isotherms\n",
    "   <br>4) Ensuring that the composition list matches the number of source isotherms\n",
    "   <br>5) Ensuring that the source isotherms are single-component\n",
    " \n",
    "   These checks can, of course, be overridden. IAST may, then, yield nonsensical results.\n",
    "\n",
    "2. Units of input isotherms can be modified to be compatible. An end user is responsible for writing the code to make those conversions.\n",
    "\n",
    "3. In principle, IAST can operate on any set of single-component adsorption isotherms measured at the same temperature; it does not check whether the adsorbent material is consistent between those source isotherms. Therefore, a user could apply IAST to single-component isotherms from different materials.\n",
    "\n",
    "\n",
    "### Acknowledgments\n",
    "\n",
    "NIST thanks Tom Van Assche, PhD, of Vrije Universiteit Brussel in Brussels, Belgium, for providing a MATLAB code that served as the example of this Python code."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Import Python Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NOTE: This tool uses the pyIAST package, which requires Python 3.x.\n",
    "#End User is responsible for installation of missing packages using an appropriate Python package manager\n",
    "\n",
    "%matplotlib inline\n",
    "import os\n",
    "import copy\n",
    "import itertools\n",
    "import matplotlib.pyplot as plt\n",
    "from tqdm.notebook import tqdm as tqdm_notebook\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.optimize import fsolve\n",
    "import pyiast\n",
    "\n",
    "#Uncomment the following lines to suppress warnings\n",
    "#import warnings ; warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Import Custom Class and Package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Custom Packages\n",
    "from NISTISODB import *\n",
    "from NISTPFR import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. User Selection of Pure-Component Isotherms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Instructions:\n",
    "#1. Select pure-component isotherms from NIST-ISODB, by their filename(s), defined as string variables.\n",
    "#2. Assemble the pure-component isotherm string variables into a list named \"isotherm_files\"\n",
    "#3. Specify the Isotherm Fitting Models (available models listed via the \"pyiast._MODELS\" command) for the\n",
    "#   pure-component isotherms in a list named \"models\"\n",
    "\n",
    "#Select the Isotherm Files\n",
    "isotherm1 = '10.1002chem.200902144.Isotherm4.json' #CH4 on ZIF-8 @ 303K http://dx.doi.org/10.1002/chem.200902144\n",
    "isotherm2 = '10.1002chem.200902144.Isotherm5.json' #CO2 on ZIF-8 @ 303K http://dx.doi.org/10.1002/chem.200902144\n",
    "isotherm3 = '10.1002chem.200902144.Isotherm6.json' #N2  on ZIF-8 @ 303K http://dx.doi.org/10.1002/chem.200902144\n",
    "\n",
    "#Assemble the isotherm filenames into a list\n",
    "isotherm_files = [ isotherm1, isotherm2, isotherm3 ]\n",
    "#isotherm_files = [ isotherm1, isotherm2 ]\n",
    "#isotherm_files = [ isotherm1, isotherm3 ]\n",
    "\n",
    "#Specify the Isotherm Fitting Models (Langmuir,)\n",
    "model1 = \"Langmuir\"\n",
    "model2 = \"Langmuir\"\n",
    "model3 = \"Langmuir\"\n",
    "models = [model1, model2, model3]\n",
    "#models = [model1, model2]\n",
    "#models = [model1, model3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Instantiate Isotherms and Conduct Basic Error Checks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ESSENTIAL SYSTEM CHECKS: \n",
      "Pressure units are compatible: bar\n",
      "Adsorption units are compatible: mmol/g\n",
      "Same material is specified: ZIF-8\n",
      "Temperature is consistent: 303.0\n"
     ]
    }
   ],
   "source": [
    "#Instructions: This block of code should not be altered unless a user is\n",
    "# overriding an error check\n",
    "\n",
    "#Instantiate the pure-component isotherms\n",
    "isotherms = [ NIST_ISODB_isotherm(file) for file in isotherm_files ]\n",
    "num_species = len(isotherms)\n",
    "spec_pairs = list(itertools.combinations(range(num_species), 2))\n",
    "\n",
    "#Actual Error Check\n",
    "error_check_isotherms(isotherms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Common pyIAST Operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.1002chem.200902144.Isotherm4.json\n",
      "Methane adsorption on ZIF-8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/local/NIST/dsideriu/anaconda3/lib/python3.6/site-packages/pyiast/isotherms.py:60: FutureWarning: 'argmin' is deprecated, use 'idxmin' instead. The behavior of 'argmin'\n",
      "will be corrected to return the positional minimum in the future.\n",
      "Use 'series.values.argmin' to get the position of the minimum now.\n",
      "  idx_min = df_nonzero[loading_key].argmin()\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.1002chem.200902144.Isotherm5.json\n",
      "Carbon Dioxide adsorption on ZIF-8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.1002chem.200902144.Isotherm6.json\n",
      "Nitrogen adsorption on ZIF-8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instructions: User must choose an isotherm model or select an interpolation option\n",
    "\n",
    "#List Models:\n",
    "#pyiast._MODELS\n",
    "\n",
    "#Compose dataframes from the single-component isotherm data; generate models in pyIAST and plot the data/fits\n",
    "Spec_Isotherms = []\n",
    "for isotherm,model in zip(isotherms,models):\n",
    "    #Option 1: Fit the single-component isotherms to the model(s) specified previously.\n",
    "    Spec_Isotherms.append(\n",
    "        pyiast.ModelIsotherm(\n",
    "            pd.DataFrame({ isotherm.p_label: isotherm.p_list, isotherm.ads_label: isotherm.ads_list }),\n",
    "            loading_key=isotherm.ads_label,\n",
    "            pressure_key=isotherm.p_label,\n",
    "            model=model)\n",
    "        )\n",
    "    #Option 2: Interpolate the single-component isotherm\n",
    "    #Spec_Isotherms.append(\n",
    "    #    pyiast.InterpolatorIsotherm(\n",
    "    #        pd.DataFrame({ isotherm.p_label: isotherm.p_list, isotherm.ads_label: isotherm.ads_list }),\n",
    "    #        loading_key=isotherm.ads_label,\n",
    "    #        pressure_key=isotherm.p_label)\n",
    "    #    )\n",
    "    print(isotherm.filename)\n",
    "    print(isotherm.adsorbate[\"name\"]+' adsorption on '+isotherm.adsorbent[\"name\"])\n",
    "    pyiast.plot_isotherm(Spec_Isotherms[-1],withfit=True,\n",
    "                         xlogscale=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Set Relevant Thermophysical Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Constants\n",
    "#   NOTE: NIST-ISODB uses bar as native units for pressure\n",
    "#Rg = 8.31415 #J/mol K  = kPa l/mol K\n",
    "Rg = 8.31415e-5 #bar m^3/mol K\n",
    "T = isotherms[0].temperature #This assumes that the same temperature is in all isotherms\n",
    "RT = Rg*T\n",
    "\n",
    "\n",
    "#   NOTE: The user is responsible for ensuring that the units of Rg, sorbent density, and\n",
    "#       the selected isotherms are compatible. This clode block is based on a 'default'\n",
    "#       set of units (bar for pressure, m^3 for volume, mol/kg for adsorption, and kg/m3 for density)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Column Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * * * * * PASSING BREAKTHROUGH COLUMN  * * * * *\n",
      " ____________________________________________________\n",
      " \n",
      " based on original MATLAB code written by Tom Van Assche, 2014, Department of Chemical Engineering, VUB\n",
      " \n",
      " \n",
      " *** CONFIRMING INPUT: please verify\n",
      " \n",
      " dx is (m): 0.006666666666666667\n",
      " dt is (s): 0.06666666666666667\n",
      "\n",
      " column length  (m)                       : 0.2\n",
      " interstital velocity (m/s)               : 0.1\n",
      " final simulation time (s)                : 30.0\n",
      "\n",
      " Porosity                                 :  0.5\n",
      "\n",
      " p init COMP.1                            :  0.1\n",
      " p init COMP.2                            :  0.1\n",
      " p init COMP.3                            :  0.1\n",
      "\n",
      " p feed COMP.1                            :  1.5\n",
      " p feed COMP.2                            :  1.5\n",
      " p feed COMP.3                            :  0.5\n",
      "\n",
      " X positions                              :  30\n",
      " Passing moves (time)                     :  450\n",
      " Number of Points                         :  13500\n",
      " _________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "print(' * * * * * PASSING BREAKTHROUGH COLUMN  * * * * *')\n",
    "print(' ____________________________________________________');\n",
    "print(' ');\n",
    "print(' based on original MATLAB code written by Tom Van Assche, 2014, Department of Chemical Engineering, VUB');\n",
    "print(' ');\n",
    "\n",
    "print(' ');\n",
    "print(' *** CONFIRMING INPUT: please verify');\n",
    "print(' ');\n",
    "\n",
    "#  Adsorbent Mechanical Properties\n",
    "dens = 350.  # adsorbent density (in kg/m3)\n",
    "eps = 0.5  # porosity (all external to adsorbent phase)\n",
    "\n",
    "#  Adsorbate Partial Pressures\n",
    "#   Feed Composition\n",
    "p_feed = [1.5,1.5,0.5]\n",
    "#p_feed = [1.5,1.5]\n",
    "#   Initial Column Conditions\n",
    "p_initial = [0.1, 0.1,0.1]\n",
    "#p_initial = [0.1, 0.1]\n",
    "\n",
    "#  Physical Parameters\n",
    "xs=30    # number of cells in the column (determines size of x positions). \n",
    "L_col=0.2 # [m] column length\n",
    "velocity_inter=0.1 # [m/s] interstitial velocity (depends on porosity and superficial velocity!)\n",
    "t_final = 30.  # [s] simulation time\n",
    "\n",
    "#  Calculations\n",
    "d_x=L_col/(xs)\n",
    "d_t=d_x/velocity_inter\n",
    "ts = int(t_final/d_t)\n",
    "eqs=xs*ts;\n",
    "\n",
    "#Parameter Output\n",
    "print(' dx is (m): '+repr(d_x))\n",
    "print(' dt is (s): '+repr(d_t))\n",
    "print()\n",
    "print(' column length  (m)                       : '+repr(L_col))\n",
    "print(' interstital velocity (m/s)               : '+repr(velocity_inter))\n",
    "print(' final simulation time (s)                : '+repr(t_final))\n",
    "print()\n",
    "\n",
    "print(' Porosity                                 : ', repr(eps))\n",
    "print();\n",
    "for (i,x) in enumerate(p_initial):\n",
    "    print(' p init COMP.'+str(i+1)+'                            : ', repr(x))\n",
    "print();\n",
    "for (i,x) in enumerate(p_feed):\n",
    "    print(' p feed COMP.'+str(i+1)+'                            : ', repr(x))\n",
    "print();\n",
    "print(' X positions                              : ', repr(xs))\n",
    "print(' Passing moves (time)                     : ', repr(ts))\n",
    "print(' Number of Points                         : ', repr(eqs))\n",
    "print(' _________________________________________________________');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Simulate the Plug-flow Adsorption Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " *** INITIALIZING\n",
      " \n",
      " *** CALCULATING\n",
      " \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "390df3db61e1416289600b0ca66edeeb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(IntProgress(value=0, description='Progress:', max=449), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " *** DONE\n"
     ]
    }
   ],
   "source": [
    "#Initialization of the Grids\n",
    "print(' *** INITIALIZING')\n",
    "\n",
    "# initial concentration / adsorption\n",
    "p = [ [ p_initial for i in range(xs) ]  for j in range(ts) ]\n",
    "\n",
    "# initial adsorbed amounts\n",
    "ads_init = list(pyiast.iast(p_initial,Spec_Isotherms,verboseflag=False,warningoff=True))\n",
    "q = [[ads_init for i in range(xs)] for j in range(ts)]\n",
    "\n",
    "print(' ');\n",
    "print(' *** CALCULATING'); # - - - - - - - - - - - - - - - - - - -\n",
    "print(' ');\n",
    "\n",
    "for j in tqdm_notebook(range(1,ts), desc='Progress:'):\n",
    "    #every gas cell is moved one forward for next timestep\n",
    "    p[j][1:] = p[j-1][:-1]\n",
    "    #every adsorbed part cell remains at position for next timestep\n",
    "    q[j] = q[j-1]\n",
    "    #Boundary Conditions\n",
    "    p[j][0] = p_feed\n",
    "   \n",
    "    for i in range(xs):  #Secondary Loop: X-positions\n",
    "        # Compute the total mass in the segment\n",
    "        N_total = [ (p[j][i][k]/RT)*eps + q[j][i][k]*(1.-eps)*dens for k in range(num_species) ]\n",
    "    \n",
    "        #Check for stability (don't try to equilibrate negative moles)\n",
    "        if sum(N_total) <= 0.:\n",
    "            p_new = [ 0. for k in range(num_species) ]\n",
    "            q_new = [ 0. for k in range(num_species) ]\n",
    "            print(' Total moles present: negative --> set to zero')\n",
    "        else:\n",
    "            #Calculate equilibrium conditions\n",
    "            guess = p[j-1][i] # use current partial pressure to seed the non-linear solver\n",
    "\n",
    "            #Mass Balance\n",
    "            p_new = solverloading(N_total,RT,eps,dens,Spec_Isotherms,guess,1.e-8)\n",
    "            \n",
    "            #IAST Calculation\n",
    "            q_new = list(pyiast.iast(p_new,Spec_Isotherms,verboseflag=False,warningoff=True))\n",
    "                \n",
    "            # Basic Error checks\n",
    "            for k in range(num_species):\n",
    "                if p_new[k] < 0.:\n",
    "                    if p_new[k] < -1.e-5:\n",
    "                        p_new[i] = 0.\n",
    "                    else:\n",
    "                        print(' NOTE !!!: P NEGATIVE         row ', j, ' column ', i, ' ... P is ', p_new[k])\n",
    "            \n",
    "        #Save the solution\n",
    "        p[j][i] = p_new\n",
    "        q[j][i] = q_new\n",
    "         \n",
    "        #print(j,i)    \n",
    "    #End of X-Loop\n",
    "    #break\n",
    "#End of Time-Loop\n",
    "print(' *** DONE')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Plot Partial Pressures at exit, and as function of time and position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "from matplotlib import cm\n",
    "\n",
    "#Plot the outlet concentrations as a function of time\n",
    "fig = plt.figure()\n",
    "for i in range(num_species):\n",
    "    plt.plot([float(j+1)*d_t for j in range(ts)],[x[xs-1][i] for x in p],'-',label=isotherms[i].adsorbate[\"name\"])\n",
    "x1,x2,y1,y2 = plt.axis()\n",
    "#plt.axis((0,10,0,1.5))\n",
    "plt.xlabel('time (s)')\n",
    "plt.ylabel('Partial Pressure in Mobile Phase')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "#Create 2-D grids to simplify 3D plots\n",
    "posL = []\n",
    "for i in range(xs):\n",
    "    posL.append(float(i+1)*d_x - d_x/2.)\n",
    "posL = np.array(posL)\n",
    "time = []\n",
    "for j in range(ts):\n",
    "    time.append(float(j)*d_t)\n",
    "time = np.array(time)\n",
    "posL, time = np.meshgrid(posL,time)\n",
    "\n",
    "#\n",
    "for i in range(num_species):\n",
    "    Z = np.array([ [ x[i] for x in y ] for y in p ])\n",
    "    fig = plt.figure()\n",
    "    ax = fig.gca(projection='3d')\n",
    "    surf = ax.plot_surface(posL,time, Z, cmap = cm.gist_rainbow,\n",
    "                       linewidth=0, antialiased=False)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.06666666666666667 [0.1, 0.1, 0.1]\n",
      "0.13333333333333333 [0.1 0.1 0.1]\n",
      "0.2 [0.1 0.1 0.1]\n",
      "0.26666666666666666 [0.1 0.1 0.1]\n",
      "0.3333333333333333 [0.1 0.1 0.1]\n",
      "0.4 [0.1 0.1 0.1]\n",
      "0.4666666666666667 [0.1 0.1 0.1]\n",
      "0.5333333333333333 [0.1 0.1 0.1]\n",
      "0.6 [0.1 0.1 0.1]\n",
      "0.6666666666666666 [0.1 0.1 0.1]\n",
      "0.7333333333333333 [0.1 0.1 0.1]\n",
      "0.8 [0.1 0.1 0.1]\n",
      "0.8666666666666667 [0.1 0.1 0.1]\n",
      "0.9333333333333333 [0.1 0.1 0.1]\n",
      "1.0 [0.1 0.1 0.1]\n",
      "1.0666666666666667 [0.1 0.1 0.1]\n",
      "1.1333333333333333 [0.1 0.1 0.1]\n",
      "1.2 [0.1 0.1 0.1]\n",
      "1.2666666666666666 [0.1 0.1 0.1]\n",
      "1.3333333333333333 [0.1 0.1 0.1]\n",
      "1.4 [0.1 0.1 0.1]\n",
      "1.4666666666666666 [0.1 0.1 0.1]\n",
      "1.5333333333333332 [0.1 0.1 0.1]\n",
      "1.6 [0.1 0.1 0.1]\n",
      "1.6666666666666667 [0.1 0.1 0.1]\n",
      "1.7333333333333334 [0.1 0.1 0.1]\n",
      "1.8 [0.1 0.1 0.1]\n",
      "1.8666666666666667 [0.1 0.1 0.1]\n",
      "1.9333333333333333 [0.1 0.1 0.1]\n",
      "2.0 [0.1 0.1 0.1]\n",
      "2.0666666666666664 [0.1        0.1        0.10000001]\n",
      "2.1333333333333333 [0.1        0.1        0.10000014]\n",
      "2.2 [0.1        0.1        0.10000103]\n",
      "2.2666666666666666 [0.10000001 0.10000001 0.10000527]\n",
      "2.3333333333333335 [0.10000005 0.10000003 0.10002079]\n",
      "2.4 [0.10000016 0.1000001  0.10006772]\n",
      "2.466666666666667 [0.10000046 0.10000028 0.10018944]\n",
      "2.533333333333333 [0.10000113 0.10000069 0.10046776]\n",
      "2.6 [0.10000252 0.10000153 0.1010401 ]\n",
      "2.6666666666666665 [0.10000512 0.1000031  0.10211465]\n",
      "2.7333333333333334 [0.10000964 0.10000584 0.10397824]\n",
      "2.8 [0.10001696 0.10001026 0.10699187]\n",
      "2.8666666666666667 [0.10002811 0.10001697 0.11157084]\n",
      "2.933333333333333 [0.10004419 0.10002662 0.11814936]\n",
      "3.0 [0.10006627 0.1000398  0.12713349]\n",
      "3.0666666666666664 [0.10009532 0.10005698 0.13884901]\n",
      "3.1333333333333333 [0.10013214 0.10007846 0.15349276]\n",
      "3.2 [0.10017734 0.10010427 0.1710958 ]\n",
      "3.2666666666666666 [0.10023147 0.10013421 0.19150468]\n",
      "3.3333333333333335 [0.10029519 0.10016777 0.2143841 ]\n",
      "3.4 [0.10036961 0.10020425 0.23924047]\n",
      "3.466666666666667 [0.10045673 0.10024276 0.26546251]\n",
      "3.533333333333333 [0.10056006 0.10028233 0.29237257]\n",
      "3.6 [0.1006853  0.10032198 0.31928134]\n",
      "3.6666666666666665 [0.1008412  0.10036079 0.34553895]\n",
      "3.7333333333333334 [0.10104051 0.10039801 0.37057685]\n",
      "3.8 [0.10130098 0.10043302 0.39393697]\n",
      "3.8666666666666667 [0.10164654 0.10046546 0.41528694]\n",
      "3.933333333333333 [0.1021084  0.10049515 0.43442225]\n",
      "4.0 [0.10272627 0.10052213 0.45125768]\n",
      "4.066666666666666 [0.10354951 0.10054662 0.46581085]\n",
      "4.133333333333333 [0.10463822 0.10056903 0.47818147]\n",
      "4.2 [0.10606417 0.10058988 0.48852904]\n",
      "4.266666666666667 [0.10791163 0.10060979 0.49705143]\n",
      "4.333333333333333 [0.11027778 0.10062948 0.50396593]\n",
      "4.4 [0.1132729  0.10064972 0.50949379]\n",
      "4.466666666666667 [0.11702013 0.10067132 0.51384848]\n",
      "4.533333333333333 [0.12165471 0.10069512 0.51722756]\n",
      "4.6 [0.12732278 0.10072197 0.51980775]\n",
      "4.666666666666667 [0.13417955 0.10075273 0.52174267]\n",
      "4.733333333333333 [0.142387   0.10078825 0.52316253]\n",
      "4.8 [0.15211091 0.10082934 0.52417524]\n",
      "4.866666666666666 [0.16351745 0.10087683 0.52486842]\n",
      "4.933333333333334 [0.17676927 0.10093145 0.52531191]\n",
      "5.0 [0.1920212  0.10099393 0.52556042]\n",
      "5.066666666666666 [0.20941565 0.1010649  0.52565611]\n",
      "5.133333333333333 [0.22907793 0.1011449  0.52563106]\n",
      "5.2 [0.25111149 0.10123438 0.52550935]\n",
      "5.266666666666667 [0.2755934  0.10133368 0.52530894]\n",
      "5.333333333333333 [0.30257019 0.10144299 0.52504317]\n",
      "5.4 [0.33205423 0.10156236 0.52472204]\n",
      "5.466666666666667 [0.36402082 0.1016917  0.52435315]\n",
      "5.533333333333333 [0.39840627 0.10183073 0.52394252]\n",
      "5.6 [0.43510692 0.10197903 0.52349513]\n",
      "5.666666666666667 [0.47397941 0.10213602 0.52301534]\n",
      "5.733333333333333 [0.51484211 0.10230094 0.52250724]\n",
      "5.8 [0.5574778  0.10247291 0.52197479]\n",
      "5.866666666666666 [0.6016375  0.10265092 0.52142195]\n",
      "5.933333333333334 [0.64704536 0.10283385 0.52085277]\n",
      "6.0 [0.69340446 0.10302049 0.52027135]\n",
      "6.066666666666666 [0.74040325 0.10320958 0.51968187]\n",
      "6.133333333333333 [0.7877224  0.10339985 0.51908851]\n",
      "6.2 [0.83504185 0.10359001 0.51849537]\n",
      "6.266666666666667 [0.88204764 0.10377881 0.51790646]\n",
      "6.333333333333333 [0.92843841 0.10396503 0.51732559]\n",
      "6.4 [0.97393123 0.10414757 0.51675631]\n",
      "6.466666666666667 [1.01826661 0.10432539 0.51620185]\n",
      "6.533333333333333 [1.06121255 0.10449758 0.51566511]\n",
      "6.6 [1.1025675  0.10466336 0.51514856]\n",
      "6.666666666666667 [1.14216233 0.10482208 0.5146543 ]\n",
      "6.733333333333333 [1.17986114 0.10497322 0.51418397]\n",
      "6.8 [1.21556117 0.10511639 0.51373883]\n",
      "6.866666666666666 [1.24919183 0.10525137 0.51331971]\n",
      "6.933333333333334 [1.28071291 0.10537801 0.51292707]\n",
      "7.0 [1.31011233 0.10549632 0.51256103]\n",
      "7.066666666666666 [1.33740339 0.1056064  0.51222138]\n",
      "7.133333333333333 [1.36262168 0.10570845 0.51190765]\n",
      "7.2 [1.38582195 0.10580275 0.51161913]\n",
      "7.266666666666667 [1.40707487 0.10588964 0.5113549 ]\n",
      "7.333333333333333 [1.42646389 0.10596954 0.51111391]\n",
      "7.3999999999999995 [1.44408227 0.10604292 0.51089497]\n",
      "7.466666666666667 [1.46003031 0.10611028 0.51069683]\n",
      "7.533333333333333 [1.47441284 0.10617215 0.51051815]\n",
      "7.6 [1.48733701 0.10622911 0.51035761]\n",
      "7.666666666666667 [1.49891037 0.10628172 0.51021384]\n",
      "7.733333333333333 [1.50923921 0.10633059 0.51008552]\n",
      "7.8 [1.51842728 0.10637633 0.50997136]\n",
      "7.866666666666666 [1.52657467 0.10641958 0.50987011]\n",
      "7.933333333333334 [1.53377697 0.10646095 0.50978057]\n",
      "8.0 [1.54012473 0.10650112 0.50970161]\n",
      "8.066666666666666 [1.54570298 0.10654073 0.50963218]\n",
      "8.133333333333333 [1.55059102 0.10658048 0.50957129]\n",
      "8.2 [1.5548623  0.10662107 0.50951802]\n",
      "8.266666666666666 [1.55858444 0.10666322 0.50947153]\n",
      "8.333333333333334 [1.56181935 0.1067077  0.50943104]\n",
      "8.4 [1.5646234  0.1067553  0.50939587]\n",
      "8.466666666666667 [1.56704765 0.10680686 0.50936535]\n",
      "8.533333333333333 [1.56913814 0.10686327 0.50933893]\n",
      "8.6 [1.57093622 0.10692547 0.50931608]\n",
      "8.666666666666666 [1.57247884 0.10699445 0.50929634]\n",
      "8.733333333333333 [1.5737989  0.1070713  0.50927929]\n",
      "8.8 [1.57492557 0.10715716 0.50926457]\n",
      "8.866666666666667 [1.57588462 0.10725326 0.50925184]\n",
      "8.933333333333334 [1.57669876 0.10736094 0.50924081]\n",
      "9.0 [1.57738789 0.10748162 0.50923124]\n",
      "9.066666666666666 [1.57796941 0.10761684 0.50922289]\n",
      "9.133333333333333 [1.57845848 0.10776824 0.50921556]\n",
      "9.2 [1.57886826 0.10793761 0.50920908]\n",
      "9.266666666666666 [1.57921015 0.10812685 0.50920329]\n",
      "9.333333333333334 [1.57949397 0.10833802 0.50919806]\n",
      "9.4 [1.57972817 0.10857331 0.50919326]\n",
      "9.466666666666667 [1.57991998 0.1088351  0.50918878]\n",
      "9.533333333333333 [1.58007558 0.10912589 0.50918454]\n",
      "9.6 [1.58020021 0.10944838 0.50918045]\n",
      "9.666666666666666 [1.58029832 0.10980546 0.50917643]\n",
      "9.733333333333333 [1.58037366 0.11020018 0.5091724 ]\n",
      "9.8 [1.58042937 0.1106358  0.50916832]\n",
      "9.866666666666667 [1.58046806 0.11111578 0.50916411]\n",
      "9.933333333333334 [1.58049191 0.11164379 0.50915973]\n",
      "10.0 [1.58050269 0.11222372 0.50915513]\n",
      "10.066666666666666 [1.58050186 0.11285965 0.50915025]\n",
      "10.133333333333333 [1.58049057 0.11355592 0.50914505]\n",
      "10.2 [1.58046975 0.11431707 0.50913948]\n",
      "10.266666666666666 [1.58044011 0.11514788 0.5091335 ]\n",
      "10.333333333333334 [1.58040218 0.11605338 0.50912706]\n",
      "10.4 [1.58035633 0.11703882 0.50912013]\n",
      "10.466666666666667 [1.58030282 0.11810969 0.50911265]\n",
      "10.533333333333333 [1.58024178 0.11927172 0.50910458]\n",
      "10.6 [1.58017324 0.12053088 0.50909588]\n",
      "10.666666666666666 [1.58009717 0.12189339 0.50908649]\n",
      "10.733333333333333 [1.58001345 0.12336568 0.50907638]\n",
      "10.8 [1.5799219  0.12495443 0.5090655 ]\n",
      "10.866666666666667 [1.57982229 0.12666653 0.5090538 ]\n",
      "10.933333333333334 [1.57971436 0.12850911 0.50904122]\n",
      "11.0 [1.57959778 0.13048951 0.50902771]\n",
      "11.066666666666666 [1.5794722  0.13261527 0.50901324]\n",
      "11.133333333333333 [1.57933726 0.13489411 0.50899773]\n",
      "11.2 [1.57919255 0.13733397 0.50898114]\n",
      "11.266666666666666 [1.57903764 0.13994293 0.50896341]\n",
      "11.333333333333334 [1.57887209 0.14272924 0.50894448]\n",
      "11.4 [1.57869543 0.14570129 0.5089243 ]\n",
      "11.466666666666667 [1.5785072  0.14886759 0.50890282]\n",
      "11.533333333333333 [1.57830691 0.15223676 0.50887997]\n",
      "11.6 [1.57809406 0.1558175  0.50885569]\n",
      "11.666666666666666 [1.57786815 0.15961856 0.50882993]\n",
      "11.733333333333333 [1.57762867 0.16364874 0.50880262]\n",
      "11.8 [1.57737512 0.16791686 0.50877371]\n",
      "11.866666666666667 [1.577107   0.17243169 0.50874315]\n",
      "11.933333333333334 [1.57682378 0.17720199 0.50871086]\n",
      "12.0 [1.57652497 0.18223642 0.50867679]\n",
      "12.066666666666666 [1.57621007 0.18754353 0.50864089]\n",
      "12.133333333333333 [1.57587859 0.19313174 0.5086031 ]\n",
      "12.2 [1.57553005 0.19900928 0.50856336]\n",
      "12.266666666666666 [1.57516399 0.20518416 0.50852163]\n",
      "12.333333333333334 [1.57477997 0.21166414 0.50847785]\n",
      "12.4 [1.57437754 0.2184567  0.50843197]\n",
      "12.466666666666667 [1.57395629 0.22556897 0.50838395]\n",
      "12.533333333333333 [1.57351584 0.23300771 0.50833373]\n",
      "12.6 [1.57305583 0.24077926 0.50828129]\n",
      "12.666666666666666 [1.57257591 0.24888951 0.50822658]\n",
      "12.733333333333333 [1.57207578 0.25734383 0.50816957]\n",
      "12.8 [1.57155517 0.26614707 0.50811023]\n",
      "12.866666666666667 [1.57101385 0.27530349 0.50804853]\n",
      "12.933333333333334 [1.5704516  0.2848167  0.50798445]\n",
      "13.0 [1.56986829 0.29468968 0.50791797]\n",
      "13.066666666666666 [1.56926378 0.30492467 0.50784908]\n",
      "13.133333333333333 [1.568638   0.31552319 0.50777777]\n",
      "13.2 [1.56799094 0.32648595 0.50770405]\n",
      "13.266666666666666 [1.56732262 0.33781287 0.5076279 ]\n",
      "13.333333333333334 [1.5666331  0.34950302 0.50754935]\n",
      "13.4 [1.56592253 0.36155457 0.50746841]\n",
      "13.466666666666667 [1.56519107 0.37396482 0.5073851 ]\n",
      "13.533333333333333 [1.56443897 0.38673013 0.50729945]\n",
      "13.6 [1.56366652 0.3998459  0.50721149]\n",
      "13.666666666666666 [1.56287406 0.41330661 0.50712126]\n",
      "13.733333333333333 [1.56206201 0.42710574 0.50702881]\n",
      "13.799999999999999 [1.56123082 0.44123581 0.50693419]\n",
      "13.866666666666667 [1.56038102 0.45568838 0.50683747]\n",
      "13.933333333333334 [1.55951319 0.47045401 0.50673871]\n",
      "14.0 [1.55862795 0.48552234 0.50663798]\n",
      "14.066666666666666 [1.55772599 0.50088203 0.50653536]\n",
      "14.133333333333333 [1.55680807 0.51652084 0.50643094]\n",
      "14.2 [1.55587496 0.53242565 0.50632481]\n",
      "14.266666666666666 [1.55492752 0.54858244 0.50621707]\n",
      "14.333333333333334 [1.55396664 0.56497639 0.50610782]\n",
      "14.4 [1.55299325 0.58159189 0.50599716]\n",
      "14.466666666666667 [1.55200832 0.5984126  0.5058852 ]\n",
      "14.533333333333333 [1.55101288 0.61542149 0.50577207]\n",
      "14.6 [1.55000797 0.63260093 0.50565789]\n",
      "14.666666666666666 [1.54899468 0.64993268 0.50554277]\n",
      "14.733333333333333 [1.54797412 0.66739806 0.50542684]\n",
      "14.799999999999999 [1.54694741 0.68497793 0.50531024]\n",
      "14.866666666666667 [1.54591571 0.7026528  0.50519308]\n",
      "14.933333333333334 [1.54488017 0.7204029  0.50507552]\n",
      "15.0 [1.54384198 0.73820825 0.50495767]\n",
      "15.066666666666666 [1.54280232 0.75604877 0.50483968]\n",
      "15.133333333333333 [1.54176236 0.77390429 0.50472167]\n",
      "15.2 [1.54072327 0.7917547  0.50460378]\n",
      "15.266666666666666 [1.53968623 0.80957999 0.50448615]\n",
      "15.333333333333334 [1.53865238 0.82736033 0.5043689 ]\n",
      "15.4 [1.53762287 0.84507616 0.50425216]\n",
      "15.466666666666667 [1.5365988  0.86270824 0.50413606]\n",
      "15.533333333333333 [1.53558126 0.88023775 0.50402072]\n",
      "15.6 [1.53457131 0.89764631 0.50390626]\n",
      "15.666666666666666 [1.53356998 0.91491611 0.50379279]\n",
      "15.733333333333333 [1.53257824 0.9320299  0.50368043]\n",
      "15.799999999999999 [1.53159706 0.9489711  0.50356929]\n",
      "15.866666666666667 [1.53062732 0.96572381 0.50345946]\n",
      "15.933333333333334 [1.52966991 0.98227288 0.50335104]\n",
      "16.0 [1.52872563 0.99860394 0.50324413]\n",
      "16.066666666666666 [1.52779524 1.01470343 0.50313881]\n",
      "16.133333333333333 [1.52687946 1.03055864 0.50303516]\n",
      "16.2 [1.52597895 1.04615773 0.50293325]\n",
      "16.266666666666666 [1.52509433 1.06148972 0.50283316]\n",
      "16.333333333333332 [1.52422615 1.07654457 0.50273494]\n",
      "16.4 [1.52337491 1.09131311 0.50263865]\n",
      "16.466666666666665 [1.52254108 1.10578709 0.50254435]\n",
      "16.53333333333333 [1.52172504 1.11995916 0.50245207]\n",
      "16.6 [1.52092716 1.13382288 0.50236185]\n",
      "16.666666666666668 [1.52014771 1.14737269 0.50227374]\n",
      "16.733333333333334 [1.51938695 1.16060389 0.50218774]\n",
      "16.8 [1.51864507 1.17351264 0.50210389]\n",
      "16.866666666666667 [1.51792223 1.18609593 0.50202221]\n",
      "16.933333333333334 [1.51721852 1.19835156 0.50194269]\n",
      "17.0 [1.516534   1.21027809 0.50186535]\n",
      "17.066666666666666 [1.5158687  1.22187483 0.5017902 ]\n",
      "17.133333333333333 [1.51522258 1.23314182 0.50171721]\n",
      "17.2 [1.51459559 1.24407974 0.5016464 ]\n",
      "17.266666666666666 [1.51398762 1.25468996 0.50157774]\n",
      "17.333333333333332 [1.51339855 1.26497441 0.50151122]\n",
      "17.4 [1.5128282  1.27493562 0.50144682]\n",
      "17.466666666666665 [1.51227639 1.28457664 0.50138452]\n",
      "17.53333333333333 [1.51174289 1.29390101 0.5013243 ]\n",
      "17.6 [1.51122746 1.30291273 0.50126612]\n",
      "17.666666666666668 [1.51072982 1.31161621 0.50120995]\n",
      "17.733333333333334 [1.51024968 1.32001627 0.50115576]\n",
      "17.8 [1.50978674 1.32811802 0.50110352]\n",
      "17.866666666666667 [1.50934067 1.33592694 0.50105318]\n",
      "17.933333333333334 [1.50891113 1.34344875 0.50100471]\n",
      "18.0 [1.50849775 1.35068941 0.50095807]\n",
      "18.066666666666666 [1.50810019 1.35765511 0.50091322]\n",
      "18.133333333333333 [1.50771805 1.36435221 0.50087011]\n",
      "18.2 [1.50735097 1.37078721 0.5008287 ]\n",
      "18.266666666666666 [1.50699854 1.37696674 0.50078895]\n",
      "18.333333333333332 [1.50666038 1.38289753 0.50075081]\n",
      "18.4 [1.50633609 1.38858638 0.50071423]\n",
      "18.466666666666665 [1.50602527 1.39404014 0.50067918]\n",
      "18.53333333333333 [1.50572752 1.39926568 0.5006456 ]\n",
      "18.6 [1.50544243 1.40426986 0.50061345]\n",
      "18.666666666666668 [1.50516962 1.40905957 0.50058269]\n",
      "18.733333333333334 [1.50490869 1.41364163 0.50055327]\n",
      "18.8 [1.50465924 1.41802282 0.50052514]\n",
      "18.866666666666667 [1.50442088 1.42220988 0.50049827]\n",
      "18.933333333333334 [1.50419324 1.42620945 0.5004726 ]\n",
      "19.0 [1.50397592 1.4300281  0.5004481 ]\n",
      "19.066666666666666 [1.50376857 1.43367229 0.50042472]\n",
      "19.133333333333333 [1.50357081 1.43714838 0.50040243]\n",
      "19.2 [1.50338228 1.44046262 0.50038118]\n",
      "19.266666666666666 [1.50320263 1.44362112 0.50036093]\n",
      "19.333333333333332 [1.50303153 1.44662988 0.50034164]\n",
      "19.4 [1.50286863 1.44949476 0.50032328]\n",
      "19.466666666666665 [1.5027136  1.45222147 0.5003058 ]\n",
      "19.53333333333333 [1.50256612 1.45481558 0.50028918]\n",
      "19.6 [1.50242589 1.45728252 0.50027337]\n",
      "19.666666666666668 [1.5022926  1.45962756 0.50025835]\n",
      "19.733333333333334 [1.50216597 1.46185585 0.50024408]\n",
      "19.8 [1.50204569 1.46397234 0.50023052]\n",
      "19.866666666666667 [1.5019315  1.46598187 0.50021765]\n",
      "19.933333333333334 [1.50182314 1.4678891  0.50020544]\n",
      "20.0 [1.50172034 1.46969856 0.50019385]\n",
      "20.066666666666666 [1.50162285 1.47141462 0.50018287]\n",
      "20.133333333333333 [1.50153044 1.47304149 0.50017245]\n",
      "20.2 [1.50144286 1.47458324 0.50016258]\n",
      "20.266666666666666 [1.50135991 1.47604382 0.50015323]\n",
      "20.333333333333332 [1.50128135 1.47742699 0.50014438]\n",
      "20.4 [1.50120699 1.47873639 0.500136  ]\n",
      "20.466666666666665 [1.50113662 1.47997554 0.50012807]\n",
      "20.53333333333333 [1.50107005 1.4811478  0.50012057]\n",
      "20.6 [1.5010071  1.4822564  0.50011348]\n",
      "20.666666666666668 [1.5009476  1.48330445 0.50010677]\n",
      "20.733333333333334 [1.50089136 1.48429492 0.50010044]\n",
      "20.8 [1.50083823 1.48523068 0.50009445]\n",
      "20.866666666666667 [1.50078806 1.48611445 0.50008879]\n",
      "20.933333333333334 [1.50074069 1.48694887 0.50008346]\n",
      "21.0 [1.50069598 1.48773642 0.50007842]\n",
      "21.066666666666666 [1.5006538  1.48847951 0.50007367]\n",
      "21.133333333333333 [1.50061401 1.48918044 0.50006918]\n",
      "21.2 [1.50057649 1.48984139 0.50006496]\n",
      "21.266666666666666 [1.50054113 1.49046444 0.50006097]\n",
      "21.333333333333332 [1.5005078  1.4910516  0.50005722]\n",
      "21.4 [1.5004764  1.49160476 0.50005368]\n",
      "21.466666666666665 [1.50044683 1.49212574 0.50005035]\n",
      "21.53333333333333 [1.50041899 1.49261627 0.50004721]\n",
      "21.6 [1.50039279 1.49307798 0.50004426]\n",
      "21.666666666666668 [1.50036813 1.49351245 0.50004148]\n",
      "21.733333333333334 [1.50034494 1.49392117 0.50003886]\n",
      "21.8 [1.50032312 1.49430555 0.50003641]\n",
      "21.866666666666667 [1.50030261 1.49466694 0.5000341 ]\n",
      "21.933333333333334 [1.50028334 1.49500661 0.50003192]\n",
      "22.0 [1.50026522 1.49532579 0.50002988]\n",
      "22.066666666666666 [1.50024821 1.49562563 0.50002797]\n",
      "22.133333333333333 [1.50023223 1.49590722 0.50002617]\n",
      "22.2 [1.50021723 1.49617159 0.50002448]\n",
      "22.266666666666666 [1.50020315 1.49641975 0.50002289]\n",
      "22.333333333333332 [1.50018993 1.49665261 0.5000214 ]\n",
      "22.4 [1.50017754 1.49687106 0.50002   ]\n",
      "22.466666666666665 [1.50016591 1.49707594 0.50001869]\n",
      "22.53333333333333 [1.50015501 1.49726804 0.50001747]\n",
      "22.6 [1.50014479 1.49744811 0.50001631]\n",
      "22.666666666666668 [1.50013522 1.49761687 0.50001523]\n",
      "22.733333333333334 [1.50012625 1.49777497 0.50001422]\n",
      "22.8 [1.50011784 1.49792306 0.50001328]\n",
      "22.866666666666667 [1.50010997 1.49806173 0.50001239]\n",
      "22.933333333333334 [1.50010261 1.49819155 0.50001156]\n",
      "23.0 [1.50009571 1.49831306 0.50001078]\n",
      "23.066666666666666 [1.50008926 1.49842675 0.50001006]\n",
      "23.133333333333333 [1.50008323 1.49853311 0.50000938]\n",
      "23.2 [1.50007758 1.49863258 0.50000874]\n",
      "23.266666666666666 [1.50007231 1.49872558 0.50000815]\n",
      "23.333333333333332 [1.50006737 1.49881252 0.50000759]\n",
      "23.4 [1.50006276 1.49889377 0.50000707]\n",
      "23.466666666666665 [1.50005846 1.49896969 0.50000659]\n",
      "23.53333333333333 [1.50005443 1.4990406  0.50000613]\n",
      "23.6 [1.50005068 1.49910682 0.50000571]\n",
      "23.666666666666668 [1.50004717 1.49916865 0.50000531]\n",
      "23.733333333333334 [1.50004389 1.49922636 0.50000495]\n",
      "23.8 [1.50004084 1.49928022 0.5000046 ]\n",
      "23.866666666666667 [1.50003799 1.49933047 0.50000428]\n",
      "23.933333333333334 [1.50003533 1.49937735 0.50000398]\n",
      "24.0 [1.50003285 1.49942106 0.5000037 ]\n",
      "24.066666666666666 [1.50003053 1.49946182 0.50000344]\n",
      "24.133333333333333 [1.50002838 1.49949981 0.5000032 ]\n",
      "24.2 [1.50002637 1.49953521 0.50000297]\n",
      "24.266666666666666 [1.5000245  1.4995682  0.50000276]\n",
      "24.333333333333332 [1.50002276 1.49959892 0.50000256]\n",
      "24.4 [1.50002113 1.49962754 0.50000238]\n",
      "24.466666666666665 [1.50001962 1.49965418 0.50000221]\n",
      "24.53333333333333 [1.50001821 1.49967899 0.50000205]\n",
      "24.6 [1.5000169  1.49970207 0.5000019 ]\n",
      "24.666666666666668 [1.50001568 1.49972355 0.50000177]\n",
      "24.733333333333334 [1.50001455 1.49974353 0.50000164]\n",
      "24.8 [1.5000135  1.49976211 0.50000152]\n",
      "24.866666666666667 [1.50001252 1.49977939 0.50000141]\n",
      "24.933333333333334 [1.5000116  1.49979546 0.50000131]\n",
      "25.0 [1.50001076 1.49981039 0.50000121]\n",
      "25.066666666666666 [1.50000997 1.49982427 0.50000112]\n",
      "25.133333333333333 [1.50000924 1.49983716 0.50000104]\n",
      "25.2 [1.50000856 1.49984914 0.50000096]\n",
      "25.266666666666666 [1.50000793 1.49986026 0.50000089]\n",
      "25.333333333333332 [1.50000734 1.49987058 0.50000083]\n",
      "25.4 [1.5000068  1.49988017 0.50000077]\n",
      "25.466666666666665 [1.50000629 1.49988907 0.50000071]\n",
      "25.53333333333333 [1.50000583 1.49989732 0.50000066]\n",
      "25.6 [1.50000539 1.49990498 0.50000061]\n",
      "25.666666666666668 [1.50000499 1.49991208 0.50000056]\n",
      "25.733333333333334 [1.50000461 1.49991867 0.50000052]\n",
      "25.8 [1.50000427 1.49992478 0.50000048]\n",
      "25.866666666666667 [1.50000395 1.49993044 0.50000044]\n",
      "25.933333333333334 [1.50000365 1.49993569 0.50000041]\n",
      "26.0 [1.50000337 1.49994055 0.50000038]\n",
      "26.066666666666666 [1.50000312 1.49994506 0.50000035]\n",
      "26.133333333333333 [1.50000288 1.49994923 0.50000032]\n",
      "26.2 [1.50000266 1.49995309 0.5000003 ]\n",
      "26.266666666666666 [1.50000246 1.49995667 0.50000028]\n",
      "26.333333333333332 [1.50000227 1.49995998 0.50000026]\n",
      "26.4 [1.5000021  1.49996305 0.50000024]\n",
      "26.466666666666665 [1.50000194 1.49996588 0.50000022]\n",
      "26.53333333333333 [1.50000179 1.49996851 0.5000002 ]\n",
      "26.599999999999998 [1.50000165 1.49997094 0.50000019]\n",
      "26.666666666666668 [1.50000152 1.49997318 0.50000017]\n",
      "26.733333333333334 [1.5000014  1.49997526 0.50000016]\n",
      "26.8 [1.50000129 1.49997718 0.50000015]\n",
      "26.866666666666667 [1.50000119 1.49997895 0.50000013]\n",
      "26.933333333333334 [1.5000011  1.49998059 0.50000012]\n",
      "27.0 [1.50000102 1.49998211 0.50000011]\n",
      "27.066666666666666 [1.50000094 1.4999835  0.50000011]\n",
      "27.133333333333333 [1.50000086 1.4999848  0.5000001 ]\n",
      "27.2 [1.50000079 1.49998599 0.50000009]\n",
      "27.266666666666666 [1.50000073 1.49998709 0.50000008]\n",
      "27.333333333333332 [1.50000067 1.49998811 0.50000008]\n",
      "27.4 [1.50000062 1.49998905 0.50000007]\n",
      "27.466666666666665 [1.50000057 1.49998992 0.50000006]\n",
      "27.53333333333333 [1.50000053 1.49999071 0.50000006]\n",
      "27.599999999999998 [1.50000048 1.49999145 0.50000005]\n",
      "27.666666666666668 [1.50000045 1.49999213 0.50000005]\n",
      "27.733333333333334 [1.50000041 1.49999276 0.50000005]\n",
      "27.8 [1.50000038 1.49999334 0.50000004]\n",
      "27.866666666666667 [1.50000035 1.49999387 0.50000004]\n",
      "27.933333333333334 [1.50000032 1.49999436 0.50000004]\n",
      "28.0 [1.50000029 1.49999481 0.50000003]\n",
      "28.066666666666666 [1.50000027 1.49999523 0.50000003]\n",
      "28.133333333333333 [1.50000025 1.49999562 0.50000003]\n",
      "28.2 [1.50000023 1.49999597 0.50000003]\n",
      "28.266666666666666 [1.50000021 1.4999963  0.50000002]\n",
      "28.333333333333332 [1.50000019 1.4999966  0.50000002]\n",
      "28.4 [1.50000018 1.49999687 0.50000002]\n",
      "28.466666666666665 [1.50000016 1.49999713 0.50000002]\n",
      "28.53333333333333 [1.50000015 1.49999736 0.50000002]\n",
      "28.599999999999998 [1.50000014 1.49999758 0.50000002]\n",
      "28.666666666666668 [1.50000013 1.49999777 0.50000001]\n",
      "28.733333333333334 [1.50000012 1.49999796 0.50000001]\n",
      "28.8 [1.50000011 1.49999812 0.50000001]\n",
      "28.866666666666667 [1.5000001  1.49999828 0.50000001]\n",
      "28.933333333333334 [1.50000009 1.49999842 0.50000001]\n",
      "29.0 [1.50000008 1.49999855 0.50000001]\n",
      "29.066666666666666 [1.50000008 1.49999867 0.50000001]\n",
      "29.133333333333333 [1.50000007 1.49999878 0.50000001]\n",
      "29.2 [1.50000006 1.49999888 0.50000001]\n",
      "29.266666666666666 [1.50000006 1.49999897 0.50000001]\n",
      "29.333333333333332 [1.50000005 1.49999906 0.50000001]\n",
      "29.4 [1.50000005 1.49999914 0.50000001]\n",
      "29.466666666666665 [1.50000004 1.49999921 0.50000001]\n",
      "29.53333333333333 [1.50000004 1.49999928 0.5       ]\n",
      "29.599999999999998 [1.50000004 1.49999934 0.5       ]\n",
      "29.666666666666668 [1.50000003 1.49999939 0.5       ]\n",
      "29.733333333333334 [1.50000003 1.49999944 0.5       ]\n",
      "29.8 [1.50000003 1.49999949 0.5       ]\n",
      "29.866666666666667 [1.50000003 1.49999953 0.5       ]\n",
      "29.933333333333334 [1.50000002 1.49999957 0.5       ]\n",
      "30.0 [1.50000002 1.49999961 0.5       ]\n"
     ]
    }
   ],
   "source": [
    "# Output the outlet partial pressures\n",
    "for j in range(ts):\n",
    "    #print(j+1,p[j][-1][0])\n",
    "    print((j+1)*d_t,p[j][-1][:])"
   ]
  }
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