{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# ThermoSTEAM 101"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pure component chemical models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thermosteam packages chemical and mixture thermodynamic models in a flexible framework that allows users to fully customize and extend the models, as well as create new models. Central to all thermodynamic algorithms is the [Chemical](../API/thermosteam/Chemical.txt) object, which contain the same thermodynamic models and data as [thermo](https://thermo.readthedocs.io)'s Chemical objects, but present an API more suitable for [BioSTEAM](../index.txt)'s needs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chemical: Water (phase_ref='l')\n",
      "[Names]  CAS: 7732-18-5\n",
      "         InChI: H2O/h1H2\n",
      "         InChI_key: XLYOFNOQVPJJNP-U...\n",
      "         common_name: water\n",
      "         iupac_name: ('oxidane',)\n",
      "         pubchemid: 962\n",
      "         smiles: O\n",
      "         formula: H2O\n",
      "[Groups] Dortmund: <1H2O>\n",
      "         UNIFAC: <1H2O>\n",
      "         PSRK: <1H2O>\n",
      "         NIST: <Empty>\n",
      "[Data]   MW: 18.015 g/mol\n",
      "         Tm: 273.15 K\n",
      "         Tb: 373.12 K\n",
      "         Tt: 273.16 K\n",
      "         Tc: 647.1 K\n",
      "         Pt: 611.65 Pa\n",
      "         Pc: 2.2064e+07 Pa\n",
      "         Vc: 5.5948e-05 m^3/mol\n",
      "         Hf: -2.8582e+05 J/mol\n",
      "         S0: 70 J/K/mol\n",
      "         LHV: -44011 J/mol\n",
      "         HHV: -0 J/mol\n",
      "         Hfus: 6010 J/mol\n",
      "         Sfus: None\n",
      "         omega: 0.3443\n",
      "         dipole: 1.85 Debye\n",
      "         similarity_variable: 0.16653\n",
      "         iscyclic_aliphatic: 0\n",
      "         combustion: {'H2O': 1.0}\n"
     ]
    }
   ],
   "source": [
    "import thermosteam as tmo\n",
    "# Initialize chemical with an identifier (e.g. by name, CAS, InChI...)\n",
    "Water = tmo.Chemical('Water') \n",
    "Water.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Access pure component data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7732-18-5'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CAS number\n",
    "Water.CAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.01528"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Molecular weight (g/mol)\n",
    "Water.MW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "373.124295848"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boiling point (K)\n",
    "Water.Tb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperature (in Kelvin) and pressure (in Pascal) dependent properties can be computed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101417.99665995422"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vapor pressure (Pa)\n",
    "Water.Psat(T=373.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07197220523022962"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Surface tension (N/m)\n",
    "Water.sigma(T=298.15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.8068319928499427e-05"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Liquid molar volume (m^3/mol)\n",
    "Water.V(phase='l', T=298.15, P=101325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02350635143332423"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vapor molar volume (m^3/mol)\n",
    "Water.V(phase='g', T=298.15, P=101325)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "997.0644792261086"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# With user-specified units of measure:\n",
    "Water.get_property('rho', 'kg/m3', phase='l', T=298.15, P=101325)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperature dependent properties are managed by objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VaporPressure(CASRN=\"7732-18-5\", Tb=373.124295848, Tc=647.096, Pc=22064000.0, omega=0.3443, extrapolation=\"AntoineAB|DIPPR101_ABC\", method=\"IAPWS\")"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Water.Psat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Phase dependent properties have attributes with model handles for each phase:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PhaseTPHandle(phase, T, P=None) -> V [m^3/mol]\n"
     ]
    }
   ],
   "source": [
    "Water.V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "VolumeLiquid(CASRN=\"7732-18-5\", MW=18.01528, Tb=373.124295848, Tc=647.096, Pc=22064000.0, Vc=5.59480372671e-05, Zc=0.22943845208106295, omega=0.3443, dipole=1.85, Psat=VaporPressure(CASRN=\"7732-18-5\", Tb=373.124295848, Tc=647.096, Pc=22064000.0, omega=0.3443, extrapolation=\"AntoineAB|DIPPR101_ABC\", method=\"IAPWS\"), eos=[PR(Tc=647.096, Pc=22064000.0, omega=0.3443, T=298.15, P=101325.0)], extrapolation=\"constant\", method=\"HEOS_FIT\", method_P=\"COSTALD_COMPRESSED\", tabular_extrapolation_permitted=True)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Water.V.l"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new model can be added easily using `add_method`, for example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'USER_METHOD'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def User_antoine_model(T):\n",
    "     return 10.0**(10.116 -  1687.537 / (T - 42.98))\n",
    "Water.Psat.add_method(f=User_antoine_model, Tmin=273.20, Tmax=473.20)\n",
    "Water.Psat.method"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `add_method` method is a high level interface that even lets you create a constant model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.31"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Water.Cn.l.add_method(75.31) \n",
    "Water.Cn('l', 350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose what model to use through the `method` attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'COOLPROP',\n",
       " 'CRCSTD',\n",
       " 'DADGOSTAR_SHAW',\n",
       " 'HEOS_FIT',\n",
       " 'JANAF',\n",
       " 'POLING_CONST',\n",
       " 'ROWLINSON_BONDI',\n",
       " 'ROWLINSON_POLING',\n",
       " 'USER_METHOD',\n",
       " 'WEBBOOK_SHOMATE',\n",
       " 'ZABRANSKY_SPLINE_C'}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Water.Cn.l.all_methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75.6223925836403"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Water.Cn.l.method = 'ZABRANSKY_SPLINE_C'\n",
    "Water.Cn('l', 350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Managing chemical sets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define multiple chemicals as a [Chemicals](../API/thermosteam/Chemicals.txt) object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chemicals([Water, Ethanol])\n"
     ]
    }
   ],
   "source": [
    "chemicals = tmo.Chemicals(['Water', 'Ethanol'])\n",
    "chemicals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The chemicals are attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Chemical('Water'), Chemical('Ethanol'))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(chemicals.Water, chemicals.Ethanol)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chemicals are indexable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chemical('Water')\n"
     ]
    }
   ],
   "source": [
    "Water = chemicals['Water']\n",
    "print(repr(Water))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Chemical('Ethanol'), Chemical('Water')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chemicals['Ethanol', 'Water']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chemicals are also iterable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chemical('Water')\n",
      "Chemical('Ethanol')\n"
     ]
    }
   ],
   "source": [
    "for chemical in chemicals:\n",
    "    print(repr(chemical))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More chemicals can also be appended:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chemicals([Water, Ethanol, Propanol])\n"
     ]
    }
   ],
   "source": [
    "Propanol = tmo.Chemical('Propanol')\n",
    "chemicals.append(Propanol)\n",
    "chemicals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main benefit of using a Chemicals object, is that they can be compiled and used as part of a thermodynamic property package, as defined through a [Thermo](../API/thermosteam/Thermo.txt) object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Thermo(\n",
      "    chemicals=CompiledChemicals([Water, Ethanol, Propanol]),\n",
      "    mixture=IdealMixture(...\n",
      "        include_excess_energies=False\n",
      "    ),\n",
      "    Gamma=DortmundActivityCoefficients,\n",
      "    Phi=IdealFugacityCoefficients,\n",
      "    PCF=MockPoyintingCorrectionFactors\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "# A Thermo object is built with an iterable of Chemicals or their IDs.\n",
    "# Default mixture, thermodynamic equilibrium models are selected.\n",
    "thermo = tmo.Thermo(chemicals)\n",
    "thermo.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Creating a thermo property package](./Property_packages.ipynb), may be a little challenging if some chemicals cannot be found in the database, in which case they can be built from scratch. A complete example on how this can be done is available in another [tutorial](./Property_packages.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Material and energy balance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A [Stream](../API/thermosteam/Stream.txt) object is the main interface for estimating thermodynamic properties, vapor-liquid equilibrium, and material and energy balances. First set the thermo property package and we can start creating streams:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s1\n",
      "phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "flow (kg/hr): Water    20\n",
      "              Ethanol  20\n"
     ]
    }
   ],
   "source": [
    "# This also works: tmo.settings.set_thermo(['Water', 'Ethanol', 'Propanol'])\n",
    "tmo.settings.set_thermo(thermo)\n",
    "s1 = tmo.Stream('s1', Water=20, Ethanol=20, units='kg/hr')\n",
    "s1.show(flow='kg/hr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create another stream at a higher temperature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s2\n",
      "phase: 'l', T: 350 K, P: 101325 Pa\n",
      "flow (kg/hr): Water  10\n"
     ]
    }
   ],
   "source": [
    "s2 = tmo.Stream('s2', Water=10, units='kg/hr', T=350, P=101325)\n",
    "s2.show(flow='kg/hr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mix both streams into a new one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s_mix\n",
      "phase: 'l', T: 310.54 K, P: 101325 Pa\n",
      "flow (kg/hr): Water    30\n",
      "              Ethanol  20\n"
     ]
    }
   ],
   "source": [
    "s_mix = tmo.Stream('s_mix')\n",
    "s_mix.mix_from([s1, s2])\n",
    "s_mix.show(flow='kg/hr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the energy balance through enthalpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.092726157978177e-12"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_mix.H - (s1.H + s2.H)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the balance is not perfect as the solver stops within a small temperature tolerance. However, the approximation is less than 0.01% off:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00%\n"
     ]
    }
   ],
   "source": [
    "error = s_mix.H - (s1.H + s2.H)\n",
    "percent_error = 100 * error / (s1.H + s2.H)\n",
    "print(f\"{percent_error:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split the mixture to two streams by defining the component splits:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s1\n",
      "phase: 'l', T: 310.54 K, P: 101325 Pa\n",
      "flow (kg/hr): Ethanol  20\n",
      "Stream: s2\n",
      "phase: 'l', T: 310.54 K, P: 101325 Pa\n",
      "flow (kg/hr): Water  30\n"
     ]
    }
   ],
   "source": [
    "# First define an array of component splits\n",
    "component_splits = s_mix.chemicals.array(['Water', 'Ethanol'], [0, 1])\n",
    "s_mix.split_to(s1, s2, component_splits)\n",
    "s1.T = s2.T = s_mix.T # Take care of energy balance\n",
    "s1.show(flow='kg/hr')\n",
    "s2.show(flow='kg/hr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Separate out stream from mixture:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s_mix\n",
      "phase: 'l', T: 310.54 K, P: 101325 Pa\n",
      "flow (kg/hr): Ethanol  20\n"
     ]
    }
   ],
   "source": [
    "s_mix.separate_out(s2)\n",
    "s_mix.show(flow='kg/hr') # Only enthanol will remain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the energy balance still holds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00%\n"
     ]
    }
   ],
   "source": [
    "error = s_mix.H - s1.H\n",
    "percent_error = 100 * error / s2.H\n",
    "print(f\"{percent_error:.2%}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flow rates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most convenient way to get and set flow rates is through the `get_flow` and `set_flow` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set and get flow of a single chemical\n",
    "# in gallons per minute\n",
    "s1.set_flow(1, 'gpm', 'Water')\n",
    "s1.get_flow('gpm', 'Water')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., 20.])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set and get flows of many chemicals\n",
    "# in kilograms per hour\n",
    "s1.set_flow([10, 20], 'kg/hr', ('Ethanol', 'Water'))\n",
    "s1.get_flow('kg/hr', ('Ethanol', 'Water'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to index flow rate data using chemical IDs through the `imol`, `imass`, and `ivol` [indexers](../API/thermosteam/indexer/index.txt):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ChemicalMolarFlowIndexer (kmol/hr):\n",
      "(l) Water    1.11\n",
      "    Ethanol  0.217\n"
     ]
    }
   ],
   "source": [
    "s1.imol.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1101687012358397"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.imol['Water']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.217, 1.11 ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.imol['Ethanol', 'Water']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All flow rates are stored as a sparse array in the `mol` attribute. These arrays work just like numpy arrays, but are more scalable (saving memory and increasing speed) for sparse chemical data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sparse([1.11 , 0.217, 0.   ])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.mol # Molar flow rates [kmol/hr]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mass and volumetric flow rates are also available for convenience:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sparse([20., 10.,  0.])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sparse([0.02 , 0.013, 0.   ])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.vol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data of these arrays are linked to the molar flows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.01528"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mass flows are always up to date with molar flows\n",
    "s1.mol[0] = 1\n",
    "s1.mass[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Changing mass flows changes molar flows\n",
    "s1.mass[0] *= 2\n",
    "s1.mol[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sparse([38.031, 12.   ,  2.   ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New arrays are not linked to molar flows\n",
    "s1.mass + 2 # A new array is created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15.34352"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Array methods are also the same\n",
    "s1.mass.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thermal condition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Temperature and pressure can be get and set through the `T` and `P` attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s1\n",
      "phase: 'l', T: 400 K, P: 202650 Pa\n",
      "flow (kmol/hr): Water    2\n",
      "                Ethanol  0.217\n"
     ]
    }
   ],
   "source": [
    "s1.T = 400.\n",
    "s1.P = 2 * 101325.\n",
    "s1.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The phase may also be changed ('s' for solid, 'l' for liquid, and 'g' for gas):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "s1.phase = 'g'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that VLE is not enforced, but it is possible to perform. For now, just check that the dew point is lower than the actual temperature to assert it must be gas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DewPointValues(T=390.81, P=202650, IDs=('Water', 'Ethanol'), z=[0.902 0.098], x=[0.991 0.009])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp = s1.dew_point_at_P() # Dew point at constant pressure\n",
    "dp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp.T < s1.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is also possible to get and set in other units of measure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.set_property('P', 1, 'atm')\n",
    "s1.get_property('P', 'atm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "256.99999999999994"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.set_property('T', 125, 'degC')\n",
    "s1.get_property('T', 'degF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Enthalpy and entropy can also be set. In both cases, an energy balance is made to solve for temperature at isobaric conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184.95887197182628"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.H = 1.05 * s1.H \n",
    "s1.get_property('T', 'degC') # Temperature should go up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "74.98140649417962"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1.S = 0.95 * s1.S\n",
    "s1.get_property('T', 'degC') # Temperature should go down"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thermal properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thermodynamic properties are pressure, temperature and phase dependent. In the following examples, let's just use water as it is easier to check properties:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "997.0644792261086"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_water = tmo.Stream('s_water', Water=1, units='kg/hr')\n",
    "s_water.rho # Density [kg/m^3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "973.7419512246709"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_water.T = 350\n",
    "s_water.rho # Density changes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get properties in different units:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06324769600985489"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_water.get_property('sigma', 'N/m') # Surface tension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01850108232200766"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_water.get_property('V', 'm3/kmol') # Molar volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flow properties"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several flow properties are available, such as net material and energy flow rates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05550843506179199"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net molar flow rate [kmol/hr]\n",
    "s_water.F_mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net mass flow rate [kg/hr]\n",
    "s_water.F_mass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00102696612664403"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Net volumetric flow rate [m3/hr]\n",
    "s_water.F_vol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216.91884940751328"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Enthalpy flow rate [kJ/hr]\n",
    "s_water.H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.556326412564778"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Entropy flow rate [kJ/hr]\n",
    "s_water.S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.194462916586701"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Capacity flow rate [J/K]\n",
    "s_water.C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Phase equilibrium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When streams are created, by default, they do not perform phase equilibrium. To perform phase equilibrium assuming 2 liquid phases and 1 gas phase is possible, pass `vlle=True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: s\n",
      "phase: 'g', T: 400 K, P: 101325 Pa\n",
      "flow (kmol/hr): Water    1\n",
      "                Ethanol  1\n"
     ]
    }
   ],
   "source": [
    "s = tmo.Stream('s', Water=1, Ethanol=1, T=400, P=101325,\n",
    "               phase='l', # Guess phase\n",
    "               vlle=True)\n",
    "s.show() # Notice how stream is a gas (not a liquid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Performing VLLE is quite slow and it is not recommended to perform VLLE calculations unless 3 phases can exist. When possible, perform phase equilibrium assuming 2 phases instead. Before moving into vapor-liquid and liquid-liquid equilibrium calculations, it may be useful to have a look at the phase envelopes to understand chemical interactions and ultimately how they partition between phases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the binary phase evelope of two chemicals in vapor-liquid equilibrium at constant pressure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "eq = tmo.equilibrium # Thermosteam's equilibrium module\n",
    "eq.plot_vle_binary_phase_envelope(['Ethanol', 'Water'], P=101325)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the ternary phase diagram of three chemicals in liquid-liquid equilibrium at constant pressure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# You'll need to \"pip install python-ternary\" to run this line\n",
    "eq.plot_lle_ternary_diagram('Water', 'Ethanol', 'EthylAcetate', T=298.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vapor-liquid equilibrium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vapor-liquid equilibrium can be performed by setting 2 degrees of freedom from the following list: `T` (Temperature; in K), `P` (Pressure; in Pa), `V` (Vapor fraction), `H` (Enthalpy; in kJ/hr), and `S` (Entropy; in kJ/K/hr).\n",
    "\n",
    "For example, set vapor fraction and pressure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiStream: s_eq\n",
      "phases: ('g', 'l'), T: 353.94 K, P: 101325 Pa\n",
      "composition (%): (g) Water    38.7\n",
      "                     Ethanol  61.3\n",
      "                     -------  10 kmol/hr\n",
      "                 (l) Water    61.3\n",
      "                     Ethanol  38.7\n",
      "                     -------  10 kmol/hr\n"
     ]
    }
   ],
   "source": [
    "s_eq = tmo.Stream('s_eq', Water=10, Ethanol=10)\n",
    "s_eq.vle(V=0.5, P=101325)\n",
    "s_eq.show(composition=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the stream is now a MultiStream to manage multiple phases. Each phase can be accessed separately too:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: \n",
      "phase: 'l', T: 353.94 K, P: 101325 Pa\n",
      "flow (kmol/hr): Water    6.13\n",
      "                Ethanol  3.87\n"
     ]
    }
   ],
   "source": [
    "s_eq['l'].show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stream: \n",
      "phase: 'g', T: 353.94 K, P: 101325 Pa\n",
      "flow (kmol/hr): Water    3.87\n",
      "                Ethanol  6.13\n"
     ]
    }
   ],
   "source": [
    "s_eq['g'].show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the phase of these substreams cannot be changed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "tags": [
     "nbval-raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "phase is locked",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[68], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s_eq[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mg\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mphase \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124ml\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
      "File \u001b[1;32m~\\code\\biosteam\\thermosteam\\thermosteam\\_stream.py:973\u001b[0m, in \u001b[0;36mStream.phase\u001b[1;34m(self, phase)\u001b[0m\n\u001b[0;32m    971\u001b[0m \u001b[38;5;129m@phase\u001b[39m\u001b[38;5;241m.\u001b[39msetter\n\u001b[0;32m    972\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mphase\u001b[39m(\u001b[38;5;28mself\u001b[39m, phase):\n\u001b[1;32m--> 973\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_imol\u001b[38;5;241m.\u001b[39m_phase\u001b[38;5;241m.\u001b[39mphase \u001b[38;5;241m=\u001b[39m phase\n",
      "File \u001b[1;32m~\\code\\biosteam\\thermosteam\\thermosteam\\_phase.py:189\u001b[0m, in \u001b[0;36mLockedPhase.__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m    187\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setattr__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name, value):\n\u001b[0;32m    188\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m value \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mphase:\n\u001b[1;32m--> 189\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mphase is locked\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: phase is locked"
     ]
    }
   ],
   "source": [
    "s_eq['g'].phase = 'l'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, the most convenient way to get and set flow rates is through the `get_flow` and `set_flow` methods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set flow of liquid water\n",
    "s_eq.set_flow(1, 'gpm', ('l', 'Water'))\n",
    "s_eq.get_flow('gpm', ('l', 'Water'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10., 20.])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set multiple liquid flows\n",
    "key = ('l', ('Ethanol', 'Water'))\n",
    "s_eq.set_flow([10, 20], 'kg/hr', key)\n",
    "s_eq.get_flow('kg/hr', key)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chemical flows across all phases can be retrieved if no phase is given:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 89.791, 292.216])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get water and ethanol flows summed across all phases\n",
    "s_eq.get_flow('kg/hr', ('Water', 'Ethanol'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, setting chemical data of MultiStream objects requires the phase to be specified:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": [
     "nbval-raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "multiple phases present; must include phase key to set chemical data",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[72], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s_eq\u001b[38;5;241m.\u001b[39mset_flow([\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m20\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkg/hr\u001b[39m\u001b[38;5;124m'\u001b[39m, (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWater\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEthanol\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
      "File \u001b[1;32m~\\code\\biosteam\\thermosteam\\thermosteam\\_multi_stream.py:458\u001b[0m, in \u001b[0;36mMultiStream.set_flow\u001b[1;34m(self, data, units, key)\u001b[0m\n\u001b[0;32m    456\u001b[0m name, factor \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_flow_name_and_factor(units)\n\u001b[0;32m    457\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mi\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m name)\n\u001b[1;32m--> 458\u001b[0m indexer[key] \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(data, dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mfloat\u001b[39m) \u001b[38;5;241m/\u001b[39m factor\n",
      "File \u001b[1;32m~\\code\\biosteam\\thermosteam\\thermosteam\\indexer.py:1036\u001b[0m, in \u001b[0;36mMaterialIndexer.__setitem__\u001b[1;34m(self, key, data)\u001b[0m\n\u001b[0;32m   1034\u001b[0m index, kind, sum_across_phases \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_index_data(key)\n\u001b[0;32m   1035\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sum_across_phases:\n\u001b[1;32m-> 1036\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultiple phases present; must include phase key \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1037\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto set chemical data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1038\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1039\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mIndexError\u001b[0m: multiple phases present; must include phase key to set chemical data"
     ]
    }
   ],
   "source": [
    "s_eq.set_flow([10, 20], 'kg/hr', ('Water', 'Ethanol'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to Stream objects, all flow rates can be accessed through the `imol`, `imass`, and `ivol` attributes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MolarFlowIndexer (kmol/hr):\n",
      "(g) Water     3.87\n",
      "    Ethanol   6.13\n",
      "(l) Water     1.11\n",
      "    Ethanol   0.217\n"
     ]
    }
   ],
   "source": [
    "s_eq.imol # Molar flow rates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.1101687012358397"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index a single chemical in the liquid phase\n",
    "s_eq.imol['l', 'Water']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.217, 1.11 ])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index multiple chemicals in the liquid phase\n",
    "s_eq.imol['l', ('Ethanol', 'Water')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sparse([3.874, 6.126, 0.   ])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index the vapor phase\n",
    "s_eq.imol['g']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.343, 4.984])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index flow of chemicals summed across all phases\n",
    "s_eq.imol['Ethanol', 'Water']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because multiple phases are present, overall chemical flows in MultiStream objects cannot be set like in Stream objects:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": [
     "nbval-raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "multiple phases present; must include phase key to set chemical data",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[78], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s_eq\u001b[38;5;241m.\u001b[39mimol[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mEthanol\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mWater\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m0\u001b[39m]\n",
      "File \u001b[1;32m~\\code\\biosteam\\thermosteam\\thermosteam\\indexer.py:1036\u001b[0m, in \u001b[0;36mMaterialIndexer.__setitem__\u001b[1;34m(self, key, data)\u001b[0m\n\u001b[0;32m   1034\u001b[0m index, kind, sum_across_phases \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_index_data(key)\n\u001b[0;32m   1035\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sum_across_phases:\n\u001b[1;32m-> 1036\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mIndexError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmultiple phases present; must include phase key \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1037\u001b[0m                      \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mto set chemical data\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m   1038\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kind \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1039\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[1;31mIndexError\u001b[0m: multiple phases present; must include phase key to set chemical data"
     ]
    }
   ],
   "source": [
    "s_eq.imol['Ethanol', 'Water'] = [1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Chemical flows must be set by phase:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "s_eq.imol['l', ('Ethanol', 'Water')] = [1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One main difference between a [MultiStream](../API/thermosteam/MultiStream.txt) object and a [Stream](../API/thermosteam/Stream.txt) object is that the `mol` attribute no longer stores any data, it simply returns the total flow rate of each chemical. Setting an element of the array raises an error to prevent the wrong assumption that the data is linked:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sparse([3.874, 7.126, 0.   ])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s_eq.mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": [
     "nbval-raises-exception"
    ]
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "assignment destination is read-only",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[81], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m s_eq\u001b[38;5;241m.\u001b[39mmol[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[1;32m~\\code\\biosteam\\thermosteam\\thermosteam\\base\\sparse.py:1646\u001b[0m, in \u001b[0;36mSparseVector.__setitem__\u001b[1;34m(self, index, value)\u001b[0m\n\u001b[0;32m   1645\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__setitem__\u001b[39m(\u001b[38;5;28mself\u001b[39m, index, value):\n\u001b[1;32m-> 1646\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mread_only: \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124massignment destination is read-only\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m   1647\u001b[0m     dct \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdct\n\u001b[0;32m   1648\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m index\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28mtuple\u001b[39m:\n",
      "\u001b[1;31mValueError\u001b[0m: assignment destination is read-only"
     ]
    }
   ],
   "source": [
    "s_eq.mol[0] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that for both Stream and MultiStream objects, `get_flow`, `imol`, and `mol` return chemical flows across all phases when given only chemical IDs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Liquid-liquid equilibrium"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Liquid-liquid equilibrium (LLE) only requires the temperature. Pressure is not a significant variable as liquid fungacity coefficients are not a strong function of pressure. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "tags": [
     "nbval-skip"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiStream: liquid_mixture\n",
      "phases: ('L', 'l'), T: 300 K, P: 101325 Pa\n",
      "flow (kmol/hr): (L) Water    98.5\n",
      "                    Butanol  1.21\n",
      "                    Octane   0.00198\n",
      "                (l) Water    1.46\n",
      "                    Butanol  3.79\n",
      "                    Octane   100\n"
     ]
    }
   ],
   "source": [
    "tmo.settings.set_thermo(['Water', 'Butanol', 'Octane'])\n",
    "liquid_mixture = tmo.Stream('liquid_mixture', Water=100, Octane=100, Butanol=5)\n",
    "liquid_mixture.lle(T=300)\n",
    "liquid_mixture.show()"
   ]
  }
 ],
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