PIS22-24_v2 (1).ipynb

{
 "cells": [
  {
   "attachments": {
    "Sketch%20pile.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "2d0bb5f6",
   "metadata": {},
   "source": [
    "![Sketch%20pile.png](attachment:Sketch%20pile.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "9f74a2cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "from pyomo.environ import ConcreteModel, SolverFactory, value, units,  TransformationFactory  # we import the solver model, object model, and results in units\n",
    "from idaes.core import FlowsheetBlock # we import the flowsheet to contain our model\n",
    "from pyomo.network import Arc, SequentialDecomposition\n",
    "import idaes.logger as idaeslog  # we import to set output levels\n",
    "from idaes.generic_models.properties import iapws95  # property of ideal water and steam\n",
    "from idaes.generic_models.properties.helmholtz.helmholtz import (PhaseType)\n",
    "from idaes.core.util.model_statistics import degrees_of_freedom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "ebf01474",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = ConcreteModel()\n",
    "m.fs = FlowsheetBlock(default={\"dynamic\": False}) # false in this case steady state\n",
    "m.fs.properties = iapws95.Iapws95ParameterBlock(default={\"phase_presentation\":PhaseType.L}) # we set only liquid water phase is in our system"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4a7e7e8",
   "metadata": {},
   "source": [
    "### PUMPS PARAMETERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "cd2afffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from idaes.generic_models.unit_models.pressure_changer import Pump"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "05768dbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.pump_24_A = Pump(default={\"property_package\": m.fs.properties}) # we include a pump to our flowsheet \n",
    "m.fs.pump_24_B = Pump(default={\"property_package\": m.fs.properties}) # we include a pump to our flowsheet \n",
    "m.fs.pump_24_C = Pump(default={\"property_package\": m.fs.properties}) # we include a pump to our flowsheet "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "5042f040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The initial DOF is 15\n"
     ]
    }
   ],
   "source": [
    "DOF_initial = degrees_of_freedom(m)\n",
    "# help (degrees_of_freedom) # help (func) describes how the function works\n",
    "print(\"The initial DOF is {0}\".format(DOF_initial)) # we have # inputs to fulffill to make the problem square, those are describe in the unit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "f3c0dd7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "f_24A = 100\n",
    "f_24B = 102\n",
    "f_24C = 100\n",
    "\n",
    "m.fs.pump_24_A.inlet.flow_mol[0].fix(f_24A) # set properties of flow inlet in this case water inlet (0)\n",
    "m.fs.pump_24_A.inlet.enth_mol[0].fix(value(iapws95.htpx(T=333.15*units.K, P=101325*units.Pa))) # J/mol # conditions of water inlyer\n",
    "m.fs.pump_24_A.inlet.pressure[0].fix(101325) # Pa # set properties of pressure in this case water inlet (0)\n",
    "m.fs.pump_24_A.outlet.pressure[0].fix(1896058) # set pressure outlet \n",
    "m.fs.pump_24_A.efficiency_pump.fix(0.75) # set efficiency of the pump \n",
    "\n",
    "m.fs.pump_24_B.inlet.flow_mol[0].fix(f_24B) # set properties of flow inlet in this case water inlet (0)\n",
    "m.fs.pump_24_B.inlet.enth_mol[0].fix(value(iapws95.htpx(T=333.15*units.K, P=101325*units.Pa))) # J/mol # conditions of water inlyer\n",
    "m.fs.pump_24_B.inlet.pressure[0].fix(101325) # Pa # set properties of pressure in this case water inlet (0)\n",
    "m.fs.pump_24_B.outlet.pressure[0].fix(1896058) # set pressure outlet \n",
    "m.fs.pump_24_B.efficiency_pump.fix(0.9) # set efficiency of the pump \n",
    "\n",
    "m.fs.pump_24_C.inlet.flow_mol[0].fix(f_24C) # set properties of flow inlet in this case water inlet (0)\n",
    "m.fs.pump_24_C.inlet.enth_mol[0].fix(value(iapws95.htpx(T=333.15*units.K, P=101325*units.Pa))) # J/mol # conditions of water inlyer\n",
    "m.fs.pump_24_C.inlet.pressure[0].fix(101325) # Pa # set properties of pressure in this case water inlet (0)\n",
    "m.fs.pump_24_C.outlet.pressure[0].fix(1896058) # set pressure outlet \n",
    "m.fs.pump_24_C.efficiency_pump.fix(0.6) # set efficiency of the pump "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "f9b11dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The final degrees of freedom is: 0\n"
     ]
    }
   ],
   "source": [
    "DOF_final = degrees_of_freedom(m)\n",
    "print('The final degrees of freedom is: {0}'.format(DOF_final)) # we check to ensure that the system is now square. You should see that the degrees of freedom is now 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81d7c91f",
   "metadata": {},
   "source": [
    "### MIXER PARAMETERS MX_24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "93f20975",
   "metadata": {},
   "outputs": [],
   "source": [
    " from idaes.generic_models.unit_models.mixer import Mixer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "95cae035",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.MX_24 = Mixer (default={\"property_package\": m.fs.properties,\n",
    "                    \"inlet_list\":[m.fs.pump_24_A.inlet.flow_mol[0], \n",
    "                                  m.fs.pump_24_B.inlet.flow_mol[0],\n",
    "                                  m.fs.pump_24_C.inlet.flow_mol[0]]})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b0fb939",
   "metadata": {},
   "source": [
    "### VALVE PARAMETERS PV_25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "77b8d727",
   "metadata": {},
   "outputs": [],
   "source": [
    "from idaes.generic_models.unit_models.valve import Valve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "ecc8b952",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.PV_25 = Valve (default={\"property_package\": m.fs.properties})\n",
    "fin = 202 # mol/s # 🤷‍♂️ how to fis this value as the difference from the product to Water_PAD from the total transfered from the pumps or from the CV of the valve\n",
    "pin = 1895058 # Pa # 🤷‍♂️ how to fix this with a pressure from from pumps 22\n",
    "pout = 1007058 # Pa # 🤷‍♂️ how to set this from the CV of the valve \n",
    "tin = 340 # K\n",
    "hin = iapws95.htpx(T=tin*units.K, P=pin*units.Pa) # J/mol\n",
    "# Calculate the flow coefficient to give 1000 mol/s flow with given P\n",
    "cv = fin/numpy.sqrt(pin - pout)/0.5\n",
    "# set inlet\n",
    "m.fs.PV_25.inlet.enth_mol[0].fix(hin)\n",
    "m.fs.PV_25.inlet.flow_mol[0].fix(fin)\n",
    "m.fs.PV_25.inlet.flow_mol[0].unfix()\n",
    "m.fs.PV_25.inlet.pressure[0].fix(pin)\n",
    "m.fs.PV_25.outlet.pressure[0].fix(pout)\n",
    "m.fs.PV_25.Cv.fix(cv)\n",
    "m.fs.PV_25.valve_opening.fix(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82ce8e1a",
   "metadata": {},
   "source": [
    "### VALVE PARAMETERS PV_24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "6b96096e",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.PV_24 = Valve (default={\"property_package\": m.fs.properties})\n",
    "fin = 0.1 # mol/s # 🤷‍♂️ how to fis this value as the difference from the product to Water_PAD from the total transfered from the pumps or from the CV of the valve\n",
    "pin = 1895058 # Pa # 🤷‍♂️ how to fix this with a pressure from from pumps 22\n",
    "pout = 101325 # Pa # 🤷‍♂️ how to set this from the CV of the valve \n",
    "tin = 340 # K\n",
    "hin = iapws95.htpx(T=tin*units.K, P=pin*units.Pa) # J/mol\n",
    "# Calculate the flow coefficient to give 1000 mol/s flow with given P\n",
    "cv = fin/numpy.sqrt(pin - pout)/0.5\n",
    "# set inlet\n",
    "m.fs.PV_24.inlet.enth_mol[0].fix(hin)\n",
    "m.fs.PV_24.inlet.flow_mol[0].fix(fin)\n",
    "m.fs.PV_24.inlet.flow_mol[0].unfix()\n",
    "m.fs.PV_24.inlet.pressure[0].fix(pin)\n",
    "m.fs.PV_24.outlet.pressure[0].fix(pout)\n",
    "m.fs.PV_24.Cv.fix(cv)\n",
    "m.fs.PV_24.valve_opening.fix(0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afe277c",
   "metadata": {},
   "source": [
    "### PRODUCT BLOCK PARAMETERS _ Inj_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "b0674456",
   "metadata": {},
   "outputs": [],
   "source": [
    "from idaes.generic_models.unit_models.product import Product\n",
    "m.fs.Inj_4 = Product (default={\"property_package\": m.fs.properties})\n",
    "f_pad = 100\n",
    "m.fs.Inj_4.inlet.flow_mol[0].fix(f_pad) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d75e451d",
   "metadata": {},
   "source": [
    "### SEPARATOR PARAMETERS _ 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "aa8555ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.S24 = Separator (default={\"property_package\": m.fs.properties,\n",
    "                                \"outlet_list\": [m.fs.Inj_4.inlet.flow_mol[0] , m.fs.PV_24.inlet.flow_mol[0]], \n",
    "                                })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85a8eeb2",
   "metadata": {},
   "source": [
    "### SEPARATOR PARAMETERS_25"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "3cb9224f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from idaes.generic_models.unit_models import Separator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "cb50ad3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.S25 = Separator (default={\"property_package\": m.fs.properties,\n",
    "                                \"outlet_list\": [m.fs.S24 , m.fs.PV_25.inlet.flow_mol[0]], \n",
    "                                })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6541d41",
   "metadata": {},
   "source": [
    "### ARCS DEFINITION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "141ecf88",
   "metadata": {},
   "outputs": [],
   "source": [
    "m.fs.Arc_24 = Arc(source=m.fs.MX_24.outlet, destination=m.fs.S25.inlet)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "e1ca8b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "TransformationFactory(\"network.expand_arcs\").apply_to(m) # connect the arcs and tranfer the data from the constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "feb50f04",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Degrees of freedom on P_24A are 0\n",
      "Degrees of freedom on P_24B are 0\n",
      "Degrees of freedom on P_24C are 0\n",
      "Degrees of freedom on PV_24 are 0\n",
      "Degrees of freedom on PV_25 are 0\n",
      "Degrees of freedom on Inj_4 are 0\n",
      "Degrees of freedom on MX_24 are 9\n",
      "Degrees of freedom on S25 are 4\n",
      "Degrees of freedom on S24 are 4\n",
      "Total degrees of freedom on m are 14\n"
     ]
    }
   ],
   "source": [
    "print(\"Degrees of freedom on P_24A are\", degrees_of_freedom(m.fs.pump_24_A))\n",
    "print(\"Degrees of freedom on P_24B are\", degrees_of_freedom(m.fs.pump_24_B))\n",
    "print(\"Degrees of freedom on P_24C are\", degrees_of_freedom(m.fs.pump_24_C))\n",
    "print(\"Degrees of freedom on PV_24 are\", degrees_of_freedom(m.fs.PV_24))\n",
    "print(\"Degrees of freedom on PV_25 are\", degrees_of_freedom(m.fs.PV_25))\n",
    "print(\"Degrees of freedom on Inj_4 are\", degrees_of_freedom(m.fs.Inj_4))\n",
    "print(\"Degrees of freedom on MX_24 are\", degrees_of_freedom(m.fs.MX_24))\n",
    "print(\"Degrees of freedom on S25 are\", degrees_of_freedom(m.fs.S25))\n",
    "print(\"Degrees of freedom on S24 are\", degrees_of_freedom(m.fs.S24))\n",
    "print(\"Total degrees of freedom on m are\", degrees_of_freedom(m))\n",
    "# print(\"Degrees of freedom on Arc_24 are\", degrees_of_freedom(m.fs.Arc_24))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "baaa0a06",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.fs.S25.split_fraction[0,(m.fs.PV_25,m.fs.S25)]\n",
    "#iscale.calculate_scaling_factors(m.fs.S25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "b300f635",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No variables match your requested type.\n"
     ]
    }
   ],
   "source": [
    "%who [m]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc91849f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.fs.pump_22_D.initialize(outlvl=idaeslog.INFO) # call this initialize method on the units"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d61bfc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.fs.pump_22_D.initialize(outlvl=idaeslog.DEBUG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3bb60147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# opt = SolverFactory('ipopt') # set solver \n",
    "# solve_status = opt.solve(m.fs.pump_22_D, tee=True) # set the solver status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e90e9fa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# m.fs.pump_22_D.report()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5bc0050",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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