MRF Economics Aug 23.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# This notebook describes the costs of operating a Materials Recovery Factory\n",
    "\n",
    "Data based on Understanding Plastics Recycling by Rudolph et al. 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some key assumptions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assumptions\n",
    "\n",
    "assumptions = {\n",
    "    \"Percentage of PET in plastic Waste[%]\": 14.16,\n",
    "    \"Average Price of Recycled PET Pellets[$/lb]\": 0.58,\n",
    "    \"Price of Recycled PET pellets[$/Kg]\": 1.26,\n",
    "    \"Electricity Price[$/kWh]\": 0.1027,\n",
    "    \"Diesel Price [$/gallon]\": 2.198,\n",
    "    \"Diesel Price [$/l]\": 0.5807,\n",
    "    \"Water Price [$/gallon]\": 0.015,\n",
    "    \"Water Price [$/l]\": 0.004,\n",
    "    \"Lifetime [years]\": 10,\n",
    "    \"Processing Building size(Sq.ft)\": 0,\n",
    "    \"Waste handled per hour [t]\": 30,\n",
    "    \"Yearly waste handling [t]\": 120000,\n",
    "    \"Total waste handling (10 years) [t]\": 1200000,\n",
    "    \"Residues rate [%]\": 2,\n",
    "    \"Correctness of manual sorting [%]\": 91,\n",
    "    \"Shifts per Day\": 2,\n",
    "    \"Hours per Shift [h]\": 8.5,\n",
    "    \"Breaktime per shift [h]\": 0.5,\n",
    "    \"Effective working time [h]\": 8,\n",
    "    \"Work days per week [days] \": 5,\n",
    "    \"Weeks per Year [weeks]\": 52,\n",
    "    \"Yearly working hours [h]\": 4160\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import biosteam as bst\n",
    "from biosteam import units\n",
    "from biosteam.units.decorators import cost, CostItem\n",
    "from compounds import *\n",
    "from tea import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Process Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sys_mrf.diagram('thorough')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "System: mrf\n",
      "ins...\n",
      "[0] Waste\n",
      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): C10H10O4       21.9\n",
      "                    Al             22.2\n",
      "                    PubChem=24261  419\n",
      "outs...\n",
      "[0] s4\n",
      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): C10H10O4       0.219\n",
      "                    Al             0.222\n",
      "                    PubChem=24261  419\n",
      "[1] s6\n",
      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): C10H10O4  0.217\n",
      "                    Al        0.22\n",
      "[2] s8\n",
      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): C10H10O4  0.214\n",
      "                    Al        0.218\n",
      "[3] Plastic\n",
      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): C10H10O4  21\n",
      "                    Al        0.216\n",
      "[4] OrganicWaste\n",
      "    phase: 'l', T: 298.15 K, P: 101325 Pa\n",
      "    flow (kmol/hr): C10H10O4  0.212\n",
      "                    Al        21.4\n"
     ]
    }
   ],
   "source": [
    "bst.main_flowsheet.clear()\n",
    "flowsheet = bst.main_flowsheet.set_flowsheet(\"MRF\")\n",
    "\n",
    "wastecomposition = chemicals.kwarray(dict(\n",
    "    PET = assumptions[\"Percentage of PET in plastic Waste[%]\"]/100,\n",
    "    Al = assumptions[\"Residues rate [%]\"]/100,\n",
    "    Ash = 1 - assumptions[\"Percentage of PET in plastic Waste[%]\"]/100 - assumptions[\"Residues rate [%]\"]/100))\n",
    "\n",
    "waste = bst.Stream(\"Waste\",\n",
    "    flow = assumptions[\"Waste handled per hour [t]\"] * wastecomposition, units=\"tonnes/hour\")\n",
    "\n",
    "u_belt = units.ConveyingBelt('Belt', ins=waste)\n",
    "# Separate metals\n",
    "u_mag_sep = units.MagneticSeparator('U102', ins=u_belt-0)\n",
    "\n",
    "# Shredder\n",
    "# u_shredder = units.Shredder('U103', ins=u_mag_sep-0)\n",
    "\n",
    "# u201 = units.CrushingMill('U201', split=dict(PET=0.9, PE=0.9, PP=0.9, Al=0.1), moisture_content=0)\n",
    "\n",
    "# Screen out fibers\n",
    "u_OCC_screen = units.VibratingScreen('OCC', ins=u_mag_sep-0,\n",
    "                             split=dict(PET=0.99, PE=0.01, PP=0.01, Al=0.99), outs=('', ''))\n",
    "\n",
    "u_debris_screen = units.VibratingScreen('DebrisRoll', ins=u_OCC_screen-0,\n",
    "                             split=dict(PET=0.99, PE=0.01, PP=0.01, Al=0.99), outs=('', ''))\n",
    "\n",
    "u_newspaper_screen = units.VibratingScreen('Newspaper', ins=u_debris_screen-0,\n",
    "                             split=dict(PET=0.99, PE=0.01, PP=0.01, Al=0.99), outs=('', ''))\n",
    "\n",
    "u_polishing_screen = units.VibratingScreen('Polishing', ins=u_newspaper_screen-0,\n",
    "                             split=dict(PET=0.99, PE=0.01, PP=0.01, Al=0.01), outs=('Plastic', 'OrganicWaste'))\n",
    "\n",
    "plastic = u_polishing_screen-1\n",
    "residues = u_polishing_screen-0\n",
    "\n",
    "sys_mrf = bst.main_flowsheet.create_system('mrf')\n",
    "\n",
    "sys_mrf.simulate()\n",
    "# sys_mrf.diagram('thorough')\n",
    "sys_mrf.show()\n",
    "\n",
    "# the process follows these steps:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "## Labor costs  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total labor cost: $2,552,000.00\n"
     ]
    }
   ],
   "source": [
    "personnel = [\n",
    "    [\"Plant Manager \",  1, 89000, 2],\n",
    "    [\"Operations Foreman\",  2, 55000, 2],\n",
    "    [\"Sorters \",  25, 30000, 2],\n",
    "    [\"Scale House Attendant \",  2, 37500, 2],\n",
    "    [\"Equipment Operators\",  4, 35000, 2],\n",
    "    [\"Spotters on Tip Floor \",  2, 31000, 2],\n",
    "    [\"Marketing Manager \",  2, 50000, 1]]\n",
    "\n",
    "def get_labor_cost(position, number, salary, shifts):\n",
    "    total = (number) * salary * shifts\n",
    "    return total\n",
    "\n",
    "labor_costs = sum([get_labor_cost(p, n, s, s1) for (p, n, s, s1) in personnel])\n",
    "labor_costs\n",
    "print (\"Total labor cost: ${:,.2f}\".format(labor_costs))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installation Costs and Waste price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The waste price is based on an assumed tipping fee of $35/tonne."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "waste.price = -0.035 # $/kg\n",
    "plastic.price = 0.085 #$/kg\n",
    "residues.price = 0.04 #$/kg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "installed_costs = [[\"Metering bin\", 150000.00],\n",
    "            [\"OCC Screen\", 175000.00],\n",
    "            [\"Debris Roll Screen\", 220000.00],\n",
    "            [\"News paper Screen\", 400000.00],\n",
    "            [\"Polishing screen\", 280000.00],\n",
    "            [\"Recycling Magnets\", 35000.00],\n",
    "            [\"Eddy Current separator\", 128000.00],\n",
    "            [\"Optical Plastic sorting machine \", 225000.00],\n",
    "            [\"Baler \", 550000.00],\n",
    "            [\"Conveyor\", 50000.00],\n",
    "            [\"Rolling Stock\", 350000.00],\n",
    "            [\"Collection Cars\", 1000000.00],\n",
    "            [\"Land\",\t675000.00],\n",
    "            [\"Site Work\", \t720000.00],\n",
    "            [\"Scale House\", \t600000.00],\n",
    "            [\"MRF Building\",\t8600000.00],\n",
    "            [\"Construction, planning & surveying\",\t3500000.00]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TEA: mrf\n",
      " NPV: 12,639,266 USD at 4.0% IRR\n"
     ]
    }
   ],
   "source": [
    "sys_mrf.simulate()\n",
    "tea = TEA(\n",
    "    system=sys_mrf,\n",
    "    IRR=0.04,\n",
    "    duration=(2018, 2038),\n",
    "    depreciation='MACRS7',\n",
    "    income_tax=0.35,\n",
    "    operating_days=330,\n",
    "    lang_factor=1.0,\n",
    "    construction_schedule=(0.08, 0.6, 0.32),\n",
    "    WC_over_FCI=0.05,\n",
    "    labor_cost=labor_costs,\n",
    "    fringe_benefits=0.4,\n",
    "    property_tax=0.01,\n",
    "    property_insurance=0.01,\n",
    "    supplies=0.20,\n",
    "    maintenance=0.02,\n",
    "    administration=0.95,\n",
    "    equipment=installed_costs)\n",
    "\n",
    "tea.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MFSP ($/tonne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum selling price of the plastic is $-178.65/tonne\n"
     ]
    }
   ],
   "source": [
    "print (\"The minimum selling price of the plastic is ${:,.2f}/tonne\".format(1000*tea.solve_price(plastic))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "waste_prices = []\n",
    "plastic_prices = []\n",
    "\n",
    "for wp in range(-35, -25, 1):\n",
    "    waste.price = wp/1000\n",
    "    pp = tea.solve_price(plastic)\n",
    "    waste_prices.append(wp)\n",
    "    plastic_prices.append(pp*1000)\n",
    "\n",
    "plt.plot(waste_prices, plastic_prices)\n",
    "plt.xlabel(\"Waste price [$/tonne]\")\n",
    "plt.ylabel(\"Plastic price [$/tonne]\")\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we assume that the waste has a tipping of $35/tonne but the MRF has to pay for its transportation costs as it scales.\n",
    "\n",
    "The base case transportation cost is assumed to be $36.8/tonne with a scaling factor of 1.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Plastic price [$/tonne]')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "capacities = range(5*24, 50*24, 1*24)\n",
    "plastic_prices = []\n",
    "waste.price = -0.035\n",
    "\n",
    "for c in capacities:\n",
    "    waste.price = 0.0368*(c/24/30)**1.3 - 0.035\n",
    "    waste.set_total_flow(c/24, 'tonnes/hour')\n",
    "    tea.system.simulate()\n",
    "    plastic_prices.append(tea.solve_price(plastic))\n",
    "\n",
    "plt.plot(capacities, plastic_prices)\n",
    "plt.xlabel(\"Capacity [tonnes/day]\")\n",
    "plt.ylabel(\"Plastic price [$/tonne]\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "14a458be60918420de05fbf6a8cad2e55521128ca3c6fc3cd6f378b33df122bb"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}