CSTR_GEKKO.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.integrate import odeint, quad\n",
    "import numpy as np\n",
    "from gekko import GEKKO, chemical\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Objective: Max Cb 0.13828950457\n",
      "Ua: 10000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#---- Initialize Model ----#\n",
    "\n",
    "m = GEKKO(remote=False)\n",
    "\n",
    "nt = 101\n",
    "tf = 10\n",
    "m.time = np.linspace(0, tf, nt)\n",
    "\n",
    "#---- Constants ----#\n",
    "\n",
    "A_1 = m.Const(8.2e14)\n",
    "Ea_1 = m.Const(2.845e5) # J/mol\n",
    "\n",
    "A_2 = m.Const(1.7e17)\n",
    "Ea_2 = m.Const(3.259e5) # J/mol\n",
    "\n",
    "R = m.Const(8.314) # J/mol-K\n",
    "\n",
    "Hf_a = m.Const(-2.4700e5) # kJ/kmol\n",
    "Hf_b = m.Const(-47500)\n",
    "Hf_c = m.Const(-74520)\n",
    "Hf_d = m.Const(-1.1053e5)\n",
    "Hf_e = m.Const(52510)\n",
    "\n",
    "Ta_0 = 575 + 273.15\n",
    "\n",
    "#---- Parameters ----#\n",
    "\n",
    "Ta = m.MV(value=Ta_0, lb=300, ub=Ta_0 + 500, name='Ta')\n",
    "Ta.STATUS = 1\n",
    "Ta.DMAX = 50\n",
    "Ta.DCOST = .1\n",
    "Ta.FSTATUS = 0\n",
    "\n",
    "Vr0 = 100\n",
    "\n",
    "Vr = m.FV(value=Vr0, lb=50, ub=200)\n",
    "Vr.STATUS = 0\n",
    "\n",
    "v̇0 = 10\n",
    "\n",
    "v̇ = m.FV(value=v̇0, lb=5, ub=50)\n",
    "v̇.STATUS = 0\n",
    "Ua = m.FV(1000, lb=1e4, ub=1e7)\n",
    "Ua.STATUS = 0\n",
    "\n",
    "p = np.zeros(nt)\n",
    "p[-1] = 1.0\n",
    "final = m.Param(value=p)\n",
    "\n",
    "#---- Initial Conditions ----#\n",
    "\n",
    "Ca0 = 1 \n",
    "Cb0 = 0\n",
    "Cc0 = 0\n",
    "Cd0 = 0\n",
    "Ce0 = 0\n",
    "T_0 = 700+273.15\n",
    "x0 = np.array([Ca0, Cb0, Cc0, Cd0, Ce0, T_0])\n",
    "\n",
    "#---- Inlet Conditions ----#\n",
    "Ta_f = 700 + 273.15\n",
    "Ca_f = 1 # kmol/m3\n",
    "\n",
    "#---- Variables ----#\n",
    "Ca = m.Var(value=Ca0, lb=0, name='Ca')\n",
    "Cb = m.CV(value=Cb0, lb=0, name='Cb')\n",
    "Cc = m.CV(value=Cc0, lb=0, name='Cc')\n",
    "Cd = m.Var(value=Cd0, lb=0, name='Cd')\n",
    "Ce = m.Var(value=Ce0, lb=0, name='Ce')\n",
    "T = m.CV(value=T_0, lb=600+273.15, ub=800+273.15, name='T')\n",
    "\n",
    "\n",
    "#CV tuning\n",
    "\n",
    "Cb.STATUS = 1\n",
    "Cb.FSTATUS = 1\n",
    "Cb.TR_INIT = 1\n",
    "Cb.TAU = 1\n",
    "Cb.COST = 0\n",
    "Cb.BIAS = 1e-1\n",
    "DT = 0.5\n",
    "\n",
    "Cc.STATUS = 0\n",
    "Cc.FSTATUS = 0\n",
    "Cc.TR_INIT = 1\n",
    "Cc.TAU = 1.0\n",
    "Cc.COST = 5\n",
    "\n",
    "T.STATUS = 0\n",
    "T.FSTATUS = 0\n",
    "T.TR_INIT = 0\n",
    "T.TAU = 1\n",
    "T.COST = 0\n",
    "\n",
    "\n",
    "#---- Intermediates ----#\n",
    "\n",
    "A, B, C, D, E, F, G = [33258, 31740, 309.1, 76072, 1374.7, 78900, 3334.7]\n",
    "Cp_a = m.Intermediate(A + B*(((C/T)**2*m.exp(C/T))/(m.exp(C/T) -1)**2) + D*(((E/T)**2*m.exp(E/T))/(m.exp(E/T) -1)**2) + F*(((G/T)**2*m.exp(G/T))/(m.exp(G/T) -1)**2))\n",
    "A, B, C, D, E = [36940, 68650, 1490, 45240, 640]\n",
    "Cp_b = m.Intermediate(A + B*(C/T/(m.sinh(C/T)))**2 + D*(E/T/(m.cosh(E/T)))**2)\n",
    "A, B, C, D, E = [33298, 79933, 2086.9, 41602, 991.96]\n",
    "Cp_c = m.Intermediate(A + B*(C/T/(m.sinh(C/T)))**2 + D*(E/T/(m.cosh(E/T)))**2)\n",
    "A, B, C, D, E = [29108, 8773, 3085.1, 8455.3, 1538.2]\n",
    "Cp_d = m.Intermediate(A + B*(C/T/(m.sinh(C/T)))**2 + D*(E/T/(m.cosh(E/T)))**2)\n",
    "A, B, C, D, E = [33380, 94790, 1596, 55100, 740.8]\n",
    "Cp_e = m.Intermediate(A + B*(C/T/(m.sinh(C/T)))**2 + D*(E/T/(m.cosh(E/T)))**2)\n",
    "\n",
    "\n",
    "Na = m.Intermediate(Ca*Vr)\n",
    "Nb = m.Intermediate(Cb*Vr)\n",
    "Nc = m.Intermediate(Cc*Vr)\n",
    "Nd = m.Intermediate(Cd*Vr)\n",
    "Ne = m.Intermediate(Ce*Vr)\n",
    "\n",
    "k_1 = m.Intermediate(A_1*m.exp(-Ea_1/(R*T))) # 1/hr\n",
    "k_2 = m.Intermediate(A_2*m.exp(-Ea_2/(R*T)))\n",
    "\n",
    "ra = m.Intermediate(k_1*Ca)\n",
    "rb = m.Intermediate(k_2*Cb)\n",
    "\n",
    "ΔHr_1 = m.Intermediate((   Hf_c + Hf_b - Hf_a) + (   Cp_c + Cp_b - Cp_a)*(T - 298.15))\n",
    "ΔHr_2 = m.Intermediate((.5*Hf_e + Hf_d - Hf_b) + (.5*Cp_e + Cp_d - Cp_b)*(T - 298.15))\n",
    "\n",
    "#---- Equations ----#\n",
    "\n",
    "m.Equation(Ca.dt() == v̇*(Ca_f - Ca)/Vr - ra     )\n",
    "m.Equation(Cb.dt() == v̇*(0    - Cb)/Vr + ra - rb)\n",
    "m.Equation(Cc.dt() == v̇*(0    - Cc)/Vr + ra     )\n",
    "m.Equation(Cd.dt() == v̇*(0    - Cd)/Vr +      rb)\n",
    "m.Equation(Ce.dt() == v̇*(0    - Ce)/Vr +   .5*rb)\n",
    "m.Equation(T.dt() ==  (Ua*(Ta - T) - (Ca_f*v̇)*Cp_a*(T - Ta_f) - ra*ΔHr_1 - rb*ΔHr_2/ \\\n",
    "                      (Na*Cp_a + Nb*Cp_b + Nc*Cp_c + Nd*Cp_d + Ne*Cp_e)))\n",
    "           \n",
    "# m.Equation(Cb*final > .10)\n",
    "m.Obj(-Cb*final)\n",
    "# m.Equation(Ca*final < .5)\n",
    "\n",
    "m.options.IMODE = 6\n",
    "m.options.NODES = 3\n",
    "m.options.SOLVER = 3\n",
    "m.options.CV_TYPE = 1\n",
    "#m.open_folder()\n",
    "m.solve(disp=False)\n",
    "\n",
    "\n",
    "print('Objective: Max Cb', Cb.value[-1])\n",
    "print('Ua:', Ua.value[-1])\n",
    "# print('Objective: Min Cc', Cc.value[-1])\n",
    "# print('Optimal Volume:', Vr.value[0])\n",
    "# print('Volumetric Flow Rate:', v̇.value)\n",
    "\n",
    "def plot():\n",
    "\n",
    "    plotsize=(12,10)\n",
    "    fontsize=16\n",
    "\n",
    "    plt.figure(1, figsize=plotsize)\n",
    "    plt.rc('font', size=fontsize)\n",
    "\n",
    "    plt.subplot(3,1,1)\n",
    "    plt.plot(m.time, Ca.value, label='Acetone')\n",
    "    plt.plot(m.time, Cb.value, label='Ketene')\n",
    "    plt.plot(m.time, Cc.value, label='Methane')\n",
    "    plt.plot(m.time, Cd.value, label='Carbon Monoxide')\n",
    "    plt.plot(m.time, Ce.value, label='Ethylene')\n",
    "    plt.xlabel('t (hr)')\n",
    "    plt.ylabel('Concentration (kmol/m3)')\n",
    "    plt.title('Reactor Concentration')\n",
    "    plt.legend(loc='best')\n",
    "    plt.grid()\n",
    "\n",
    "\n",
    "    plt.subplot(3,1,2)\n",
    "    plt.plot(m.time,np.array([T.value])[0,:]-273.15, label='Reactor Temperature')\n",
    "    plt.xlabel('t (hr)')\n",
    "    plt.ylabel('Temperature (C)')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Reactor Temperature')\n",
    "    plt.grid()\n",
    "\n",
    "\n",
    "    plt.subplot(3,1,3)\n",
    "    plt.plot(m.time,np.array([Ta.value])[0,:]-273.15, label='Heat')\n",
    "    plt.xlabel('t (hr)')\n",
    "    plt.ylabel('Ambient Temperature (K)')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Cooling/Heating')\n",
    "    plt.grid()\n",
    "    \n",
    "    plt.tight_layout()\n",
    "plot()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CSTR(y, t, Ca_f, Ta_f, v̇, Vr, Tc):\n",
    "    Ca, Cb, Cc, Cd, Ce, T = y\n",
    "    \n",
    "    Na, Nb, Nc, Nd, Ne  = Ca*Vr, Cb*Vr, Cc*Vr, Cd*Vr, Ce*Vr\n",
    "     \n",
    "    \n",
    "    def Cps(T, A, B, C, D, E, F, G):\n",
    "        return A + B*(((C/T)**2*np.exp(C/T))/(np.exp(C/T) -1)**2) + D*(((E/T)**2*np.exp(E/T))/(np.exp(E/T) -1)**2) + F*(((G/T)**2*np.exp(G/T))/(np.exp(G/T) -1)**2)\n",
    "    \n",
    "    def Cp(T, A, B, C, D, E):\n",
    "        return A + B*(C/T/(np.sinh(C/T)))**2 + D*(E/T/(np.cosh(E/T)))**2\n",
    "    \n",
    "    coeff_a = [33258, 31740, 309.1, 76072, 1374.7, 78900, 3334.7]\n",
    "    coeff_b = [36940, 68650, 1490, 45240, 640]\n",
    "    coeff_c = [33298, 79933, 2086.9, 41602, 991.96]\n",
    "    coeff_d = [29108, 8773, 3085.1, 8455.3, 1538.2]\n",
    "    coeff_e = [33380, 94790, 1596, 55100, 740.8]\n",
    "    \n",
    "    Cpa = Cps(T, *coeff_a)\n",
    "    Cpb = Cp(T, *coeff_b)\n",
    "    Cpc = Cp(T, *coeff_c)\n",
    "    Cpd = Cp(T, *coeff_d)\n",
    "    Cpe = Cp(T, *coeff_e)\n",
    " \n",
    "    #---- Kinetics ----#\n",
    "    \n",
    "    def k(A, Ea, T):\n",
    "        R = 8.314 # J/mole-K\n",
    "        return A*np.exp(-Ea/(R*T))\n",
    "    \n",
    "    A_1 = 8.2e14\n",
    "    Ea_1 = 2.845e5 # J/mol\n",
    "    k_1_1 = k(A_1, Ea_1, T)\n",
    "    \n",
    "    A_2 = 1.7e17\n",
    "    Ea_2 = 3.259e5 # J/mol\n",
    "    k_2_1 = k(A_2, Ea_2, T)\n",
    "    \n",
    "    ra_1 = k_1_1*Ca\n",
    "    rb_1 = k_2_1*Cb\n",
    "    \n",
    "    Ua = 1.64e7\n",
    "    \n",
    "    #---- Thermodynamics ----#\n",
    "    \n",
    "    Hr_a = -2.4700e5 # kJ/kmol\n",
    "    Hr_b = -47500\n",
    "    Hr_c = -74520\n",
    "    Hr_d = -1.1053e5\n",
    "    Hr_e = 52510\n",
    "    \n",
    "#     ΔHr_1 = lambda T: (Hr_c + Hr_b - Hr_a) + quad(lambda T: Cp(T, *coeff_c) + Cp(T, *coeff_b)\\\n",
    "#                                                  -Cps(T, *coeff_a), 298.15, T)[0]\n",
    "#     ΔHr_2 = lambda T:(.5*Hr_e + Hr_d - Hr_b) + quad(lambda T: .5*Cp(T, *coeff_e) + Cp(T, *coeff_d)\\\n",
    "#                                                  -Cp(T, *coeff_b), 298.15, T)[0]\n",
    "    \n",
    "    ΔHr_1 = (Hr_c + Hr_b - Hr_a) + (Cpc + Cpb -Cpa)*(T - 298.15)\n",
    "    ΔHr_2 = (.5*Hr_e + Hr_d - Hr_b) + (.5*Cpe + Cpd -Cpb)*(T - 298.15)\n",
    "    \n",
    "    #---- Equations ----#\n",
    "    \n",
    "    \n",
    "    \n",
    "    dCadt = v̇*(Ca_f - Ca)/Vr - ra_1\n",
    "    dCbdt = v̇*(0    - Cb)/Vr + ra_1 - rb_1\n",
    "    dCcdt = v̇*(0    - Cc)/Vr + ra_1\n",
    "    dCddt = v̇*(0    - Cd)/Vr + rb_1\n",
    "    dCedt = v̇*(0    - Ce)/Vr + .5*rb_1\n",
    "    \n",
    "#     dTdt = (Ua*(Tc - T) - (v̇*Ca_f)*Cpa(T)*(T - Ta_f) - ra_1*ΔHr_1(T) - rb_1*ΔHr_2(T))/\\\n",
    "#             (Na*Cpa(T) + Nb*Cpb(T) + Nc*Cpc(T) + Nd*Cpd(T) + Ne*Cpe(T))\n",
    "    \n",
    "    dTdt = (Ua*(Tc - T) - (v̇*Ca_f)*Cpa*(T - Ta_f) - ra_1*ΔHr_1 - rb_1*ΔHr_2)/\\\n",
    "            (Na*Cpa + Nb*Cpb + Nc*Cpc + Nd*Cpd + Ne*Cpe)\n",
    "    \n",
    "    \n",
    "    equations = np.array([dCadt, dCbdt, dCcdt, dCddt, dCedt, dTdt])\n",
    "    return equations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib\n",
    "\n",
    "nt = 201\n",
    "t = np.linspace(0, tf, nt)\n",
    "\n",
    "Ca_arr = np.ones(len(t))\n",
    "Cb_arr = np.zeros(len(t))\n",
    "Cc_arr = np.zeros(len(t))\n",
    "Cd_arr = np.zeros(len(t))\n",
    "Ce_arr = np.zeros(len(t))\n",
    "T_arr = np.ones(len(t)) * T_0\n",
    "SP = np.ones(len(t))\n",
    "Ta_u = np.ones(len(t)) * Ta_0\n",
    "\n",
    "# Set point steps\n",
    "SP[0:int((nt-1)/4)] = .1\n",
    "SP[int((nt-1)/4):int((nt-1)/2)] = .2\n",
    "SP[int((nt-1)/2):int((nt-1)*3/4)] = .05\n",
    "SP[int((nt-1)*3/4):] = .125\n",
    "\n",
    "# Create plot\n",
    "plt.figure(figsize=(16,10))\n",
    "plt.rc('font', size=20)\n",
    "plt.ion()\n",
    "plt.show()\n",
    "\n",
    "# Simulate CSTR\n",
    "for i in range(len(t)-1):\n",
    "    # simulate one time period (0.05 sec each loop)\n",
    "    ts = [t[i],t[i+1]]\n",
    "    y = odeint(CSTR,x0,ts,args=(Ca_f,Ta_f,v̇0,Vr0,Ta_u[i]))\n",
    "    # retrieve measurements\n",
    "    Ca_arr[i+1] = y[-1][0]\n",
    "    Cb_arr[i+1] = y[-1][1]\n",
    "    Cc_arr[i+1] = y[-1][2]\n",
    "    Cd_arr[i+1] = y[-1][3]\n",
    "    Ce_arr[i+1] = y[-1][4]\n",
    "    T_arr[i+1]  = y[-1][5]\n",
    "    # insert measurement\n",
    "    Cb.MEAS = Cb_arr[i+1]\n",
    "    \n",
    "    # solve MPC\n",
    "    m.open_folder()\n",
    "    m.solve(disp=True)\n",
    "\n",
    "    Cb.SPHI = SP[i+1] + DT\n",
    "    Cb.SPLO = SP[i+1] - DT\n",
    "\n",
    "    # retrieve new Q value\n",
    "    Ta_u[i+1] = Ta.NEWVAL\n",
    "    # update initial conditions\n",
    "    x0[0] = Ca_arr[i+1]\n",
    "    x0[1] = Cb_arr[i+1]\n",
    "    x0[2] = Cc_arr[i+1]\n",
    "    x0[3] = Cd_arr[i+1]\n",
    "    x0[4] = Ce_arr[i+1]\n",
    "    x0[5] = T_arr[i+1]\n",
    "\n",
    "    #%% Plot the results\n",
    "    plt.clf()\n",
    "    plt.subplot(3,1,1)\n",
    "    plt.plot(t[0:i],Ta_u[0:i],'b--',linewidth=3)\n",
    "    plt.ylabel('Temperature (K)')\n",
    "    plt.legend(['Heating/Cooling'],loc='best')\n",
    "\n",
    "    plt.subplot(3,1,2)\n",
    "   # plt.plot(t[0:i],Ca_arr[0:i],'--',linewidth=3,label=r'$C_a$')\n",
    "    plt.plot(t[0:i],Cb_arr[0:i],'y--',linewidth=3,label=r'$C_{b,meas}$')\n",
    "   # plt.plot(t[0:i],Cc_arr[0:i],'g--',linewidth=3,label=r'$C_c$')\n",
    "    #plt.plot(t[0:i],Cd_arr[0:i],'r--',linewidth=3,label=r'$C_d$')\n",
    "    #plt.plot(t[0:i],Ce_arr[0:i],'m--',linewidth=3,label=r'$C_e$')\n",
    "    plt.plot(t[0:i],SP[0:i],'k-',linewidth=3,label=r'$SP$')\n",
    "    plt.ylabel(r'$C_b$ (mol/L)')\n",
    "    plt.legend(loc='best')\n",
    "\n",
    "    plt.subplot(3,1,3)\n",
    "    plt.plot(t[0:i],T_arr[0:i],'b:',linewidth=3,label=r'$T$')\n",
    "    \n",
    "    plt.ylabel('T (K)')\n",
    "    plt.xlabel('Time (hr)')\n",
    "    plt.legend(loc='best')\n",
    "    plt.draw()\n",
    "    plt.tight_layout()\n",
    "    plt.pause(0.01)\n",
    "\n",
    "#%% Plot the results\n",
    "# plotsize=(16,10)\n",
    "# fontsize=16\n",
    "# plt.figure(1, figsize=plotsize)\n",
    "# plt.rc('font', size=fontsize)\n",
    "# plt.subplot(3,1,1)\n",
    "# plt.plot(t,Ta_u,'b--',linewidth=3)\n",
    "# plt.ylabel('Temperature (K)')\n",
    "# plt.legend(['Heating/Cooling'],loc='best')\n",
    "\n",
    "# plt.subplot(3,1,2)\n",
    "# plt.plot(t,Ca_arr,'--',linewidth=3,label=r'$C_a$')\n",
    "# plt.plot(t,Cb_arr,'y--',linewidth=3,label=r'$C_{b,meas}$')\n",
    "# plt.plot(t,Cc_arr,'g--',linewidth=3,label=r'$C_c$')\n",
    "# plt.plot(t,Cd_arr,'r--',linewidth=3,label=r'$C_d$')\n",
    "# plt.plot(t,Ce_arr,'m--',linewidth=3,label=r'$C_e$')\n",
    "# plt.ylabel(r'$C_b$ (mol/L)')\n",
    "# plt.legend(loc='best')\n",
    "\n",
    "# plt.subplot(3,1,3)\n",
    "# plt.plot(t,T_arr,'b:',linewidth=3,label=r'$T$')\n",
    "# plt.plot(t,SP,'k-',linewidth=3,label=r'$SP$')\n",
    "# plt.ylabel('T (K)')\n",
    "# plt.xlabel('Time (hr)')\n",
    "# plt.legend(loc='best')\n",
    "# plt.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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