{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "981687d8",
   "metadata": {},
   "source": [
    "# Fit Pure Fluid Parameters\n",
    "\n",
    "This example shows how to use the new fitting class of version 0.21 of teqp \n",
    "to fit the model parameters for the SAFT-VR-Mie model based on fitting\n",
    "pseudo-experimental data obtained from a reference equation\n",
    "of state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0bace5e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T15:20:42.941406Z",
     "iopub.status.busy": "2024-10-29T15:20:42.940933Z",
     "iopub.status.idle": "2024-10-29T15:20:43.555865Z",
     "shell.execute_reply": "2024-10-29T15:20:43.555236Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.21.0'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import teqp\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import CoolProp.CoolProp as CP\n",
    "import scipy.optimize\n",
    "display(teqp.__version__)\n",
    "\n",
    "nonpolar = {\n",
    "    \"kind\": \"SAFT-VR-Mie\",\n",
    "    \"model\": {\n",
    "        \"coeffs\": [\n",
    "        {\n",
    "          \"name\": \"R32\",\n",
    "          \"BibTeXKey\": \"Bell\",\n",
    "          \"m\": 1.2476268271391935,\n",
    "          \"sigma_m\": 3.6080717234117107e-10,\n",
    "          \"epsilon_over_k\": 172.53065054286867,\n",
    "          \"lambda_r\": 14.634722358167384,\n",
    "          \"lambda_a\": 6\n",
    "        }\n",
    "      ]\n",
    "    }\n",
    "}\n",
    "template = {  \n",
    "  'kind': 'genericSAFT', \n",
    "  'model': {\n",
    "      'nonpolar': nonpolar\n",
    "  }\n",
    "}\n",
    "\n",
    "# NOTE: '/' inside field names MUST be escaped as ~1; see https://datatracker.ietf.org/doc/html/rfc6901#section-3\n",
    "pointers = [\n",
    "    '/model/nonpolar/model/coeffs/0/m',\n",
    "    '/model/nonpolar/model/coeffs/0/sigma_m',\n",
    "    '/model/nonpolar/model/coeffs/0/epsilon_over_k',\n",
    "    '/model/nonpolar/model/coeffs/0/lambda_r',\n",
    "    '/model/nonpolar/model/coeffs/0/lambda_a'\n",
    "]\n",
    "x0 = [1.5, 3e-10, 150, 19, 5.7]\n",
    "bounds = [(1,5), (2e-10,5e-10), (100,400), (12,50), (5.1, 6.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f5463428",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-10-29T15:20:43.558309Z",
     "iopub.status.busy": "2024-10-29T15:20:43.557990Z",
     "iopub.status.idle": "2024-10-29T15:21:04.439157Z",
     "shell.execute_reply": "2024-10-29T15:21:04.438541Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 1: f(x)= 5818.421956412952\n",
      "differential_evolution step 2: f(x)= 499.1768555361148\n",
      "differential_evolution step 3: f(x)= 487.6265883142837\n",
      "differential_evolution step 4: f(x)= 457.7391656468499\n",
      "differential_evolution step 5: f(x)= 457.7391656468499\n",
      "differential_evolution step 6: f(x)= 457.7391656468499\n",
      "differential_evolution step 7: f(x)= 418.0474196262808\n",
      "differential_evolution step 8: f(x)= 418.0474196262808\n",
      "differential_evolution step 9: f(x)= 418.0474196262808\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 10: f(x)= 338.1635147679501\n",
      "differential_evolution step 11: f(x)= 338.1635147679501\n",
      "differential_evolution step 12: f(x)= 338.1635147679501\n",
      "differential_evolution step 13: f(x)= 338.1635147679501\n",
      "differential_evolution step 14: f(x)= 338.1635147679501\n",
      "differential_evolution step 15: f(x)= 338.1635147679501\n",
      "differential_evolution step 16: f(x)= 330.10096482774765\n",
      "differential_evolution step 17: f(x)= 330.10096482774765\n",
      "differential_evolution step 18: f(x)= 330.10096482774765\n",
      "differential_evolution step 19: f(x)= 330.10096482774765\n",
      "differential_evolution step 20: f(x)= 330.10096482774765\n",
      "differential_evolution step 21: f(x)= 275.1544998794892\n",
      "differential_evolution step 22: f(x)= 275.1544998794892\n",
      "differential_evolution step 23: f(x)= 241.81895513378925\n",
      "differential_evolution step 24: f(x)= 241.81895513378925\n",
      "differential_evolution step 25: f(x)= 241.81895513378925\n",
      "differential_evolution step 26: f(x)= 241.81895513378925\n",
      "differential_evolution step 27: f(x)= 241.81895513378925\n",
      "differential_evolution step 28: f(x)= 241.81895513378925\n",
      "differential_evolution step 29: f(x)= 241.81895513378925\n",
      "differential_evolution step 30: f(x)= 241.81895513378925\n",
      "differential_evolution step 31: f(x)= 241.81895513378925\n",
      "differential_evolution step 32: f(x)= 241.81895513378925\n",
      "differential_evolution step 33: f(x)= 241.81895513378925\n",
      "differential_evolution step 34: f(x)= 241.81895513378925\n",
      "differential_evolution step 35: f(x)= 237.2929366395986\n",
      "differential_evolution step 36: f(x)= 237.2929366395986\n",
      "differential_evolution step 37: f(x)= 237.2929366395986\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 38: f(x)= 237.2929366395986\n",
      "differential_evolution step 39: f(x)= 237.2929366395986\n",
      "differential_evolution step 40: f(x)= 230.68631809484037\n",
      "differential_evolution step 41: f(x)= 230.20609180473951\n",
      "differential_evolution step 42: f(x)= 209.69348566431282\n",
      "differential_evolution step 43: f(x)= 209.69348566431282\n",
      "differential_evolution step 44: f(x)= 209.69348566431282\n",
      "differential_evolution step 45: f(x)= 182.23116564860715\n",
      "differential_evolution step 46: f(x)= 182.23116564860715\n",
      "differential_evolution step 47: f(x)= 146.77960931385533\n",
      "differential_evolution step 48: f(x)= 146.77960931385533\n",
      "differential_evolution step 49: f(x)= 144.65095762969202\n",
      "differential_evolution step 50: f(x)= 122.62697814799093\n",
      "differential_evolution step 51: f(x)= 63.07678081956036\n",
      "differential_evolution step 52: f(x)= 63.07678081956036\n",
      "differential_evolution step 53: f(x)= 63.07678081956036\n",
      "differential_evolution step 54: f(x)= 63.07678081956036\n",
      "differential_evolution step 55: f(x)= 63.07678081956036\n",
      "differential_evolution step 56: f(x)= 63.07678081956036\n",
      "differential_evolution step 57: f(x)= 46.53073192085213\n",
      "differential_evolution step 58: f(x)= 46.53073192085213\n",
      "differential_evolution step 59: f(x)= 46.53073192085213\n",
      "differential_evolution step 60: f(x)= 46.53073192085213\n",
      "differential_evolution step 61: f(x)= 46.53073192085213\n",
      "differential_evolution step 62: f(x)= 46.53073192085213\n",
      "differential_evolution step 63: f(x)= 39.1040859298758\n",
      "differential_evolution step 64: f(x)= 39.1040859298758\n",
      "differential_evolution step 65: f(x)= 39.1040859298758\n",
      "differential_evolution step 66: f(x)= 39.1040859298758\n",
      "differential_evolution step 67: f(x)= 39.1040859298758\n",
      "differential_evolution step 68: f(x)= 39.1040859298758\n",
      "differential_evolution step 69: f(x)= 39.1040859298758\n",
      "differential_evolution step 70: f(x)= 39.1040859298758\n",
      "differential_evolution step 71: f(x)= 35.91213962638507\n",
      "differential_evolution step 72: f(x)= 35.91213962638507\n",
      "differential_evolution step 73: f(x)= 35.91213962638507\n",
      "differential_evolution step 74: f(x)= 31.991928955829998\n",
      "differential_evolution step 75: f(x)= 25.69596469618272\n",
      "differential_evolution step 76: f(x)= 25.69596469618272\n",
      "differential_evolution step 77: f(x)= 25.69596469618272\n",
      "differential_evolution step 78: f(x)= 25.69596469618272\n",
      "differential_evolution step 79: f(x)= 25.69596469618272\n",
      "differential_evolution step 80: f(x)= 25.69596469618272\n",
      "differential_evolution step 81: f(x)= 25.69596469618272\n",
      "differential_evolution step 82: f(x)= 25.69596469618272\n",
      "differential_evolution step 83: f(x)= 25.69596469618272\n",
      "differential_evolution step 84: f(x)= 25.69596469618272\n",
      "differential_evolution step 85: f(x)= 25.40977267008008\n",
      "differential_evolution step 86: f(x)= 25.08654968764155\n",
      "differential_evolution step 87: f(x)= 21.732540180244946\n",
      "differential_evolution step 88: f(x)= 21.732540180244946\n",
      "differential_evolution step 89: f(x)= 21.732540180244946\n",
      "differential_evolution step 90: f(x)= 21.732540180244946\n",
      "differential_evolution step 91: f(x)= 21.732540180244946\n",
      "differential_evolution step 92: f(x)= 21.732540180244946\n",
      "differential_evolution step 93: f(x)= 21.732540180244946\n",
      "differential_evolution step 94: f(x)= 21.732540180244946\n",
      "differential_evolution step 95: f(x)= 21.5950406845984\n",
      "differential_evolution step 96: f(x)= 21.565973142204154\n",
      "differential_evolution step 97: f(x)= 21.565973142204154\n",
      "differential_evolution step 98: f(x)= 21.565973142204154\n",
      "differential_evolution step 99: f(x)= 21.530760903539687\n",
      "differential_evolution step 100: f(x)= 21.530760903539687\n",
      "differential_evolution step 101: f(x)= 21.316727944016527\n",
      "differential_evolution step 102: f(x)= 21.25499340116124\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 103: f(x)= 20.559872546488993\n",
      "differential_evolution step 104: f(x)= 20.020765011191784\n",
      "differential_evolution step 105: f(x)= 20.020765011191784\n",
      "differential_evolution step 106: f(x)= 20.020765011191784\n",
      "differential_evolution step 107: f(x)= 20.020765011191784\n",
      "differential_evolution step 108: f(x)= 19.549324351102406\n",
      "differential_evolution step 109: f(x)= 19.532246269269777\n",
      "differential_evolution step 110: f(x)= 19.532246269269777\n",
      "differential_evolution step 111: f(x)= 19.01797540712929\n",
      "differential_evolution step 112: f(x)= 19.01797540712929\n",
      "differential_evolution step 113: f(x)= 18.895398559399503\n",
      "differential_evolution step 114: f(x)= 18.776806394241568\n",
      "differential_evolution step 115: f(x)= 18.749622014848157\n",
      "differential_evolution step 116: f(x)= 18.749622014848157\n",
      "differential_evolution step 117: f(x)= 18.56057810916112\n",
      "differential_evolution step 118: f(x)= 18.47984249770556\n",
      "differential_evolution step 119: f(x)= 18.47984249770556\n",
      "differential_evolution step 120: f(x)= 18.47984249770556\n",
      "differential_evolution step 121: f(x)= 18.346929054564406\n",
      "differential_evolution step 122: f(x)= 18.060391807258394\n",
      "differential_evolution step 123: f(x)= 18.060391807258394\n",
      "differential_evolution step 124: f(x)= 18.060391807258394\n",
      "differential_evolution step 125: f(x)= 17.744114151073173\n",
      "differential_evolution step 126: f(x)= 17.68251103024557\n",
      "differential_evolution step 127: f(x)= 17.68251103024557\n",
      "differential_evolution step 128: f(x)= 17.68251103024557\n",
      "differential_evolution step 129: f(x)= 17.52261388130418\n",
      "differential_evolution step 130: f(x)= 17.2817439273557\n",
      "differential_evolution step 131: f(x)= 16.54892785495758\n",
      "differential_evolution step 132: f(x)= 16.54892785495758\n",
      "differential_evolution step 133: f(x)= 16.31867885756894\n",
      "differential_evolution step 134: f(x)= 15.911061208426595\n",
      "differential_evolution step 135: f(x)= 15.614991653902301\n",
      "differential_evolution step 136: f(x)= 15.614991653902301\n",
      "differential_evolution step 137: f(x)= 14.789691475222233\n",
      "differential_evolution step 138: f(x)= 14.58866564278831\n",
      "differential_evolution step 139: f(x)= 14.58866564278831\n",
      "differential_evolution step 140: f(x)= 14.58866564278831\n",
      "differential_evolution step 141: f(x)= 14.349150893719104\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "differential_evolution step 142: f(x)= 13.423324045866506\n",
      "differential_evolution step 143: f(x)= 13.21096960040352\n",
      "differential_evolution step 144: f(x)= 13.040709831794738\n",
      "differential_evolution step 145: f(x)= 13.040709831794738\n",
      "differential_evolution step 146: f(x)= 13.040709831794738\n",
      "differential_evolution step 147: f(x)= 13.040709831794738\n",
      "differential_evolution step 148: f(x)= 13.040709831794738\n",
      "differential_evolution step 149: f(x)= 13.040709831794738\n",
      "differential_evolution step 150: f(x)= 13.040709831794738\n",
      "differential_evolution step 151: f(x)= 13.040709831794738\n",
      "differential_evolution step 152: f(x)= 13.040709831794738\n",
      "differential_evolution step 153: f(x)= 13.040709831794738\n",
      "differential_evolution step 154: f(x)= 13.040709831794738\n",
      "differential_evolution step 155: f(x)= 12.8860672106379\n",
      "differential_evolution step 156: f(x)= 12.8860672106379\n",
      "differential_evolution step 157: f(x)= 12.8860672106379\n",
      "differential_evolution step 158: f(x)= 12.8860672106379\n",
      "differential_evolution step 159: f(x)= 12.8860672106379\n",
      "differential_evolution step 160: f(x)= 12.8860672106379\n",
      "differential_evolution step 161: f(x)= 12.884275329003621\n",
      "differential_evolution step 162: f(x)= 12.884275329003621\n",
      "differential_evolution step 163: f(x)= 12.884275329003621\n",
      "differential_evolution step 164: f(x)= 12.884275329003621\n",
      "differential_evolution step 165: f(x)= 12.862938445406227\n",
      "differential_evolution step 166: f(x)= 12.862938445406227\n",
      "differential_evolution step 167: f(x)= 12.862938445406227\n",
      "differential_evolution step 168: f(x)= 12.843987764028336\n",
      "differential_evolution step 169: f(x)= 12.843987764028336\n",
      "differential_evolution step 170: f(x)= 12.843987764028336\n",
      "differential_evolution step 171: f(x)= 12.843987764028336\n",
      "differential_evolution step 172: f(x)= 12.843987764028336\n",
      "differential_evolution step 173: f(x)= 12.843987764028336\n",
      "Polishing solution with 'L-BFGS-B'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             message: Optimization terminated successfully.\n",
      "             success: True\n",
      "                 fun: 12.843987764028336\n",
      "                   x: [ 1.159e+00  3.550e-10  1.153e+02  1.284e+01\n",
      "                        5.170e+00]\n",
      "                 nit: 173\n",
      "                nfev: 7086\n",
      "          population: [[ 1.159e+00  3.550e-10 ...  1.284e+01  5.170e+00]\n",
      "                       [ 1.151e+00  3.559e-10 ...  1.291e+01  5.169e+00]\n",
      "                       ...\n",
      "                       [ 1.174e+00  3.531e-10 ...  1.260e+01  5.160e+00]\n",
      "                       [ 1.147e+00  3.563e-10 ...  1.298e+01  5.156e+00]]\n",
      " population_energies: [ 1.284e+01  1.299e+01 ...  1.335e+01  1.298e+01]\n",
      "194.75971762562753 9153.1514317028\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.5, '$(w_{fit}/w_{\\\\rm pexp}-1)\\\\times 100$'),\n",
       " Text(0.5, 0, '$T$ / K')]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "FLD = 'Methane'\n",
    "ppo = teqp.PureParameterOptimizer(template, pointers)\n",
    "\n",
    "Ts = np.linspace(100, 170, 10)\n",
    "\n",
    "# Generate some artificial pseudo-experimental data from \n",
    "# the reference EOS as implemented in CoolProp\n",
    "for T in Ts:\n",
    "    pt = teqp.SatRhoLPWPoint()\n",
    "    rhoL, rhoV = [CP.PropsSI('Dmolar','Q',Q,'T',T,FLD) for Q in [0,1]]\n",
    "    p = CP.PropsSI('P','Q',0,'T',T,FLD)\n",
    "    w = CP.PropsSI('speed_of_sound','Q',0,'T',T,FLD)\n",
    "    pt.T = T\n",
    "    # Measurands (here, pseudo-experimental values)\n",
    "    pt.p_exp = p\n",
    "    pt.rhoL_exp = rhoL\n",
    "    pt.w_exp = w\n",
    "    \n",
    "    # Additional parameters\n",
    "    pt.rhoL_guess = rhoL\n",
    "    pt.rhoV_guess = rhoV\n",
    "    pt.R = 8.31446261815324\n",
    "    pt.M = CP.PropsSI('molemass',FLD)\n",
    "    AS = CP.AbstractState('HEOS',FLD)\n",
    "    AS.update(CP.DmolarT_INPUTS, 1e-10, T)\n",
    "    pt.Ao20 = AS.tau()**2*AS.d2alpha0_dTau2() # -cv0/R\n",
    "    \n",
    "    # Weights (multiplied by 100 to put on a percentage basis)\n",
    "    pt.weight_p = 1*100\n",
    "    pt.weight_rho = 1*100\n",
    "    pt.weight_w = 0.25*100\n",
    "    ppo.add_one_contribution(pt)\n",
    "\n",
    "def cost_function(x):\n",
    "#     return ppo.cost_function_threaded(x, 10) # This is an option if you have lots of threads\n",
    "    return ppo.cost_function(x)\n",
    "\n",
    "r = scipy.optimize.differential_evolution(cost_function, bounds=bounds, disp=True, maxiter=10000, popsize=8)\n",
    "print(r)\n",
    "x = r.x\n",
    "model = teqp.make_model(ppo.build_JSON(x))\n",
    "\n",
    "Tc, rhoc = model.solve_pure_critical(400, 5000)\n",
    "print(Tc, rhoc)\n",
    "anc = teqp.build_ancillaries(model, Tc, rhoc, 0.5*Tc)\n",
    "\n",
    "def _get_SOS(model, T, rho, z, *, R, M, Ao20):\n",
    "    \"\"\" Helper function to calculate speed of sound \"\"\"\n",
    "    Ar0n = model.get_Ar02n(T, rho, z)\n",
    "    Ar01 = Ar0n[1]; Ar02 = Ar0n[2]\n",
    "    Ar11 = model.get_Ar11(T, rho, z)\n",
    "    Ar20 = model.get_Ar20(T, rho, z)\n",
    "\n",
    "    # M*w^2/(R*T) where w is the speed of sound\n",
    "    # from the definition w = sqrt(dp/drho|s)\n",
    "    Mw2RT = 1 + 2*Ar01 + Ar02 - (1 + Ar01 - Ar11)**2/(Ao20 + Ar20)\n",
    "    if Mw2RT < 0:\n",
    "        return 1e6\n",
    "    w = (Mw2RT*R*T/M)**0.5\n",
    "    return w\n",
    "\n",
    "TcREF = CP.PropsSI('Tcrit', FLD)\n",
    "Tsverify = np.linspace(100, TcREF*0.99999, 1000)\n",
    "RHOL, RHOV, PPP, WWW = [],[],[],[]\n",
    "z = np.array([1.0])\n",
    "for T in Tsverify:\n",
    "    rhoL, rhoV = model.pure_VLE_T(T, anc.rhoL(T)*1.01, anc.rhoV(T)*0.9, 10)\n",
    "    pL = rhoL*model.get_R(z)*T*(1+model.get_Ar01(T, rhoL, z))\n",
    "    pV = rhoV*model.get_R(z)*T*(1+model.get_Ar01(T, rhoV, z))\n",
    "    RHOL.append(rhoL)\n",
    "    RHOV.append(rhoV)\n",
    "    PPP.append(pL)\n",
    "    AS = CP.AbstractState('HEOS',FLD)\n",
    "    AS.update(CP.DmolarT_INPUTS, 1e-10, T)\n",
    "    Ao20 = AS.d2alpha0_dTau2()*AS.tau()**2\n",
    "    WWW.append(_get_SOS(model, T, rhoL, z, R=8.314462618,M=CP.PropsSI('molemass',FLD),Ao20=Ao20))\n",
    "    \n",
    "# Plot the T-rho for VLE\n",
    "line, = plt.plot(np.array(RHOL), Tsverify)\n",
    "plt.plot(np.array(RHOV), Tsverify, color=line.get_color())\n",
    "for Q in [0, 1]:\n",
    "    D = CP.PropsSI('Dmolar','T',Tsverify,'Q',Q,FLD)\n",
    "    plt.plot(D, Tsverify, lw=2, color='k')\n",
    "plt.gca().set(xlabel=r'$\\rho$ / mol/m$^3$', ylabel='$T$ / K')\n",
    "\n",
    "# And a deviation plot, much closer to the critical point\n",
    "# than the fitting region\n",
    "fig, (ax1, ax2, ax3) = plt.subplots(3,1, figsize=(10,5), sharex=True)\n",
    "\n",
    "ax1.plot(Tsverify, (np.array(PPP)/CP.PropsSI('P','T',Tsverify,'Q',0,FLD)-1)*100)\n",
    "ax1.set(ylabel=r'$(p_{fit}/p_{\\rm pexp}-1)\\times 100$')\n",
    "\n",
    "ax2.plot(Tsverify, (np.array(RHOL)/CP.PropsSI('Dmolar','T',Tsverify,'Q',0,FLD)-1)*100)\n",
    "ax2.set(ylabel=r'$(\\rho_{fit}/\\rho_{\\rm pexp}-1)\\times 100$')\n",
    "\n",
    "ax3.plot(Tsverify, (np.array(WWW)/CP.PropsSI('speed_of_sound','T',Tsverify,'Q',0,FLD)-1)*100)\n",
    "ax3.set(ylabel=r'$(w_{fit}/w_{\\rm pexp}-1)\\times 100$', xlabel='$T$ / K')"
   ]
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