{ "cells": [ { "cell_type": "markdown", "id": "1ec37f01", "metadata": {}, "source": [ "# VLLE @ constant pressure\n", "\n", "Following the approach described in Bell et al.: https://doi.org/10.1021/acs.iecr.1c04703, but slightly different because the pressure is fixed rather than the temperature, but the same basic principles hold\n", "\n", "for the mixture of nitrogen + ethane, with the default thermodynamic model in teqp, which is the GERG-2008 mixing parameters (no departure function).\n", "\n", "Two traces are made, and the intersection is obtained, this gives you the VLLE solution." ] }, { "cell_type": "code", "execution_count": 1, "id": "29a2031a", "metadata": { "execution": { "iopub.execute_input": "2024-12-12T18:10:33.744862Z", "iopub.status.busy": "2024-12-12T18:10:33.744437Z", "iopub.status.idle": "2024-12-12T18:10:34.701934Z", "shell.execute_reply": "2024-12-12T18:10:34.701441Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rhovec / mol/m^3 | T / K\n", "[4921.97976373 9.6755684 ] 125.1472901887422\n", "[ 6008.68040253 15630.22353351] 125.1472901887422\n", "[18948.39537895 1540.60935171] 125.1472901887422\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import teqp, numpy as np, matplotlib.pyplot as plt, pandas\n", "import CoolProp.CoolProp as CP \n", "\n", "names = ['Nitrogen', 'Ethane']\n", "model = teqp.build_multifluid_model(names, teqp.get_datapath())\n", "pures = [teqp.build_multifluid_model([name], teqp.get_datapath()) for name in names]\n", "p = 29e5 # Pa\n", "\n", "# Trace from both pure fluid endpoints\n", "traces = []\n", "for ipure in [1,0]:\n", " # Init at the pure fluid endpoint\n", " anc = pures[ipure].build_ancillaries()\n", " rhoLpure, rhoVpure = [CP.PropsSI('Dmolar','P',p,'Q',Q,names[ipure]) for Q in [0,1]]\n", " T = CP.PropsSI('T','P',p,'Q',0,names[ipure])\n", "\n", " rhovecL = np.array([0.0, 0.0])\n", " rhovecV = np.array([0.0, 0.0])\n", " rhovecL[ipure] = rhoLpure\n", " rhovecV[ipure] = rhoVpure\n", " j = model.trace_VLE_isobar_binary(p, T, rhovecL, rhovecV)\n", " df = pandas.DataFrame(j)\n", " plt.plot(df['xL_0 / mole frac.'], df['T / K'])\n", " plt.plot(df['xV_0 / mole frac.'], df['T / K'])\n", " traces.append(j)\n", " \n", "# Do the VLLE solving\n", "for soln in model.find_VLLE_p_binary(traces):\n", " T = soln['polished'][-1]\n", " print('rhovec / mol/m^3 | T / K')\n", " for rhovec in soln['polished'][0:3]:\n", " rhovec = np.array(rhovec)\n", " rhotot = sum(rhovec)\n", " x = rhovec/rhotot\n", " p = rhotot*model.get_R(x)*T*(1+model.get_Ar01(T, rhotot, x))\n", " plt.plot(x[0], T, 'X')\n", " print(rhovec, T)\n", " \n", " # And also carry out the LLE trace for the two liquid phases\n", " opt = teqp.PVLEOptions()\n", " opt.integration_order = 5\n", " opt.init_dt = 1e-10\n", " # Or could be 1 depending on the initial integration direction, do not know the direction \n", " # a priori because not starting at a pure fluid endpoint\n", " for init_dt in [-1]: \n", " opt.init_c = init_dt \n", " rhovecV, rhovecL1, rhovecL2, T = soln['polished']\n", " j = model.trace_VLE_isobar_binary(p, T, np.array(rhovecL1), np.array(rhovecL2), opt)\n", " df = pandas.DataFrame(j)\n", " plt.plot(df['xL_0 / mole frac.'], df['T / K'], 'k')\n", " plt.plot(df['xV_0 / mole frac.'], df['T / K'], 'k')\n", "\n", "# Plotting niceties\n", "plt.ylim(top=280, bottom=100)\n", "plt.gca().set(xlabel='$x_1$ / mole frac.', ylabel='$T$ / K', title='nitrogen(1) + ethane(2)')\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.11.0" } }, "nbformat": 4, "nbformat_minor": 5 }