{ "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": "2024-03-15T22:39:51.973103Z", "iopub.status.busy": "2024-03-15T22:39:51.972931Z", "iopub.status.idle": "2024-03-15T22:39:53.062859Z", "shell.execute_reply": "2024-03-15T22:39:53.062296Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rhovec / mol/m^3 | T / K\n", "[4921.97976373 9.6755684 ] 125.14729018874252\n", "[ 6008.68040253 15630.22353351] 125.14729018874252\n", "[18948.39537895 1540.60935171] 125.14729018874252\n" ] }, { "data": { "image/png": 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", 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" ] }, "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 }