{ "cells": [ { "cell_type": "markdown", "id": "1ec37f01", "metadata": {}, "source": [ "# VLLE\n", "\n", "Following the approach described in Bell et al.: https://doi.org/10.1021/acs.iecr.1c04703\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": "2024-03-15T22:39:48.542005Z", "iopub.status.busy": "2024-03-15T22:39:48.541842Z", "iopub.status.idle": "2024-03-15T22:39:49.544920Z", "shell.execute_reply": "2024-03-15T22:39:49.544338Z" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "rhovec / mol/m^3 | p / Pa\n", "[3.66984834e+03 3.25893958e+00] 2321103.087319132\n", "[19890.16767481 1698.86505766] 2321103.087318946\n", "[ 5641.24690517 16140.85769908] 2321103.0873195715\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", "\n", "def get_traces(*, T, ipures):\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", " traces = []\n", " for ipure in ipures:\n", " # Init at the pure fluid endpoint\n", " anc = pures[ipure].build_ancillaries()\n", " rhoLpure, rhoVpure = pures[ipure].pure_VLE_T(T, anc.rhoL(T), anc.rhoV(T), 10)\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", " opt = teqp.TVLEOptions()\n", " opt.p_termination = 1e8 \n", " opt.crit_termination=1e-4\n", " opt.calc_criticality=True\n", " j = model.trace_VLE_isotherm_binary(T, rhovecL, rhovecV, opt)\n", " traces.append(j)\n", " return model, traces\n", "\n", "T = 120.3420\n", "model, traces = get_traces(T=T, ipures=[0,1])\n", "for trace in traces:\n", " df = pandas.DataFrame(trace)\n", " plt.plot(df['xL_0 / mole frac.'], df['pL / Pa'])\n", " plt.plot(df['xV_0 / mole frac.'], df['pV / Pa'])\n", " \n", "# Do the VLLE solving\n", "for soln in model.find_VLLE_T_binary(traces):\n", " print('rhovec / mol/m^3 | p / Pa')\n", " for rhovec in soln['polished']:\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], p, 'X')\n", " print(rhovec, p)\n", " \n", " # And also carry out the LLE trace for the two liquid phases\n", " j = model.trace_VLE_isotherm_binary(T, np.array(soln['polished'][1]), np.array(soln['polished'][2]))\n", " df = pandas.DataFrame(j)\n", " plt.plot(df['xL_0 / mole frac.'], df['pL / Pa'], 'k')\n", " plt.plot(df['xV_0 / mole frac.'], df['pV / Pa'], 'k')\n", "\n", "# Plotting niceties\n", "plt.ylim(top=3e6, bottom=0)\n", "plt.gca().set(xlabel='$x_1$ / mole frac.', ylabel='$p$ / Pa', title='nitrogen(1) + ethane(2)')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 2, "id": "297f0c92", "metadata": { "execution": { "iopub.execute_input": "2024-03-15T22:39:49.547156Z", "iopub.status.busy": "2024-03-15T22:39:49.546788Z", "iopub.status.idle": "2024-03-15T22:39:49.988374Z", "shell.execute_reply": "2024-03-15T22:39:49.987833Z" } }, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Nitrogen + ethane VLLE curve')" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Trace from both pure fluid endpoints\n", "T = 113\n", "model, traces = get_traces(T=T, ipures = [0,1])\n", "\n", "# Find the VLLE solution for the starting temperature\n", "solns = model.find_VLLE_T_binary(traces)\n", "rhovecV, rhovecL1, rhovecL2 = solns[0]['polished']\n", "\n", "# Obtain the VLLE trace towards higher temperatures\n", "opt = teqp.VLLETracerOptions()\n", "a = lambda x: np.array(x)\n", "VLLE = model.trace_VLLE_binary(T, a(rhovecV), a(rhovecL1), a(rhovecL2), opt)\n", "df = pandas.DataFrame(VLLE)\n", "\n", "# Add the pressure to the DataFrame\n", "def add_ps(row, key):\n", " T = row['T / K']\n", " rhovec = np.array(row[key])\n", " rhotot = sum(rhovec)\n", " x = rhovec/rhotot\n", " p = rhotot*model.get_R(x)*T*(1+model.get_Ar01(T, rhotot, x))\n", " return p\n", "df['p / Pa'] = df.apply(add_ps, axis=1, key='rhoV / mol/m^3')\n", "\n", "# Plot the p-T curve\n", "plt.plot(df['T / K'], df['p / Pa'])\n", "plt.gca().set(xlabel='$T$ / K', ylabel='$p$ / Pa');\n", "plt.title('Nitrogen + ethane VLLE curve')" ] } ], "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 }