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844 lines
25 KiB
Plaintext
844 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "51bd3ee9-400e-4ee1-ae81-3781c4acf453",
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"metadata": {},
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"outputs": [],
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"source": [
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"#!/usr/bin/env python\n",
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"# coding: utf-8\n",
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"\n",
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"# In[1]:\n",
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"\n",
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"\n",
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"import numpy as np\n",
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"from scipy.optimize import minimize\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"\n",
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"# #### Theory\n",
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"\n",
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"# \n",
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"\n",
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"# #### Preliminary Calculation\n",
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"\n",
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"# In[2]:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "90be5fde-d54c-48d5-9cfc-fb76d02a2ce8",
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"metadata": {},
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"outputs": [],
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"source": [
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"PmmHg = 440.8 \n",
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"TC = 9.158144271\n",
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"zis = [0.66,0.135,0.162,0.043]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "ba673fb5-82b6-4ca4-98eb-36bef06cf96c",
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"metadata": {},
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"outputs": [],
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"source": [
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"def AntoinePsat(coeff,TC):\n",
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" A,B,C = coeff\n",
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" return 10**(A-B/(TC + C))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "2ddecd48-5434-40ec-af2c-ae372e7cd9e3",
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"metadata": {},
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"outputs": [],
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"source": [
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"coeffs = [[7.94917,1657.462,227.02],\n",
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" [7.5788,863.35,273.15],\n",
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" [6.49454,255.6784,266.55],\n",
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" [6.69147,319.0117,266.7]]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "67f34a74-e710-45a3-b96d-c87b95df1883",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[8.537355065914953, 33160.18044351948, 369137.6636043228, 342796.30727095477]"
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]
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},
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"execution_count": 23,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\" Calculate Psats \"\"\"\n",
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"Psats = [AntoinePsat(coeff,TC) for coeff in coeffs]\n",
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"Psats "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "2668abbd-1477-43ee-9936-f52ec9000d36",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([1.93678654e-02, 7.52272696e+01, 8.37426642e+02, 7.77668574e+02])"
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]
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},
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"execution_count": 24,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\" Calculate Kis\"\"\"\n",
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"Kis = np.divide(Psats,PmmHg)\n",
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"Kis\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "d3523741-f974-4bb2-acd5-606f3cbcb574",
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"metadata": {},
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"outputs": [],
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"source": [
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"# define the mass balance\n",
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"def vapor_phase_mass_balance(V,zis):\n",
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" \"\"\"\n",
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" Equation 10.17 SVA\n",
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" \"\"\"\n",
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" return np.abs(1-sum([(zi*Ki)/(1+V*(Ki-1)) for zi,Ki in list(zip(zis,Kis))]))\n",
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"\n",
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"# minimize vapor_phase_mass_balance to solve for V\n",
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"\n",
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"F_vapor_phase_mass_balance = lambda x : vapor_phase_mass_balance(x,zis)\n",
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"res = minimize(F_vapor_phase_mass_balance, x0 = 0.5 ,method='Nelder-Mead', tol=1e-6)\n",
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"\n",
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"\n",
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"# evaluate L and V\n",
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"if res.success:\n",
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" V,L = res.x[0],1-res.x[0]\n",
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"else:\n",
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" print('Did not converge')\n",
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" raise\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "7bbf9028-887f-410f-b404-2a7d2ae11b1c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.019254965906916017,\n",
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" 0.38406230477729975,\n",
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" 0.4715420278978388,\n",
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" 0.12514042283686558]"
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]
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},
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"execution_count": 26,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\" evaluate yis\"\"\"\n",
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"yis = [(zi*Ki)/(1+V*(Ki-1)) for zi,Ki in list(zip(zis,Kis))]\n",
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"yis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "24958634-7976-4350-98b3-7111567a359d",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[0.9941707831334012,\n",
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" 0.0051053601543027535,\n",
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" 0.0005630845789828888,\n",
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" 0.00016091742301905547]"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"\"\"\" evaluate xis\"\"\"\n",
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"xis = [yi/Ki for yi,Ki in list(zip(yis,Kis))]\n",
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"xis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "b082d83b-1f2f-4013-812c-14f0d2eccbb9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(0.65339999728938, 0.6534)"
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]
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},
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"execution_count": 28,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"L*xis[0],1*zis[0]*0.99"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 29,
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"id": "346bfd1a-1421-4d15-aa5a-9bf1331605da",
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"metadata": {},
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"outputs": [],
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"source": [
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"zis = [0.66,0.135,0.162,0.043]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 30,
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"id": "2ee4387f-e366-4643-bbd6-7483e7977fb3",
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"metadata": {},
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"outputs": [],
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"source": [
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"def calcT98(TC,zis):\n",
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" \"\"\" Calculate Psats \"\"\"\n",
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" Psats = [AntoinePsat(coeff,TC) for coeff in coeffs]\n",
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"\n",
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" \"\"\" Calculate Kis\"\"\"\n",
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" Kis = np.divide(Psats,PmmHg)\n",
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" \n",
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" \"\"\" solve for the vapor fraction,V\"\"\"\n",
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" F_vapor_phase_mass_balance = lambda x : vapor_phase_mass_balance(x,zis)\n",
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" res = minimize(F_vapor_phase_mass_balance, x0 = 0.5 ,method='Nelder-Mead', tol=1e-6)\n",
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" \n",
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" # evaluate L and V\n",
|
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" if res.success:\n",
|
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" V,L = res.x[0],1-res.x[0]\n",
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" else:\n",
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" print('Did not converge')\n",
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" raise\n",
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" \n",
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" \"\"\" evaluate yis\"\"\"\n",
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" yis = [(zi*Ki)/(1+V*(Ki-1)) for zi,Ki in list(zip(zis,Kis))]\n",
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" \n",
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" \"\"\" evaluate xis\"\"\"\n",
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" xis = [yi/Ki for yi,Ki in list(zip(yis,Kis))]\n",
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" \n",
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" return [np.abs(L*xis[0] - (1*zis[0]*0.98)),yis[0]]\n",
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" \n",
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"\n",
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"F_calcT98 = lambda x : calcT98(x,zis)[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 31,
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"id": "4c67cb7d-9041-4d3c-8e1b-67775d20510d",
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"metadata": {},
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"outputs": [],
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"source": [
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"T98 = minimize(F_calcT98, x0 = 10 ,method='Nelder-Mead', tol=1e-6)"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 32,
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|
"id": "2ab69a19-7bf0-48f5-a61c-b9023127d53b",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"19.905107498168945"
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]
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},
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"execution_count": 32,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"T98 = T98.x[0]\n",
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"T98"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"id": "51f22d53-8f85-4103-8ab6-63b693946b3c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# #### Effect of Composition to T98\n",
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"\n",
|
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"# In[15]:\n",
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"\n",
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"\n",
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"\"\"\"water_to_carbondioxide_mass_ratios\"\"\" \n",
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"r1s = np.linspace(0.05,2,11)\n",
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"\"\"\"wetcarbondioxide_to_air_mole_ratios\"\"\"\n",
|
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"r2s = 1-np.linspace(0.00,0.40,11) "
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]
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},
|
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{
|
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"cell_type": "code",
|
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"execution_count": 34,
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"id": "4f7b6eee-9b90-4016-83bb-b05bebc3e769",
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"metadata": {},
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"outputs": [],
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"source": [
|
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"r1 = r1s[0]\n",
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"r2 = r2s[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "5a475a55-7ef5-4c72-96ce-a07dfc6ff273",
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"metadata": {},
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"outputs": [],
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"source": [
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"results = []\n",
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"\n",
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"for r1 in r1s:\n",
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" for r2 in r2s:\n",
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"\n",
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" mCO2 = 1\n",
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" mwater = r1*mCO2\n",
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"\n",
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" nCO2 = mCO2/ 44.01\n",
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" nwater = mwater / 18.01528\n",
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"\n",
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" z1_pure = nwater / (nwater + nCO2)\n",
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" z2_pure = nCO2 / (nwater + nCO2)\n",
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"\n",
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" zs = [ r2*z1_pure,\n",
|
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" r2*z2_pure,\n",
|
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" 0.79*(1 - r2*z1_pure - r2*z2_pure),\n",
|
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" 0.21*(1 - r2*z1_pure - r2*z2_pure)]\n",
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"\n",
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" zijs = [np.round(z,3) for z in zs]\n",
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"\n",
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"\n",
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" \"\"\"define a lambda funtion at the calculated zijs\"\"\"\n",
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" F_calcT98 = lambda x : calcT98(x,zijs)[0]\n",
|
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" \"\"\"solve for T98\"\"\"\n",
|
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" T98 = minimize(F_calcT98, x0 = 50 ,method='Nelder-Mead', tol=1e-6) \n",
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"\n",
|
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" \"\"\"append the result and the calculation parameter levels\"\"\"\n",
|
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" results.append({'zH2O' : zijs[0],\n",
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" 'zCO2' : zijs[1],\n",
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" 'zN2' : zijs[2],\n",
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" 'zO2' : zijs[3],\n",
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" 'r1' : r1,\n",
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" 'r2' : 1-r2,\n",
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" 'yH2O' : calcT98(T98.x[0],zijs)[1],\n",
|
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" 'T98' : T98.x[0]})"
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 36,
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"id": "f666ddb2-00fb-4ef2-b63f-9e6b433fc311",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
|
" }\n",
|
|
"\n",
|
|
" .dataframe tbody tr th {\n",
|
|
" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
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" }\n",
|
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"</style>\n",
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
|
" <thead>\n",
|
|
" <tr style=\"text-align: right;\">\n",
|
|
" <th></th>\n",
|
|
" <th>zH2O</th>\n",
|
|
" <th>zCO2</th>\n",
|
|
" <th>zN2</th>\n",
|
|
" <th>zO2</th>\n",
|
|
" <th>r1</th>\n",
|
|
" <th>r2</th>\n",
|
|
" <th>yH2O</th>\n",
|
|
" <th>T98</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
|
|
" <th>0</th>\n",
|
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" <td>0.109</td>\n",
|
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" <td>0.891</td>\n",
|
|
" <td>-0.000</td>\n",
|
|
" <td>-0.000</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.00</td>\n",
|
|
" <td>0.002403</td>\n",
|
|
" <td>-19.445789</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
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" <td>0.104</td>\n",
|
|
" <td>0.856</td>\n",
|
|
" <td>0.032</td>\n",
|
|
" <td>0.008</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.04</td>\n",
|
|
" <td>0.002280</td>\n",
|
|
" <td>-20.161733</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>0.100</td>\n",
|
|
" <td>0.820</td>\n",
|
|
" <td>0.063</td>\n",
|
|
" <td>0.017</td>\n",
|
|
" <td>0.05</td>\n",
|
|
" <td>0.08</td>\n",
|
|
" <td>0.002182</td>\n",
|
|
" <td>-20.759943</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>0.096</td>\n",
|
|
" <td>0.784</td>\n",
|
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" <td>0.095</td>\n",
|
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" <td>0.025</td>\n",
|
|
" <td>0.05</td>\n",
|
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" <td>0.12</td>\n",
|
|
" <td>0.002085</td>\n",
|
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" <td>-21.381680</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
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" <td>0.091</td>\n",
|
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" <td>0.749</td>\n",
|
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" <td>0.126</td>\n",
|
|
" <td>0.034</td>\n",
|
|
" <td>0.05</td>\n",
|
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" <td>0.16</td>\n",
|
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" <td>0.001966</td>\n",
|
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" <td>-22.193559</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
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],
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"text/plain": [
|
|
" zH2O zCO2 zN2 zO2 r1 r2 yH2O T98\n",
|
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"0 0.109 0.891 -0.000 -0.000 0.05 0.00 0.002403 -19.445789\n",
|
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"1 0.104 0.856 0.032 0.008 0.05 0.04 0.002280 -20.161733\n",
|
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"2 0.100 0.820 0.063 0.017 0.05 0.08 0.002182 -20.759943\n",
|
|
"3 0.096 0.784 0.095 0.025 0.05 0.12 0.002085 -21.381680\n",
|
|
"4 0.091 0.749 0.126 0.034 0.05 0.16 0.001966 -22.193559"
|
|
]
|
|
},
|
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"execution_count": 36,
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"metadata": {},
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"output_type": "execute_result"
|
|
}
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],
|
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"source": [
|
|
"results = pd.DataFrame(results)\n",
|
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"results.head()"
|
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]
|
|
},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": 37,
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"id": "9e120445-4d0d-4aee-8edb-7826ff67710c",
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"metadata": {},
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"outputs": [
|
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{
|
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"data": {
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th>r2</th>\n",
|
|
" <th>0.00</th>\n",
|
|
" <th>0.04</th>\n",
|
|
" <th>0.08</th>\n",
|
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" <th>0.12</th>\n",
|
|
" <th>0.16</th>\n",
|
|
" <th>0.20</th>\n",
|
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" <th>0.24</th>\n",
|
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" <th>0.28</th>\n",
|
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" <th>0.32</th>\n",
|
|
" <th>0.36</th>\n",
|
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" <th>0.40</th>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>r1</th>\n",
|
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0.050</th>\n",
|
|
" <td>0.002403</td>\n",
|
|
" <td>0.002280</td>\n",
|
|
" <td>0.002182</td>\n",
|
|
" <td>0.002085</td>\n",
|
|
" <td>0.001966</td>\n",
|
|
" <td>0.001871</td>\n",
|
|
" <td>0.001777</td>\n",
|
|
" <td>0.001661</td>\n",
|
|
" <td>0.001568</td>\n",
|
|
" <td>0.001477</td>\n",
|
|
" <td>0.001364</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0.245</th>\n",
|
|
" <td>0.011811</td>\n",
|
|
" <td>0.011061</td>\n",
|
|
" <td>0.010348</td>\n",
|
|
" <td>0.009669</td>\n",
|
|
" <td>0.009062</td>\n",
|
|
" <td>0.008441</td>\n",
|
|
" <td>0.007846</td>\n",
|
|
" <td>0.007277</td>\n",
|
|
" <td>0.006731</td>\n",
|
|
" <td>0.006208</td>\n",
|
|
" <td>0.005706</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0.440</th>\n",
|
|
" <td>0.021381</td>\n",
|
|
" <td>0.019616</td>\n",
|
|
" <td>0.018075</td>\n",
|
|
" <td>0.016586</td>\n",
|
|
" <td>0.015214</td>\n",
|
|
" <td>0.013945</td>\n",
|
|
" <td>0.012821</td>\n",
|
|
" <td>0.011722</td>\n",
|
|
" <td>0.010696</td>\n",
|
|
" <td>0.009780</td>\n",
|
|
" <td>0.008878</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0.635</th>\n",
|
|
" <td>0.031056</td>\n",
|
|
" <td>0.028006</td>\n",
|
|
" <td>0.025211</td>\n",
|
|
" <td>0.022829</td>\n",
|
|
" <td>0.020694</td>\n",
|
|
" <td>0.018694</td>\n",
|
|
" <td>0.016955</td>\n",
|
|
" <td>0.015371</td>\n",
|
|
" <td>0.013863</td>\n",
|
|
" <td>0.012535</td>\n",
|
|
" <td>0.011310</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>0.830</th>\n",
|
|
" <td>0.040917</td>\n",
|
|
" <td>0.036093</td>\n",
|
|
" <td>0.032009</td>\n",
|
|
" <td>0.028500</td>\n",
|
|
" <td>0.025555</td>\n",
|
|
" <td>0.022866</td>\n",
|
|
" <td>0.020486</td>\n",
|
|
" <td>0.018363</td>\n",
|
|
" <td>0.016458</td>\n",
|
|
" <td>0.014797</td>\n",
|
|
" <td>0.013230</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1.025</th>\n",
|
|
" <td>0.050890</td>\n",
|
|
" <td>0.043965</td>\n",
|
|
" <td>0.038326</td>\n",
|
|
" <td>0.033781</td>\n",
|
|
" <td>0.029791</td>\n",
|
|
" <td>0.026477</td>\n",
|
|
" <td>0.023494</td>\n",
|
|
" <td>0.020964</td>\n",
|
|
" <td>0.018644</td>\n",
|
|
" <td>0.016579</td>\n",
|
|
" <td>0.014788</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1.220</th>\n",
|
|
" <td>0.060927</td>\n",
|
|
" <td>0.051696</td>\n",
|
|
" <td>0.044438</td>\n",
|
|
" <td>0.038565</td>\n",
|
|
" <td>0.033705</td>\n",
|
|
" <td>0.029612</td>\n",
|
|
" <td>0.026114</td>\n",
|
|
" <td>0.023089</td>\n",
|
|
" <td>0.020444</td>\n",
|
|
" <td>0.018112</td>\n",
|
|
" <td>0.016040</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1.415</th>\n",
|
|
" <td>0.071202</td>\n",
|
|
" <td>0.059247</td>\n",
|
|
" <td>0.050184</td>\n",
|
|
" <td>0.043050</td>\n",
|
|
" <td>0.037275</td>\n",
|
|
" <td>0.032358</td>\n",
|
|
" <td>0.028355</td>\n",
|
|
" <td>0.024934</td>\n",
|
|
" <td>0.021974</td>\n",
|
|
" <td>0.019388</td>\n",
|
|
" <td>0.017109</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1.610</th>\n",
|
|
" <td>0.081214</td>\n",
|
|
" <td>0.066234</td>\n",
|
|
" <td>0.055573</td>\n",
|
|
" <td>0.047118</td>\n",
|
|
" <td>0.040417</td>\n",
|
|
" <td>0.034965</td>\n",
|
|
" <td>0.030437</td>\n",
|
|
" <td>0.026614</td>\n",
|
|
" <td>0.023341</td>\n",
|
|
" <td>0.020505</td>\n",
|
|
" <td>0.018025</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1.805</th>\n",
|
|
" <td>0.091741</td>\n",
|
|
" <td>0.073664</td>\n",
|
|
" <td>0.060515</td>\n",
|
|
" <td>0.050717</td>\n",
|
|
" <td>0.043308</td>\n",
|
|
" <td>0.037178</td>\n",
|
|
" <td>0.032291</td>\n",
|
|
" <td>0.028075</td>\n",
|
|
" <td>0.024500</td>\n",
|
|
" <td>0.021514</td>\n",
|
|
" <td>0.018836</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2.000</th>\n",
|
|
" <td>0.102358</td>\n",
|
|
" <td>0.080387</td>\n",
|
|
" <td>0.065397</td>\n",
|
|
" <td>0.054160</td>\n",
|
|
" <td>0.045827</td>\n",
|
|
" <td>0.039220</td>\n",
|
|
" <td>0.033845</td>\n",
|
|
" <td>0.029383</td>\n",
|
|
" <td>0.025513</td>\n",
|
|
" <td>0.022305</td>\n",
|
|
" <td>0.019526</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
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"text/plain": [
|
|
"r2 0.00 0.04 0.08 0.12 0.16 0.20 0.24 \\\n",
|
|
"r1 \n",
|
|
"0.050 0.002403 0.002280 0.002182 0.002085 0.001966 0.001871 0.001777 \n",
|
|
"0.245 0.011811 0.011061 0.010348 0.009669 0.009062 0.008441 0.007846 \n",
|
|
"0.440 0.021381 0.019616 0.018075 0.016586 0.015214 0.013945 0.012821 \n",
|
|
"0.635 0.031056 0.028006 0.025211 0.022829 0.020694 0.018694 0.016955 \n",
|
|
"0.830 0.040917 0.036093 0.032009 0.028500 0.025555 0.022866 0.020486 \n",
|
|
"1.025 0.050890 0.043965 0.038326 0.033781 0.029791 0.026477 0.023494 \n",
|
|
"1.220 0.060927 0.051696 0.044438 0.038565 0.033705 0.029612 0.026114 \n",
|
|
"1.415 0.071202 0.059247 0.050184 0.043050 0.037275 0.032358 0.028355 \n",
|
|
"1.610 0.081214 0.066234 0.055573 0.047118 0.040417 0.034965 0.030437 \n",
|
|
"1.805 0.091741 0.073664 0.060515 0.050717 0.043308 0.037178 0.032291 \n",
|
|
"2.000 0.102358 0.080387 0.065397 0.054160 0.045827 0.039220 0.033845 \n",
|
|
"\n",
|
|
"r2 0.28 0.32 0.36 0.40 \n",
|
|
"r1 \n",
|
|
"0.050 0.001661 0.001568 0.001477 0.001364 \n",
|
|
"0.245 0.007277 0.006731 0.006208 0.005706 \n",
|
|
"0.440 0.011722 0.010696 0.009780 0.008878 \n",
|
|
"0.635 0.015371 0.013863 0.012535 0.011310 \n",
|
|
"0.830 0.018363 0.016458 0.014797 0.013230 \n",
|
|
"1.025 0.020964 0.018644 0.016579 0.014788 \n",
|
|
"1.220 0.023089 0.020444 0.018112 0.016040 \n",
|
|
"1.415 0.024934 0.021974 0.019388 0.017109 \n",
|
|
"1.610 0.026614 0.023341 0.020505 0.018025 \n",
|
|
"1.805 0.028075 0.024500 0.021514 0.018836 \n",
|
|
"2.000 0.029383 0.025513 0.022305 0.019526 "
|
|
]
|
|
},
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|
"execution_count": 37,
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"metadata": {},
|
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"output_type": "execute_result"
|
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}
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|
],
|
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"source": [
|
|
"df=results.pivot('r1','r2','yH2O')\n",
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"df"
|
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]
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},
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{
|
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"cell_type": "code",
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"execution_count": 38,
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"id": "1dcb241f-de8d-4dac-b8f1-b261629428c1",
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|
"metadata": {},
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"outputs": [
|
|
{
|
|
"ename": "NameError",
|
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"evalue": "name 'plt' is not defined",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
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"\u001b[1;32mC:\\Users\\SHAHLA~1\\AppData\\Local\\Temp/ipykernel_24248/944468034.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdpi\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0mZ\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmeshgrid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
|
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"\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.figure(dpi=100)\n",
|
|
"X=df.columns.values\n",
|
|
"Y=df.index.values\n",
|
|
"Z=df.values\n",
|
|
"x,y=np.meshgrid(X, Y)\n",
|
|
"plt.contourf(x, y, Z) #the NAN will be plotted as white spaces\n",
|
|
"plt.colorbar(label = 'y$_{water}$')\n",
|
|
"plt.xlabel('mole air / (mole CO$_2$ + H$_2$O)')\n",
|
|
"plt.ylabel('mass H$_2$O / mass CO$_2$')"
|
|
]
|
|
},
|
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{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "3e1d8849-7598-442f-a80b-95daa51a11ba",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df=results.pivot('r1','r2','T98')\n",
|
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"df"
|
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]
|
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},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": null,
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"id": "c97006bc-911e-40ca-8c0c-271c1c530431",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"plt.figure(dpi=100)\n",
|
|
"X=df.columns.values\n",
|
|
"Y=df.index.values\n",
|
|
"Z=df.values\n",
|
|
"x,y=np.meshgrid(X, Y)\n",
|
|
"plt.contourf(x, y, Z) #the NAN will be plotted as white spaces\n",
|
|
"plt.colorbar(label = 'T$_{98}$,[°C]')\n",
|
|
"plt.xlabel('mole air / (mole CO$_2$ + H$_2$O)')\n",
|
|
"plt.ylabel('mass H$_2$O / mass CO$_2$')\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
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"execution_count": null,
|
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"id": "20dec83e-56a6-4100-8730-8e497e6400f1",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
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},
|
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{
|
|
"cell_type": "code",
|
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"execution_count": null,
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"id": "ce2f975b-555e-4a04-a9e6-356628c3b55b",
|
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"metadata": {},
|
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"outputs": [],
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"source": []
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},
|
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{
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|
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"source": []
|
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}
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],
|
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"metadata": {
|
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"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
|
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"name": "ipython",
|
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|
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},
|
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|
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"mimetype": "text/x-python",
|
|
"name": "python",
|
|
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|
|
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|
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|
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|
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|
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