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correlations.py
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correlations.py
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import time
import streamlit as st
import numpy as np
import pandas as pd
import requests
import scipy.constants as constants
from scipy.special import xlogy
from antoine import get_psat
from volume import get_volume
import models.margules, models.redlichkister, models.vanlaar, models.alphagm, models.wohls
import lxml
def main():
st.title("Isothermal Binary VLE Data")
st.write(
""" The *Margules* model, *Redlich-Kister Expansion* truncated to two terms, *van Laar* model and the *Truncated Wohls expansion* are implemented here. """
r"In case $\alpha$ fits the data with an accuracy of 80% or above, the $\alpha_{GM}$ value is displayed.")
compounds = ['Acetonitrile', 'Acetone', '1,2-Ethanediol', 'Ethanol',
'Diethyl ether', 'Ethyl acetate', 'Benzene', '1-Butanol',
'Chloroform', 'Cyclohexane', 'Acetic acid butyl ester', 'Acetic acid',
'Hexane', '2-Propanol', '1-Hexene', 'Methanol',
'Tetrahydrofuran', 'Water', 'm-Xylene', 'p-Xylene', '1,3-Butadiene', 'Hexadecane']
menu_options = compounds.copy()
for i, compound in enumerate(compounds):
if ' ' in compound:
compounds[i] = compound.replace(' ', '%20')
compound1 = st.selectbox('Select compound 1', menu_options, key='compound1')
compound2 = st.selectbox('Select compound 2', menu_options, key='compound2')
i1 = menu_options.index(compound1)
i2 = menu_options.index(compound2)
st.info("You have chosen %s and %s" % (compound1, compound2))
def link_generator(i1, i2):
url = 'https://web.archive.org/web/20200220211155/http://www.ddbst.com/en/EED/VLE%20' + compounds[i1] + '%3B' + compounds[i2] + '.php'
if requests.get(url).status_code == 404:
url = 'https://web.archive.org/web/20200220211155/http://www.ddbst.com/en/EED/VLE%20' + compounds[i2] + '%3B' + compounds[i1] + '.php'
return url
try:
if compound1 == compound2:
st.warning('Choose different compounds')
else:
url = link_generator(i1, i2)
if requests.get(url).status_code == 404:
st.error("VLE data for this pair of compounds doesn't exist at DDBST.")
dataframes = pd.read_html(url)
isothermal_vledata = []
T = []
for i, data in enumerate(dataframes):
col = data.columns
if col.dtype == object:
if len(col) == 3 and 'P' in col[0] and 'x1' in col[1] and 'y1' in col[2]:
T.append(float(dataframes[i - 1][1]))
isothermal_vledata.append(dataframes[i])
if isothermal_vledata == []:
st.error('There is no isothermal data available for this pair of compounds at DDBST')
else:
for i in range(len(T)):
st.write('%d)' % (i + 1), 'T = ', T[i], 'K')
st.write(isothermal_vledata[i])
if len(T) == 1:
choice = 1
else:
choice = st.number_input('Choose a dataset', value=1, min_value=1, max_value=len(T))
st.info('Analysing dataset %d ...' % choice)
P = isothermal_vledata[choice - 1]['P [kPa]']
x1 = isothermal_vledata[choice - 1]['x1 [mol/mol]']
y1 = isothermal_vledata[choice - 1]['y1 [mol/mol]']
T = T[choice - 1]
st.write(r'$T = %0.2f K$' % T)
p1sat = get_psat(compounds[i1], T)
p2sat = get_psat(compounds[i2], T)
if p1sat > p2sat:
st.info('The more volatile component is %s' % menu_options[i1])
s1, s2 = compounds[i1], compounds[i2]
else:
st.info('The more volatile component is %s' % menu_options[i2])
s1, s2 = compounds[i2], compounds[i1]
p1_s = max(p1sat, p2sat)
p2_s = min(p1sat, p2sat)
st.write(r'$p_1^s = %0.3f kPa$' % p1_s)
st.write(r'$p_2^s = %0.3f kPa$' % p2_s)
x = np.linspace(0, 1, 50)
P_raoult = x * p1_s + (1 - x) * p2_s
y_raoult = x * p1_s / P_raoult
n_points = len(x1) - 1
try:
if x1[0] == 0 and x1[n_points] != 1:
x1, y1, P = x1[1:], y1[1:], P[1:]
if x1[0] != 0 and x1[n_points] == 1:
x1, y1, P = x1[:n_points], y1[:n_points], P[:n_points]
if x1[0] == 0 and x1[n_points] == 1:
x1, y1, P = x1[1:n_points], y1[1:n_points], P[1:n_points]
except KeyError:
pass
q1, q2 = get_volume(s1, T), get_volume(s2, T)
z1 = x1 * q1 / (x1 * q1 + (1 - x1) * q2)
gamma1 = np.divide(P * y1, x1 * p1_s)
gamma2 = np.divide(P * (1 - y1), ((1 - x1) * p2_s))
G_e = constants.R * T * (xlogy(x1, gamma1) + xlogy(1 - x1, gamma2))
alpha_gm = models.alphagm.get_alpha_gm(x1, y1)
if alpha_gm == 0:
pass
else:
st.success(r"$\alpha_{GM}=%0.3f$" % alpha_gm)
st.text("Try using this value in the McCabe-Thiele Plotter!")
model = st.selectbox("Choose a model",
["Select", "Margules", "Redlich Kister", "van Laar", "Truncated Wohls"], key='model')
if model == "Select":
st.info("Select a model")
else:
if model != "Truncated Wohls":
MODELS = {"Margules": models.margules, "Redlich Kister": models.redlichkister,
"van Laar": models.vanlaar}
latest_iteration = st.empty()
bar = st.progress(0)
for i in range(100):
latest_iteration.text(f'{i + 1}%')
bar.progress(i + 1)
time.sleep(0.03)
A, acc, fig4, fig5, fig6 = MODELS[model].main(x1, y1, P, G_e, x, p1_s, p2_s, T, P_raoult)
if model == "Margules":
st.write(r"$G^E = %0.3fx_1x_2$" % A)
if model == "Redlich Kister":
st.write(r"$G^E = x_1x_2(%0.3f + (%0.3f)(x_1-x_2))$" % (A[0], A[1]))
if model == "van Laar":
st.write(r"$\frac{x_1x_2}{G^E} = \frac{x_1}{%0.3f} + \frac{x_2}{%0.3f}$" % (A[1], A[0]))
st.write(r"$R^2$ score = %0.3f" % acc)
st.write(fig4, fig5, fig6)
else:
latest_iteration = st.empty()
bar = st.progress(0)
for i in range(100):
latest_iteration.text(f'{i + 1}%')
bar.progress(i + 1)
time.sleep(0.03)
A, acc, fig4, fig5, fig6 = models.wohls.main(x1, y1, P, G_e, T, s1, s2)
st.write(r"Molar volumes: $q_1=%0.3e$, $q_2=%0.3e$" % (q1, q2))
st.write(r"$\frac{G^E/RT}{x_1q_1 + x_2q_2} = 2(%0.3f)z_1z_2$" % A)
st.write(r"$R^2$ score = %0.3f" % acc)
st.write(fig4, fig5, fig6)
except:
pass
st.sidebar.title("Note")
st.sidebar.info(""" The saturation pressures are obtained from
[DDBST's database](http://ddbonline.ddbst.com/AntoineCalculation/AntoineCalculationCGI.exe?component=Ethanol).""")
st.sidebar.info(""" The densities are obtained from
[DDBST's database](http://ddbonline.ddbst.de/DIPPR105DensityCalculation/DIPPR105CalculationCGI.exe?component=Diethyl%20ether).""")
st.sidebar.info("These models can only be used for binary isothermal vapor-liquid equilibrium data.")