def __init__(self, s1, s2, T):
self.s1 = s1
self.s2 = s2
self.p1_s = get_psat(s1, T)
self.p2_s = get_psat(s2, T)
self.q1 = get_volume(self.s1, T)
self.q2 = get_volume(self.s2, T)
def main(x1, y1, P, G_e, T, s1, s2):
style.use('classic')
w = Wohls(s1, s2, T)
A = get_parameter(x1, G_e, s1, s2, T)
acc = get_accuracy(G_e, w.Ge(x1, A))
x = np.linspace(0, 1, 50)
fig4 = plt.figure(facecolor='white')
plt.title(r"$G^E-x$")
plt.xlim(0, 1)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$G^E\ (J/mol)$')
plt.scatter(x1, G_e)
plt.plot(x, w.Ge(x, A), label=r"$Wohls\ model$", color='red')
plt.axhline(0, color='black')
q1 = get_volume(s1, T)
q2 = get_volume(s2, T)
z = x * q1 / (x * q1 + (1 - x) * q2)
p1_s = get_psat(s1, T)
p2_s = get_psat(s2, T)
P_Wohls = x * p1_s * w.gamma1(z, A) + (1 - x) * p2_s * w.gamma2(z, A)
y_Wohls = x * p1_s * w.gamma1(z, A) / P_Wohls
P_raoult = x * p1_s + (1 - x) * p2_s
fig5 = plt.figure(facecolor='white')
plt.title(r"$P-x$")
plt.xlim(0, 1)
plt.ylim(0, 1.2 * max(P))
plt.xlabel(r'$x_1$')
plt.ylabel(r'$P\ (kPa)$')
plt.scatter(x1, P)
plt.plot(x, P_Wohls, label=r"$Wohls\ model$", color='red')
plt.plot(x, P_raoult, color='black', label=r"$Raoult's\ Law$")
plt.legend(loc='best', fontsize=10)
fig6 = plt.figure(facecolor='white')
plt.gca().set_aspect('equal', adjustable='box')
plt.title(r"$y-x$")
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$y_1$')
plt.scatter(x1, y1)
plt.plot(x, y_Wohls, label=r"$Wohls\ model$", color='red')
plt.plot(x, x, color='black')
plt.legend(loc='best', fontsize=10)
return A, acc, fig4, fig5, fig6
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))
if st.button('Go!'):
st.write('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.write('The more volatile component is %s' % menu_options[i1])
else:
st.write('The more volatile component is %s' % menu_options[i2])
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
def main():
st.title("Correlations")
st.write(
""" The *Margules* model, *Redlich-Kister Expansion* truncated to two terms and the *Van Laar* predictive model 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', 'N-Methyl-2-pyrrolidone', '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))
@st.cache(suppress_st_warning=True)
def link_generator(i1, i2):
url = 'http://www.ddbst.com/en/EED/VLE/VLE%20' + compounds[
i1] + '%3B' + compounds[i2] + '.php'
if requests.get(url).status_code == 404:
url = 'http://www.ddbst.com/en/EED/VLE/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 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])
else:
st.info('The more volatile component is %s' % menu_options[i2])
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
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"The geometric mean of $\alpha$ is $\alpha_{GM}=%0.3f$" %
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"])
if model == "Select":
st.info("Select a model")
else:
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(0.1)
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)
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(
"These models can only be used for binary isothermal vapor-liquid equilibrium data."
)
st.info('Analysing dataset %d ...' % choice)
T = isobaric_vledata[choice - 1]['T [K]']
x1 = np.array(isobaric_vledata[choice - 1]['x1 [mol/mol]'])
y1 = np.array(isobaric_vledata[choice - 1]['y1 [mol/mol]'])
P = P[choice - 1]
st.write(r'$P = %0.3f kPa$' % P)
n_points = len(x1) - 1
T1 = T[n_points]
T2 = T[0]
if T1 > T2:
st.info('The more volatile component is %s' % menu_options[i2])
p1_s = np.array(get_psat(compounds[i2], T))
p2_s = np.array(get_psat(compounds[i1], T))
else:
st.info('The more volatile component is %s' % menu_options[i1])
p1_s = np.array(get_psat(compounds[i1], T))
p2_s = np.array(get_psat(compounds[i2], T))
x = np.linspace(0, 1, 50)
try:
if x1[0] == 0 and x1[n_points] != 1:
x1, y1, T = x1[1:], y1[1:], T[1:]
if x1[0] != 0 and x1[n_points] == 1:
x1, y1, T = x1[:n_points], y1[:n_points], T[:n_points]
if x1[0] == 0 and x1[n_points] == 1:
x1, y1, T = x1[1:n_points], y1[1:n_points], T[1:n_points]
def main():
st.title('Binary VLE Data')
st.markdown(
"Vapor-liquid equlibrium data of 30 important components from "
"[Dortmund Data Bank](http://www.ddbst.com/en/EED/VLE/VLEindex.php) can be accessed from here. "
)
style.use("classic")
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', 'N-Methyl-2-pyrrolidone', '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))
@st.cache(suppress_st_warning=True)
def link_generator(i1, i2):
url = 'http://www.ddbst.com/en/EED/VLE/VLE%20' + compounds[
i1] + '%3B' + compounds[i2] + '.php'
if requests.get(url).status_code == 404:
url = 'http://www.ddbst.com/en/EED/VLE/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)
vledata = []
S = []
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]) or (len(col) == 3 and 'T' in col[0]
and 'x1' in col[1]
and 'y1' in col[2]):
S.append(float(dataframes[i - 1][1]))
vledata.append(dataframes[i])
if vledata == []:
st.error('Complete VLE data is not available at DDBST')
else:
for i in range(len(S)):
if vledata[i].columns[0] == 'P [kPa]':
st.write('%d)' % (i + 1), 'T = ', S[i], 'K')
if vledata[i].columns[0] == 'T [K]':
st.write('%d)' % (i + 1), 'P = ', S[i], 'kPa')
st.write(vledata[i])
if len(S) == 1:
choice = 1
else:
choice = st.number_input('Choose a dataset',
value=1,
min_value=1,
max_value=len(S))
st.info('Analysing dataset %d ...' % choice)
try:
P = vledata[choice - 1]['P [kPa]']
x1 = vledata[choice - 1]['x1 [mol/mol]']
y1 = vledata[choice - 1]['y1 [mol/mol]']
T = S[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])
else:
st.info('The more volatile component is %s' %
menu_options[i2])
p1_s = max(p1sat, p2sat)
p2_s = min(p1sat, p2sat)
fig1, fig2 = isothermalPlots(x1, y1, P, p1_s, p2_s)
st.write(fig1, fig2)
except:
pass
try:
T = vledata[choice - 1]['T [K]']
x1 = vledata[choice - 1]['x1 [mol/mol]']
y1 = vledata[choice - 1]['y1 [mol/mol]']
P = S[choice - 1]
st.write(r'$P = %0.2f kPa$' % P)
p1sat = get_psat(compounds[i1], T[0])
p2sat = get_psat(compounds[i2], T[0])
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 = get_psat(s1, T)
p2_s = get_psat(s2, T)
fig1, fig2 = isobaricPlots(x1, y1, T)
st.write(fig1, fig2)
except:
pass
except:
''
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)."""
)
def main():
st.title('Isothermal Binary VLE Data')
st.markdown("Vapor-liquid equlibrium data of 30 important components from "
"[Dortmund Data Bank](http://www.ddbst.com/en/EED/VLE/VLEindex.php) can be accessed from here. "
"Find out which pair of components have isothermal data available and see the $y-x$, $P-x$ and $G^E-x$ graphs.")
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',
'N-Methyl-2-pyrrolidone', '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))
@st.cache(suppress_st_warning=True)
def link_generator(i1, i2):
url = 'http://www.ddbst.com/en/EED/VLE/VLE%20' + compounds[i1] + '%3B' + compounds[i2] + '.php'
if requests.get(url).status_code == 404:
url = 'http://www.ddbst.com/en/EED/VLE/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 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))
if st.button('Go!'):
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])
else:
st.info('The more volatile component is %s' % menu_options[i2])
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
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))
fig1 = plt.figure(facecolor='white')
plt.gca().set_aspect('equal', adjustable='box')
plt.title(r"$y-x$")
plt.xlim(0, 1)
plt.ylim(0, 1)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$y_1$')
plt.scatter(x1, y1)
plt.plot(x, y_raoult, label=r"$Raoult's\ Law$", color='black')
plt.plot(x, x, color='grey')
plt.legend(loc='best', fontsize=10)
st.write(fig1)
fig2 = plt.figure(facecolor='white')
plt.title(r"$P-x$")
plt.xlim(0, 1)
plt.ylim(0, 1.2*max(P))
plt.xlabel(r'$x_1$')
plt.ylabel(r'$P\ (kPa)$')
plt.scatter(x1, P)
plt.plot(x, P_raoult, color='black', label=r"$Raoult's\ Law$")
plt.legend(loc='best', fontsize=10)
st.write(fig2)
fig3 = plt.figure(facecolor='white')
plt.title(r"$G^E-x$")
plt.xlim(0, 1)
plt.xlabel(r'$x_1$')
plt.ylabel(r'$G^E\ (J/mol)$')
plt.scatter(x1, G_e)
plt.axhline(0, color='black')
st.write(fig3)
except:
''
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).""")