def __init__(self, _subs: List[SubstanceProp], _k):
self.substances = _subs
self.k = _k
self.eosname = ""
self.mixRuleBehavior = MixtureRuleBehavior()
self.thetaiBehavior = ThetaiBehavior()
self.biBehavior = BiBehavior()
# TODO remove deltai and epsiloni?
self.deltaiBehavior = DeltaiBehavior()
self.epsiloniBehavior = EpsiloniBehavior()
self.deltaMixBehavior = DeltaMixtureRuleBehavior()
self.epsilonMixBehavior = EpsilonMixtureRuleBehavior()
self.n = len(self.substances)
self.Vcs = np.zeros(self.n)
self.Pcs = np.zeros(self.n)
self.Tcs = np.zeros(self.n)
self.omegas = np.zeros(self.n)
self.subs_ids = self.getSubstancesIDs()
self.vle_method = "phi-phi"
self.has_UNIFAC = self.hasUNIFAC()
if self.has_UNIFAC:
self.unifac_model = UNIFAC(self.subs_ids)
for i in range(self.n):
self.Vcs[i] = self.substances[i].Vc
self.Tcs[i] = self.substances[i].Tc
self.Pcs[i] = self.substances[i].Pc
self.omegas[i] = self.substances[i].omega
def diffEpsilonm(
self, i: int, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
c = self.c.getCm(y, T, substances)
cline = self.c.getDiffCm(i, y, T, substances)
b = bmb.bm(y, T, bib, substances)
bline = bmb.diffBm(i, y, T, bib, substances)
return -bline * c - cline * b
def diffEpsilonm(
self, i: int, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
w = getW(y, substances)
diffw = substances[i].omega
b = bmb.bm(y, T, bib, substances)
diffb = bmb.diffBm(i, y, T, bib, substances)
return diffw * b * b + w * 2.0 * b * diffb
def diffDeltam(
self, i: int, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
u = 1.0 - getW(y, substances)
return (
u * bmb.diffBm(i, y, T, bib, substances)
- bmb.bm(y, T, bib, substances) * substances[i].omega
)
def diffDeltam(self, i: int, y, T: float, bib: BiBehavior,
bmb: MixtureRuleBehavior, substances) -> float:
return 2.0 * bmb.diffBm(i, y, T, bib, substances
) + 4.0 * self.cm.diffCm(i, y, T, substances)
def deltam(self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior,
substances) -> float:
return 2.0 * bmb.bm(y, T, bib, substances) + 4.0 * self.cm.cm(
y, T, substances)
def diffEpsilonm(self, i: int, y, T: float, bib: BiBehavior,
bmb: MixtureRuleBehavior, substances) -> float:
return -2.0 * bmb.bm(y, T, bib, substances) * bmb.diffBm(
i, y, T, bib, substances) + 4.0 * self.cm.cm(
y, T, substances) * self.cm.diffCm(i, y, T, substances)
def epsilonm(self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior,
substances) -> float:
return (-(bmb.bm(y, T, bib, substances))**2 + 2.0 *
(self.cm.cm(y, T, substances))**2)
def epsilonm(self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior,
substances) -> float:
b = bmb.bm(y, T, bib, substances)
c = self.cm.cm(y, T, substances)
return b * c + 2.0 * c * c
def diffDeltam(self, i: int, y, T: float, bib: BiBehavior,
bmb: MixtureRuleBehavior, substances) -> float:
return (bmb.diffBm(i, y, T, bib, substances) +
self.cm.ci.getCi(i, T, substances) * 3.0)
def deltam(self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior,
substances) -> float:
b = bmb.bm(y, T, bib, substances)
c = self.cm.cm(y, T, substances)
return b + 3.0 * c
class EOSMixture:
"""
Main class for modeling a system with multiple substances, using a cubic equation of state.
This is the main class of the software. It's responsable for calculating all properties,
and all the vapor-liquid equilibrium data. It uses a generalized cubic equation of state for all
its calculations.
"""
def __init__(self, _subs: List[SubstanceProp], _k):
self.substances = _subs
self.k = _k
self.eosname = ""
self.mixRuleBehavior = MixtureRuleBehavior()
self.thetaiBehavior = ThetaiBehavior()
self.biBehavior = BiBehavior()
# TODO remove deltai and epsiloni?
self.deltaiBehavior = DeltaiBehavior()
self.epsiloniBehavior = EpsiloniBehavior()
self.deltaMixBehavior = DeltaMixtureRuleBehavior()
self.epsilonMixBehavior = EpsilonMixtureRuleBehavior()
self.n = len(self.substances)
self.Vcs = np.zeros(self.n)
self.Pcs = np.zeros(self.n)
self.Tcs = np.zeros(self.n)
self.omegas = np.zeros(self.n)
self.subs_ids = self.getSubstancesIDs()
self.vle_method = "phi-phi"
self.has_UNIFAC = self.hasUNIFAC()
if self.has_UNIFAC:
self.unifac_model = UNIFAC(self.subs_ids)
for i in range(self.n):
self.Vcs[i] = self.substances[i].Vc
self.Tcs[i] = self.substances[i].Tc
self.Pcs[i] = self.substances[i].Pc
self.omegas[i] = self.substances[i].omega
def hasUNIFAC(self):
if len(self.subs_ids) < 2:
return False
return has_unifac_in_db(self.subs_ids)
def getZfromPT(self, P: float, T: float, y):
b = self.mixRuleBehavior.bm(y, T, self.biBehavior, self.substances)
theta = self.mixRuleBehavior.thetam(y, T, self.thetaiBehavior,
self.substances, self.k)
delta = self.deltaMixBehavior.deltam(y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
epsilon = self.epsilonMixBehavior.epsilonm(y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
return _getZfromPT_helper(b, theta, delta, epsilon, T, P, R_IG)
def getPfromTV(self, T: float, V: float, y) -> float:
b = self.mixRuleBehavior.bm(y, T, self.biBehavior, self.substances)
theta = self.mixRuleBehavior.thetam(y, T, self.thetaiBehavior,
self.substances, self.k)
delta = self.deltaMixBehavior.deltam(y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
epsilon = self.epsilonMixBehavior.epsilonm(y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
p = R_IG * T / (V - b) - theta / (V * (V + delta) + epsilon)
return p
def getPhi_i(self, i: int, y, P: float, T: float, Z: float):
bm = self.mixRuleBehavior.bm(y, T, self.biBehavior, self.substances)
thetam = self.mixRuleBehavior.thetam(y, T, self.thetaiBehavior,
self.substances, self.k)
deltam = self.deltaMixBehavior.deltam(y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
epsilonm = self.epsilonMixBehavior.epsilonm(y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
# derivatives
diffthetam = self.mixRuleBehavior.diffThetam(i, y, T,
self.thetaiBehavior,
self.substances, self.k)
diffbm = self.mixRuleBehavior.diffBm(i, y, T, self.biBehavior,
self.substances)
diffdeltam = self.deltaMixBehavior.diffDeltam(i, y, T, self.biBehavior,
self.mixRuleBehavior,
self.substances)
diffepsilonm = self.epsilonMixBehavior.diffEpsilonm(
i, y, T, self.biBehavior, self.mixRuleBehavior, self.substances)
return _getPhi_i_helper(
P,
T,
Z,
R_IG,
bm,
thetam,
deltam,
epsilonm,
diffthetam,
diffbm,
diffdeltam,
diffepsilonm,
DBL_EPSILON,
)
def getFugacity(self, y, _P: float, _T: float, _V: float,
_Z: float) -> float:
f = 0.0
for i in range(self.n):
f += y[i] * self.getPhi_i(i, y, _P, _T, _Z)
return f * _P
def getAllProps(self, y, Tref: float, T: float, Pref: float,
P: float) -> (Props, Props):
log = ""
zs = self.getZfromPT(P, T, y)
zliq, zvap = np.min(zs), np.max(zs)
vliq, vvap = zliq * R_IG * T / P, zvap * R_IG * T / P
MixSubs = MixtureProp(self.substances, y)
avgMolWt = MixSubs.getMolWt()
if avgMolWt:
rholiq, rhovap = avgMolWt * 1e-3 / vliq, avgMolWt * 1e-3 / vvap
else:
rholiq, rhovap = 0, 0
if MixSubs.hasCp():
igprops = MixSubs.getIGProps(Tref, T, Pref, P)
log += MixSubs.getCpLog(Tref, T)
pliq, pvap = self.getCpHSGUA(y, Tref, T, Pref, P)
else:
igprops = 0
pliq, pvap = 0, 0
log += "Couldn't calculate properties: missing Cp paramaters"
fl, fv = (
self.getFugacity(y, P, T, vliq, zliq),
self.getFugacity(y, P, T, vvap, zvap),
)
retPropsliq, retPropsvap = Props(), Props()
retPropsliq.Z, retPropsvap.Z = zliq, zvap
retPropsliq.V, retPropsvap.V = vliq, vvap
retPropsliq.rho, retPropsvap.rho = rholiq, rhovap
retPropsliq.P, retPropsvap.P = P, P
retPropsliq.T, retPropsvap.T = T, T
retPropsliq.Fugacity, retPropsvap.Fugacity = fl, fv
retPropsliq.IGProps, retPropsvap.IGProps = igprops, igprops
retPropsliq.Props, retPropsvap.Props = pliq, pvap
retPropsliq.log, retPropsvap.log = log, log
return retPropsliq, retPropsvap
def getdZdT(self, P: float, T: float, y) -> [float, float]:
h = 1e-5
z_plus_h = self.getZfromPT(P, T + h, y)
z_minus_h = self.getZfromPT(P, T - h, y)
zs = (z_plus_h - z_minus_h) / (2.0 * h)
return np.min(zs), np.max(zs)
# TODO speed up this part with numba
def getDepartureProps(self, y, P, T, V, Z):
def _Zfunc(v, t):
bm = self.mixRuleBehavior.bm(y, t, self.biBehavior,
self.substances)
thetam = self.mixRuleBehavior.thetam(y, t, self.thetaiBehavior,
self.substances, self.k)
delta = self.deltaMixBehavior.deltam(y, t, self.biBehavior,
self.mixRuleBehavior,
self.substances)
epsilon = self.epsilonMixBehavior.epsilonm(y, t, self.biBehavior,
self.mixRuleBehavior,
self.substances)
return v / (v - bm) - (thetam /
(R_IG * t)) * v / (v**2 + v * delta +
epsilon)
def _dZdT(v, t):
h = 1e-5
return (_Zfunc(v, t + h) - _Zfunc(v, t - h)) / (2.0 * h)
def _URfunc(v, t):
return t * _dZdT(v, t) / v
def _ARfunc(v, t):
return (1.0 - _Zfunc(v, t)) / v
# calculate UR
# nhau = _URfunc(V, T)
UR_RT = quad(_URfunc, V, np.inf, args=(T, ))[0]
UR = UR_RT * T * R_IG
# calculate AR
AR_RT = quad(_ARfunc, V, np.inf, args=(T, ))[0] + np.log(Z)
AR = AR_RT * T * R_IG
# calculate HR
HR_RT = UR_RT + 1.0 - Z
HR = HR_RT * R_IG * T
# calculate SR
SR_R = UR_RT - AR_RT
SR = SR_R * R_IG
# calculate GR
GR_RT = AR_RT + 1 - Z
GR = GR_RT * R_IG * T
ret = DeltaProp(0, HR, SR, GR, UR, AR)
return ret
def getDeltaDepartureProps(
self,
y,
_Pref: float,
_Tref: float,
_Vref: float,
_Zref: float,
_P: float,
_T: float,
_V: float,
_Z: float,
) -> DeltaProp:
ref = self.getDepartureProps(y, _Pref, _Tref, _Vref, _Zref)
state = self.getDepartureProps(y, _P, _T, _V, _Z)
delta = state.subtract(ref)
return delta
def getCpHSGUA(self, y, Tref: float, T: float, Pref: float, P: float):
zs = self.getZfromPT(P, T, y)
zsref = self.getZfromPT(Pref, Tref, y)
zliq, zvap = np.min(zs), np.max(zs)
zliqref, zvapref = np.min(zsref), np.max(zsref)
vliq, vvap = zliq * R_IG * T / P, zvap * R_IG * T / P
vliqref, vvapref = zliqref * R_IG * Tref / Pref, zvapref * R_IG * Tref / Pref
MixSubs = MixtureProp(self.substances, y)
igprop = MixSubs.getIGProps(
Tref, T, Pref,
P) # make sure that mixture can handle single substances
ddp_liq = self.getDeltaDepartureProps(y, Pref, Tref, vliqref, zliqref,
P, T, vliq, zliq)
ddp_vap = self.getDeltaDepartureProps(y, Pref, Tref, vvapref, zvapref,
P, T, vvap, zvap)
pliq = igprop.subtract(ddp_liq)
pvap = igprop.subtract(ddp_vap)
return pliq, pvap
def _getPb_guess(self, x, T):
return _helper_getPb_guess(x, T, self.Pcs, self.Tcs, self.omegas)
def _getPd_guess(self, y, T):
return _helper_getPd_guess(y, T, self.Pcs, self.Tcs, self.omegas)
def getCapPhi_i(self, i: int, y, P: float, T: float) -> float:
zv = np.max(self.getZfromPT(P, T, y))
return self.getPhi_i(i, y, P, T, zv)
def getPSat_i(self, i: int, T: float) -> float:
has_antoine = self.substances[i].hasAntoine()
check_antoine_range = self.substances[i].checkAntoineRange(T)
if has_antoine and check_antoine_range:
P = self.substances[i].getPvpAntoine(T)
else:
P = self.substances[i].getPvpAW(T)
from EOSPureSubstanceInterface import EOSPureSubstanceInterface
system = EOSPureSubstanceInterface([self.substances[i]],
self.eosname)
P, it = system.getPvp(T, P)
return P
def getTSat_i(self, i: int, P: float) -> float:
has_antoine = self.substances[i].hasAntoine()
if has_antoine:
t = self.substances[i].getAntoineTsat(P)
else:
t = 300.0 # check this!
return t
def getTsat(self, P: float):
tsat = np.asarray([self.getTSat_i(i, P) for i in range(self.n)])
return tsat
def getCapPhiSat_i(self, i: int, y, T: float) -> float:
P = self.getPSat_i(i, T)
zv = np.max(self.getZfromPT(P, T, y))
return self.getPhi_i(i, y, P, T, zv)
def getDefCapPhi_i(self, i: int, y, P: float, T: float) -> float:
return self.getCapPhi_i(i, y, P, T) / self.getCapPhiSat_i(i, y, T)
def get_y_eq_12_9(self, x, gamma, Psat, CapPhi, P):
return x * gamma * Psat / (CapPhi * P)
def get_P_eq_12_11(self, x, gamma, Psat, CapPhi):
return np.sum(x * gamma * Psat / CapPhi)
def getPhiVap(self, y, P, T):
phivap = np.zeros(self.n, dtype=np.float64)
zsvap = self.getZfromPT(P, T, y)
zvap = np.max(zsvap)
for i in range(self.n):
phivap[i] = self.getPhi_i(i, y, P, T, zvap)
return phivap
def getCapPhi(self, y, P, T):
capphi = np.ones(self.n, dtype=np.float64)
for i in range(self.n):
capphi[i] = self.getCapPhi_i(i, y, P, T)
return capphi
def getBubblePointPressure(self,
x,
T: float,
tol=1e3 * DBL_EPSILON,
kmax=1000):
if self.vle_method == "phi-phi":
return self.getBubblePointPressure_phi_phi(x,
T,
tol=tol,
kmax=kmax)
elif self.vle_method == "UNIFAC":
return self.getBubblePointPressure_UNIFAC(x, T, tol=tol, kmax=kmax)
else:
raise NotImplementedError("gamma-phi not implemented")
def getSubstancesIDs(self):
subs_ids = [s.getSubstanceID() for s in self.substances]
return subs_ids
def getPsat(self, T: float):
Psat = np.asarray([self.getPSat_i(i, T) for i in range(self.n)])
return Psat
def getBubblePointPressure_UNIFAC(self,
x,
T,
tol=1e3 * DBL_EPSILON,
kmax=100):
assert len(x) == self.n
assert np.sum(x) == 1.0
x = np.atleast_1d(x)
gamma = self.unifac_model.getGamma(x, T)
capphi = np.ones(self.n, dtype=np.float64)
PSat = self.getPsat(T)
pb = self.get_P_eq_12_11(x, gamma, PSat, capphi)
err = 100
ite = 0
while err > tol and ite < kmax:
ite += 1
y = self.get_y_eq_12_9(x, gamma, PSat, capphi, pb)
capphi = self.getCapPhi(y, pb, T)
pb_old = pb
pb = self.get_P_eq_12_11(x, gamma, PSat, capphi)
err = np.abs((pb - pb_old) / pb)
phivap = self.getPhiVap(y, pb, T)
k = self.get_k_gamma_phi(gamma, PSat, pb, capphi)
return y, pb, phivap, gamma, k, ite
def getBubblePointPressure_phi_phi(self,
x,
T,
tol=1e3 * DBL_EPSILON,
kmax=1000):
assert len(x) == self.n
assert np.sum(x) == 1.0
x = np.atleast_1d(x)
pb = self._getPb_guess(x, T)
k = np.exp(
np.log(self.Pcs / pb) + 5.373 * (1 + self.omegas) *
(1.0 - self.Tcs / T))
y = x * k / np.sum(x * k)
err = 100
ite = 0
phivap = np.empty(self.n, dtype=np.float64)
philiq = np.empty(self.n, dtype=np.float64)
while err > tol and ite < kmax:
ite += 1
zsvap = self.getZfromPT(pb, T, y)
zsliq = self.getZfromPT(pb, T, x)
zvap = np.max(zsvap)
zliq = np.min(zsliq)
for i in range(self.n):
phivap[i] = self.getPhi_i(i, y, pb, T, zvap)
philiq[i] = self.getPhi_i(i, x, pb, T, zliq)
k = philiq / phivap
y = x * k
yt = np.sum(y)
pb = pb * yt
err = np.abs(1.0 - yt)
return y, pb, phivap, philiq, k, ite
####### DEW POINT ###########
def getDewPointPressure(self,
y,
T: float,
tol=1e3 * DBL_EPSILON,
kmax=1000):
if self.vle_method == "phi-phi":
return self.getDewPointPressure_phi_phi(y, T, tol=tol, kmax=kmax)
elif self.vle_method == "UNIFAC":
return self.getDewPointPressure_UNIFAC(y, T, tol=tol, kmax=kmax)
else:
raise NotImplementedError("gamma-phi not implemented")
def getDewPointPressure_phi_phi(self,
y,
T,
tol=1e3 * DBL_EPSILON,
kmax=1000):
assert len(y) == self.n
assert np.sum(y) == 1.0
y = np.atleast_1d(y)
pd = self._getPd_guess(y, T)
k = np.exp(
np.log(self.Pcs / pd) + 5.373 * (1 + self.omegas) *
(1.0 - self.Tcs / T))
x = y / k
x = x / np.sum(x)
err = 100
ite = 0
phivap = np.empty(self.n, dtype=np.float64)
philiq = np.empty(self.n, dtype=np.float64)
while err > tol and ite < kmax:
ite += 1
zsvap = self.getZfromPT(pd, T, y)
zsliq = self.getZfromPT(pd, T, x)
zvap = np.max(zsvap)
zliq = np.min(zsliq)
for i in range(self.n):
phivap[i] = self.getPhi_i(i, y, pd, T, zvap)
philiq[i] = self.getPhi_i(i, x, pd, T, zliq)
k = philiq / phivap
x = y / k
xt = np.sum(x)
pd = pd / xt
err = np.abs(1.0 - xt)
x = x / xt
return x, pd, phivap, philiq, k, ite
def getP_eq_12_12(self, y, gamma, Psat, capphi):
return 1.0 / np.sum(y * capphi / (gamma * Psat))
def get_x_eq_12_10(self, y, gamma, Psat, capphi, p):
return y * capphi * p / (gamma * Psat)
def getDewPointPressure_UNIFAC(self,
y,
T: float,
tol=1e3 * DBL_EPSILON,
kmax=1000):
assert len(y) == self.n
assert np.sum(y) == 1.0
y = np.atleast_1d(y)
Psat = self.getPsat(T)
capphi = np.ones(self.n, dtype=np.float64)
gamma = np.ones(self.n, dtype=np.float64)
pd = self.getP_eq_12_12(y, gamma, Psat, capphi)
x = self.get_x_eq_12_10(y, gamma, Psat, capphi, pd)
x = x / np.sum(x)
gamma = self.unifac_model.getGamma(x, T)
pd = self.getP_eq_12_12(y, gamma, Psat, capphi)
capphi = self.getCapPhi(y, pd, T)
err = 100
ite = 0
while err > tol and ite < kmax:
ite += 1
capphi = self.getCapPhi(y, pd, T)
err2 = 100
ite2 = 0
while err2 > tol and ite2 < kmax:
ite2 += 1
x = self.get_x_eq_12_10(y, gamma, Psat, capphi, pd)
x = x / np.sum(x)
gamma_old = gamma
gamma = self.unifac_model.getGamma(x, T)
err2 = np.max(np.abs(gamma_old - gamma))
pd_old = pd
pd = self.getP_eq_12_12(y, gamma, Psat, capphi)
err = np.abs((pd - pd_old) / pd)
phivap = self.getPhiVap(y, pd, T)
k = self.get_k_gamma_phi(gamma, Psat, pd, capphi)
return x, pd, phivap, gamma, k, ite
def getBubblePointTemperature(self,
x,
P: float,
tol=1e3 * DBL_EPSILON,
kmax=100):
if self.vle_method == "phi-phi":
return self.getBubblePointTemperature_phi_phi(x,
P,
tol=tol,
kmax=kmax)
elif self.vle_method == "UNIFAC":
return self.getBubblePointTemperature_UNIFAC(x,
P,
tol=tol,
kmax=kmax)
else:
raise NotImplementedError("gamma-phi not implemented")
def get_k_gamma_phi(self, gamma, psat, P, capphi):
k = gamma * psat / (P * capphi)
return k
def getBubblePointTemperature_UNIFAC(self,
x,
P,
tol=1e3 * DBL_EPSILON,
kmax=100):
assert len(x) == self.n
x = np.atleast_1d(x)
assert np.sum(x) == 1.0
tsat = self.getTsat(P)
tb = np.float(np.sum(x * tsat))
capphi = np.ones(self.n, dtype=np.float64)
psat = self.getPsat(tb)
gamma = self.unifac_model.getGamma(x, tb)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
tb2 = tb
f2 = np.sum(x * k) - 1.0
tb1 = tb * 1.1
y = x * k / np.sum(x * k)
capphi = self.getCapPhi(y, P, tb1)
psat = self.getPsat(tb1)
gamma = self.unifac_model.getGamma(x, tb1)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
f1 = np.sum(x * k) - 1.0
y = x * k / np.sum(x * k)
err = 100
ite = 0
while err > tol and ite < kmax:
ite += 1
tb = tb1 - f1 * ((tb1 - tb2) / (f1 - f2))
capphi = self.getCapPhi(y, P, tb)
psat = self.getPsat(tb)
gamma = self.unifac_model.getGamma(x, tb)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
y = x * k
yt = np.sum(y)
err = np.abs(1.0 - yt)
y = y / yt
tb2 = tb1
tb1 = tb
f2 = f1
f1 = np.sum(k * x) - 1.0
phivap = self.getPhiVap(y, P, tb)
return y, tb, phivap, gamma, k, ite
# TODO optimize this! here, I used the secant method for Tb convergence.
def getBubblePointTemperature_phi_phi(self,
x,
P,
tol=1e3 * DBL_EPSILON,
kmax=100):
assert len(x) == self.n
x = np.atleast_1d(x)
assert np.sum(x) == 1.0
Tbi = np.empty(self.n)
for i in range(self.n):
if self.substances[i].Tb > 0:
Tbi[i] = self.substances[i].Tb
else:
Tbi[i] = 100.0
tb = _helper_bubble_T_guess_from_wilson(x, P, np.sum(x * Tbi),
self.Pcs, self.Tcs,
self.omegas)
k = np.exp(
np.log(self.Pcs / P) + 5.373 * (1 + self.omegas) *
(1.0 - self.Tcs / tb))
err = 100
ite = 0
tb2 = tb
f2 = np.sum(x * k) - 1.0
tb1 = tb * 1.1
k = np.exp(
np.log(self.Pcs / P) + 5.373 * (1 + self.omegas) *
(1.0 - self.Tcs / tb1))
f1 = np.sum(x * k) - 1.0
y = x * k / np.sum(x * k)
phivap = np.empty(self.n, dtype=np.float64)
philiq = np.empty(self.n, dtype=np.float64)
while err > tol and ite < kmax:
ite += 1
tb = tb1 - f1 * ((tb1 - tb2) / (f1 - f2))
zsvap = self.getZfromPT(P, tb, y)
zsliq = self.getZfromPT(P, tb, x)
zvap = np.max(zsvap)
zliq = np.min(zsliq)
for i in range(self.n):
phivap[i] = self.getPhi_i(i, y, P, tb, zvap)
philiq[i] = self.getPhi_i(i, x, P, tb, zliq)
k = philiq / phivap
y = x * k
yt = np.sum(y)
err = np.abs(1.0 - yt)
y = y / yt
tb2 = tb1
tb1 = tb
f2 = f1
f1 = np.sum(k * x) - 1.0
return y, tb, phivap, philiq, k, ite
def getDewPointTemperature(self,
y,
P: float,
tol=1e3 * DBL_EPSILON,
kmax=100):
if self.vle_method == "phi-phi":
return self.getDewPointTemperature_phi_phi(y,
P,
tol=tol,
kmax=kmax)
elif self.vle_method == "UNIFAC":
return self.getDewPointTemperature_UNIFAC(y, P, tol=tol, kmax=kmax)
else:
raise NotImplementedError("gamma-phi not implemented")
def getDewPointTemperature_UNIFAC(self,
y,
P: float,
tol: float = 1e4 * DBL_EPSILON,
kmax: int = 1000):
assert len(y) == self.n
y = np.atleast_1d(y)
assert np.sum(y) == 1.0
td = float(np.sum(y * self.getTsat(P)))
gamma = np.ones(self.n, dtype=np.float64)
capphi = self.getCapPhi(y, P, td)
psat = self.getPsat(td)
x = self.get_x_eq_12_10(y, gamma, psat, capphi, P)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
x = x / np.sum(x)
td2 = td
f2 = np.sum(y / k) - 1.0
td1 = td * 1.1
capphi = self.getCapPhi(y, P, td1)
psat = self.getPsat(td1)
gamma = self.unifac_model.getGamma(x, td1)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
f1 = np.sum(y / k) - 1.0
x = self.get_x_eq_12_10(y, gamma, psat, capphi, P)
x = x / np.sum(x)
err = 100
ite = 0
while err > tol and ite < kmax:
ite += 1
td = td1 - f1 * ((td1 - td2) / (f1 - f2))
capphi = self.getCapPhi(y, P, td)
psat = self.getPsat(td)
gamma = self.unifac_model.getGamma(x, td)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
x = self.get_x_eq_12_10(y, gamma, psat, capphi, P)
xt = np.sum(x)
err = np.abs(1.0 - xt)
x = x / xt
td2 = td1
td1 = td
f2 = f1
f1 = np.sum(y / k) - 1.0
phivap = self.getPhiVap(y, P, td)
return x, td, phivap, gamma, k, ite
def getDewPointTemperature_phi_phi(self,
y,
P: float,
tol: float = 1e4 * DBL_EPSILON,
kmax: int = 1000):
assert len(y) == self.n
y = np.atleast_1d(y)
assert np.sum(y) == 1.0
Tdi = np.empty(self.n)
for i in range(self.n):
if self.substances[i].Tb > 0:
Tdi[i] = self.substances[i].Tb
else:
Tdi[i] = 100.0
td = np.sum(y * Tdi)
k = np.exp(
np.log(self.Pcs / P) + 5.373 * (1 + self.omegas) *
(1.0 - self.Tcs / td))
td2 = td
f2 = np.sum(y / k) - 1.0
td1 = td * 1.1
k = np.exp(
np.log(self.Pcs / P) + 5.373 * (1 + self.omegas) *
(1.0 - self.Tcs / td1))
f1 = np.sum(y / k) - 1.0
err = 100
ite = 0
# x = np.full(self.n, 1.0 / self.n)
x = (y / k) / np.sum(y / k)
phivap = np.empty(self.n, dtype=np.float64)
philiq = np.empty(self.n, dtype=np.float64)
while err > tol and ite < kmax:
ite += 1
td = td1 - f1 * ((td1 - td2) / (f1 - f2))
zsvap = self.getZfromPT(P, td, y)
zsliq = self.getZfromPT(P, td, x)
zvap = np.max(zsvap)
zliq = np.min(zsliq)
for i in range(self.n):
phivap[i] = self.getPhi_i(i, y, P, td, zvap)
philiq[i] = self.getPhi_i(i, x, P, td, zliq)
k = philiq / phivap
x = y / k
xt = np.sum(x)
err = np.abs(1.0 - xt)
x = x / xt
td2 = td1
td1 = td
f2 = f1
f1 = np.sum(y / k) - 1.0
return x, td, phivap, philiq, k, ite
def getFlash(self,
z,
P: float,
T: float,
tol=1e5 * DBL_EPSILON,
kmax=1000):
if self.vle_method == "phi-phi":
return self.getFlash_phi_phi(z, P, T, tol=tol, kmax=kmax)
elif self.vle_method == "UNIFAC":
return self.getFlash_UNIFAC(z, P, T, tol=tol, kmax=kmax)
else:
raise NotImplementedError("gamma-phi not implemented")
def getFlash_phi_phi(self,
z,
P: float,
T: float,
tol=1e5 * DBL_EPSILON,
kmax=1000):
assert self.n == len(z)
z = np.atleast_1d(z)
assert np.sum(z) == 1.0
# check if is flash problem
y, pd, pv, pl, k, ite = self.getDewPointPressure(z, T)
x, pb, pv, pl, k, ite = self.getBubblePointPressure(z, T)
if not (pd <= P <= pb):
raise ValueError("P is not between Pdew and Pbubble")
v = (pb - P) / (pb - pd)
err = 100
ite = 0
phivap = np.empty(self.n, dtype=np.float64)
philiq = np.empty(self.n, dtype=np.float64)
y = np.full(self.n, 1.0 / self.n)
x = np.full(self.n, 1.0 / self.n)
while err > tol and ite < kmax:
ite += 1
zsvap = self.getZfromPT(P, T, y)
zsliq = self.getZfromPT(P, T, x)
zvap = np.max(zsvap)
zliq = np.min(zsliq)
for i in range(self.n):
phivap[i] = self.getPhi_i(i, y, P, T, zvap)
philiq[i] = self.getPhi_i(i, x, P, T, zliq)
k = philiq / phivap
vold = v
v = _RachfordRice(v, k, z, tol=1e-8, kmax=500)
x = z / (1.0 + v * (k - 1.0))
y = k * x
err = np.abs(v - vold)
return x, y, v, phivap, philiq, k, ite
def getFlash_UNIFAC(self,
z,
P: float,
T: float,
tol=1e5 * DBL_EPSILON,
kmax=1000):
assert self.n == len(z)
z = np.atleast_1d(z)
assert np.sum(z) == 1.0
# check if is flash problem
y, pd, pv, pl, k, ite = self.getDewPointPressure(z, T)
x, pb, pv, pl, k, ite = self.getBubblePointPressure(z, T)
if not (pd <= P <= pb):
raise ValueError("P is not between Pdew and Pbubble")
v = (pb - P) / (pb - pd)
psat = self.getPsat(T)
y = np.full(self.n, 1.0 / self.n)
x = np.full(self.n, 1.0 / self.n)
err = 100
ite = 0
while err > tol and ite < kmax:
ite += 1
phivap = self.getPhiVap(y, P, T)
gamma = self.unifac_model.getGamma(x, T)
capphi = self.getCapPhi(y, P, T)
k = self.get_k_gamma_phi(gamma, psat, P, capphi)
vold = v
v = _RachfordRice(v, k, z, tol=1e-8, kmax=500)
x = z / (1.0 + v * (k - 1.0))
y = k * x
err = np.abs(v - vold)
return x, y, v, phivap, gamma, k, ite
def isobaricBinaryMixtureGenData(self, P, x=None, Punit="Pa", Tunit="K"):
assert self.n == 2
if x is None:
x = x_vec_for_plot
x = np.atleast_1d(x)
xmix = np.empty(2, dtype=np.float64)
y = np.empty(len(x), dtype=np.float64)
T = np.empty(len(x), dtype=np.float64)
kvec = np.empty(len(x), dtype=np.float64)
phi_vap_vec = np.empty(len(x), dtype=np.float64)
phi_liq_vec = np.empty(len(x), dtype=np.float64)
pv = np.empty(len(x), dtype=np.float64)
pl = np.empty(len(x), dtype=np.float64)
k = np.empty(len(x), dtype=np.float64)
for i in range(len(x)):
xmix[0] = x[i]
xmix[1] = 1.0 - x[i]
try:
yres, T[i], pv, pl, k, ite = self.getBubblePointTemperature(
xmix, P)
except:
try:
yres = [0, 0]
yres[0] = y[i - 1]
T[i] = T[i - 1]
x[i] = x[i - 1]
pv[0] = phi_vap_vec[i - 1]
pl[0] = phi_liq_vec[i - 1]
k[0] = kvec[i - 1]
except:
yres = [0, 0]
yres[0] = y[i + 1]
T[i] = T[i + 1]
x[i] = x[i + 1]
pv[0] = phi_vap_vec[i + 1]
pl[0] = phi_liq_vec[i + 1]
k[0] = kvec[i + 1]
T[i] = conv_unit(T[i], "K", Tunit)
y[i] = yres[0]
phi_vap_vec[i] = pv[0]
phi_liq_vec[i] = pl[0]
kvec[i] = k[0]
return x, y, T, phi_vap_vec, phi_liq_vec, kvec
def isothermalBinaryMixtureGenData(self, T, x=None, Punit="Pa", Tunit="K"):
assert self.n == 2
if x is None:
x = x_vec_for_plot
x = np.atleast_1d(x)
xmix = np.empty(2, dtype=np.float64)
y = np.empty(len(x), dtype=np.float64)
P = np.empty(len(x), dtype=np.float64)
kvec = np.empty(len(x), dtype=np.float64)
phi_vap_vec = np.empty(len(x), dtype=np.float64)
phi_liq_vec = np.empty(len(x), dtype=np.float64)
phi_vap = np.empty(len(x), dtype=np.float64)
phi_liq = np.empty(len(x), dtype=np.float64)
kv = np.empty(len(x), dtype=np.float64)
for i in range(len(x)):
xmix[0] = x[i]
xmix[1] = 1.0 - x[i]
try:
yres, P[
i], phi_vap, phi_liq, kv, ite = self.getBubblePointPressure(
xmix, T, tol=1e-5, kmax=100)
except:
try:
yres = [0, 0]
yres[0] = y[i - 1]
P[i] = P[i - 1]
x[i] = x[i - 1]
phi_vap[0] = phi_vap_vec[i - 1]
phi_liq[0] = phi_liq_vec[i - 1]
kv[0] = kvec[i - 1]
except:
yres = [0, 0]
yres[0] = y[i + 1]
P[i] = P[i + 1]
x[i] = x[i + 1]
phi_vap[0] = phi_vap_vec[i + 1]
phi_liq[0] = phi_liq_vec[i + 1]
kv[0] = kv[i + 1]
P[i] = conv_unit(P[i], "Pa", Punit)
y[i] = yres[0]
phi_vap_vec[i] = phi_vap[0]
phi_liq_vec[i] = phi_liq[0]
kvec[i] = kv[0]
return x, y, P, phi_vap_vec, phi_liq_vec, kvec
def isobaricBinaryMixturePlot(self,
P,
x=None,
Punit="Pa",
Tunit="K",
expfilename="",
plottype="both"):
assert self.n == 2
if x is None:
x = x_vec_for_plot
x, y, T, phiv, phil, kvec = self.isobaricBinaryMixtureGenData(
P, x, Punit=Punit, Tunit=Tunit)
if self.vle_method == "UNIFAC":
gamma_title = "UNIFAC + "
else:
gamma_title = ""
title = "{} (1) / {} (2) at {:0.3f} {}\n{}Equation of state: {}".format(
self.substances[0].Name,
self.substances[1].Name,
conv_unit(P, "Pa", Punit),
Punit,
gamma_title,
self.eosname,
)
vleplot = VLEBinaryDiagrams.VLEBinaryMixturePlot(
"isobaric", T, x, y, Tunit, title, plottype)
if os.path.exists(expfilename):
vleplot.expPlot(expfilename)
vleplot.plot()
def setVLEmethod(self, method: str):
if not self.has_UNIFAC:
self.vle_method = "phi-phi"
return
self.vle_method = method
def isothermalBinaryMixturePlot(self,
T,
x=None,
Punit="Pa",
Tunit="K",
expfilename="",
plottype="both"):
assert self.n == 2
if x is None:
x = x_vec_for_plot
x, y, P, phiv, phil, kvec = self.isothermalBinaryMixtureGenData(
T, x, Punit=Punit, Tunit=Tunit)
if self.vle_method == "UNIFAC":
gamma_title = "UNIFAC + "
else:
gamma_title = ""
title = "{} (1) / {} (2) at {:0.3f} {}\n{}Equation of state: {}".format(
self.substances[0].Name,
self.substances[1].Name,
conv_unit(T, "K", Tunit),
Tunit,
gamma_title,
self.eosname,
)
vleplot = VLEBinaryDiagrams.VLEBinaryMixturePlot(
"isothermal", P, x, y, Punit, title, plottype)
if os.path.exists(expfilename):
vleplot.expPlot(expfilename)
vleplot.plot()
def epsilonm(
self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
c = self.c.getCm(y, T, substances)
b = bmb.bm(y, T, bib, substances)
return -b * c
def diffDeltam(
self, i: int, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
cline = self.c.getDiffCm(i, y, T, substances)
bline = bmb.diffBm(i, y, T, bib, substances)
return cline + bline
def epsilonm(
self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
w = getW(y, substances)
return w * (bmb.bm(y, T, bib, substances)) ** 2
def deltam(
self, y, T: float, bib: BiBehavior, bmb: MixtureRuleBehavior, substances
) -> float:
u = 1.0 - getW(y, substances)
return u * bmb.bm(y, T, bib, substances)