def _run(self):
wet_solids, air, natural_gas = self.ins
dry_solids, hot_air, emissions = self.outs
wet_solids.split_to(hot_air, dry_solids, self.split)
sep.adjust_moisture_content(dry_solids, hot_air, self.moisture_content)
emissions.phase = air.phase = natural_gas.phase = hot_air.phase = 'g'
design_results = self.design_results
design_results['Evaporation'] = evaporation = hot_air.F_mass
air.imass['N2', 'O2'] = np.array([0.78, 0.32]) * self.R * evaporation
hot_air.mol += air.mol
dry_solids.T = hot_air.T = self.T
emissions.T = self.T + 30.
natural_gas.empty()
emissions.empty()
if self.utility_agent == 'Natural gas':
LHV = self.chemicals.CH4.LHV
def f(CH4):
CO2 = CH4
H2O = 2. * CH4
natural_gas.imol['CH4'] = CH4
emissions.imol['CO2', 'H2O'] = [CO2, H2O]
duty = (dry_solids.H + hot_air.H + emissions.H) - (wet_solids.H + air.H + natural_gas.H)
CH4 = duty / LHV
return CH4
flx.wegstein(f, 0., 1e-3)
def simulate_multi_stage_lle_without_side_draws(self):
f = self.multi_stage_lle_without_side_draws_iter
extract_flow_rates = self.initialize_multi_stage_lle_without_side_draws(
)
self.extract_flow_rates = extract_flow_rates = flx.wegstein(
f, extract_flow_rates, xtol=0.1, maxiter=10, checkiter=False)
self.update_multi_stage_lle_without_side_draws(extract_flow_rates)
for i in self.stages:
i.balance_raffinate_flows()
def solve_y(y_phi, phi, T, P, y_guess):
if isinstance(phi, IdealFugacityCoefficients): return y_phi
return flx.wegstein(y_iter,
y_phi,
1e-9,
args=(y_phi, phi, T, P),
checkiter=False,
checkconvergence=False,
convergenceiter=3)
def _solve_x(self, T):
solute_chemical = self.chemicals.tuple[self._solute_index]
Tm = solute_chemical.Tm
if Tm is None: raise RuntimeError(f"solute {solute_chemical} does not have a melting temperature, Tm")
Cpl = solute_chemical.Cn.l(T)
Cps = solute_chemical.Cn.s(T)
Hm = solute_chemical.Hfus
if Tm is None: raise RuntimeError(f"solute {solute_chemical} does not have a heat of fusion, Hfus")
gamma = 1.
x = solubility_eutectic(T, Tm, Hm, Cpl, Cps, gamma) # Initial guess
args = (T, Tm, Cpl, Cps, Hm)
return flx.wegstein(self._x_iter, x, xtol=1e-6, args=args)
def test_fixedpoint_array_solvers2():
original_feed = feed.copy()
p = flx.Profiler(f2)
solution = flx.wegstein(p, feed, xtol=1e-8, maxiter=200)
assert_allclose(solution, real_solution2)
p.archive('Wegstein')
with pytest.raises(RuntimeError):
solution = flx.wegstein(f2, feed, convergenceiter=4, xtol=1e-8, maxiter=200)
with pytest.raises(RuntimeError):
solution = flx.wegstein(f2, feed, xtol=1e-8, maxiter=20)
with pytest.raises(RuntimeError):
solution = flx.aitken(f2, feed, convergenceiter=4, xtol=1e-8, maxiter=200)
solution = flx.wegstein(p, feed, checkconvergence=False, convergenceiter=4, xtol=1e-8)
p.archive('Wegstein early termination')
assert_allclose(solution, real_solution2, rtol=1e-3)
solution = flx.aitken(p, feed, xtol=1e-8, maxiter=10000)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution2)
p.archive('Aitken')
solution = flx.aitken(p, feed, checkconvergence=False, convergenceiter=4, xtol=1e-8)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution2, rtol=1e-3)
p.archive('Aitken early termination')
solution = flx.fixed_point(p, feed, xtol=5e-8, maxiter=200)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution2)
p.archive('Fixed point')
solution = flx.fixed_point(p, feed, maxiter=500, checkconvergence=False, convergenceiter=4, xtol=5e-8)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution2, rtol=1e-3)
p.archive('Fixed point early termination')
assert p.sizes() == {'Wegstein': 61, 'Wegstein early termination': 18,
'Aitken': 392, 'Aitken early termination': 91,
'Fixed point': 191, 'Fixed point early termination': 191}
def solve_x(x_guess, x_gamma_poyinting, T, f_gamma, gamma_args, f_pcf,
pcf_args):
args = (x_gamma_poyinting, T, f_gamma, gamma_args, f_pcf, pcf_args)
try:
x = flx.wegstein(x_iter,
x_guess,
1e-10,
args=args,
checkiter=False,
checkconvergence=False,
convergenceiter=3)
except:
return x_gamma_poyinting
return x
def simulate_multi_stage_lle_without_side_draws(self):
f = self.multi_stage_lle_without_side_draws_iter
if hasattr(self, 'extract_flow_rates'):
extract_flow_rates = self.extract_flow_rates
else:
extract_flow_rates = self.initialize_multi_stage_lle_without_side_draws(
)
extract_flow_rates = flx.wegstein(f,
extract_flow_rates,
xtol=0.1,
maxiter=10,
checkiter=False)
self.extract_flow_rates = extract_flow_rates
self.update_multi_stage_lle_without_side_draws(extract_flow_rates)
def _y_iter(self, y, Psats_over_P, T, P):
phi = self._phi(y, T, P)
Psat_over_P_phi = Psats_over_P / phi
try:
x = flx.wegstein(self._x_iter,
self._x,
1e-12,
args=(Psat_over_P_phi, ))
except flx.SolverError as solver_error:
x = flx.aitken(self._x_iter,
solver_error.x,
1e-6,
args=(Psat_over_P_phi, ))
self._x = x
v = self._F_mol_vle * self._V * x * self._Ks
return fn.normalize(v)
def _solve_v(self, T, P):
"""Solve for vapor mol"""
Psats_over_P = np.array([i(T) for i in self._bubble_point.Psats]) / P
self._T = T
v = self._v
y = fn.normalize(v)
l = self._mol - v
self._x = fn.normalize(l)
if isinstance(self._phi, IdealFugacityCoefficients):
self._y = self._y_iter(y, Psats_over_P, T, P)
else:
self._y = flx.wegstein(self._y_iter,
v / v.sum(),
1e-6,
args=(Psats_over_P, T, P))
self._v = self._F_mol_vle * self._V * self._y
return self._v
def test_fixedpoint_array_solvers():
original_feed = feed.copy()
p = flx.Profiler(f)
solution = flx.wegstein(p, feed, convergenceiter=4, xtol=1e-8, maxiter=200)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution)
p.archive('Wegstein')
solution = flx.aitken(p, feed, convergenceiter=4, xtol=1e-8, maxiter=200)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution)
p.archive('Aitken')
solution = flx.fixed_point(p, feed, convergenceiter=4, xtol=5e-8, maxiter=200)
assert_allclose(feed, original_feed)
assert_allclose(solution, real_solution)
p.archive('Fixed point')
assert p.sizes() == {'Wegstein': 5, 'Aitken': 5, 'Fixed point': 194}
p.archive('[Scipy] Brent-Q') # Save/archive results with given name
x_brenth = opt.brenth(p, x0, x1)
p.archive('[Scipy] Brent-H')
x_IQ = flx.IQ_interpolation(p, x0, x1)
p.archive('IQ-interpolation')
x_false_position = flx.false_position(p, x0, x1)
p.archive('False position')
p.plot(r'$f(x) = 0 = x^3 + 2 \cdot x - 40$ where $-5 < x < 5$')
p = flx.Profiler(f)
x_guess = -5
x_aitken_secant = flx.aitken_secant(p, x_guess)
p.archive('Aitken')
x_secant = flx.secant(p, x_guess)
p.archive('Secant')
x_newton = opt.newton(p, x_guess)
p.archive('[Scipy] Newton')
p.plot(r'$f(x) = 0 = x^3 + 2 \cdot x - 40$')
# Note that x = 40/x^2 - 2/x is the same
# objective function as x**3 - 40 + 2*x = 0
f = lambda x: 40/x**2 - 2/x
p = flx.Profiler(f)
x_guess = 5.
x_wegstein = flx.wegstein(p, x_guess)
p.archive('Wegstein')
x_aitken = flx.aitken(p, x_guess)
p.archive('Aitken')
p.plot(r'$f(x) = x = \frac{40}{x^2} - \frac{2}{x}$', markbounds=False)
# ^ Fixed-point iteration is non-convergent for this equation; so we don't include it here