def input_properties_case_REFPROP___ButaneOctane(comp1, comp2, kij):
this_function_name = sys._getframe().f_code.co_name
print('\n---\n THE DATA USED IN THIS SIMULATION WAS OBTAINED FROM: ',
this_function_name)
print('\n---\n')
'''
SOURCE: REFPROP -- BUTANE AND OCTANE.
'''
critical_pressure = np.array([comp1.pC, comp2.pC])
critical_temperature = np.array([comp1.Tc, comp2.Tc])
acentric_factor = np.array([comp1.AcF, comp2.AcF])
molar_mass = np.array([comp1.MM, comp2.MM])
omega_a = 0.45724 * np.ones_like(molar_mass)
omega_b = 0.07780 * np.ones_like(molar_mass)
binary_interaction = np.array([[0.0000, kij], [kij, 0.0000]])
specific_heat_mass_base = np.array([comp1.Cp, comp2.Cp])
specific_heat = Tools_Convert.convert_specific_heat_massbase_TO_molarbase(
specific_heat_mass_base, molar_mass)
return (critical_pressure, critical_temperature, acentric_factor,
molar_mass, omega_a, omega_b, binary_interaction, specific_heat)
===================================
@(PRESSURE, TEMPERATURE, GLOBAL MOLAR FRACTION): These are the conditions you are interested to evaluate
===================================
LEGEND:
p: Interested pressure (evaluate the enthalpy for this pressure) [Pa]
T: Interested temperature (evaluate the enthalpy considering this isotherm) [K]
OCR: oil-circulation ratio [-]: it is a mass fraction of oil per total mass mixture
z_mass: global mass fraction
z: global molar fraction
'''
p = 0.3e5 # <=============================== change here
T = (30. + 273.15) # <=============================== change here
LC, base = 0.40, 'mass' # <=============================== change here
zin = np.array([LC, (1. - LC)])
z, z_mass = Tools_Convert.frac_input(MM, zin, base)
'''
=======================================
CREATING OBJECTS - [to a better identification, all objects' names are followed by "_obj"]
=======================================
'''
rr_obj = RachfordRice()
eos_obj = PengRobinsonEos(pC, Tc, AcF, omega_a, omega_b, kij)
michelsen_obj = Michelsen()
flash_obj = Flash(max_iter=50, tolerance=1.0e-13, print_statistics=False)
prop_obj = Properties(pC, Tc, AcF, omega_a, omega_b, kij)
def getting_the_results_from_FlashAlgorithm_main(p, T, pC, Tc, AcF, z):
initial_K_values = calculate_K_values_wilson(p, T, pC, Tc, AcF)
=============================================================================================================
Class Molecule("name", MM, Tc, pC, AcF, Cp)
LEGEND:
name: compost name
MM: molar mass of each component [kg/kmol]
Tc: critical temperature [K]
pC: critical pressure [Pa]
AcF: acentric factor [-]
Cp: specific heat [J / kg K] !<=== HERE IT IS IN MASS BASE, BUT IT IS CONVERTED TO MOLAR BASE INSIDE THE FUNCTION
=============================================================================================================
'''
MM = np.array([58.12, 240.])
specific_heat_mass_base = np.array([1663.0, 1490.0]) #(J/kg K)
specific_heat = Tools_Convert.convert_specific_heat_massbase_TO_molarbase(
specific_heat_mass_base, MM)
#sort as name, molar_mass, Tc, pC, AcF, Cp
comp1 = Molecule("R600a", 58.12, (134.7 + 273.15), 36.4e5, 0.1853,
specific_heat[0])
comp2 = Molecule("AB_ISO5", 240., (675.9 + 273.15), 20.6e5, 0.9012,
specific_heat[1])
kij = -0.02668
'''
===============================================================================================================
Do you have kij above? If you don't have it ==> uncomment the CODE's 4 lines below
===============================================================================================================
'''
# p_C = np.array([comp1.pC, comp2.pC])
# T_C = np.array([comp1.TC, comp2.TC])
# kij_obj = Kij_class(p_C, T_C)
def __call__(self):
zin = np.array([self.LC, (1. - self.LC)])
z, z_mass = Tools_Convert.frac_input(self.MM, zin, self.base)
return (self.p, self.T, z, z_mass)
Building the isotherm and molar concentration to feed the code. Why does it necessary?
Because Pb = Pb @(T, z): We want Pb for a specific global molar fraction and isotherm
=========================================================================================================
LEGEND:
T: Interested temperature (evaluate the enthalpy considering this isotherm) [K]
LC: Lighter component of your binary mixture
base: It is the base of your fraction, i.e., you must specify 'molar' ou 'mass'
z_mass: global {mass} fraction
z: global {molar} fraction
'''
T = 520. #(40. + 273.15) # <=================================== change here
LC, base = 0.10, 'mass' # <=============================== change here
zin = np.array([LC, (1. - LC)])
z, z_mass = Tools_Convert.frac_input(BubbleP.MM, zin, base)
'''
=========================================================================================================
CHANGE HERE (II)
BUILDING A RANGE: this 4 lines below it is necessary to create a range of pressure necessary in...
... executable() function.
To do this it's necessary to import the function calculate_pressure_guess()...
... from BubbleP.py
VERY IMPORTANT!: the step size determines how close we are to the correct root. So,
smaller step produces more precise results
=========================================================================================================
LEGEND:
pG: Guess pressure [Pa] - It is necessary to initialize the search for correct bubble pressure
CompN = MM.shape[0] #components number
Pbubble_store = np.zeros([LN, CN]) #matrix with LN lines and CN columns
# Sy_store = np.zeros([LN, CN])
# y_store = np.zeros([LN, 2 * CN])
'''
========================
PUTTING TO RUN: with a for loop
========================
'''
for index_out, T in enumerate(T_list):
for index, LC_mass in enumerate(LC_mass_list):
logging.debug('LC mass concentration =======> ' + str(LC_mass))
logging.debug('Temperatura =======> ' + str(T))
z_mass = np.array([LC_mass, (1. - LC_mass)])
z = Tools_Convert.convert_massfrac_TO_molarfrac(MM, z_mass)
pBubble, y, Sy, counter = bubble_obj(T, z)
Pbubble_store[index, index_out] = pBubble
# Sy_store[index, index_out] = Sy
# y_store[index, 2 * index_out:2 * index_out + 2] = y
# print('%.2f \t %.2e \t %.5f' % (T, pBubble, Sy))
'''
=====================
PLOTTING
=====================
'''
colors = ['r', 'b', 'k', 'y', 'm', 'c', 'g', 'lightgray']
markers = ['o', 'v', '<', '>', '.', 'p', 'P', 's', '*']
plt.title('Bubble Pressure of Mixture of ' + comp1.name + comp2.name +
'(Dados Artigo Moisés)')
def calculate_enthalpy_entropy(p,
pR,
pC,
T,
TR,
Tc,
AcF,
Cp,
z,
MM,
hR,
sR,
printResults=False):
pB, y_sat, Sy, counter = bubble_obj(T, z)
if printResults:
print('You\'re running pressure = %.3e [Pa] and temperature = %.2f C' %
(p, (T - 273.15)))
print(
'For this temperature the bubble pressure is: pB = %.3e [Pa] at T = (%.2f C)'
% (pB, (T - 273.15)))
print('\n---------------------------------------------------------)')
if (p >= pB):
F_V = 0.0
H_subcooled = prop_obj.calculate_enthalpy(TR, T, pR, p, z, z, hR, Cp,
'liquid')
M_L = prop_obj.calculate_weight_molar_mixture(MM, z, 'liquid')
H_subcooled_mass = H_subcooled * np.reciprocal(M_L)
S_subcooled = prop_obj.calculate_entropy(TR, T, pR, p, z, z, sR, Cp,
'liquid')
S_subcooled_mass = S_subcooled * np.reciprocal(M_L)
h, s = H_subcooled_mass, S_subcooled_mass
if printResults:
print('--> This @(p,T) falls into single phase region')
print('\nSubcooled liquid with h = %.3e [J/ kg]' %
H_subcooled_mass)
print('\nSubcooled liquid with s = %.3e [J/(kg K)]' %
S_subcooled_mass)
else:
if printResults:
print('This @(p,T) falls into two phase region')
F_V, is_stable, K_values_newton, initial_K_values = \
FlashAlgorithm_main.getting_the_results_from_FlashAlgorithm_main(p, T, pC, Tc, AcF, z)
x = z / (F_V * (K_values_newton - 1.) + 1.)
y = K_values_newton * x
x_mass = Tools_Convert.convert_molarfrac_TO_massfrac(MM, x)
y_mass = Tools_Convert.convert_molarfrac_TO_massfrac(MM, y)
H_sat_vapor = prop_obj.calculate_enthalpy(TR, T, pR, p, y, z, hR, Cp,
'saturated_vapor')
M_V = prop_obj.calculate_weight_molar_mixture(MM, y, 'saturated_vapor')
H_sat_vapor_mass = H_sat_vapor * np.reciprocal(M_V)
H_sat_liquid = prop_obj.calculate_enthalpy(TR, T, pR, p, x, z, hR, Cp,
'saturated_liquid')
M_L = prop_obj.calculate_weight_molar_mixture(MM, x,
'saturated_liquid')
H_sat_liquid_mass = H_sat_liquid * np.reciprocal(M_L)
enthalpy_mixture = (1. - F_V) * H_sat_liquid + F_V * H_sat_vapor
F_V_mass = np.reciprocal((M_L / M_V) * (1 / F_V - 1.) + 1.)
enthalpy_mixture_mass = (
1. - F_V_mass) * H_sat_liquid_mass + F_V_mass * H_sat_vapor_mass
if printResults:
print(
'The mixture\'s state is ELV with a quality = %.3f [molar base]'
% F_V)
print(
'The mixture\'s state is ELV with a quality = %.3f [mass base]'
% F_V_mass)
print('x [molar base] = ', x)
print('y [molar base] = ', y)
print('x_mass [mass base] = ', x_mass)
print('y_mass [mass base] = ', y_mass)
print(
'\n======\nThe mixture liquid/vapor with h = %.3e [J/ kmol]' %
enthalpy_mixture)
print(
'The mixture liquid/vapor with h_mass = %.3e [J/ kg] {mass base}'
% enthalpy_mixture_mass)
S_sat_vapor = prop_obj.calculate_entropy(TR, T, pR, p, y, z, sR, Cp,
'vapor')
S_sat_vapor_mass = S_sat_vapor * np.reciprocal(M_V)
S_sat_liquid = prop_obj.calculate_entropy(TR, T, pR, p, x, z, sR, Cp,
'liquid')
S_sat_liquid_mass = S_sat_liquid * np.reciprocal(M_L)
entropy_mixture = (1. - F_V) * S_sat_liquid + F_V * S_sat_vapor
entropy_mixture_mass = (
1. - F_V_mass) * S_sat_liquid_mass + F_V_mass * S_sat_vapor_mass
h, s = enthalpy_mixture_mass, entropy_mixture_mass
if printResults:
print(
'\n======\nThe mixture liquid/vapor with s = %.3e [J/(kmol K)]'
% entropy_mixture)
print(
'The mixture liquid/vapor with s_mass = %.3e [J/(kg K)] {mass base}'
% entropy_mixture_mass)
return F_V, h, s
sR: reference entropy (in molar base) [J/kmol K]
MM: molar mass of each component [kg/kmol]
M: mixture molar mass [kg/kgmol] ==> depend on phase composition
'''
'''
=========================================================================================================
CHANGE HERE (I)
@(PRESSURE, TEMPERATURE, GLOBAL MOLAR FRACTION): These are the conditions you are interested to evaluate
=========================================================================================================
'''
p = 200e3
T = (20. + 273.15)
LC, base = 99. / 100, 'mass' # <=============================== change here
zin = np.array([LC, (1. - LC)])
z, z_mass = Tools_Convert.frac_input(MM, zin, base)
'''
=========================================================================================================
CHANGE HERE (II)
BUBBLE PRESSURE - First you MUST load BubbleP.py to get the bubble pressure at the interested
... temperature. Such value is used as reference_pressure {i.e., pR = pB (T,z)}
=========================================================================================================
'''
TR = 273.15
pR, y_sat, Sy_sat, counter = bubble_obj(TR, z)
hR_mass = 200e3
hR = hR_mass * prop_obj.calculate_weight_molar_mixture(MM, z,
'saturated_liquid')
sR_mass = 1000.
sR = sR_mass * prop_obj.calculate_weight_molar_mixture(MM, z,
f_L, Z_L = eos_obj.calculate_fugacities_with_minimum_gibbs_energy(p, T, x, 'liquid')
f_V, Z_V = eos_obj.calculate_fugacities_with_minimum_gibbs_energy(p, T, y, 'vapor')
PHI_L = f_L / (x * p)
PHI_V = f_V / (y * p)
delta = np.sum(np.abs(x * PHI_L - y * PHI_V))
print('delta before = ', delta)
if delta < epsilon:
print('delta', delta)
print('Liquid Composition', x)
print('Vapor Composition', y)
return x,y, counter
else:
x[0] = PHI_V[0] * (PHI_V[1] - PHI_L[1]) / (PHI_L[0] * PHI_V[1] - PHI_V[0] * PHI_L[1])
x[1] = 1. - x[0]
y[0] = x[0] * PHI_L[0] / PHI_V[0]
y[1] = 1. - y[0]
T = 500.0
p = 2 * 1e6
xguess = np.array([0.1, 0.9])
yguess = np.array([0.9, 0.1])
x, y, counter = calculate(xguess, yguess, p, T)
print('Loop', counter)
x_mass = Tools_Convert.convert_molarfrac_TO_massfrac(MM, x)
y_mass = Tools_Convert.convert_molarfrac_TO_massfrac(MM, y)
print('Liquid Mass Fraction [-] = ', x_mass)
print('Vapor Mass Fraction [-] = ', y_mass)