- {
- “cells”: [
- {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“# An hour blitz to practical thermodynamics”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Pure component chemical models”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Thermosteam packages chemical and mixture thermodynamic models in a flexible framework that allows users to fully customize and extend the models, as well as create new models. Central to all thermodynamic algorithms is the [Chemical](../Chemical.txt) object, which contains constant chemical properties, as well as thermodynamic and transport properties as a function of temperature and pressure:”
]
}, {
“cell_type”: “code”, “execution_count”: 1, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Chemical: Water (phase_ref=’l’)n”, “[Names] CAS: 7732-18-5n”, ” InChI: H2O/h1H2n”, ” InChI_key: XLYOFNOQVPJJNP-U…n”, ” common_name: watern”, ” iupac_name: (‘oxidane’,)n”, ” pubchemid: 962n”, ” smiles: On”, ” formula: H2On”, “[Groups] Dortmund: <1H2O>n”, ” UNIFAC: <1H2O>n”, ” PSRK: <1H2O>n”, “[Data] MW: 18.015 g/moln”, ” Tm: 273.15 Kn”, ” Tb: 373.12 Kn”, ” Tt: 273.15 Kn”, ” Tc: 647.14 Kn”, ” Pt: 610.88 Pan”, ” Pc: 2.2048e+07 Pan”, ” Vc: 5.6e-05 m^3/moln”, ” Hf: -2.8582e+05 J/moln”, ” S0: 70 J/K/moln”, ” LHV: 44011 J/moln”, ” HHV: 0 J/moln”, ” Hfus: 6010 J/moln”, ” Sfus: Nonen”, ” omega: 0.344n”, ” dipole: 1.85 Debyen”, ” similarity_variable: 0.16653n”, ” iscyclic_aliphatic: 0n”, ” combustion: {‘H2O’: 1.0}n”
]
}
], “source”: [
“import thermosteam as tmon”, “# Initialize chemical with an identifier (e.g. by name, CAS, InChI…)n”, “Water = tmo.Chemical(‘Water’) n”, “Water.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“All fields can be easility accessed, for example:”
]
}, {
“cell_type”: “code”, “execution_count”: 2, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“‘7732-18-5’”
]
}, “execution_count”: 2, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# CAS numbern”, “Water.CAS”
]
}, {
“cell_type”: “code”, “execution_count”: 3, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“18.01528”
]
}, “execution_count”: 3, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Molecular weight (g/mol)n”, “Water.MW”
]
}, {
“cell_type”: “code”, “execution_count”: 4, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“373.124”
]
}, “execution_count”: 4, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Boiling point (K)n”, “Water.Tb”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Temperature (in Kelvin) and pressure (in Pascal) dependent properties can be computed:”
]
}, {
“cell_type”: “code”, “execution_count”: 5, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“101284.55179999319”
]
}, “execution_count”: 5, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Vapor pressure (Pa)n”, “Water.Psat(T=373.15)”
]
}, {
“cell_type”: “code”, “execution_count”: 6, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“0.07197220523022964”
]
}, “execution_count”: 6, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Surface tension (N/m)n”, “Water.sigma(T=298.15)”
]
}, {
“cell_type”: “code”, “execution_count”: 7, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.8069204487889095e-05”
]
}, “execution_count”: 7, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Liquid molar volume (m^3/mol)n”, “Water.V(phase=’l’, T=298.15, P=101325)”
]
}, {
“cell_type”: “code”, “execution_count”: 8, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“0.023505774739491968”
]
}, “execution_count”: 8, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Vapor molar volume (m^3/mol)n”, “Water.V(phase=’g’, T=298.15, P=101325)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Temperature dependent properties are managed by indexable model handles, which contain many models ordered in decreasing priority:”
]
}, {
“cell_type”: “code”, “execution_count”: 9, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TDependentModelHandle(T, P=None) -> Psat [Pa]n”, “[0] Wagner originaln”, “[1] Antoinen”, “[2] EQ101n”, “[3] Wagnern”, “[4] boiling critical relationn”, “[5] Lee Keslern”, “[6] Ambrose Waltonn”, “[7] Sanjarin”, “[8] Edalatn”
]
}
], “source”: [
“Water.Psat.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Each model is applicable to a certain domain, as given by their Tmin and Tmax:”
]
}, {
“cell_type”: “code”, “execution_count”: 10, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TDependentModel(T, P=None) -> Psat [Pa]n”, ” name: Wagner originaln”, ” Tmin: 275 Kn”, ” Tmax: 647.35 Kn”
]
}
], “source”: [
“Wagner_origial = Water.Psat[0]n”, “Wagner_origial.show()”
]
}, {
“cell_type”: “code”, “execution_count”: 11, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(647.35, 275.0)”
]
}, “execution_count”: 11, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Note that these attributes can be get/set toon”, “Wagner_origial.Tmax, Wagner_origial.Tmin”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“When called, the model handle searches through each model until it finds one with an applicable domain. If none are applicable, a domain error is raised:”
]
}, {
“cell_type”: “code”, “execution_count”: 12, “metadata”: {}, “outputs”: [
- {
“ename”: “DomainError”, “evalue”: “Water (CAS: 7732-18-5) has no valid saturated vapor pressure model at T=1000.00 K”, “output_type”: “error”, “traceback”: [
“u001b[1;31m—————————————————————————u001b[0m”, “u001b[1;31mDomainErroru001b[0m Traceback (most recent call last)”, “u001b[1;32m<ipython-input-12-5818a3190dca>u001b[0m in u001b[0;36m<module>u001b[1;34mu001b[0mnu001b[1;32m—-> 1u001b[1;33m u001b[0mWateru001b[0mu001b[1;33m.u001b[0mu001b[0mPsatu001b[0mu001b[1;33m(u001b[0mu001b[1;36m1000.0u001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0m”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\base\thermo_model_handle.pyu001b[0m in u001b[0;36m__call__u001b[1;34m(self, T, P)u001b[0mnu001b[0;32m 284u001b[0m u001b[1;32mifu001b[0m u001b[0mmodelu001b[0mu001b[1;33m.u001b[0mu001b[0mindomainu001b[0mu001b[1;33m(u001b[0mu001b[0mTu001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0m u001b[1;32mreturnu001b[0m u001b[0mmodelu001b[0mu001b[1;33m.u001b[0mu001b[0mevaluateu001b[0mu001b[1;33m(u001b[0mu001b[0mTu001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 285u001b[0m raise DomainError(f"{no_valid_model(self._chemical, self._var)} "nu001b[1;32m–> 286u001b[1;33m f"at T={T:.2f} K", chemical=self._chemical)nu001b[0mu001b[0;32m 287u001b[0m u001b[1;33mu001b[0mu001b[0mnu001b[0;32m 288u001b[0m u001b[0mat_Tu001b[0m u001b[1;33m=u001b[0m u001b[0m__call__u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;31mDomainErroru001b[0m: Water (CAS: 7732-18-5) has no valid saturated vapor pressure model at T=1000.00 K”
]
}
], “source”: [
“Water.Psat(1000.0)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Model handles as well as the models themselves have tabulation and plotting methods to help visualize how properties depend on temperature and pressure.”
]
}, {
“cell_type”: “code”, “execution_count”: 13, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“import matplotlib.pyplot as pltn”, “Water.Psat.plot_vs_T([Water.Tm, Water.Tb], ‘degC’, ‘atm’, label="Water")n”, “plt.show()”
]
}, {
“cell_type”: “code”, “execution_count”: 14, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“# Plot all modelsn”, “Water.Psat.plot_models_vs_T([Water.Tm, Water.Tb], ‘degC’, ‘atm’)n”, “plt.show()”
]
}, {
“cell_type”: “code”, “execution_count”: 15, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“# Plot only the ‘Wagner original model’n”, “Water.Psat[0].plot_vs_T(T_units=’degC’, units=’atm’) # Bounds are the model’s Tmin and Tmaxn”, “plt.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Manage the model order with the set_model_priority and move_up_model_priority methods:”
]
}, {
“cell_type”: “code”, “execution_count”: 16, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TDependentModel(T, P=None) -> Psat [Pa]n”, ” name: Antoinen”, ” Tmin: 273.2 Kn”, ” Tmax: 473.2 Kn”
]
}
], “source”: [
“# Note: In this case, we pass the model name, but itsn”, “# also possible to pass the current index, or the model itself.n”, “Water.Psat.move_up_model_priority(‘Antoine’)n”, “Water.Psat[0].show() # Notice how Antoine is now in the top priority”
]
}, {
“cell_type”: “code”, “execution_count”: 17, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TDependentModel(T, P=None) -> Psat [Pa]n”, ” name: Wagner originaln”, ” Tmin: 275 Kn”, ” Tmax: 647.35 Kn”
]
}
], “source”: [
“Water.Psat.set_model_priority(‘Wagner original’)n”, “Water.Psat[0].show() # Notice how Wagner original is back on top priority”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“When setting a model priority, the default priority is 0 (or top priority), but you can choose any priority:”
]
}, {
“cell_type”: “code”, “execution_count”: 18, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TDependentModel(T, P=None) -> Psat [Pa]n”, ” name: Antoinen”, ” Tmin: 273.2 Kn”, ” Tmax: 473.2 Kn”
]
}
], “source”: [
“Water.Psat.set_model_priority(‘Antoine’, 2)n”, “Water.Psat[2].show() # Moved Antoine to priority #2”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Thermodynamic properties dependent on the phase are handled by phase properties:”
]
}, {
“cell_type”: “code”, “execution_count”: 19, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“PhaseTPHandle(phase, T, P) -> V [m^3/mol]n”
]
}
], “source”: [
“Water.V.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Phase properties contain model handles as attributes:”
]
}, {
“cell_type”: “code”, “execution_count”: 20, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TPDependentModelHandle(T, P) -> V.l [m^3/mol]n”, “[0] volume VDI PPDSn”, “[1] Campbell Thodosn”, “[2] Yen Woods saturationn”, “[3] Rackettn”, “[4] Yamada Gunnn”, “[5] Bhirud normaln”, “[6] Townsend Halesn”, “[7] CRC inorganic liquid constantn”, “[8] Rackettn”, “[9] COSTALDn”, “[10] COSTALD compressedn”
]
}
], “source”: [
“Water.V.l.show()”
]
}, {
“cell_type”: “code”, “execution_count”: 21, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TPDependentModelHandle(T, P) -> V.g [m^3/mol]n”, “[0] Tsonopoulos extendedn”, “[1] Tsonopoulosn”, “[2] Abbottn”, “[3] Pitzer Curln”, “[4] CRCVirialn”, “[5] ideal gasn”
]
}
], “source”: [
“Water.V.g.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“A new model can be added easily to a model handle through the add_model method, for example:”
]
}, {
“cell_type”: “code”, “execution_count”: 22, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“TDependentModel(T) -> Psat [Pa]n”, ” name: User antoine modeln”, ” Tmin: 273.2 Kn”, ” Tmax: 473.2 Kn”
]
}
], “source”: [
“# Set top_priority=True to place model in postion [0]n”, “@Water.Psat.add_model(Tmin=273.20, Tmax=473.20, top_priority=True)n”, “def User_antoine_model(T):n”, ” return 10.0**(10.116 - 1687.537 / (T - 42.98))n”, “Water.Psat[0].show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The add_model method is a high level interface that even lets you create a constant model:”
]
}, {
“cell_type”: “code”, “execution_count”: 23, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“ConstantThermoModel(T=None, P=None) -> V.l [m^3/mol]n”, ” name: User constantn”, ” value: 1.687e-05n”, ” Tmin: 0 Kn”, ” Tmax: inf Kn”, ” Pmin: 0 Pan”, ” Pmax: inf Pan”
]
}
], “source”: [
“Water.V.l.add_model(1.687e-05, name=’User constant’)n”, “# Model is appended at the end by defaultn”, “Water.V.l[-1].show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Lastly, all default models in thermosteam have functors (i.e. functions with adjustable parameters):”
]
}, {
“cell_type”: “code”, “execution_count”: 24, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Functor: Wagner_original(T, P=None) -> Psat [Pa]n”, ” Tc: 647.35 Kn”, ” Pc: 2.2122e+07 Pan”, ” a: -7.7645n”, ” b: 1.4584n”, ” c: -2.7758n”, ” d: -1.233n”
]
}
], “source”: [
“# The saturated vapor pressure model from beforen”, “Wagner_origial.evaluate.show()”
]
}, {
“cell_type”: “code”, “execution_count”: 25, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Functor: Wagner_original(T, P=None) -> Psat [Pa]n”, ” Tc: 647.35 Kn”, ” Pc: 2.2064e+07 Pan”, ” a: -7.7645n”, ” b: 1.4584n”, ” c: -2.7758n”, ” d: -1.233n”
]
}
], “source”: [
“Wagner_origial.evaluate.Pc = 22.064e6n”, “Wagner_origial.evaluate.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“## Managing chemical sets”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Define multiple chemicals as a [Chemicals](../Chemicals.txt) object:”
]
}, {
“cell_type”: “code”, “execution_count”: 26, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Chemicals([Water, Ethanol])n”
]
}
], “source”: [
“chemicals = tmo.Chemicals([‘Water’, ‘Ethanol’])n”, “chemicals”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The chemicals are attributes:”
]
}, {
“cell_type”: “code”, “execution_count”: 27, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“(Chemical(‘Water’), Chemical(‘Ethanol’))”
]
}, “execution_count”: 27, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“(chemicals.Water, chemicals.Ethanol)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Chemicals are indexable:”
]
}, {
“cell_type”: “code”, “execution_count”: 28, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Chemical(‘Water’)n”
]
}
], “source”: [
“Water = chemicals[‘Water’]n”, “print(repr(Water))”
]
}, {
“cell_type”: “code”, “execution_count”: 29, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“[Chemical(‘Ethanol’), Chemical(‘Water’)]”
]
}, “execution_count”: 29, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“chemicals[‘Ethanol’, ‘Water’]”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Chemicals are also iterable:”
]
}, {
“cell_type”: “code”, “execution_count”: 30, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Chemical(‘Water’)n”, “Chemical(‘Ethanol’)n”
]
}
], “source”: [
“for chemical in chemicals:n”, ” print(repr(chemical))”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“More chemicals can also be appended:”
]
}, {
“cell_type”: “code”, “execution_count”: 31, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Chemicals([Water, Ethanol, Propanol])n”
]
}
], “source”: [
“Propanol = tmo.Chemical(‘Propanol’)n”, “chemicals.append(Propanol)n”, “chemicals”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The main benefit of using a Chemicals object, is that they can be compiled and used as part of a thermodynamic property package, as defined through a [Thermo](../Thermo.txt) object:”
]
}, {
“cell_type”: “code”, “execution_count”: 32, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Thermo(n”, ” chemicals=CompiledChemicals([Water, Ethanol, Propanol]),n”, ” mixture=Mixture(n”, ” rule=’ideal mixing’, …n”, ” include_excess_energies=Falsen”, ” ),n”, ” Gamma=DortmundActivityCoefficients,n”, ” Phi=IdealFugacityCoefficients,n”, ” PCF=IdealPoyintingCorrectionFactorsn”, “)n”
]
}
], “source”: [
“# A Thermo object is built with an iterable of Chemicals or their IDs.n”, “# Default mixture, thermodynamic equilibrium models are selected.n”, “thermo = tmo.Thermo(chemicals)n”, “thermo.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“[Creating a thermo property package](./Thermo_property_packages.ipynb), may be a little challenging if some chemicals cannot be found in the database, in which case they can be built from scratch. A complete example on how this can be done is available in another [tutorial](./Thermo_property_packages.ipynb).”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Material and energy balance”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“A [Stream](../Stream.txt) object is the main interface for estimating thermodynamic properties, vapor-liquid equilibrium, and material and energy balances. First set the thermo property package and we can start creating streams:”
]
}, {
“cell_type”: “code”, “execution_count”: 33, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: s1n”, ” phase: ‘l’, T: 298.15 K, P: 101325 Pan”, ” flow (kg/hr): Water 20n”, ” Ethanol 20n”
]
}
], “source”: [
“tmo.settings.set_thermo(thermo)n”, “s1 = tmo.Stream(‘s1’, Water=20, Ethanol=20, units=’kg/hr’)n”, “s1.show(flow=’kg/hr’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Create another stream at a higher temperature:”
]
}, {
“cell_type”: “code”, “execution_count”: 34, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: s2n”, ” phase: ‘l’, T: 350 K, P: 101325 Pan”, ” flow (kg/hr): Water 10n”
]
}
], “source”: [
“s2 = tmo.Stream(‘s2’, Water=10, units=’kg/hr’, T=350, P=101325)n”, “s2.show(flow=’kg/hr’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Mix both stream into a new one:”
]
}, {
“cell_type”: “code”, “execution_count”: 35, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: s_mixn”, ” phase: ‘l’, T: 310.53 K, P: 101325 Pan”, ” flow (kg/hr): Water 30n”, ” Ethanol 20n”
]
}
], “source”: [
“s_mix = tmo.Stream(‘s_mix’)n”, “s_mix.mix_from([s1, s2])n”, “s_mix.show(flow=’kg/hr’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Check the energy balance through enthalpy:”
]
}, {
“cell_type”: “code”, “execution_count”: 36, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“9.094947017729282e-12”
]
}, “execution_count”: 36, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s_mix.H - (s1.H + s2.H)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Note that the balance is not perfect as the solver stops within a small temperature tolerance. However, the approximation is less than 0.01% off:”
]
}, {
“cell_type”: “code”, “execution_count”: 37, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“0.00%n”
]
}
], “source”: [
“error = s_mix.H - (s1.H + s2.H)n”, “percent_error = 100 * error / (s1.H + s2.H)n”, “print(f"{percent_error:.2%}")”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Split the mixture to two streams by defining the component splits:”
]
}, {
“cell_type”: “code”, “execution_count”: 38, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: s1n”, ” phase: ‘l’, T: 310.53 K, P: 101325 Pan”, ” flow (kg/hr): Ethanol 20n”, “Stream: s2n”, ” phase: ‘l’, T: 310.53 K, P: 101325 Pan”, ” flow (kg/hr): Water 30n”
]
}
], “source”: [
“# First define an array of component splitsn”, “component_splits = s_mix.chemicals.array([‘Water’, ‘Ethanol’], [0, 1])n”, “s_mix.split_to(s1, s2, component_splits)n”, “s1.T = s2.T = s_mix.T # Take care of energy balancen”, “s1.show(flow=’kg/hr’)n”, “s2.show(flow=’kg/hr’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Flow rates”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The most convenient way to get and set flow rates is through the get_flow and set_flow methods:”
]
}, {
“cell_type”: “code”, “execution_count”: 39, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.0”
]
}, “execution_count”: 39, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Set and get flow of a single chemicaln”, “# in gallons per minuten”, “s1.set_flow(1, ‘gpm’, ‘Water’)n”, “s1.get_flow(‘gpm’, ‘Water’)”
]
}, {
“cell_type”: “code”, “execution_count”: 40, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([10., 20.])”
]
}, “execution_count”: 40, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Set and get flows of many chemicalsn”, “# in kilograms per hourn”, “s1.set_flow([10, 20], ‘kg/hr’, (‘Ethanol’, ‘Water’))n”, “s1.get_flow(‘kg/hr’, (‘Ethanol’, ‘Water’))”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“It is also possible to index flow rate data using chemical IDs through the imol, imass, and ivol [indexers](../indexer/indexer_module.txt):”
]
}, {
“cell_type”: “code”, “execution_count”: 41, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“ChemicalMolarFlowIndexer (kmol/hr):n”, ” (l) Water 1.11n”, ” Ethanol 0.2171n”
]
}
], “source”: [
“s1.imol.show()”
]
}, {
“cell_type”: “code”, “execution_count”: 42, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.1101687012358397”
]
}, “execution_count”: 42, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.imol[‘Water’]”
]
}, {
“cell_type”: “code”, “execution_count”: 43, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([0.217, 1.11 ])”
]
}, “execution_count”: 43, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.imol[‘Ethanol’, ‘Water’]”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“All flow rates are stored as an array in the mol attribute:”
]
}, {
“cell_type”: “code”, “execution_count”: 44, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([1.11 , 0.217, 0. ])”
]
}, “execution_count”: 44, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.mol # Molar flow rates [kmol/hr]”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Mass and volumetric flow rates are available as [property arrays](https://free-properties.readthedocs.io/en/latest/property_array.html):”
]
}, {
“cell_type”: “code”, “execution_count”: 45, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“property_array([<Water: 20 kg/hr>, <Ethanol: 10 kg/hr>,n”, ” <Propanol: 0 kg/hr>])”
]
}, “execution_count”: 45, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.mass”
]
}, {
“cell_type”: “code”, “execution_count”: 46, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“property_array([<Water: 0.020166 m^3/hr>, <Ethanol: 0.012898 m^3/hr>,n”, ” <Propanol: 0 m^3/hr>])”
]
}, “execution_count”: 46, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.vol”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“These arrays work just like ordinary arrays, but the data is linked to the molar flows:”
]
}, {
“cell_type”: “code”, “execution_count”: 47, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“<Water: 18.015 kg/hr>”
]
}, “execution_count”: 47, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Mass flows are always up to date with molar flowsn”, “s1.mol[0] = 1n”, “s1.mass[0]”
]
}, {
“cell_type”: “code”, “execution_count”: 48, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“2.0”
]
}, “execution_count”: 48, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Changing mass flows changes molar flowsn”, “s1.mass[0] *= 2n”, “s1.mol[0]”
]
}, {
“cell_type”: “code”, “execution_count”: 49, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([38.031, 12. , 2. ])”
]
}, “execution_count”: 49, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Property arrays act just like normal arraysn”, “s1.mass + 2 # A new array is created”
]
}, {
“cell_type”: “code”, “execution_count”: 50, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“15.34352”
]
}, “execution_count”: 50, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Array methods are also the samen”, “s1.mass.mean()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Thermal condition”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Temperature and pressure can be get and set through the T and P attributes:”
]
}, {
“cell_type”: “code”, “execution_count”: 51, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: s1n”, ” phase: ‘l’, T: 400 K, P: 202650 Pan”, ” flow (kmol/hr): Water 2n”, ” Ethanol 0.217n”
]
}
], “source”: [
“s1.T = 400.n”, “s1.P = 2 * 101325.n”, “s1.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“The phase may also be changed (‘s’ for solid, ‘l’ for liquid, and ‘g’ for gas):”
]
}, {
“cell_type”: “code”, “execution_count”: 52, “metadata”: {}, “outputs”: [], “source”: [
“s1.phase = ‘g’”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Notice that VLE is not enforced, but it is possible to perform. For now, just check that the dew point is lower than the actual temperature to assert it must be gas:”
]
}, {
“cell_type”: “code”, “execution_count”: 53, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“DewPointValues(T=390.84, P=202650, IDs=(‘Water’, ‘Ethanol’), z=[0.902 0.098], x=[0.991 0.009])”
]
}, “execution_count”: 53, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“dp = s1.dew_point_at_P() # Dew point at constant pressuren”, “dp”
]
}, {
“cell_type”: “code”, “execution_count”: 54, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“True”
]
}, “execution_count”: 54, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“dp.T < s1.T”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“It is also possible to get and set in other units of measure:”
]
}, {
“cell_type”: “code”, “execution_count”: 55, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.0”
]
}, “execution_count”: 55, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.set_property(‘P’, 1, ‘atm’)n”, “s1.get_property(‘P’, ‘atm’)”
]
}, {
“cell_type”: “code”, “execution_count”: 56, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“256.99999999999994”
]
}, “execution_count”: 56, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.set_property(‘T’, 125, ‘degC’)n”, “s1.get_property(‘T’, ‘degF’)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Enthalpy can also be set. An energy balance is made to solve for temperature at isobaric conditions:”
]
}, {
“cell_type”: “code”, “execution_count”: 57, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“130.80215821464316”
]
}, “execution_count”: 57, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s1.H = s1.H + 500n”, “s1.get_property(‘T’, ‘degC’) # Temperature should go up”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Thermal properties”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Thermodynamic properties are pressure, temperature and phase dependent. In the following examples, let’s just use water as it is easier to check properties:”
]
}, {
“cell_type”: “code”, “execution_count”: 58, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“997.0156689562489”
]
}, “execution_count”: 58, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s_water = tmo.Stream(‘s_water’, Water=1, units=’kg/hr’)n”, “s_water.rho # Density [kg/m^3]”
]
}, {
“cell_type”: “code”, “execution_count”: 59, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“971.4430230945908”
]
}, “execution_count”: 59, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s_water.T = 350n”, “s_water.rho # Density changes”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Get properties in different units:”
]
}, {
“cell_type”: “code”, “execution_count”: 60, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“0.06324769600985489”
]
}, “execution_count”: 60, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s_water.get_property(‘sigma’, ‘N/m’) # Surface tension”
]
}, {
“cell_type”: “code”, “execution_count”: 61, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“0.01854486528979459”
]
}, “execution_count”: 61, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s_water.get_property(‘V’, ‘m3/kmol’) # Molar volume”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Flow properties”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Several flow properties are available, such as net material and energy flow rates:”
]
}, {
“cell_type”: “code”, “execution_count”: 62, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“0.05550843506179199”
]
}, “execution_count”: 62, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Net molar flow rate [kmol/hr]n”, “s_water.F_mol”
]
}, {
“cell_type”: “code”, “execution_count”: 63, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.0”
]
}, “execution_count”: 63, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Net mass flow rate [kg/hr]n”, “s_water.F_mass”
]
}, {
“cell_type”: “code”, “execution_count”: 64, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“0.0010293964506682433”
]
}, “execution_count”: 64, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Net volumetric flow rate [m3/hr]n”, “s_water.F_vol”
]
}, {
“cell_type”: “code”, “execution_count”: 65, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“216.85387645424356”
]
}, “execution_count”: 65, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Enthalpy flow rate [kJ/hr]n”, “s_water.H”
]
}, {
“cell_type”: “code”, “execution_count”: 66, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“4.556131378540336”
]
}, “execution_count”: 66, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Entropy flow rate [kJ/hr]n”, “s_water.S”
]
}, {
“cell_type”: “code”, “execution_count”: 67, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“4.197680667946338”
]
}, “execution_count”: 67, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Capacity flow rate [J/K]n”, “s_water.C”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Thermodynamic equilibrium”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Before moving into performing vapor-liquid and liquid-liquid equilibrium calculations, it may be useful to have a look at the phase envelopes to understand chemical interactions and ultimately how they separate between phases.”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Plot the binary phase evelope of two chemicals in vapor-liquid equilibrium at constant pressure:”
]
}, {
“cell_type”: “code”, “execution_count”: 68, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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n”, “text/plain”: [
“<Figure size 432x288 with 3 Axes>”
]
}, “metadata”: {
“needs_background”: “light”
}, “output_type”: “display_data”
}
], “source”: [
“eq = tmo.equilibrium # Thermosteam’s equilibrium modulen”, “eq.plot_vle_binary_phase_envelope([‘Ethanol’, ‘Water’], P=101325)n”, “plt.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Plot the ternary phase diagram of three chemicals in liquid-liquid equilibrium at constant pressure:”
]
}, {
“cell_type”: “code”, “execution_count”: 69, “metadata”: {}, “outputs”: [
- {
- “data”: {
“image/png”: 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MohkXlpeX7wC+rqoRJY9EitfjawI2AwV2xrabQNA/HBgMrEqkTQBVvb+pqen3ra2tqTk5Oc+ISEEs17HnfQ+YraozgQHAGZj+anfYwvXNmD5xDscegXNojn5NaH0JuEhETgEQkSMmT558Z+XKlbS1tV2oqv9Lhm2vx7cWSANGhPYFgv5BwGigKpYkkEhQ1R+sW7fu5eEjho8cMGDAUyKSEeOlUoFBIpKKccA1wDFAwL4fdeG6w5FM3BqaY69ARA7CtFA5Y8SIEU8CeRs2bLhNVa9Otu1A0L8PpvdZPaYX2Q7r7JKGiAxPTU19f9KkSROWLl36APDNCIvEw69xBXATsB34D6Yx6NuqOsW+Pw74t53BORy9jpuhOfYKVPUd4AbgXxkZGXmbN29+gRh0HmPB6/F9gnFkY4FByXZmAKq6sbW1dX5VVdX2wsLC84HLoznfih7Pw4x7DDAEOLEjU3EO1eFIGG6G5tgrEMMjI0aM+MbYcWO3zD7ggIOO//LxqXwmjVUBZGHW28B0m04HxtntSvs60b5WAy2YtHsw4bgGIKTpWAvUAUV2eztwALAEU0S9xZ5TaO00AKvt9QfZ4ysxTjDL2lpux5cD7MJ0yB4F5FkbZfb73fe04I4Fp40YOeKOZ57uuhxPVT+3ziciPuAEVb3Qbp+L6XbtA0araqtNOvmZqn6ly4s7HD1Eam8PwOHoIX44btzYb9TVbWo8aM6ci4859pg1tvartN1x5e22l7fbrmy3Xd1uu/3sa70tnt4f8APDgSVh62fr2x8f5Xb7ZJb1fP6ePjjj9G/8Ojc390epaamba9fXHqCqK+meVcDBIjIY41yPBd4HXgK8wONEWLjucPQULuTo6PeIyFfGFIy5ZfPmLWzbtu2cu+/+4xPAge2zD5NBIOhPBWZjFPQ3YLIFc5Nt19reF/h0165d19fX1z+XnpY2bMiQIc+KyJDuzrUh2gDwAfAJ5rPiT8CPgO+LyDLMbPAvybsDhyM6XMjR0a8RkSkjRox4X7Vt6MaNdTeq6s9C7wWC/qO8Ht/LybJtHeZ+wFIrixXafyjGwbUm0fZwIN+u3yEiOVlZWe8PHZpduHZtzZNtbW1nRJsk4nDs6bgZmqPfIiJZmZmZf09PTx+6adPmhcDP2x3ySiDoPyqJQxgDNIQ7MwCvx/cGcEiyZoh2VlgUcmYAqrqloaHh5Kbt2xvz8vJOw8y0HI5+hXNojl7DKk+8KyIfWUWKG+3+uNUoRCQlJSXloezs7Blbt24tb2trO1dVP9cB2q5jvZ2EjtMh0eEJXo9vaUfvez2+V4Ejk+TUDvN6fK+136mqZZvqNp2VkpJCTk7OzVb70uHoNziH5uhNmoFjVHVfYBZwgogcTGLUKP4vJydnfmtr69aGhoaTO+uV5vX4dgBrrK5iIjnE6/F1p9r/BkZkOWEEgv6juwqjquqz69ev/8ngwYMlOzv7MRFJSKdth2NPwDk0R6+hhka7mWa/lDjVKERk/pAhQ342ZMiQttra2tNVtcNZUgivx7cKyAsE/d0mS0RCpGtztjP10kDQX5wgu0cAr0Zw6E01NTVP5QzLyR48ePDfRSQ7EfYdjt7GOTRHryIiA0RkESb9/AVMmvwWVQ0lTKwGItYjFJGSQYMG/W3EyBFUV1dfq6rPRXKe1+N7lwRkPto1uVciPd7r8a0HBgaC/qFx2v0SsNjr8e3q7lhVbVPV8+o21pXm5eVNGzBgwBfEmx2Ovoj7I3Z0ilWkTyqquktVZ2EKiOfwWSHy5w6L5FoiMmzgwIEL88fkZ1ZVVj0G/CbK4bwCHBnlObsJBP2zgf9Fq9Ho9fg+BGYFgv6Y/h9tRmOb1+Ori/QcVW3ctm3bKTtbd9YPHZp9EnBjLLYdjj0J59Acuwk5sFAIqifTulV1C/AycDCQYwVxwTi6bqWiRGRAamrqE/lj8gvXrF6zSFW/Fe34rSN6JRD0R+3UAkF/hxmNUdh+BTgiBrsDgBnA4mjPVdXl62rW+YYNG9aWnp5+g4h4or2Gw7En4RyaYzeqqjb0dKmI5Hd7QpyIyAgRybHfDwKOw8g3hdQoIHI1il/l5g47vrGxcVNzc/N8VW2KZUzWqb0XCPrnRHqOzWic5PX42quMRGv75RjKCA4B3oxVuV9VX1i+fMU1EydNZMCAAQ+KyD6xXMfh2BNwDs3RngxgJnBSaIeIfElEJotIojMB84GXRORj4D3gBVX9B1GqUYjImRkZA6/OzMzatXHDxlNVtSqeQdleZusDQf/ECE85xNaWJYI3AkH/YZEcaAu03/J6fG3dHtw1t1eUVzw8deqUISkpKQtFpEeUTByOROOUQhxfQETmAF9W1V+KyNnAOcAmYBvwjHU6PTmeAmCDqrZ08N7+IvLGjBkzMsrKyi5X1bsSYdOG8r4ElFsH19lxR3s9vpcSYTPsmqOBYV6Pr6yLY+YAH9uyg7gRkUEpKSmvTZs29YAlS8r/H3BiWGKOw9EncDO0vZxQdpstZh4hImMxyvPHichhmBT664FLgJV2u6f5OfD79jtFZCTwTFFxUUZZWdlfgT8kyqDNFiwDZnaW+WjDgy8nymaY7XXAoM4yHwNB/zSgOlHODEBVt7e1tX19zZq1G8ZPGH8cphYwJkRkuogsCvvaKiJXikiuiLxgC+ZfsC1qHI6E4RzaXoyIiKq2icghwH+BhzHOYwamDiwd84H9HUzH4geAY0WkoCcyIMO4EjhMRC4OG3sa4C8uKR5Xurj0beC7iU5isQ5jMUYp/3MEgv4DgA+S1XXa6/F9QAeZj4GgfximXq+9yn7cqGp1Q0ODR1Vbs7Ozvi8i58R4nXJVnWWzVw8AmoCnMf3nXrQF8y/SQ/3oHHsPzqHtxdgkkAGYNavbMIocS4FTgSrgUmAZ5gPpUeAZoExV1/RwBmQDptnkjSJyuN19x9SpU49YtnRZDeBR1eZk2LYtZpZZ5Xpgd0Zjo9fj61B9JIG2P5f5aMOg44GqSOrNYkFVX6teVX35mIICUlJS7hOR2XFe8lhguV3XnIcplIcYCuYdju5wa2gOROQqTJ+uX2Oe/h8FvgdcB1yLqQObABSoatCek9JeG7EHxvkV4H7g9pEjR/xGoWVD7YYjbKuTpBII+vMxXZtXAQcmMAkkEttH2QzIYmCd1+PblGybInJvSUnJRYsXL14NzFbV9n3YIr3OX4EPVPUuEdmiqjlh721WVRd2dCQMN0PbiwkLG74OCMahXY950CkHPgZOU9UaVX07zJkN6GlnBqCqzwNBEbk1MyuLDbUbLu4JZ2ZZj+lldiLQnUZjonkjEPSfDtT1hDOzXL548eI3SmaWjMX8zGMRiE4HTsE0NnU4ko6boTkAEJF9gVHASOA/qlorIscAV2Gc2vZeHSC7sx3fnzKlcPTIkSP/fdX3r7oAk9Y/0h5SAWRhygHAyGilA+PsdqV9nWhfq4EWIFSOUAM0ACHB3lqgjs/US2YAn/JZp/ct9pxCa6cBI9U1ERiE6fRciSkOz7K2ltvx5QC7MB2yR9n7AJOI0v6eptmvHZgavUTeUx3GWU/HOOzd91RRUZH/8kuv3PfCCy90O4tS1S+sqYrIPOBSVf2y3S4HjlLVGlvn+LKqTu/u2g5HpDiHtpdjE0M6/SMQkb9glO8/7cFhdTSODOCV4pLiOaWLS1968KEHfjl48OCXkpWU0Z5A0H84ZmY2Gkj3enwre8huHjAC4/hmASu8Hl99D9medueC3xUuWrTo6by8vIFVVVUXq+q9kZ4vIo8Dz6vq/Xb7N0Cdqt4iIj8GclX1miQN37EX4kKOezmdOTMrGpwCLNgDnJkAfywpKZlTuri0Cjj93HPO+y/J6yf2Oazw76dej2+X1+NbA6QFgv6R3Z2XALvpGAe62jrujzFlBEn/vw0E/dlA1muvvfbvxsbGi9ra2sjKyvq9LeXoFhEZDBwPPBW2+xbgeBFZat+7JeEDd+zVOIfm6BArGtymqp90f3TSuXzK1CnnL126dDswX1U32P1xiQlHgi1ybsaE4gDwenwVwIhA0J9sRY18jD5ko7W7C3gXOCiZRu1Dwv5ej+9/AKr6UHV19YKCsQVpKSkpAVur2CWq2qSqeapaH7avTlWPVdWp9rWn1gMdewnOoTnixs7mPhSRf9jtuDtOh1376BEjRty+tb6elpaWC1R1Ueg9O2v5n1W5Tzh2hlQArOwgtFkKjLczmWTYHgZk215tu7E91D4MBP3JdGod9XO7eknZkhdnFM0YBTxttTf7FKESBNcqp//ifrGORHAFJpkhRCI6TiMiE9PT0/05OTkDams3/FpVn2h/jFW3b7C1YYlmH6DM6/F9QXLLOrglwCTr+BJGIOgfDEz2enwdzo5twfcSGwpNKLZE4AtSXlYG6/TSxaUri0uKZwP39nBxfcyISI6IPAe8KyInWjEB99nXD3G/1L0AEdknFCYSkSdFZGb4h1E8/9z2ul8D/my3hTg7TtvrDAGemTJ1St7SpUv/jSkn6BCrcp9Qx2ILqZd2peNoHUsdMC5Ra3mBoD8DmBkK93XBVmBrIOgf181x0dg+lC5KElS1Dpi/pGxJU1FR0TkYBZc9GhE5ABOarsUICDwiIhOdU+ufuF9oP0dEBgKXA4+LyEPA/qr6aXgySKimTESKYzCxALgGCNWl5RFHx2k7DgH+UlxSvG/p4tKlwJmq2qUyhi10PiSqkXeCLWBeHYkSiNfjW41Jyc9KgN00jCByd84sNEOsAYYGgv4hCbA9DajsaDYajqp+3NbWdl51dTUTJoy/TUSOi9d2srD93Z4C/gVcp6q/wfy9vgCf/d07+g/OofVzrCTUbzBK+WcDd4fek8+aaCIil2McX8SIyElAraqGfwB3NFOJNrX+R8UlxaeXLi5tAObZ5p/dEmM/sc9hZzzboun+7PX4FgH7WGmqWO0KplZsRaSyVl6PrxnT/HRCPDPEQNCfhVmvWxPJ8aoaaGxsvElVU7Kzs58Ukcmx2k4WInILJvSdAgxT1dUAqvpzoFRE7unN8TmSg3No/Zgwh7UCIz58P3CSiPwadq+LhIR+n8I6tCjWRg4FThGRSuBxTKhxATF0nA4b81enTJly87KlywDOVtVOW6h0wiuxOjX7wZ5FFOMNEZohxpFSnweI1+PbGKXdTZgMzImxGLWO8ACvx/d+lKf+ZNWq6n+OGZM/zPZQy4zFfqIRkYEi8ixwOEZU+2DgayJybugYVZ2H0SV19DOcQ+vHqGqrFR9+AnhJVS/ENMucJSIPiUiamN5nHwIb7fFdFlq3u/61qjpWVScCZwD/VdWziK3jNCIybcTIEY9t3VovLS0tP1HVZ6O6YXaH4j6wavgRYx3RGKDWZhLGwuvA4dHOluzMrsjr8cVU7+f1+NYCGgj6u02n74AjO8ho7BYbrjurvLyifPr06TOBB/aQJJEDMc1pb1TVF1V1DXA68BcR2Z1Eo6rPicgcq5Dj6Cc4h9b/GQxsAOaLSBZmJnY7pjN1md1eEFKrT5CKflQdpwFEJHvgwIHPZmVlZW/YsPEp4KZYjdu1r8YoMx/zgZZoZ0jt7CqmTmxOpE7NHne41+N7LVa71nYlMDiagu+Q6HGsNlW1XlXnlZeXb50xY7oHI2bdq6jq65i/71vD9r2JCbfvAyAiXxcRP3ADcL19sNsTnLEjTpxD68eIyBDbeuV+TDHuPKvJ+B9MIsdlwHdU9c/x2lLVl1X1JPv9ClWdo6pTVNXXXWsXm2328ISJE6avqlr1qaqeF++CfTSZj1Zeamgi5Ky8Ht92TEbdhAhPiWmG1AkrgJE2dNolNqPxrXgNqmp5W1vbmcuWLdepU6f+wq6r9iqq+kNgg4g8EbbvCVV9xGbPTgY8QJqqnqaqO3uyHZIjeTiH1k+xGV6v22SPKuAXwD0iMk8Nlar6nKr+0x6f1CdUEfmyDX92xI1Tpk45eeWKFZtbW1vnq2pjImxGkvlou0IXeD2+0kTYtHZXAqNsPVlXtg/HhCkTZbcVk/lY0NVaXlhGY0J6yKnqP1tbW2/YsKFWCgoKHheRGYm4bpycABwoIt8N7bDj+jGmA8BdwJEisl/Y+6dbh+fooziH1n/ZgVFxPwJThzMJ+ANwmYjs0/7gZD6h2hnYdZhu2MlPxmQAACAASURBVO3f80wunHzDqqpVbTt3tp6uqssTaburzEfrcAoxCvoJxevxvQMc1FnoMRD0z8QUbbd29H4cduswJRQjOrEbVUZjFPxq69atfhEZkpWV9XcRyen+lI6xhdABEVkiImUiMldEckXkBas+84KIdNkBwJZ5HIMp7EdE9sf0+DsaE2IP9fvbT0SyROQ64LuY9TZHH8U5tH5CqEhUDBnAv4EHMOtkX8b8I0/HdBD+Zk8Wldrw4WnA2SLiCxvzPgUFBQ9t3LCRlpaWa1T1hSQN4dVA0P85zUebiDEFWOb1+JJSj2QVN45qv99qQA6IZ72uG7sVGFmuz8lThTQaiaDOLVpUVdva9IKNGzd+mpeXOyUlJeWRLmbk3XEn8JyqzgD2xfwN/xh40arPvGi3uxtTpao+Zjevwfz9f1dVn7bvL8BkO/4C83PxY2rWHH0U59D6CWFrTrdinjyPBG4EjgPOUdVz7f7/A/7Z00WlqloLfB24W0S+JCK5OTk5f29raxvc0NDwMGYhPylYh7UoEPTvF7Z7NKberNvi6Tj53AzROtKZXo/vo2Qa9Xp87wGz2q0hHg68mqyWO6q6bceOHads3Fi3qaCg4KsYRxEVIpKNiSr8xV6zxdYhzsOozkCU6jM2u/Fg4HRV/Ths/2GYqMU+wBuqepeqrovDETt6GdcPrZ8hIt/BJCQcg3my/Ssmm+suVf172HERp+cneHzfAG5KTU2tHj169BF1dXUfbt++/VDtgQaigaB/OkYyajOwrw0LJh0b2tzH6/G9E29mYQy2DwXewXygvxVp0XY8iMix2dnZz2dmDhmwdm3N6ar6ZBTnzgL+hBF/3hczm7wCWKOqOWHHbVbVbhuPhh2/EMhU1WPt9lnAqZhSjWogEwio6l9FJMWpiPRNnEPrh9in3GxM6GYkMB6jFHJkWOuVXkNE3s0YlHHgwPSBG+vr6/dX1eqesBsI+lOBA4DBHQnwJtn2JOAgr8f3eA/bTQfOBAKhNjQ9gYhcOWr0qDvSUtO2r169eq6qRjQjFaOI/zZwqKq+IyJ3Yh5CLo/Hodlzvo3J+L0KIwqwGHhcVT8RkZmYrM9ZiV7HdfQczqH1IzqaddmwyhHAXFU9uXdG9rnxnJObm/vQxIkT2/Lycs+66DsXfYxxugAVGKWOfLu9HEgHQgK8lfZ1on2tBlowiR1gMvwaMBJSYNLn64Aiu11n3xuO6QC9xZ5TaO00YLQnJwKDgO3W5lg7rhY7pnwgB9hlrzMKU28HZlac18E9fRkjAfacfU3kPa3HrA8N6OCesjD93LKBjQm+p05/Tzt37uSJx598aOHChYfTDaoaLpQ9GnjbFusjIodj1sumAEepao2I5AMvq+r07q7dHhH5KXAYZq3scXu9FGAOJux9tqquiPa6jj0D59D6KOHOK/S9iAwIifiGh03sP6yo6i4RSdXPhIN7esyzc3JyXs/IGDhw3br1l/gDT74LVPTEzMEmRBwBvAYMBEpikHuK1fZMYL3X49tg19NeSdY6Vju7mZiaq1KM6PGyHlgzDNnOW7du3bhrrv7Rn/Py8g5Yv379Szt37vxyJH97IvIa8C1VLReRnwGhVPo6Vb1FRH4M5KrqNdGOS4wA90HAY6q6w/5vTACuta/nqur6sON7JTTviA2XFNIHsc4q5MwGY5+MrcMS+/3uNQA1nad32e97y5mNysrKWpiZmTlw/fraP6nqPcBHwAzbMiXZ7Acs8np8bbb4uSIQ9M9KttGwjMYNYMoISHKXbWtXgKkYseNWr8f3AbB/otrcdGN7EDD5sksvX7R9+/b5O3bs2JCTk3O0iNwW4SUux7R5+RiYBdwM3AIcLyJLgePtdtSoaqmq3m+dWRomu/FPmL+PS1R1vdWDnGuPV6ci0ndwDq0PEjbzuh2TdvyoiLwoIgV74tOkiKSnp6c/NWxYzpja2tq3VPVyAJugsBIoTOYHbSDoHw1s93p89aF9dqayIRD0J00pvouMxlcCQf/RybJrKcF02t49++0JZ2rX7Pa1WZao6uqNGzfOz8jIaB0yZPAVInJed9dQ1UWqOltVv6Sq81V1s6rWqeqxqjrVvm6KZ5xiuqifikmaqsOE5FeIabd0KvAbEbkidHg8thw9h3NofRSrAHIyRjfxBEy90w9jvFaliHwiIotE5H27L6pC1m6uf+ewYcMO2dbUVNPS0nKqqu7uuWULgRWzrpVwbI+xKV6PryPV/vXAwEDQH3MRcDcc7vX4Xm2/04Yb3wkE/Qclw2gg6J8BrPN6fF9ou5OIFjtd2E3FiAN/LntUVd+srq7+7vARI8jIyLhXjCB2r2L/Bk/D1LadAaTZhJShtnbtEuBGETnKZTz2HZxD64PYwunZwA9V9UPgLEwywXUiMkVEpsZw2aNVdZaqzrbbUReydjLWiwYPHnzxwIyBLXUb6+ap6rr2x1jZqWnW+SSaQ70eX4fyUmFSUWPi6WXWEaG1ss7et52wawNB/8QE250ANHVTtP2mTedPpN0UTHhwUUfrg6p6X+XKyj8WFBQMTE9Pf9omdvQ231DVq0RkAmZt9UFgoYgcqaqfYDIiT3chx76Dc2h9AKv+sfsDV1V3YLLV8kXkUOBXwGm2lutUjBJIvB/QMReyho370NTU1LvGjBnDqqpV31bV9zo71uouzk1k6DGSmi87i6nnswy9RNg9FHgjgsSPSoyY8KBujovU7jBMd4UuZa1sV+qVti4vUYwFNnk9vm1dHHPlypUrXxs/ftyYtLS0p2x4rzcJtQk6GSMVtz/wKPCI3T867BhHH8A5tL5BSVj24i/sB8GnmF5jT2NqdN4VkQMwIcinQsdHiAL/EZH/ichFdt8oVa0BsK8RtyWx4xybkpISLCwsTFu6dOkCVX2ou3NseO7IRDg1K3X1hXBfJ3bXAAMCQX/cs4ZA0F+EySbs9oPQOrz3gdnx3rMN940G1kZSPG17qGVFoswfge0MYIzX4+sy3V1VW9ra2ry1tRvWjho16mARuas3Zz9h683rgMmq2qyqvwceFJEmTHr/fTYxJBnRA0eCcQ5tD8fW5fxbRBaIyDPA4fYf7y7MWkUmkCkivwIewjQ2fC9KrcZDVXV/4ETgUhE5Is4xDxKRp6dNnzaqvLz8ReDqKE5/HfNBEjOBoH9/4KNoNBq9Ht9yYGgg6I95Lc/OkFIxtWKR2m3DFPR22RUgAvJpl/gSge33gQPicab23IO9Ht/bkRyvqrVbt249RUSaMzMzv4VZq+pVVDWAeaC7R0R+gVHZ+QgTkvxEREYC14hIshN5HHHiHNqezyZMu4szMVp8p4TeUNUrMR8IYzFhs+tV9XehtyM1oKpr7WstZsY3B1gfWuewrxF9SNvw6L2TJk+aXbmyshKjnxdxqYBd1yqztVtREwj6RwE7OkqIiIAKzHpadgx2UzBp8uXR1pjZe34vEPQfHK1dazsH08+tMtpzE5D5GHU/N1X9X3V19YUFBWNISUm5M94HqESgqucDbwLXA2uB81X1dTEtZy4DrgQuF9upIgEhfUcScA5tD8dmY63ByP98iOlxNjfs/QdV9f9U9RZVfQY+X6fWHSIyREwna8T0gvoyJpz5LCakiX1dGOGQr8zLyz2naVvT9h07dsxT1boIz9uNTWgYYGu4IsYmlUyNtbeZnS2tBCbEkKByAPCJXZ+KxXYLpjYuKkduQ4aTvB5fzC1wYs187C7xpStU9ZElS8p/O6NoRioQEJHxsVwnwQQwqvyX2KLuwzEtZ+ZiOrv/B/CLSGF4zadjz8E5tD2U8JChnTnNxKTnvww8KSLn2+MuF5Ebw8+NMs14FMZJfgS8i1Hif44YCllF5Li0tLTbcnNzWbdu3bkapmweLbZ2a2aU2YedZjRGYbcBaMSsR0WEDXGW2YLteNgM7Ih0Lc8mk8zwenwfxmkXosx8DAT9c4D34lQ8+XHp4tIXikuKRwBPW5GAXkNVm1T1NqBORM4AvgUMBX6pqt+xYgDvY7pYJLWHoCM2nEPbA7ESVqHi6SNEZB6mTmsX8AOMc7ldRP4F/BJ4PlZbqrpCVfe1XyWqepPdH1Uhq4hMBp6YOm1qSkXF0pvtukRc2CSRiMJRiVSxtx2nRwaC/m67FweC/mKgKhHyXdY5rAGGB4L+LjMA7QyyGIj5oaGd7Rag0naz7pJA0D+e7jMau8WGos8oX1K+YkbRjP2B+/aQWc8k4JtAE/BTVX3FhtJTMFmPS3t1dI5OcQ5tD8Nqx4UyGv+OcV5/Bm7DPDEOUtU/YBp2vgmcoqpvJjumLyLZInJFRx84IpIJPFNSUpJburj0n5ieawnB6/G91F04LJqMxijs/g/Y12YPdma3AJPuHZdqRTu72zFOrTv1lAmY4unmBNpeA2R3lfloMxoLvB7fskTYVNVNu3btmrdmzZpt48ePOxPzwNarqOoyTCnMj+z3YNRCbsWUxbzVW2NzdI1zaHsYYRqNPwcmquohmJT5/TAt4q8SkfGq+pGq/lJVX7HnJbvP1S7gAszi+G6sg7u/uKR4n8WLF5cDZyVBWeH1QNDfoWq7bdoZVUZjpHg9vjcxtXFfeFiwPc6GYdLkExp68np8mzBOsrCj923SSpZ1QInmf3St+Xiw1+NL6Ae6qn7asLXhHJEUMjMzfy0iX4nneolQvlHVl1R1a+hcTBTkLOAQVf2PiGSKSIGIDLXHuCSRPQDn0PYgQutmIjIc82F2vn3rWkyo40FMxtXNoWyrnkJVt2GKq38kIseFvXXdlCmF3uXLlm8F5qlqxGnjkWKzAJe0T5iIM6MxUt4ADgn/gLffj8Uo6O9IhlGvx7cOaLbKH7sJKXIkaN2sI7uKme1+IdSbzOakqvp0VVXVz8eNG5eSkpLyuIhMifOSCVG+sVmOKzAF64VApYhca/c9iSmpKbZJIs6p9TLOoe1BqGqbiIxS1Y3AvcAym9F4GfAVVV0AfAJUYZoT9vT4KoEzgIdFZLKInDR8+PBfNDQ0anNz85mqWp4s21atfnfmo11Dmozp3ZU07MzvY2BWmFMbA+wMKegnkdUYrcmR8FkLnI70IROJdWqvhc+K48lojIIby8rKnp1RNCMHI0EVd9F3GLEq3+wCnlDVQ1W1CbgD+Drmf9IL/AN4Vkxbpl1R1n86Eozrh7YHICKnY7Kp7geWAReo6n/te6cCJ6rqt20o5jLgQlWtlV5qFS9GGPmy1NTU/MmTJ2VVVCy9XlVv7gnbgaD/CMys6WDgHTt76wm7RZgsxF1ArtfjS6ojDbM7ENsGBpiTrBlSJ7YHY1T7BVgcbxJIJIjptv52cUlxUeni0mcAT7R/4yKyEvO7UuBeVf2TiGzR+DteX40RAD9cVSvC9j8HfB9YEpbMNSVs/c3RQ7iniV7GJlSUYFQ6SoE3VPW/YckX9cCFIvJnIIjpsltrk0d6SwX8b8Do4cOHZ1VULPVjFtB7BDs7+Q4mZbzHertZtf79MAkRPeLMrN1mTKfprwERqXEk0HYTxpkN7glnBmDXreZXlFfUH37E4fOBG2K4TEKVb8IYCNytqhWh/08R8WJUXlrDnNl5wB9FJKZCeUfsuBnaHoCIDMIUdR4P3AP8VlWrQjMwEfkaRjh1iar6rTPrlV+cXSd4dkbRjK8OGTxkzQUXnH/2qNGjavlM67ECyMJIMQEsB9L5TPy30r5OtK/VQAufJUDUAA1AKH28FtOvqshuj8J8sJ+CCb9usecUWjsNmFDdRGAQRsS5ErPmlWVtLbfjy8HMuMrtdfOsjTL7ffg9jcC0RskG/Am+pzpMK5vpwIAO7mmo3d9or5uoe+ru95QOfAWTvv6uHVei7qnL39Mbb7xxwoI77vwR3aCqXab5i+l43Qh8GzhKVWus8s3LqhqVOLOI3AoUqerJdvurwOlApar+1D6cXo7phLEMeFBVYyryd8SGc2i9iH3KE+u05mFazR+L0We8NyzseGQomzF0Xi86tJuLS4qvLV1cumnQoEEHPvS3B1OBzT2wnkQg6J8KNHo9vhqbTj/X6/G91gN2UzAfzLVej68umckRHdjOBIq8Ht97gaB/NlBqZ049YfsozLrZYMz9f5jojM5O7E4Ftvq8p50/YcKEW+rq6hobGxsPisQ5WLWbFFVtsN+/APwc839Vp6q3iMiPgVxVvSbasYlIOeaBaiamvGIdcBPQitEsHYfRI31YVZP+P+H4PC7k2EvY4mkFBovICEwG1qPA7zBPt5eJyPkich1m1rabXnRmp02ZUnjt8mXLdwGnNTU1rcDo3o1MUi+z3Vjh3zSspqQNN5bbwuZkMxxQ24wU4FVb+5ZUbBLI7FD3ZysmvE93RdcJsn0k8IrX41MbblwOJD2z1hZtN3s9vvXArVVVVY+PGzcuMyUlZWF3qfaWhCnfdMKR2EQQTKj9SswD6K8ws9+ngD84Z9Y7OIfWC1hntsvWsAQxGnEPici9GAexAFgEnIOpffmqPa/Xfl8isu/w4cMfaGxspLm5+Yeq+iKAVcjYggkVJQVbB1YAVLdrjbIBSI1W8zFK2wOByV6Pb0lon818XGQlr5JJR8K/72IU8pP2AGGzG18Pn41ZFf81yXyAsJ0OBmFCtqEHtwvLysoWFRUVTQEe7S41PlHKN11cf52q+lX1RlV9C+Mcf4EpsL9fVR9R1ZY9RPFkr8M5tF4grAj6KcxaxsHAExhF/X1sBtWvgJOAY1R1ZbgcVkeISI6IBERkiYiUicjcaItJu7j28PT09IW5ucMGrV9f+yBwZ/j7tsA3LZRengQKMeG+hnZ2FVO+MMOGBROKdaQHdtQaxX7Ab7e1cAmns7CmveePgC8l6Z5nAR931FPNzlC3JrrLtrU7EDO7qg53pDZVfv7ixYs3FpcUnwD0SDZtV4RksETkpxiJrHLMrOyF0Puqqs6p9TzOofUC9h9iMubnf7WqNmMWrQM2w3EuMFJVt6vqeohICeRO4DlVnQHsi0kCiKmYtN1YU4EnpkwpnFBRsfQ9Vb24o5Cn1+OrAEYkerZkPzybvB5fh+1r7Afve8BBCbabQjdixzbzcWqiZ0uBoH8uXWQ02hDgZhI8Kw4E/WMwCRRbuzhsLZCRyN+zDa3mA/UdrQ+qahXgW1K2ZNeMGdOvEZFvJMp2LKihDbM08BHwe1X9CD7vzOzroND+XhzyXoNzaL2AdQhbgVwgVURuAnJU9QJ7yBUY4dmIsLU7RwB/sddvUdUtxF5MGs5tRUVFx5SVLVkPnKqqnSpjeD2+xcD4RHRBht0fsClej291V8fZ7tAfWgX4RNgVTMuQbiWerMOLWKU+AtuFwOruFEhsd+gxVlsxEXYHYjoMrOoq8cOGW9cCo7vSuYySHCCzq9+zqr7c1tZ21dq1NYwdO/avIrJfgmzHjJomuzep7Sdo92no1Y7xYxGZ2Fvr3nsbzqH1EHamg4iMEZGRVg3kHcxM6uKQRI+I3AIUhsIXETIZs550v4h8KCJ/thleo1S1BsC+RhUSFJHzpk+ffkV5eflOVT1VVbt0LJYyjLBuXIkLNgkkB9OfrFusA1gSCPr3jceupQhYZh1lJLZj6ifWHqvun+f1+KojtPs2cHA8HafDmIlpTtptPzevx7cVM5MbE69RW7w9OcJ+bnc1NDTcn5IiGZmZmc/aZKpeRVWbO5p92bGdhlkfXxO2383UkohzaD2AXf9qFZFRmH5mR9tQxC+AfwJbReR6EbkTI6dzSui8CE2kYurU/qiq+wHbiCG82G7McyZMmPCn6upq2traLlXVNyM5zxYCr8fM1GL65w0E/emYJJAuZwsd2N4KbG6vfxil7RxgoM2yi4a4Mh/tz+pAr8f3bjTnJaDjNPYhYGk0xdNej28VkBlP6NE+9OxjOxt0iw31XbJ2bc27o/NHj01JSfGLSFKzayMdF3zBWX0Z0/n9r8AuEZkQOtY5teThHFoPELb+9XfgH6r6hF0fqwZ+igkxTgY+AObb4s8BEaybhVgNrFbVd+x2AOPg1tsiUuxrh+tQ7RGR/Nzc3IXNzc3p27dv/6Oq3hfhOADwenw1mGy1nO6O7YTRQEOMPcbWAVm2fisq7LrZvrEI/9pQ3EdW/T8WOspojJRXYp0h2qzF1fZhICpsZ/BxVv0/WrtpGOWV96I5T1WbW1tbv15VWVUzefKkI4Hbo7WdLMIc2wnAH4HfYCIntwBviciC8OMcicc5tB5CRL4EbFXV79vt0PrDRlV9VlUvVNUHVfVTu6AccTsYVV0HVItISPngWIyM1rPAeXbfecDCCMY5MC097ans7KzRmzZtel1Vr+zunI7wenwfA8XRrrPYD8dhXo+vKka7LZgQz7gYZohHeD2+mAV4rep/c7SZj/EK/9pZ7HvRriHamq/GsPq6WGx/hEmM6bYZaphdwYR1y2Np+6Oqa3fu3HlqdfXqlsmTJ10mIt+M9hrJQkRmYrpj/AtTZP0aJuGlBhN+dCQR59B6jm3AdBE5CnZ36wXIEZELrGwO9r1YnuAuBx4RkY+BWZj05qiKSW0o5K4x+WMOrq3dsKalpcWrqt2uqXSG1+MLtV6J6O/MJjgU2Q/JmPF6fJsxSTcTIz0nEPQfnSD1jzJgSqSZj4Gg/2Dg7XgVOGy4sM46qUjsZmMKgtd2d2wEfApMt6HiSBiB6VawOVaDqvp2c3PzJbW1tYwfP+4e2XN0E78KHI75bJ2ISS56H5OB/C8RGSmmv5ojCTiHlkA6WRxOsfs3YJ7CjxSR8HTrm4DTVTWW8NpuVHWRqs5W1S+p6nxV3RxDMenFY8eO/VZdXd2OpqameaGSgXiwYsKHdzdbCoWgvB7fO10dF4XdNfa6Bd0dGwj6D8PIFSXCrmKyI+dGYHcypkFoQnqqeT2+5UBBd5mP9gFjDKbjddwCz3bddB3QrTO1M/ZptuQhLlT1r9u2Nd21ffuOtOHDhz8jInEnqSSAP2GiIZeo6vXAufbrJhH5OvBL4Pcikg4gIiW9NtJ+iHNoCSQshn6aiMwWkTxVbbOL2VsxKvUezB/3wyJyB6Z4+kx7XlJ/HyIyR0R+0sl7R+Tn5/+uoaGBxsbGb6lqRAv1EdJlNl6ogBmT9ZkwvB7fSkziQqfZnXYNaWmkGY0R2m0D3rCOsjO7QzAdryPJHI3G9luYQv2uyMfIS8WkltGJ3bWYn/XQzo6xv//DuqrtixZV/X59ff0rGRkZozIyMp4RkbjLGERkgM0W/ofdniQi71iBgidCzqiT8WxR1b8Bm8SIis/DrPPdB/waM5tdCBwrImdjPguOjXfMDoNzaAlGRA4BHsf0NntTRG4QkWNFZLCqPg+cDHyIaQtTDRyrqpvENAhMdjuYZcB5InJWuzGPz8vLe2rXrl2p9fX1v1XVRxJp1D7Br+Yzpfbd2A+5EqAslvWUCFgGjOqoNi6OjMZuCRV825Bie7sCfAmjyJHwe7ZlBEd39J7NSsyxzj7RdhfRtYJJPIkvHaKqO1taWnwNDVtX5+YOO1BE7k5AFuEVmNBxiF8Dd1iBgs3AhRFcIxuj9lMMnIpZcthHVX+HWdv+GXAXRiko7tmqw+AcWuKpwTyF1QDXYTo8/xL4SESuAPJVdYGqXqqqt6tquU0CSXpvLxtynAcsEJEDAERk8KBBgxYOGjQob9OmTf+PONP9O8PWVg3rIPtwBNAaz3pKN3Z3YZzpODsTBOLLaIzCdjOwIhD0z2j31hzgg0TOCjvgC7Vxdt1snC2ATwq2+8EXQsy2MWtSOiOo6ob6+q2n7NzZuiM3N/cCTBPcmLDLAV8D/my3BTgGkzkMEQoUqGo98BjwPeBnqnqRrVnbD/gRprSlCdilqmujKNFxdIFzaAlGVVdiutrOwqwrHAz4MIvvvwTuFZFNIlIcdk6PpfGq6qeYBplPiciolJSU+3JycmY1NjZUtra2np5Mx2pV42eHPuzC1lOS2jPKOsudfL6wPK6MxijYCEgg6M8DCAT9BwCfWGeXNNpnPtoC5qnAx8m0a22/AhwZ9nveF/i0I33IRKGqH27YsOGbaWlpDB069I5Q8lUMLACuAUIz5zxgS9j/xWpMjWQkY/qVzVz+BEBEjsdI3O2PWWvbD7hARPaLJqvZ0TnOoSUY+0T3BvAMcIVN9sgFJgEXYxoCXqe92PhPVZ/CPGm+lZWVeSZo05Yt9SdHkDQSNzbkdJTdTOh6Sjd2l2ISJgYnMKMxErttQBUwNhD0z8QUi/dIPzOb+bjJ6mFOxaTJ99TD01vA3EDQPwLYlcj1us5Q1cfWrVt3a1ZW5oCsrMxAqJg5UkTkJKC23fpxR+HLqH+GIjIVI2ScCdymqv+wSVdzVTVpUYK9DefQEoxNANkGPAB8RUSuBx4G7lHTWmKJqt4DUSmBJIM3U1NTJ+UMG0ZNzbqz7cytR/B6fC8Fgv4re8qphNl9H9OSp1Ph3yTZbcK0FxmPmbH1pO1lGLHqHTEWqsdqtxkTlTg2QlmrRHFdTc2652fPnp2XMSjjWREZHMW5hwKniEglZh38GMyMLSesbnQssZU6HI1J4/+eqr4Gu5PAKsO+d8SJ61idBOyamIrIlZgMpwdV9QIRSemBxI9Ixjdl4MCB7+Xnj86ZfeCBT59++mk/B1r4LCRXAWRhsuHANHdMxxSKgv0n5LM6r2p7fijpowZoAKbZ7VqMMnlR2HkvAScCSzD91Grs+en23NX2uEHAdmtzrB1Xix1TPkaNZBemhccoTIgIzEJ7Xrt72tderwCzMJ+awHuqw6yLTAcGtLunUEbjCnv9jQm8p+5+TxkYaaxBGKWaRN1TJL+nEkxSzlZgYALvCbr4PdXW1s5++OFHbn3rzbe6TONX1U6TR2zI8oeqepKI+IGgqj4uIvcAH6vq3V1du4PrzQPuVtWIwpWO2HAOLUZE5DigVMOUtjs4Ziom9Pj/VPWKHhtcF4hIVnp6+jvjxo0rWr58+bPf/8FV3rlz5+5LD6zrwO5+W6u8Ht8mWwScbmcRybab5IjjDAAAIABJREFUgwn7LsI4stnAO0nKrAy3K5hw3xavx1cbCPrHAqlej68ymXat7RRMWPdVO46eWjcMFaq/ZL8/Ctv9ugfsHgh86vOeNqmgoODdxsbGIfX19T9W1V9Hc512Dm0yZsaWi8lQPltNy6eoEJHfA8tVdYHdFieDlVjcNDdKxJAPPArcLCLTOksTVtWlwNXAN0VkVm+LkopISkpKykNjxuQXrV27dglwzm9vu30n8AkmiSWp2PWU1tB6ihW4zY1GNilGuynAPsBHXo9PbXbhcszMI9mMxdR8hXQ01wDp9meRNKwDO9wWtoeSRF6z2YZJxdp4NbSdCAHlCO0WAjVej2+7qpauWbPmrBEjR5CamvorETkxmmup6suqepL9foWqzlHVKarqi8WZ2etcrqoLROQYu92hM+tEoMEJGkeAc2hRYtfIajDZi5MwIqRzpQPVb/tH+D4mbr5oD3gau2HYsGHzd+zY0bB9+/Z5ttg7tN7xSSDon50swzajcUb79RSrLn9gglqgdMYhwJvhszHrYAbamVtSCAT9ozF1brt1Ka1jWQ2MTFQvsw7sCqY/3qvh++39L4pDQDkS2x1mNCaqxU4XdrOBwZjMYgBUdeGypct+WlhYKCkpKY+JyLTOr9Az2LWyS7rKwrTLFdkiMj6UDb0HfHb0CZxDiwERSVfVFZhsvZWYFNz5YnqQ7cY6v1pVvd+e1+nPW0Smi8iisK+tInKliOSKyAtWpeAFERkW45jnpaWl3Tgsd5iuW7f+NFWtCH/fJi5U2JBgMjjM1ih9gXaZjwklEPQfggktfiEt2hYC7xup1mSUdnMxa0PLO7DbhFmPGpMkR34Aps6to87iW4GGSCTBoiUQ9A+n64zGtwOmG3ei7aZg1tBWdiDl9cvy8vKnp02fNlREnhHTDLfXsGvoF9OFKo6ITMG0lroBeFREbu2h4fV5nEOLAVVtEdPP7DBV/Ram6PJW4FIJazrYPkzQVUKIqpar6ixVnYX5QGoCnsYUOr9oVQpeJIbCZ/uU9/DUqVNYtnTZtar6XEfH2Q+7Whu6SRiRpMnbzMejEmz3AIwaR6cFzHZNKaFhuEDQH0o8WdbZupHVmszArMsk0nYBsNXr8TV0cVgVRj0lYTNEOwMv6iqj0WpWrg4E/VMSZddSDFR2lMVp/+fOW1qxdPG0adOKgId7O6NQjcbq9i7GcTAmIeenGGm8eSIysYeG16dxDi12zsH0O0JVfwb8AFOwfKN9woonTHAsZvG4CqPs8aDdH5FKQTh2RrewuKQ4s7S07HGM4+2K9UBawHSMjpv26yndkLAn+EDQPwmojSRVPZHhMDvjKgA2dpdkYwvKJ8TSu60T2wOBCV6Pr6Kr46yDX4LpUp0oOp2Bt7NdTQLXTQNB/3RgfVdKM6rasGvXrvnr1q2rHzu24GSMo+h12j/g2vX5kK7pm3Zp4zRM8lllLwyxz+EcWuz8DUgTkT9YHcanMJI5B2CeAkfHce0zMLI5AKPsHzb2tVOh3fbYf45Hi0uKp5QuLl0EXNidk7WhuRogP9peZu2JViHCPsGviXeGaHUbMwlbT4mALsWEo2AYkOE1TU67xevxfQAU2VldvMz1enyRdhZvAsoTsZ4WbT+3RK2bBkxn8iavx7ehu2NVdVl9ff1paWlpbYMHD/6JiJwaj+1kYJcodmFmZ1eLyD8xJRQPta9ZdUkiHeMcWoR0ED7cDnwf04YjNCNbgsnmekRN081Y7KQDpwD+uAZsuHly4eQTVq5YWYfphB2RQoXX46vHiCeP6+7YzohDISKk+RjTE7xdT5kIVEWjlWiPXWrV92PC9gObFkMh8f8wor6R9hPryPZR0Raq27BkpVUwidXuYcDr0abkx5v5aNcoB2GyRiNCVf+zcmXljyZOnICIPCSmGeceh6r+HPgDprXUY6r6NDBWRGaKyOn2GJck0gHOoUWIzTyaICKXiUholvQmZq3rd2HH7VDV30PMSiAnAh/oZ73I1tsyAexrbadnhiEi/5+9745vqzrff46GZVneduzEcRLHiTOchBAIBAg4Nm6hQBmtZcroYBQoETe0dDDKXoVSoHC4rEIpUFYktRRaynItm4QEMhjBjkN24sSOHe8hy5Z0fn+858bXiocsK+X7a/x8Pvo40dG9516N8573fZ/3eS9OTU35jbfbG/B6vXYZvgwbMsdjlEy9UWEoRmOY8wpQrc+iCHfws0B9vtojmPsAImQ+yns+wV5SOmoVEsk+/BrUJHPU9zxaDylk7hYAbdLbGe288wDUDELECHfuiEK9LuqdNxHUS260dYQPV1dvfiU/P98G4B/s/0izTcZYriSGXcoYOwPAmSClkmsZYy+DBBruAnCsltIYx+EYN2hhQOed/QCUpP2SMfYAKL79cwAGxtjF8rWHjFiEgqMXoz/cCJCixU/kv38C6qU00vUuMplMz6dPmIC6urqfCyE8EVyHJpuUItlro8GYGmbKEOVaEN0+bMhQZVs4Iahh5v4Mo2Q+yteeDNrgRDpvCwA/RhFSlnMvBrBhjEXLdQDihutlNsi82neiaQzzApQ3HW236UkAuiLZtEjP5qqqqqqN+fPycwG8rpO1+iYRC+o6fy6oG4MHlOf8FYBHhBAlAC4RQtwshDjiQgT/v2JcKWQYMMbMQojDwlaMsTNBi/ZiUIzbBGCrEGLQHlSjmC8OFHLLFdR+AoyxNAArQTqAewCUDiciLL3Hdfnz8qdWV1W/gDDyZsNBegwLQCyyERcQrU1INFQhJMlhUThej2T3xdij1Ocr3BCefH9OACmteKMw71IAn4YTLnW5nVkA4kcigYQ5bwpIkmrrSDlP2YbnOFChem8U5p4KUk/ZEeZ1Th6rPiRjbKrRaFyflzdzQk3NloeFEL8ay/miAcZYku53PwPA1aC1ZSIAhxCilTFmjHCjfFRg3EMbBFqITzNmjLEbGGP3M8buY4ylCGrUea8Q4iwQs5EDyJIEkYgFh4UQ3UKINO1LLZ9rEkIUCyHy5N/moRLCsrh7pTRmn4DawI/JsEjDtB1Ajgz1DAm5GK+JlsSRZAlucbmdC0aYNxlAEsjgRwWjCIflgFh2YzZmct7VAJaOFHqU+bbp0TBmct4WkBbjsPqHEseByD5jNmYStQgjbyrZoNOjIXYshNgTCARK6urq/dnZk3/JQprefhPQGbMEUOTHB4rWrAPl6yON+hw1GDdoIZCJ4ocZY3b5/6tAtV/JIHmoSsbYEk3+Rhq3h0GtISaDhFWP5PX9CtSvaTA8kj8vf1l1VXUdgO9HKtETCtmGpA3DLHaSTPH1aIgYYaIVQPdQhcAydzUJlE+J9o99WOaji3qMZeiVQKKBMAkTp0jjF815dwOwDRdilqzImmgZcDlvEMAXoFDvoIZcskDnSlZoVCCE+KitrW2FyWyGzRb3HJNNbyMBYyyWMfYpY+wLxlgVY+wu+fx0xtgnUhjhDUn6GgkZoF5pTwCoBkm0hZU7P9oxbtAOhwCpgtsZY9cB+DaAU4QQy0He2HsAXIyxHx86QAi/EGI1gCWgosgjidcBrAjVpmOMXZE7I/e6Hdt39IKMWSQtLoaEXOzSJCV+AFzUvJLhCLRGkd7ePlDt0mDU9kyQVmLrEZh7SOajXHhPtJeUDqn4MEZUutzOQY1aOIXqkcJeUloDKtk4jBgj34fdIxRtRzqvHyQTt2SQec0g5f4voj0vgKd37dz13LRp02KlkkhmhOfxAThdCLEQtPH9DmPsJAAPAnhUCiO0ALgyjHM1g5jTV4MIZ0FQd4pxjIBxgxYCIUQVqEh6G2iXvACynYYQohZEpb0XwOOMsTuAQwWRxwFoG0qFI4rXVwsio7zIpDYdY+yk1NTUp7zdXvT09FwrhDgi/b7k7vgYfX2a/HcOgO1HSk1dq08DMF2/g5fGdUI4uZcxzD0U83HUNPlRzhsE6WsOkCKTYdBB5y0oLjIVFBdlFhQXLSgoLiouKC46q6C4KBKtyq9A7/WhEKCLugR4QYvyEYEMYX7hIsV8bV4GIBdAbRRDnIcgQ/LXVVdvXjM3f242aLM66vIJWUOmFfGb5UOAmIou+XxYwghCiBYA3wNFfKpArWuqRntNRyPGSSEYuo2DjKtfD3L7VSHEOvl8HEh7cK+Q7dW158Ot9YrCNV8NirNfYDKZPDNmzJi0ZcuWJ4QQypGe2+V2ngZgtb2kNChDUFvDUeSIwryTAMTZS0q3S29tvr2kdN1/YV5N7Pcjec+F+C+0QykoLmIXnHvukk1VVcHtO3aYp0+bdlJdfb2/x+dLBIWlQh+ap6xHAKQb+AGA9wF8WllWPiLNXhqzmaAF1QLatGw9EkZlkLnTAGTaS0qr5UZimr2k9Eh4Z4cg8+br8+flZ1VXVT8lIzKjPYcRVFM4E1RH9hCAtUKImXJ8CoB/CyH+T9a//S9g3KBJMMYWAPixEOLXjLEPAXwqhLiFUVv2H4OaFL4phPinfL3WxPMb62nEGHsWgH1u/tyUzdWbPQDOGIyVGW3IBf400G5975EI9w0z91TQ7ndCJDVfY5hXM2o+ELsvohxSQXGRBcAEDG6Q9I9M+Xcob6EFlFc57JGRnp4Sa7HAZDSm+gOB44QQczs6OzNa29sZKBf6Dqhw/93KsvIh78PlduaAwl2xAFrGUg4xGsj3egaoxnNGOJJa0QBj7ESj0ViZlzfTUlOz5WohxJ8iPE8ySIf1dgAvhBi0d4QQw5KcQs413jNtFDiqDZr+y8IY+x6A5SCx2AQhxCzd644HcC2oPcXHAJ75bxiO4SCZji8kJyf/JDt7csuiRYtOPe/884D/XtdpTYS5HpF1M460Q3MyADvIa/4kyvc0UodmzTPapN2T1+vdfaChIb+ltXVKS0tLfO2+fb0xMTG5Qois3t7epK7ubjNjbJIQYkIgEEgRQgzF5OszGo3NAA4YDIZmq9XaYzab2xhQH2e1BqZOmXJMIBh8w2g0VvV0dV1oNBrnBgKB5EAgYAsGg6mxsbGfLTnppD8AaKjweNaBvqsa2iwWy3st7e3V++vrF3Z1dS0DkMoY81qt1opYi+X1c885Z0dSYqJxkM/pOBDLrmOUn9NYv3t9oBxSGSjcHM1O2kN+91555dVLjEbjTW6Xe4iPiSCG6XYNADId0Q3gRgAThRB+xtjJAO4UQpw57MnHETGOWoMWYszyhRDVjLFfQoYJAPxUCFGte3026MvZJoS49Ru5aB0YY0p+/tzHq6s3e5cvv/a6otOLXopUsWG00EJ/oMWkS6qK/FcgxWi7QTT9QRXWx4qC4iIbQjwmk8k02Wazze3o6GBms3m6z+eLlWMTQO9DKIIgksxhHlS8zRawxcUxq8XCLBaLyWwyxRoMhqYnnnjyYQBQFMc/ASwFLbYaypcVFt5tLyn1KIrjSzmmP+9HnKvPy+MLQZ5YA4BGztUBYcKC4iIzKGReCuD7IGPUCSri1zy3Hsl2TJf3+PERYJEOCRnWrQJtML8+0uFd3bwn/eTHl12akZlxXWNDY0NXV9fxMm89LBh12eiTtWJWUHj3QZAYglsI8Tpj7GkAXwohnjyyd3H04qg1aBoYY68D2COE+A1jbB6oWDoHJEH1lBDiRfm6eUKIKkZCxP5vONRYND13+gf1dfVGr9d7iRDitUi0/CKBzF3lgkggPS7qN7ZhJGX5KM2dBCDXXlL6mcvtzAAt6ltHWuwKiotMoIV5uNCe/hE3+JnQAaDBaDS2Go3G5t7e3l0Gg6HJarH0WCyWnubW1nUAGmbPmHGCJSZmgVzktHP2cK6eDACK4ngfxJ7VIAB8yrl6khy/Q15vQ1JSkq2zs3NDIBDYuqywcBOApXbZhToa0Bm3C0FEhDQAHUaj8d3JWVmr9uzd+6w0blErmB8Jsp5xnb2ktNfldk4BYIh2acQQ8+YAYKX2C/cCeG/O3Dmnb6nZsk4IUSCE6BnuWMbYMSDShxFEtlsphLibMZYLYiangiTdfhitcppxHI6j0qDp8l+3gZQ0ckLGs0ESVN8F8BGAf4JCjXOFEFv+29cbcm05qampGy0WS0pdXd3vhRA3amNH2qjpchsdkv2nPV9kLyk9orRiKS91qr2ktLKguIgBSDr+uONO2rdvn6n+wIEYDJ+PShvitH4M4kGZTaY2W1yc3xobG4yNjWUT0tPzvN3dBwwGw62cqwFFcdzIGFsuhEhBf91hL4BYzlWhKI4/gzomHNCddzfn6nUAoCiOU0BGUxs7yLl6mHct2X5VUhlfey4VwJQjQZKQxq3IYDBcJIT4vhAiCWTEX2eMPee45hqrwWCoPJJGTXrg7fpuBVLSzGsvKY1qKUrIvFYAC7W8LGMsnTG2bvac2Tk1m2teAnDZeC7r/z6OOoPGGDMIIYKS7PFXAMcK2WtI7qYWCSHccnf9LVB32SwA9wshXtCO/4auPc5kMn2ckzNt4Y4dO98NBoPf1SsHSEmiqO7g9XC5nZkAEu0lpVsHGRuTMS0oLorFMGQJq9U6x+v1mnTPDUeW0BuSBqPR2GKNje3p6enZ5g8E9mVPmpSSlJi42GgwJEqpMO1xBudqvaI4bgcJweoRBJDJuXpQURyXMcbOslgs/p6enmrdfP+SBs80mIEaDVxu5yyQLuWBQcbmg9RJok7SkBuHufvr6hpcf//7IgCXgnKWcYyxrzIyMv7d2Nh4v+eDsqgTgVxuZyKAPHtJ6YaQ5xmAOaD+dmPVjhxsXgYq0RnAXGWMLTQYDB/n5eXFbdmy5RdCiD9Ge+5xRBdHnUEDDukd7gJwsxDiMfmcGZScvlUI8Zx8TgsvzhBCbJfPDRlqZIz9AsBPQSGkTQAuByWetZDDRgA/EkKMmvosSSCvzZqV94MdO3Zu8/v9J8p6lQGQlOcse0nppsPPEjlG0tDTFoWhjFpBcVECgBKQhzeY0UocYuoeo9HYFggE9kAaKAY0WiyWLmtsbJ81NjaYmZExrdfnOxgIBl9+9pk/bVUURxGAB3B4CHEJ5+qniuK4AsDzkCFE9BskhXO1VlEcCwDkT5w0Ka2ttXWt1+vdC6CZc3VADkmGQKfZS0q/HOn9Gw3kZzgBlDsadPMkSyfWRCNvqigOA+dqUFEcLD4+/iLGWHZHR0cQMkfY5/evr/7666DBYLgmf9asY2v37+9rbW//M4A/VpaV14x1fmDk74/U9ZwFCnVHtTRGFrAP6nkyxi60WCxvZGdPDmzfvuNMIURZNOceR3RxtBq0VADvAmgEeV6rGWPvA9gvhLhM97rThBAf6f4/nDGbDFKYzxfUXn0liB59NoC/6ZLCXwghnorgmm+cOXPGA7t37+ns6+tboies6CEXhnzQDj4qyh2ygHmGvaT08zBeN9teUrpee66guCgXwHUghYREDEGWMJtMbQAa+vz+PfE2W8/kzMzF5piYWJvNlt/j9QpQTukBztX3FMVxOoj9NgAGg+HCxx7jTkVxnAzgztA5ALzDuXpAURyxABjn6nCU9ekAhL2kdNcI95wOKiHYPNzrwoUsVJ+DMBQ5hgr1KorDjP6c4QRQ/m6VHHsEtKnQe8Nvc65e6nI7LRUeTzP6NwC9oPftBc7V2xXFwXy9vX9pam3NaTx4cAmoPu1dAI8C+KCyrHwsItgjevguamWUAGBbtMKe+prKoV7DGLs/NTX15piYmNb6+vrjhBBREcAeR/RxVBo0DYyx50FK6fUAUoQQJ+jGfg7Koy0VQoy4C5YGbS2AhZA1ayDR4lcwRtouY+ysnOk5/6qvq2M9Pb4LhBDDtpCRi+JiUGJ9TMw0l9sZCzKQX4RzLpfbOcvv93c9+/zz8wxG4w1Gg+EMk9EYjI2N/dRkND5X19Dw4sL8/BQAL2Ggh2YBcAPn6qOK4pgFolEDxNY7IB+/41z9t6I4JoLYeYeMVfqECW1z587NNhgMGyLokRV6D1YAx4BU70f8gURT6V+GGlvtJaWHtPs0D0r+ezH6vdwJVqv1WK/X+xXn6i1yfBWIIalHBedqoRyvBDFEG9H//n2yrLBwJYCTKzweL/oZkh2cq4Pe/6OPPXr+3996awEAB0gNvhrAYwBeHq62bYh7Djtc7aIebPXRCD263M7jQKSiYTcOjDGj0Wh8e/LkrLMOHmz6qru7+yQhRNdY5x9H9PF/oQ/QNwYhxJWMsRUAbgPwjKb0wRg7DSRvdao0RCPmzYQQ+xhjfwApvntBtN0NAFp1BrEWJGcTNhhjsyZOnPhGW2sb6+nx3TGSMQNIF8/ldm4E9VVaM5r59JD5lBkgevyIxqyguMgAYEFMTMzv5s2enacbMkL2C3vjtTcCiuLwghbkBpDMkhb205pU7khLT5+Tl5eXfOklPzxMK5FztR7AYdRnqUA/DUDEhkXTaMQQIaghUAcgz+V2pofjFSuKwwAgBf0eVBLn6tsut3Pap5988j2v13t8hceToRvvBDBdHn43iIELAEGv19tkNpsn6E7/Bui714B+o3WorIJztWCQez5EuLGXlIZ1w7+4/hf/mJKd3fb4k08+BGJI/gLAMwDuLyguegbAk5Vl5SOWc0hGY9h95OwlpVUut3OJy+3sHAuzVrInW8PRpRRCBBhjFx882LRuwoQJ8/fu3fMCY+wH4ySR/3s4qj00DYyx74C8qfdAHtVbAG4QQrzMwuw/xBhLAeAGNQFtBdXzuAHcEalSAGMsMSEh4dPExMTZdXV1fw8Gg/bREFKkd3WMvaT003CPCTk+A0CqFKwdEgXFRUZQTdOtIBHZrXPy8vabjEan0WDYj35PoI5zdcS6sZHyKSMceyKotUlEeZZwQlChUBQHO2Xp0niz2Tx17Zo1fT6fLx/9nqcW2vsR56pfURy/B7UC0deu+U9ZujTHbDZbKjye60AGS+9B1XKu3i/nmiOPbYDM68mw58RI2qrI9/pURFBnpv+cJPP0NEg5NhCD9EUAD1SWlW8f4vjZIMbsqNmLslxkXSTdHaQHPgf0PQn7eMbYnISEhE+TkhITamv3/VYIcf9o5x7HkcW4QZNg1FDvZZBa/h+EEEO1aBnq+FIA3xFCXCn//2OQV1KKCEKOjDGDwWB4Mytr0rktra2buzq7lgghRq1yLpljuSPlvwY5LgbAYntJ6ZC7Z1nfdQmAW0CqDdUgz3ZlZVl5IFLm41gZk5FqLUpv4ZMKjycAIB4D80wVnKttknByZcjYBAAzlhUWYu2aNSt8Pp++WWQ7yPgs4VxtVhTHuSBFec1gNSYlJfUsOOaYFqPRWB1JbmgseVMX9ZqrizTfqs+bSs9zZlt7+zGd3d2XB4PBM4xGo7Gvr+8/re3tKyrLyqt1xw3KaBzFvAwUWl0zGkMsj1sEYIud2iKNCoyx7yYlJb1ltVpRX19/rhDiX6M9xziOHMYNmg5SXPRHQoi/yP+HTdFnjC0B8GdQTs4L4C+gdhgFiEApgDF2d0ZGxm1+v7+tubl5sRhD23UZXjGORHDQvf5QCGqw8YLiohgAPwIZslxQW497Afytsqw8qDvPqD2taNXShZ5HURxM1oilgjYteu8pY8qUKW/nzpjxQYXHczqAV0H6hXqczLm6VlEcF4M6LuhDeo0AHuFcbXjssUeLGhoagu3t7dtBKh3DhsUkey8PwI6xsPdki5VFADaGy3yUbMrJg7E0FcVhBb0/6QB8nKtV8vk7QMxdTUUk3WKxrDrp5JPvqfB4DoAkqwagsampb/+BAyZQ5/U7Vixf/jUi9MBDrt8K6oaxPlyP2uV2HgMi3LSN+OIhwBj7bXp6+r0AOg4ePHiiECIqTM9xjB3jBm0IhBtqDDnmLlDI0Q9SBfgpKGc2KqUAxtj3U1NT3CaTOdjQ0HCWEOL9iG5CQi52s0BCwu0jvJYBKLCXlFaEjklh3StADU+nggz2PQDeHorhNpqduF4hYqTXKopDE/nVjNJWztUdiuKYCuAOkFzVDL/fr9W3Xc25+pqiOJZhYPuVHgBNKSkpNx2zcOErFR5PPoDL0B/u04zWZs7VsHb0Lmr5sj0MliID5fwC9pLSveGce4TzDRpiDiGVnABgKmNsgi0+/vjOjo4uAPs5V38vxz8GEZv0ain/4Fy9QI7vA4lDH5SPRgDvLSssrAKwrsLj+T4o5N6ovWZTTY01GAzeAGAFAGtiYuL77e3t11aWle+Kwj1PAymJjJg3dVE/twNjJZQwxhgzsJVZk7Ls7e3tWzs6Ok4Qui7z4/jmMG7QvgEwxmxDsaQYY/OTkpI+sdni4vbvr/u1EOIP0ZhT1pFNBIVahqpt0vIgq/VhnILiIiuAq0CdsieDiCb3gDT/wmEBzgLQOVyuZKXzjdkGg6GjwuNpAEkx6T2oCQDe41x1K4ojCySGG1q39mvO1T8oiiMXpO7SAOBgTExMoLe3txrAq5yr6xXFkQgKzzUAaDzp5JP9FotlFoDN0WyN4nI7FwOoHs7riqSOTVEcDHTv6bqHhXP1bwDw85+vuMVgMJzR19dnRr+HtYdz9Vh5/BoMbELbDqBcZ7DuBhkzvcHaybn6pTb/UMzHkbzrguKijNSUlCeaW1rOA8lD/QnAfZVl5WNSAHG5nUsAbBrhvZ4GKsPYM5a5NDDG4uPjbWuTk5Pn1dXVvxMIBM4b7QZ4HNHHUc1y/AbhYoy5hBDP659kjKXabLa3k5IS4/burX0FwMPRmtBeUtricjvjMDwLcD5oEQ4Ah3Jkl4FquiYDqASJrf5nJEOmKA4jSHJqAoCMKVOnTne5nX+1l5T2KoqDg9RXDoX8YmNj33/ooYcvqvB4zKDeXRoCoEVVIxY0g8K5etJEI4CtAMC5ugM6Jqmmz6ft4DlX20HlFZoBPwbAzmgaM4kvQM1QNw12bhlqnFPh8Xxe4fFkgQgzQlEcS0Bha00UOB2AjXP1u/LQlwD8MOR0TQD+BgCBQGBaIBBINxqNHYFAYCPIKOkboF4zPTf3hJ07dvwbQFNoSJRz9fbhbmooYwYA9pJSz3AyaCuWL5+wXSSrAAAgAElEQVQB4EePP/nkBBCB6GoAVxQUFz0GIo9EpD5iLyn9xOV2FrrczkHzpi7qqRYH6gIQFQghOhlj58XF2TYkJSWd3dzcfBfonkYNSRZ7CbThDAJ4VgjxmKyXfQOkLbsLwIWDiSmMox/jHto3AMbYbJAXcb4QYo18zmQymf6dlTXpW42NBz/3er2nCCEi6rk1HGTBcK89RCFfn0+RjLXvgtQ28kEG4ObKsnIPcKgWahoGEiNqOVcfkOOfgcJW+hYb/1hWWPiIvaS0UlEcX8mxRgANVqvV7PV6X+Vcdcrjl4IW6UYALVq4bAz3POgOXoag6u0lpc1jOb8eiuKwgdqUTIiLi5tpMBgmd3Z2BgE8wbnaoSiOHwNQDAZDVjAYTEC/FmSiHH8IgEYqaUa/p7RMsiTPBYWP9R7UQWnI9fc2qEciRYYHeODRxFB5U5fbmQegW/+9O+u7Z+f7g8G7GfB9k8nUwYAnO7q67qwsK4+Ijj+Yhyjl4PJAocaoGwPG2LemTZv6XmNjo6G721sqhHCNfNRh55gEYJIQYiNjLAFU7nMBaDPZLIR4gDF2E6hW9sZhTnXUY9xD+wYghNjCGLsCgJMxdqIQYj+AB5KTk77V0+Nr8nq95x8JYwYA9pLSnS63c7bL7ZygaQHKH/08Kfy7ODM9/TWLxTLTbDZ3x1osu40GQ46ssVssT8MxMGzVAuA/uv87AbyNgV7UHgC7XG7nqZyrhzr2DsZI5FxdHeV7PmwHLxfYpqGMmQztxaHfS9rGudqqKI75oIJ77XntYedcrQZ5sCoAdHcPsJ9/B1DDGPObTKZAX19fOfrzTI0gTxSgTcRDIEr+YeQOztW3w7xtrTYu3i5b7EhGY3W0jZnMZ6bK66t77fVXN9x66y13t7W1dQJINRgME41G45S+vj63vaT0SUVxxAFomjl9up54k9jS1naTt6fnooLiol8DcEegPFIxiFGbBKDnSBgzABBCfMgY+1Vu7vRHavfte5Ex9rUQYlRSaEKIOtDnBSFEB2NsMyjKcD4o/A5QCYQH1MJqHENg3EP7BsEY+y2AcwE8HRsb+8KkSZP8O3fuPF0vt3UkIA1YPojt1e5yOwsff/LJzQDuB3D51MmTfQnx8R1Gg2E7Y+wAaMHdxrn6IAAoimOhPJWmFB92LY8sBzjeXlK6ZjgNvWhDURxsWWHhsgqPZ31SUtJ3AsFgZifpFWoG6WWZY1sKIvGkYyDT8WypUnI+qL5wgIcE4FbO1S2K4sgDhQwPkSJOXLJkltVq9YDCSRphZFhyTjTg6u9ntgVUyJ09XL5Onx+TXvgkUNg4Vf7dw7n6jBz/EOQppgLQmpb+E8BPZHlCF2hDEAAxH30gRZ6rQdGJ10ElLQMaZe7cu3dXe0dHDqjI/vrKsvJRdRVw6VTzXdSZYJK9pLRqNOcYLaTO6osz82b+aM/uPbt6e3sXCyEiIp4wxnJAof35oLZWybqxFtnhYRxDYNygfYOQP4T3GWNFM2fOMG7dum15JDqPkUASErK7u7snP/eXvywAtYu3guSL7qksKz9iC65caL8F4E17SemwfaYGg/SekjAwz7SDc7VKURzpAH6PgR7UBAC3LCssfKq2tvba7du2qSGnbAXwM87VN6RBugU6gyQfazlXG2RuMDhcLmmIe14GyuEk2ktKj3gLIkl+yUhLS8vv6+tLMZvNM5qammo4V1+V43eD6uFS0W+wNgMo5Fz1ySaiegEAAcrjLOdcfVdRHC+DPAgzSLZMM0zLOVefWrHiujOEEO/J51pB4dNmkOF/T5J3LgeFlrWx5m6vt2brzp0XgspAUkDEkdsqy8rD7iwg86YJIPZj1NvsDAbGmNVkMn00bdq043ft2lUeCATOCEcyL+Qc8SBDfp8Q4m+MsdZxgzY6jBu0bxCMsUzG2IbMzIzJU6dNe2/FCuUnFosl2q3sO0A7aYA8qkOt7Ndv3PjDT9atKw4EAjlxVuvahQsW/O6ExYs/QRRa2aO/B9lm+W/9Pc2T17AbwMaOjo54ALkJCQldwWBw18YNG84zmUwzA4FAks/nMwWDwaSExMSvFy5c+FZra2vbF59//ilCwuW2+Pi/Ll68+PmDjY3+qqqqlUajsdNgMLQDaAgKsS8jI+OzWbNmbfb5fFPq6upmAqhJSEjoSUhIqI+JidkehXsa6XMyAVBAocewPichxNyuzs64ltbWQHNTU8Bqtc6fNXv25wBa13366bFCiO8EhUgJ+P1xgUDAajAYLKeedpoCwPtRZeWdwWDwOxiIjokTJ947e86cNas++uhngUDgXDBmgBAGEOswxmazvbH4hBOe+WzjxrT29vbQfFB7YlLSK4sWLVp5oL6+eeu2bffEmM1Bo9HYHggE9jDGDk7Ozj6YlZW1r6enp9Hn882Pj4+fajQa1yHkuyf/fQBUkG8EGb06+Z7E7K2txTvvvnuJr7f3CsZYz8TMzKe+dfrpj6YkJ08K43PKAoXqngF5if+V39PHqz82rVu3zrlq1Wp9p/FBIYQY4JnKbh//BPCeEOIR+dwWAIVCiDqZZ/MIIWaPdO6jGeMG7RsCYyyGMVY2e87sU7fUbFnz/J+fu/Pyy64YU71ZuCgoLsqzWCzP+Xy+AqPRuDsmJuYX7/3znb9H6/yK4tAWe70H1cS56na5nQkfr169sq+vL5MxlimbSNoAvMa5eok8vh208AjQgnEQFBK8X3pn9+ie18J+uzlXh93Fu9zOSXIuP3TMx/8G/vrXl87ctGnT18cuWjTl4osuqZS5uFMwMKSXCuASztVuRXHcBeBmkAekxxmcqx8oikMF1Tn2oT//ZgDg4ly9XFEc3wYxH+NDjn+Fc/WHwCGh4m4M9JI+4lz9UL7PS3RjraHtc4aDJNw0gISOTxhOcWY4FBQXzQXwOMij/wKAo7KsfMgcqxTmng8yXiciAsWYSOFyOye+9OJLp2/a9NWLDQ0Npu7u7p8IIV4a6TgtZAkigPxc9/xDoI2NRgpJHa2C0dGGcVLINwTG2B9zcnJO3bN7T50QouTyy66oi5ZKxlAoKC6ygYSYb+jt7e0F8OtAIPD4VZdffrzL7TSH6trJRS0BZJDiOFe/ks9fDKK76w1WLefqRfLQMhDLUY8Kl9v5NwBz+/r6ugDUM8a+jomJ8fl8vhrQYqVhEWghbAldRGWob9T0aFngnaGFoFxu50kut7PeXlIaEflGKo7Mw+EGSZU91b4HKndIg6wVA4AvPv/8MrPZ3ADgTABajaEfZJj8oJKECwF8AjLWmjeh4U5QWcN1oM9gIvqNUROIIQfO1Q/uvueu+5sOHvwyGAweNJlMrbPnzMmMiYn5TDvRYELFujEBWd4wWsiNgw9EuhEut3Ojy+08MRJN0cqy8s0FxUVngHrpPQpgVUFx0Z8B/KayrHywPFU2gBbJaPVI0pEnkvsYDaR4wcy33nr7VcZY/Ny5c5+pqal5ljFWI4QY6b6XgpR3NjHGNIm6W0AEoZWMsStBpKrwlKOPYowbtG8AjLGrkpOTr+3t7e3t7u6+QLKcAOBjl9u51F5SGlWWHwAUFBedB2InTk1LSfGkJiercVarEcBVFR5PepzNthzAZfaS0oCiOP4I+vGko78z9F6QOghAP75vYaCHVK+b7i7QAq4fbwJwHICvOFft2gulAHKyvaT0UI0Q5+qgYraRwuV22hCiVlJVVfXp5Kysc1esuK5GCKHPI63mXN0uPajbMNBYpQG4QpYXFILIIXoEQXmn74JCUwmgENih31lXV9eDAM6ZPHmyc9++fWeA3sc29BulTvkevKMojhtB4bRm3aNejgtQEfxQ9zw/Pz//T3qNRpfbeQDAVJfbWXMEafuxoPdqh+YZ2UtKe1xu526X2zk7kvyhZDu6CoqL3gXlen8B4LyC4qJfgtrVaMxVG4B0u64fH2gjNWRtXBSxVNuMCiGeZYwdlz8v/5rqquq/McYWCyHqhzpQCLEKIeQYHYqPwLX+z2I85PhfBmNsqdFoLM/Lm2muqdlymRDiRf24y+3MApAQLeJAQXHRFAA8Py/v/M1bt1Ydu3DhM8G+vjkAloe8tPG0goLvGQyGNRUez3KQl6QnRtRzrr4LALJBpm80xAgpB7VjMHafy+3MBmAKV2tSDx1BRDM4DZyruxXFkQzgesZYhtlsziWHFKkA/iglsBaBOoiH4m2Q4PJsAP8ChewMIKOkhf/iQMbqKVBfNoCMmeYl5XOuBm+99ZY7Ozo6MoPBYD36Q3eNnKsfuNzOwo9Xr/60r6+vZ6x1dqGQ7L5BGY2yXKHTXlJad/iRY56XgWq+BvRzk2MGyPq5SIWQNRQUFy0A5cdOBpWL/GzF8uXbMYT+qJ75OJZ5h8JgBpMxFgOgLH9e/qnVVdUfAyiKpFP9OEaHcYMWAsbY9SCZJwbgT0KIP0arYp8xlg1gff68/MzqqurH9PFyPVxu5/GgxoMRMw1lb7JrQWELY0529urTCgoeverKq95RFEc+KFSoGasmzlX/WNqQDAcpfdVmLyk9MMQ4CwaDeY0NDb01NTWtst6LgbzENAz0kMo5V/+iKI54kOJJCga2YnkLwDUgA3MA/eE8ATJMAlRPVwdi0JWEXE4QwEzO1Z2K4rgUZNwGMPEAPM+56lUUxyQQWaYZQLveMLmou3KKfZhO1kcixCxLMk4GtYMZSuLsRABfRsIwHWHuKSBm4e4hxuNBfd1qImn7oof8fl8F4EEAsZmZmS8eOHDgusqy8kHPG6oYEy1I9upHg73XjLFMk8m0fsaMGdlbtmx5VghxTTTnHsfhGDdoOjDG5oPqY04EsZveBRmFqzDGin3GWCyAynnz5p1QVVX1HwBnDkfrjbQFCgAUFBfNAS3Wp4LyLT9bsXz5ZFCrjWGpxFKNfN9YBVwB6t2VkJAwhxkMk9vb2npBRmkf5+pf5fhbILV+zWDFAPgQVMu0X1Ec3SCDAZBh8oPYcJcCKAexws4eZOpvAyhLSEi4orOz8xYhxAH0e0/NIGX8vYriyAEwy2q1eidPnpy8bdu2SlCX5rEqk5gBLLGXlK4K47VRNWout/NUhNFSJdzXjWLeTFBJwtYRXjcB1GNv1BEIRXGYQd+VRBDxY6rf789p6+w8x+v1zgSwp6ml5YLKsvLPQo919TduHVbzcTRwUcfr7cMp9zPGFqekpHxktVpj9+/ff60Q4ulozD2OwTFu0HSQPc3OFEL8VP7/NlBy+0qMgT4rWUx/yc+f++PNm2t2CSFOEEKMGHYZbey/oLjIDBIQvh1AFyjX8NKK5cvzATRoyiBhzFsAYHWFx2PAwDqlNACCc/UtAFAUx82gvJjeg9oJ4Hucq02K4qhGP01bwwEA13Ku/l0atLMx0MMCgGs4V59VFMf3QZ5xc8jjQc7VVYrimALKV+nHmgDULSss1NRP1oV5z9MB+KOkeh+2kZKF5idEI2/qGmXTS/k5rwq39cow5xm2gFl62zaQN52SlJw8w+fzLejxerXvVgMo55gE2cEbRHiZAArzdoI2Oy0g0seg6PH5/Fu2b2cAfgfg3lAJLa2TBKJQzB+OB66BMfbD3NzpL9fV1fu9Xu8RF044mjFu0HRgjM0F8A9QyMYLYuutB/VIi7jAkTH28+nTpz964MCB7u7u7pNHI40T7uJYUFx0LIghtxAkPaVUlpUfcLmdqYFAIHvNxx/XBgKBNADJnKvrAEBRHOeAqNn6sJ4AcPaywsLTKjyeX4IMhh5toFYsKxXFsRLAeaDwrBH9humBZYWFt3z22Wc/bW9re1Y+50e/h/Qw5+rziuJIATEWD3lPaWlp8TEWy9pbbv5ttWwYKSIoYh6yBc4wx5hBu/6aSJmP8jyj9riikTcNR91/kGO08OTqwRZ4nbJ/MuizTQSxKuNB79V8AGkmk8kfCAR2yBKMNFB+MRf0fTKCvlMMFM41DHNJjSDj1QYyfnGgsoKvANTI5w+COgRMssTGJphNptbOzs4KAHV79u1jLW1t94Pkx74CcHllWbmeIDJir79wMBoPXANj7OH8/Lk3VFdvbgCwWAgx5o3TOA7HuEELgaTIOkA/rGqQYbs8UoPGGCtOTk5+z2azGfft23ehEMI5musZaQdfUFwUk5iQ8JjVYrnaZDL1xNts62Itlu9wrvYoiuM2AL8GLUAai0oAuJBz1aUojmdAUkR+0GID0ALk4ly96J577vptQ0PDb9Ef9gMoH/Us5+p1csFzYSBLrxnA+mWFhbV+vz9h9apVvfK5znANkwzlRNRReChx3DCPjQNJgm2IZAc/XD4ljGMjzpu63M5cAD698K8Mz2XJRzzo+5wI8noSQaSX4wGkGAyGQDAY3Cqf15iZGRjYE02DD7IEQQeNEKN9D1pARjBVzlsD6gXYCSk4DSA+OSVlUsDv/7qjo2MTKI87mjq3WACL7CWla0LHCoqLzgHwrLyP+0HeWq/uWCOAk0djkELmLsQo0wGMMROAd/Ln5X+7uqp6I4BTj5Re69GMcYM2DBhj94OUMq5HBCFHxliu0Whcl5c3M7WmZsvvhBC3RHIdLrdzMgCbntoOAAXFRfMnpKW9kxgfPyXeZoMQIsAYCwCo4Fw946677rj54MGDN4Do93p8xLlaINmKK3E4NXwD5+prLrfTuH3btmtqa2s/1o11jWSYpHJ/pr2ktDqS+5XniIi4MJbcozw+AcAMe0np5yO+eOBxI+ZTwjhH4a6dOyt2795tA31ms0EeUSKoDikJ/ZJf00AC0fGys/oOOeYFeU7WQaYAiNSUo/u/ABmpXaDPdx+o6WkcSCC3DRRGXgvyjFpB3nQwNS3NOnXq1MCVV/x0MLZoOPcbB/LkttpLSsNW2A9n01JQXJQM4I8gb+1zAD+qLCs/RHaSdYkz7CWlh+XbRpg7Yv1RSS5bN2/evNyqqqpXQJGf8QU4ihg3aCFgjGUIIRoYY1MBvA8KydyCUVbsM8ZsANbkz8tfUF1V/Q6AiBsAyh/wItAPv6OguMgIyo/dlzNlikiw2boNBsN+9BudzcsKC98E8HmFx/NtkMiuPsfUPIruy+mg/MimMF8/6qaVQ5zHAErib7SH2atMGsGvxpr0d1Ez1Mnhsj21fEqFx7MFtDnQvJMl6C88/xJkcGJBXlMGqKA2FuQV1wNIMhqNzYFAYLjN0hegsHIXyGgF5L83gozPXpAXbQAZixbQpuxjOa4ZpA4Ah0oGXKNsKyMjB8eNlQovSzbMAHaFayRGE9YtKC46H+StJQP4LYBHKsvKtXvOBbVSqg1z3tNA7NGIiTSMsQUmk2nNjBkzbFu2bPmVECJqPQ/HMW7QDgNj7CNQHqAPwA1CiDLGWBrIk5kKWbEvhBiyh5YkgbyRPy+/tLqq+msAJ461RbsWJnni6adrg8Hgi6Dk9psArqksKw+t+VkEYJu9pLRjLHPqzhcW81GGgeaAcjljrrmRu+hcAF+MtNi5qP+XyV5SOuqibJmrs4A8oTQAyQkJCbk9PT2n9vX1WUD5nK9AxoeBqOdJIIMbA6pR6wSF69aD1PaHwlqQNmAHSAWkF+RVeUAGZ19iYmJWe3v7ZpCHdhBEpFmLfoPUyrkacLmdswE0h0v2GQnhkpCkvNSSaAkAuKitTW04LV4i8cALiosmgIzaBYyxSpPJdFnZu+/vlEY5HxTeHjb8J69xnz0KvfMYYyUpKSkuq9Ua3L9//1lCiP+K5N3RgHGDFiEYYzOFENuGGLslP3/ufZs317QLIZYIIWrGOl9BcREzGo2XBYPBJ6SnpwB4KbRnlKy3CUSDrRdy3mF38Lqi2nZ7SemQqggRzDsLQMdQhcCK4rBardbpkyZNmr9jx47NIM8oBWScCkHeUBJkR2tQvidPjueBDJJepeFzUIuXweAFGZYJoPBbHmMMcnNTjv7ebxqxIU/+vx5AFcgYhROyjQGweDj9Q+ld9NhLSvcPd67RYiTvR3rOS+0lpVFl6g0WYlYURwzI6McDsGVOnLi4uampoa+v7xNZdjENpFpjk6/RHg9wrm5QFEcRgKcBxAsh4gEkMMbYtl27Oru6u6+qLCt/XdbG5QH4fCgjKdVsUkGGLyoLJmPsntzc6bfW19e3dHd7TxBCRFUd52jFuPRVBGCMWQGUMcZWCCH+ETJ2zvTp0+/dtWu3EEJcGiVjlgLgqUAg8AMAq05ftuzhO2+/883Q18n8jw1RbDWvwV5SWjmCLl4KAEu4xkx6Rekgr3ci+uvM0kDhoUSQeG+OwWBIr/B4doByOwzktWnelMHr9WLHjkMNm3fI8VDMA4XatoAM0wFQ/kiAvKVVIO9bC8m1WyyWaYlJSayluXmt3++vDe37Jo38R9Fa5DTYS0p7pVRU3mB1XTKnKqJtzCQ+crmdBUMobmi1XIe0CaXRicdAo9LIubpLURxWUM1gqMH5l2xBMxnAq9rzjLGUCo8nBsCNsu9aPohMAgA4UH/oq/UTAC+BKPz3gLzcLtBGohP0/QHos/wMQCdjrBNAl6+31+z3+4sBvFZQXHQWgOtWLF++C/T9OCzELBmNU0HGNpqf8x07duxcmD8v/9zqquo3GWMnCyE6IzkRY+zPIDZygxBivnwuKmIQ/79h3KBFACGEV9as/YsxtlUIUQ0AjLE5ySnJr/X29rLu7u5bhRD/HOtcBcVFp4J+9JNAubzfz583L8Hldh6jz1PJnfNUUFhkTCoMQ8FeUupxuZ2FFR7PWpDXMxW0u52YlJQ0ua2t7bMKj6cQtJtNAHk6M0EEgxaQIUkEGbKEIabpRL9CvADAgsFggDEWK3exB0ELahuAHJPJlOH3+3cBWAdgG8hzapR/mXx922ip/xJrZChThL6nLrdzKSifcqRCHPsBHOtyOxP0oWOZ30sCMQfDhmQ9agYlyLm6Tz5/Bmgzoo3ZYmNj60ACv5sUxfFXOW4zGAxpQohYIcTr9pLS2+Q5ByNzPASqh4wBFfhr8IM2DztBogW9oM+4DkCnEKLLarUm+Hp7tfKF3QCuNBqNvrS0tIkNDQ1fgkLDTYriOEH++zwAqzhXW6T+5lkATlMUx3dA37seALdxrtYpiuMiS0zML2fPmBEbCAbbIMSPd+7dW/T4k0+WrFi+fJfL7ZxjLykNfV8XIkohdD2EEEHG2A+rq6o/yZ+XP7+6qvpFxlipECKSmsC/AHgCZOQ13ASgTJf3vwlHQbfr8ZDjGMAY+wmojupEAEGj0fjJzJkzZ2/5eosLAheOhcEkiR+3goqkdwK4pLKs/FPg0E45B0CfltB2uZ0zAHQPp9GnK3BNBxmWHNAPNh202OyTz2thuzyQ/p4FtDD0ynET+kWLh0I3iLAQC8pH7gP1EesEGZlWEDEiHkRW+UqOd6G/7ciA8FxojmesjMZw4XI75wJo1DQIXW7nfAB10VBTCYU0ErGcqx0ut9NUu3evffv27XUgDyYxPj4+t8fn2//Iw4++IF//K1DxuuYF2QBs4Vz9mRz/FLSx0LeheYdz9Rw5XgtiM+rhXFZYeC+AfRUezzsg0orXZDYzf1/fPgDvc64+L4+/CfRZd4E2LDEg7/egvJ6pIMLKFtD37FKQobHJRxyApzhXP1UUxxIATxoMhuRgMGjUXjcpK+v2W27+7cOK4igF5bJDcSrn6mpFcfwEtLgD9H3tko8zOVdrFMVxAahMpQtAt6+3N2HHnj1Lent7MwDccs2VVzotFsuhcL2L9Ee3RysXPRgYY7MAfJo/Lz+puqr6diHEPRGeJwfAP3Ue2lHZS23coI0RjLE/gpL3gblz556zefPmTQBOiTR8AAAFxUXZAF4BET9eNhqNjvmzZwdBrLYkUDFrmtVqPb6zszPXYDBMBGAIBAJfyPEY0CKVDSIgmNCvZWgA7XynDXMJX6PfYKWCduH7QF5Qu8lk8sXGxgY7Ozs7bDbbgq6urg2gZolbQJ5RM+dq1IVYpRd6mr2ktELmXKoiqVWLcO5jQO+bBRTqrLGXlApZHK4ZZs2gxHGuugBAURzfhaTXo9/oCF3vt8cAfE83FgNgD+fqNDn+HoAzQi6nhnN1rhx/B6Sq0Yn+sNvnnKvXy/HfgDYn2ngMyNhsAhmMeaDP9jPQhuUyeY82q9U60+v1tgN4e1lhYVOFx1MFEmzWGyQbgN9yrv5RURxzQbWbofgZ5+oziuJYDPoOAWT8NEN4Hefq24riWAjgAZPJxIQQ7YFAoCkuLi61u7v7DtB3axqAItB32Sz/BkE5SgagUj63AMROtspr1R7Xca72KYrjWlCLnjghRLzf758mANvmrVvf/3Zx8T1zZ8/+EtT0sy4aJJCRwBg722Qy/TM3dzr7+uut5wsh3orgHDkYaNCOym7X4wZtjJAFk1vT0tJyWlpamoPB4AmyJigiFBQXnTMpM9OZYLNZYszmVqPRmILDW0t8CVrEQuEDeTabIENLoEWgC7RrrpDjTfKvAWQAD4Do3jtBBdAj0pJdbudCUO7LM5oaorFClgUsBSXoR0ykK4rDiIHGRvu7kXO1S1EcCwAsw+F5oN/IMNblID1Pm8xLxAkhLAAyOFfbFcXxBwC/HGRqE+dqQFEcT4G8gg70G5wmztVT5PVdBypw1gwOAy32/wQtwscaDAZTdnZ2Nxhr2LN792zQZ6Z52zaQAXtMnq8MVFCs94Be41y9SnrofThcaoxzrq6QdYka268HQLfBYPDHJyS477v3/uWK4kgE5WW60G+MukDlLRvlNWnKMmnoVwhpA0UAPgSFnmcAuFhem97o/JpzdZuiOEoMBsMfAMQGg0GD7nXzOFe3KIrjl+jvJadHNufqPkVx3A5qYQRQlKBbPmbJz0wBCV93A+gWQnR7e3pytu7cuRhAy8IFCx5cdtppb9oj6P4QKRhjN6WmpvzOao3r3Ldv34lCiBEltUKOz8G4QRs3aGMFY6x0xozclSaTScTGWn9w622/rUIELd+9PT3GV9944wddXV1XTZ44sTkjPX23wWBoCAQCOQaDIXY9J9QAACAASURBVGAwGDpiYmLKAoFAXWJiYoI5JqaTAR0Gg2FyfEJCl81mq4mLi6uHro09aBGtBYUWraDFahfIcxupjX2avLbN8t+h97QYRL54CeQBjqmNfSAQmOft7o7t6OjoaWlp6cqeMiUlMTGxp7Gxke3csSM3NjZ2uhAi3h8ImM0mU05GRsbfJ06atG3b1q25dXV1FxoMhlQA8cFgMDYYDJqyJk++KS8vb/+XX355Rktz882hn1tWVtb382bNavni888vaG1tvV4+HQQt5B3Tpk0rzZk+3fjVV18VtzQ3FzDGuqxWawoYE76enlqbzfaGzWbL7uzszO3p6Ykzmc2rkxITUzo6Oub4fL74QCDQHBMTkx0MBq0COHDyySf/GsDsT9auvbG3t3dOMBg0M8YShBBWo9G49dTTTvs2gJxVH330SiAQmKG/VrPZ/NkpS5feDyD4UWUlDwaDWaDNS4/BYPBZLJa1Jy5ZcjWAtLVr1jwuhDD7/f5Gg9EoTEaj2RIbuyvWYnkrITGxs7m5+QIIITo7O5PkuZMZY71+v/+gNS6ucsaMGYHOzs6E7du2XRgMBk0Wi2VmMBg09/b2dlmt1kcWn3BC7c4dO+bs2bPnAZAnF6tdZ1xc3BUnnHjili01NafW19c/GPqe2+LjL1q8ePG26qqqcxobG28F0M0Y62WM9THGvImJiT89ZuHCpq1ff/3t1tbWSwB0+f3+OpPJxAwGgy8tPf0POTk5vtq9e09qbW2d5fP5dptMJr/NZks0Go09EzIy/hMfH9/b3d09JxAIGE0m0zar1do90ncPUm+0cvXq1C83bXooGAxOz548eeW5Z5/9oNls3o//wu8pGAxO+tOzz9394YcfDtnjToMQ4rDeaeMhR8K4QRsDGGMLp0zJXtPW1mbt6Oi8YaXzjQ2IgPUmQ4xvgFh9TwP4RWVZ+bAKGbIWaBaIPJAIIi4ccX04RXEYpkyZkpGRmZkXHx+/qre3t2jNxx/34nAm2yecq+sVxZEJ4O6QMRuAhzhX3bIv2cfQLYwSl3KuvqoojkIQJV4PkZGRcdPc/PxHKzyeUwA8hn6Gmw9kmP4KIofMAb2vn4A8hemg3GALyBAnyWv6IWiBugmH53mCywoLTwFQu+qjj14IBAIXhFxPI+dqhnx/3gRwvrwOzZPZwrn6LTl+P2SIGv2tbRoAvANaXKfKOZeCPClTbGxsWk9Pz574+Phd03Jy/lX11Vd7QJqjmnej/X2Bc/U+GQYdLFR2O+fqPYriyAZtNELxK87VhxXFMRNAmcFgQDAYbAbQZTabY/r6+m6X7MQcAL9Cv+ejhQ//LT2sdJBotVf3mm4AB6QkGxuOpCPb0MTYS0q3H4kWO8PMa9hbW3vs2++881u/3/99g8Hwn2AweFFlWXlU6vxGmNvY2Nh4+vUrfv6HaTnTjtm2ddu7AL4brhjDIAbtIYxSDOJ/AeMGLUIwxtKSkpM22uJsU+vq6l4WQvzE6Vqp5STWhWvUCoqLzgQtvrEArqosK389nOOkoK1VC7u5QnqO6RcNRXHkgnaQeqOyn3N1jRy/A2QU9UbpPc7VpxTFYQHtKrXnNUml33Gu3vKrX92Q4fP5ButzdqtcXKeA8iaawekGGRwXgA2g3e0PQKy9Rnkd+aA8jx/9Cuz3HbNwYdqXX3yRCGLQxTHGUoQQZtBiPkNSxW8G6feFIoNztVFRHPeB2KIa1VszOkvk634A4Ez0907zm0ymOLPZ/KbX6z0AoDklJeW0tra2qcFgMAn9osw+UMnAfSAjtRK089cbnHLO1fPke74PVCOnx0rO1R/I8VZ575DvVzdj7MWCZcueBrC9wuMpl3Pqjca/ZPNSM4CbMdCYeAFs4lytluPzMNAYdYOUQ7TuzzNB+pAaQeKI9MoLhSzOPwbAentJaVCfNz2S88q5swGYHn/yyd2gllEc9J0srSwrP0wzMorzHpLyYozlxMTErJ8yZUra9u3bHxBCHBZdCAVj7DVQzWU6KH1wB0h0IWwxiP8VjBu0CMAYM5lMpvezsycX1dXVb/D5fKdpQqOSbdhnLyndM9w5ZIPC2wDcYWCsOjkp6eopWVkH0G84/JyrnwCAojguBOXCbCC2W3JcXJz3gQd+r8hxJ4DZcoG3yNd9yLl6vhyvB4U99Hidc/ViOd4OWsC1PE4fqI/aSnmum0Chlrq4uLgp3d3dSehnKtoAzEhMSlrT3tb2CigM+TuQgda8nDgA53Ou/lNRHOeCmnCG4nTO1XJFcVwMKlPwQbfQTpw48ekDBw68I4SYBlpszABizGZzUl9fXyOA1SAD+G/QgngiyKAY5WsDICN2PshAPAnqNq0ZHADYzbmaI9+TwcgY1Zyr8wDgF7+4fpPf78/DQIOwTkf24KAco96gVOnYgReDPET98XWcq1sA4Prrlcx58+dPM5lM+z7buHG/ztBMABF1vj5S7E5ZbDw3tPWOJMbUHkmihCyH+MSu69sn86Yz7CWlEWlGatA2eXKTlgoKmVoAWBITEzOm5+Y2//TKqzbKgu3jOrq6Zrd3dPxGCJHU7fXe6e3puTdUyCAaCPVCGWNFsbGxH0ycNNG4a+eui4QQb0R7zv9V/E8YtNEUFkpZqsdAfbi6AVwmhBjVD4Ux9ujUqVN+3tTU3NDV1XW8EGKAFpzL7TwZwGdDCesWFBelgryyswC8fMzcudmMsaKQl33FuboAABTFsQbElANop94nr/kOkMH4BYAYg8HQaDQaRV9fXzYo5LRTjs8CSTL9G5Sk56CcgGb8bABu4Vx9UHpzg5EtViwrLNxU4fE0gUgpmmSTF0BPTEzMywkJCVuampq2gDwgEyiPEAR5OltA7/cL6G89UgwyNmZ5XVYAp4F2xbfK+wtFAudqp6I4HpH3HQoj52pQkjUuxEAvpJlz9Vz5nl4NKlnQezFNnKvPyfGTzGZzRkpKSlJra+u23t7eNlDzz71y3LCssPBUHIHCauBQj7HswTQxXW7nVAAYadMU4bxDCv/KsaUIszGoojhM0BkNUBlGuzQox4A2PYfGZ8ycacjOzn6rwuNJARVPH2Ioms3miTab7bl77rnvXVlj9puQ402gPnoViuK4FUTUMcvnNS/6DM7VD+UG0D7IJZ/MubpWURxfIIR05e3pwdc7drwB4KeVZeURM5hDIfUhD2u8yxhbkZiY8FhCQmLPvn37ThZCjEos+2jF/4pBKwB5Fy/pDNrvMUiXacbY2SDZqLNBYabHhBBLhjr3IHP9OCsr68WOjo6+jo6OIiHEoHp2Q9VIFRQXLZqQlvaByWhMjouLq7RZrTtlLUozALe8jztAO0gT+g2Oc25+/o8yMjJOrPB4PsDhaurPAvj5ouOOy/ts48YvQF5Wj3z4QEy090H1XveCjIxG5BCgUEU7KIc3BRQKvBK0KFgYY3FCCCOIYl4B8m4O2znabLZvP/DA7z9UFMcVAJ7XDQVB3t8SztXNiuK4FGSQQvMw13OuHpS5swKLxWI2Go3+7u7uPXL875yrvYrimCrfo+709HTjpKyszE1ffvkJdGGzsUAu3jNBYdyGYV6zDFGuhQunZ1do3aFkMBo0hqqiOCaCjIHeoLRzrm6W4+eCwtD6XOFXywoL2ys8ns8BPCzHY9FvOFzLCgvV3bt3/2jXzp23gTZF2obEBOBxztXbZb3YGzicnXuPHL8DwJ2h98QYu+nxx594UFEcr4JYkAOQmpp674Jjjrm7wuNxAzg3ZNgPoJhztVJRHO+DupYD5JkHQDnT0zhXtyqK4zWQYe4D0GcwGFgwGNwG4IeS2XoPKM/pBdBjMplMB5ubs3bX1p4BCr9/v7KsfMxqPC63cx6ovvGw7xcj/DkzM+Oy3t7e2ubmluOEEEc8l/f/O/4nlEKEEJUyKarH+aC4MgC8CJJsulE+/5Isel7LGEtmjE0SQgxZkKyBMXZiZmbmn7xeLzo6Oq4bypgB/aoa0ElFnXH2d641GY2PpCYnmywxMYIxdgLIS+gFeTDJIG/yE9BisgBEFGAAcrd+/fXqms2bnwTp1yUAeBD9i9XVAK7+bOPGn598yiknffH55/7u7u716FfkuEI+ruJc/Z6iOE6U8/SCDEW2/PtXScZYBFqsvSaTyQig0+/3NwLYx7nqVxTHegA/g84gxcTEBPJmzeqRQsorQR6hZqj69IaGc/UVUK3doOBc9bjczrUgT+6zUI+Ac3UPKDcAAHC5nVhWWJg0ksjsKJAJItoMasykATFnZ2dvTk5J+ZZC3bnNnKu75HgeiO2mMQEtoPoztywluAzkOdtAmxMrgL2cqzd2dnaeuWH9+ssrPJ5HQEZDe2ziXD1f5iU/ADCxwuPRvA8jiDp/vKI4jgOFYEOJNh8DWDpU2NdkMm2wl5QurvB4fgj6roSi2l5SGlyx4rp49DMH9dC6sE8CMQI10ksf6Hv2Hzm+CyQ15gMZDHMwGOwIBoOa0sVfQIZDX7Dtu+uue16Xv6kbQWokPt2jR3vvQeohQYR85zRooXb5Pc0HsEff9odz9bbQY1xu5wnPv/ii2tXV9RcA6wqKi35YWVb+9iDvQVhwuZ3JIOLLoN8vIYRgjF3b2to2Pz09fXFnZ5eTMfZtIcQRUQH6X8H/hEEbApmakZLUVY0mOxkDGV6aUsKwBo0xNjElJflNxlhMa2vr00KIZ4d7vUSFy+0sfPzJJ1cDeGhSZub1MWZz3a69e4+fM3NmPWjnqMk8TQQZkD9yrl6tKI44eZ3doHqghEAg0CGEMMhFMQlUeB2a2F99ycWXrvf5fKd/8fnnpYFAoAMDvSCtCeR60AI8INShgXP1MwDnDJVP4VzdAeCZ0OPkDzWfc3UTyNscC44FtY8JaAYElFsMKoojHkR9PhR2ysrKKr7zzttfvvPOu9sUxTEPxG7Ucng2UJ7wdzLsdT2A0zEwbOUH8J1lhYXm1atWvez3+/MrPB69BxIEFSkLkMxWbm1tLWprD0Wc+wDEKIrDBmrzEupFd4O88EwAzw1yv/tcbue/N6xfvw1UJxWK3fJvGmgDohUW++TcmmBwEERQCYIMiQ/kqWvf2e1y/kN5yviEhPRgIPCEHK8EeeCasdDOUQUAQogXk5OT/z1l6tTcTV9+uVaO+TXjwbn6OIDHB7l+yPEXQZtMfU5wq9YYlXNViyYcBhkKHbYrAOdquD30MgF4w+lhJ7//y9Snnz4+EAy6AbxVUFx0F4C7tXY04UJ64AtHIroIIXoYY+d3dXV9lpqasuzAgYaHAawYzVxHG/6XDdpQOKyGA7QwDH0AYxaLxfJ3my1+UkNDw2ohxPXDvV6DvaRUXH7VlTstFssan893fFdX16upkyc/+cG/39PCREvRn4fSjE4nAHCudkPWrkj9vqn2ktIvtHNzrrZh8F00AODyy674z2AJdt3xQfR3qT4MiuJgFoslZv78+QUbNmxYV+HxZIMW/yYZlrGB2qTEop+KbwPw/rLCwrr77rvHXl9fX4B+hl8syDDcybm6UVEc1wBYDjIk/6+9cw+vqrzy/2edcxLIhVwIEC4CgoqKV/CCWglQWrX3S5Kpo9OZaWs71Xhatba1tb/WtvaivWg9TduZ1o62nVqboJ06VXthTHAQBQEFhMr9fhchCQkk52T9/ljvTnZODiSBnIOE/X2ePDlnr/3utd+93/Ou913XLDqz3lfGYtUL7rzzjj8cPnz4WiDsdiEhd2tXAQvorFXXge3btzNx4sRm4JdYEO/IFF37D0y1eg+dSWw9HBgzZkwxMDwej19FZ8VmT221GrPRtUWjVWAhE21AaygUiqiqpyJsxXanEUyQeO/Yq5D8JnArJgia3Dmtk84+O9+dE8Z27oeT/ryx8Yp7Xl4m+GJ8TiKOfl6KvuPoKzHHGtw1ung0Ju9+U7RvAppq59QcmjFz5siK8sqUVSd6gkv8W4rVQuuLUKivnVMz43g8H5035diK8sqXetvG8Zv5i0cemd7c3PxTzDQwpWz2rI/Om/tcXyqNl/U2FEFVt4vIB0tLS+uKioqiIrJUVf+zD7xOKQxkgbbLUyW6wEJva7+VTtsR2Er3iFnLnS47VlRUdEVzc/P21tbWclXtVVqnstmzLsbcZ0cNHTr09j/UzHnQT/fc5qFDhTWIzombaLRqQigUyhs7duy0TZs2rayvq5sF7I3Fqpe7HdzHMZWip7bKxfLsPeliib4WDodL6uvqWt21s7G8edXRaNW7MNVOxPcXBu6NxarvBT5/+PDh+xYvXpzcrTsx+8qPMbVZMj5ZUV75i89+NvpvwDuSaIrZ1ZZgDhvJ2U5agNbaOTVTWltbc7Ed0yG6xmt526FX3bPydhCHgM2bN2/+vVNLfRtzi/dntNhEZ+D3u33tDwOHi4uLD5151lkj3f0NB1qPsoPtpnKrnVMzs3ZOjcQsK395qnaubQtQndTWs6fE3QKkVwVSK8ord9dahfBhmDNNn1A7pyYPE4gv97UtZnc9t3ZOTWFvdjkpMKG1tXXPghdeaKkor/R+AyXYWPR2xREsufRu52RyCRDJycnJX7Vy5ad37969EVjnbGO5wAeT2mYB82Ox6qUuPu5mICsvL2/CwYMHd9TX1V2PhUsscA5RX03in4XFTD4fjVZdAnzz3DPPHNrW1tYSTyS2JeLx92/btWtZ2exZ1/TGrnYscXWqukBEqkaPHv3zRCLxMxFZparHVVh1oGIgC7Q/Yp5S33X//9t3/FYR+R3mFHKgB/vZp/Pz8z8ZiUQO79q16wOqmirmqhtmX3fNjWeMH/+r7Kys9kgksjccDn8lGq26G3gxFqt+X9Qyg8/D4q5CdO4cPTvHNcAz7e3toU2bNvkvPRcTFB/GvBWTUQo86c55ZyLRzRnNi326AJs82ukM8m3FVGkUFRWN2r9//zo67R+e2ulvrv2fsB+7twPxdpiPA7S3t3+5oKDgqbZ4fFdLc/Nu17bFqTLB4r0i+ARKzIpWngU0PfTQj5Nd5rsgFqu++Ui02jk1z8+YOTNRUV6Z6vl47RcktQlhu5q1FeWV7RXHUPU6ld20N3Bq2izPnuImdi/cIIKl0XrT0YZh8Wn+CVtnzJw5sXZOTUN9Xd052BjwT8otsVj10679e7EFXQTIGlJQcFZjQ8PCWKx6kaNHsRARv1BYG4tVf8/Rf4TFNkWALKe9WAt82qmG/4LtjP0C4X9isepbXfvtvvv3vFv/A/g39zmVUP4BtpDKxerR0dLSQktLh7n063TuuFPZZe/EclUOxYL8OXjwYBudJYtexXb9+ViuSI/m/c9z12kHWuPx+Pbs7OwcaWt7WVXXJhKJi4CFZbNnXT9v7nPPpuAPQO2cmqsx22afoaq/EJEp48aNvSWRSDwpIpeoajrKB53UGBACzR9YKCJbMVXAd4Hfi8gncIGF7vSnsdX5WmwC/thRrjt98ODBD5UMK2Hjho03qWqPq1gXX/bVcDj8tfy8PDBhVerKQigmGMBUcK2YIb3N9+eVnNmZlZW11MVYed6KLZi7P9gP8C66JnltotOOUoMzvE+aNGnSvn37Vu7du/dNzFhPLFZ9P3B/qj7Uzqm58qKLL76rorwylVs8rn0tFhx9JPqi2jk1K7GwiTXJ5Tdiseq9yW3cTiOMrfxTImp11Dz1pX/S3heLVTdHo1VDgNMLCgpCq19//YYdO3ZsdPTlTlU6BlOVdlnFnz5hwkvjx48/XF9XN7a+ru4mugqMCPAjt0t4OzaWkulVsVj1G/V1dWNemD9/UVtbW2MSj1ku3OALmANPx/270jhFbpfyE2wX4UcznZPqj4Abkui7KsorR9bOqbkKGxPJHoDrsHEPcAc2aQPQ2NAAFhLiqbE+gtku/RO6v9DoGZhAawPiqtrW1tbWjDm4rMK0HQfpKhRe87V/NBKJFIZCIWltbfXG/hIwNbgTqHHfX5u7Lu667/bTThs7dvLePXs8e9tuzDvRf+9xnKp2/Pjxa8aOGzfrjTfemHfLzVXdVJyxWPUyjpK02y3GZkNHgdVERXnlprLZs07HFsx/Kps96/PAAykK707GfgfH49Rx244dOy8YPXr09Hg8/qSIlKlqxvKongwYEAJNVbu5+DrMTnGuAlU9XVNExg4aNKh29JjRkfXr1v9QVX/TU5uy2bNyMWN3RSKReGTPG2/cNrykpEuyX7eCJxarfpnUNh4AZsycWQhckcr+5dqvA+7z3LUxl+02gGi0aiimXmwAIqtXr147efLkS887//xalyHeqyXlFwrbY7HqV2rn1Jy5aNGiS5oPHpxUX1fnn7CXuxgfLwtFslporgucLsB2jllAJBwO5wPZ9XV1/x6LVdc4gfJ4UtssEflO2YwZi5csXpzf2Ni4LYl3FvCJWKz6N5gNLVW15A9jO9OrgacbGhpoaOhi1ngntru8ihQlSNra2j5ZUV75i/q6uo9jHnR+JLBg791Y2qxyuk6Ybe55AmS3tbUlwpFIcSIefxMTRnE6d+BbsF1GHGjLyckZ0dLSsslHfwbbpfgFgn/S+nfgz0n8mwEqyitf2LJ5c83GjRu/197efthH93t+/gMQvvCii6auX7fupaamJk+lC0AsVn118rNJwvvceeoWF7mJRCK8ZcuWUbEfP3Q55oHY6BYXgzGHq3DUMvGHCwsLnx47blxrSUnJwvq6ugJsVxx2YRphzE65xIVujMKE7UTnNep5c/7ZLS4mbd2y5YKioqLbo9Gq9T76o45+GRafGopGq8KDBw8etXXr1qZEIrEcq6l2DTZuvHYh97/KOQ7dgC1ewknnvDcWq15/991f+mF9Xd2VF02eHFLVRGtbW0MikfjBmg0bzi+bPevmeXOfOwwdO/BBXhafY4WqtolIRUNDw5KhQ4devnPnzmoR+eTxlKkaaBgQAu1Y4KpOX6Wqc1PRsrKy/nvs2NNGrFu3/q/0ojBe2exZYzB15pTRpaWPDRs6dJ+IfAOIOCGwPxar/gJQ/+Uv3/VwY2Njsq1gcyxW/TGAO+647Q/xeHySqobcxB7BMqp/ECAarXoRsz95bcGcJK51n5diq+gOrFy58okZI0bswdRhf8bsJn78qnZOTRVQ1Hzw4A/pWj8LzGZWj028XiZzb1cZxxwd/gf70Ze5Y/FEItEWCoUi2dnZXqaSBLbbbPK1jRcWFkaA/Y2NjQ3uOSYLDK8syUZsF+Kf8NvorGy8FJu0W0OhUPvQoUPP2Ldv34b29vb10WjVcMw29U6s5EgbUDR69Ohp27ZuXejUwK9iqujljj4JU80Oj0arZmPB6uWxWPXz7l3MwLwtpzt3/DjwzauvvvrvQFt9Xd00TAX4cScAwsBTsVj1465Y6oWYve6brn0Yy0jyS3f9+4GiaLTqYTon1QWxWPVPHP1xICcarfqMo0UKCgqWTZk69c76ujrBhH/Yd+1wXn7+X4uLi+9pamoSLC4x5KcD98csp+MYnCMMnZN9CIsffBDLk/kawPp1XWLxb8JspRfhVIQeDhw4wMFVqz76wAM/eqm+ru5SOlXYfrwH21FOA55IQZ+BLS4uB36yf//+ZPrfHP1SzCbWDrQfOnSo3b2fB7FwmElYXKXn9OOp373FSQm2I00knRMGaGhoWJuVlVXW1ta2V0QS2VlZW1sSibMwrc+kstmzPvyZW27ZSy88GnsLVd0tIu8fN27c/Ly8vE80NTUtJckeeyrjlBVo2O7ot65KbEcAq3MC+fmo0aOm7Ny5a6OqXq+qKXdJHspmz5oKPIXZw94/vKTkHzGbgH9S3gxQX1fn8Zmsqg2O1g4MjkarhpeOHFksodAhVV2DTfxekcwtbsINY+rGl7EdmGIT5s5otOod2IRTg/1gd2M/zrOBffV1dWOWL1/+bcxlexnmYFGI7Wqaly5Z8p2GhoYtmFrrT5iq6nRcuqhotOo+x/9BoDpmiWinYQHY46PRqkd893d3LFa9IRqtuq69vf2W9vb2f/jMZ259v/eYgZtiseqt0WjVjSLyuYaGhtvr6+q8STkMzIxZ7sXbgc8C/5Q06Y6PWfmX+xzdm7QFi/cKAXzmM7f+p6o+kPTKGmOx6oLaOTXy4oIFf9u+ffvbk+jbY7HqMQDRaNX3MDWXH69jkznAN7HsJn4sriivvNRljPkypsLzo652Ts02zJ7yc7pPmn/CPDVxvEt8tDhd7UynY7vxjvaHDx9uxlKQrcPUdB20cDicjer2ivLKxvq6uhx3D/7JPEFnNewm4Kd0n/A9z8BdWMaODnpeXt7wrOxsT8W4DrNfJ4DEkCFDStrb2w8fPHiwztFfAa5L6nuCzsXLc8AU12c/3bMdPYEJr0Rubi5nn3PO1KVLlszH7VixJAE/mzFzZhi4sqK8ssvOPhar/jG2UEuJWKw6Rmo7tUf/CfCT5MKzZbNnfQRzuHpp6auv3vOtb9z76JGucSxQ1SUi8vFJk8767bp16x8UkRWq2i8CU0Suw37/YeAXqvrd/rhupnDKCjRV3SAiHwUeF5Fpquq5Kd9RWFhwYyIeb25qanpfTwk9y2bP+kDp8OE1JUVFoUgksk9EvJX0z2Ox6tudSrAZKIlGqxI4L8asrKyHr7zqqq/X19XtxwTTlcD1u3bu9C799Vis+p5otGo0nbFj/hgULzP62XROQHf76J+OxaofjlpRxY4V3L43Ooos3xiLVdc5Vc+dQLKKbl4sVr0lGq26GLO7JE86T2B2yDGYGiqZ7tl88oCx8Xg8EQ6HhyYSCc++EgLIyckZ1Nra2uTi5ZKvAbYjq0s63u6je67wXdpGXd4+VX0sHA6/npObW9LU2LjJ0T0V3vTW1tavY8LD397vEPIV4Id0nfD9RUU/hnmY+u+rBaCivHLB/v37v7Z82bIF7e3tHfd+6WWXnYEVj2yrr6ubdLTMJrFY9flHojl6yiw3tXNqLp0xc2ZeRXnlO33HcrHsJ393bVuwIP0jXfsAbmwcgf4GSerZ2jk1Q4BxtXNqspyd9FfueBHmJr88H65H4AAAFLlJREFUqf2fe+B/xJRPLrSl413VzqlZNGPmzAkej1hn/sur05yxv87vvThv7nOPl82etT4UCj37/Pz5D5XNnrVj3tznUsbVHStU9TERmXLOOWd/fvXqNbUicqmqbuq55ZEhImFsrngntthdJCJ/VNVURVvfkhgQqa+OByJyJ5ZmZzpwdTgcfuaMM88IrX59dbmqplJ3AFA2e5ZgAuaBoUVF60eXli4Kh8NNdE5682Ox6t8CuF2Ef0JMAAtmzJzZuGPHjhWrX3/9U0CiqKho4v79+//u6ItjserF0WhVDqnVIq+5HVIuJgyTJ/z1zo6QQ9IOYMxpp40YNmxY002f+OTyaLQq+/wLLpi9efPmJQ0HDhzyndca60Whz77AlbyZArxaUV7Z6pLOjgNWpCvRro93MZYbcbn7fjGwqaK88s108nW8OlbwbmKfUFFeubSHZv3BdyYuLZdL03UhsKGivLIvMVPHwnc80O7L1D8IuLgvMV/HyFdw2gjPYzRT5Wdc+MN5FeWVC933tz3y61/vaGhsfBKzE0bnzX3up/3J0wmgP5177rnXrlq1ailwtar22TvXd70rgXtU9Vr3/UsAqvqdfrnhDCAQaJas+NdYPNf0yedNLl752spvqupXj9SmbPasCKZ2q8IcEf5p3tzn+jyQnKv42yrKK59PVlukG7Vzai7DvMfOwAJbjyWO6Fj4DsdiptZhk8+6Y3GRP0be4+isCD28orzytR6a9BdfL+fjPDJUCsXHe6YLJ5iMeUO+0WOj/uF7IRb31wxcVlFe+UIPTfqL7yAsXdoynANRH4O2j4f3GZj2IQfLAbqzbPasIZhD0XsxVd7n5s19rt8WiiJSnJWVtWjChNPPWLNm7e9U9YZjdRIRkQrgOlW9yX3/KDBNVW/tr/tNN055gQYgIsOB3flD8mlq7Dlb04VTLmbqxRdx+NBhhg7t6luhqjQ2NtLUZNcZPXp0FxqYYby52ebw0tJS5s9/gWnTLic7OxuAffv2cfjwYUKhECNHjuzWfvfuPSQScSKRCCNGjOhG37lzJ6rKoEGDKCkp6UbfunUb4XCIffv2kZuby8SJE7vQN23aTHZ2Fvn5+RQUFHRrv379BnJyBjNkyBCGDBnSjb5mzRry8/MpLCwkLy+vG33t2vWsX7+O6dOvJjc3txt9xYrXKCkZSklJCYMGDepGX7ZsOcOHD2PYsGEdz8xPf+WVVygtLWX48OFkZWV1ob/66qu0K4wsHcGIESOIRCJd6KrK8uUrGD58WEp6e3s7r7/+OkVFRZSWlhIOh7vQ4/E4GzZsJD8/j5EjRxIKWZx8Q0MDK1a8xiWXTGXbtm0MHjyYUaNGYeupzvaHDh1iz549RCKRlPTm5mYOHLC1h39sefTksaeqvPTSQs4552wKCwu7jL1UY7M/x148nmDbtm1MnTql29jLzc2lqKioW/v+GHt5efmsWbOGyy+/rGN8efRVq/5OUVEhxcXF5OTkdGt/vGNv4cKXiUTCXHjhBd3Gnogw74UXWL60I+FPv6G4uJhDhw7R0tJyt6qmqgfYI0SkErg2SaBdrqrR/rzXdOKUtaEloQQgLy+PcCjMkCH5FBRabcUd23eQkzOYomITXLt27iLe1kYiHu+YrJIRDofJzc3rdlxEUFVCoRC5uXmoKvPmPU9LyyGys7M76OFwmJyc3C6Tmb99JBImKytCVlZ2Sno4HCEcDpGdPSgFHbKyIjQ0NPLiiwv50Ic+0I0+aFA2kciRrg+DBw9y9KyU9NzcXMLhSBdh4E6gPdHO0qVLKSws7E63kygqKiIcjhzh+QrFQ4cSDke6PR+PXlIyjEgkqxt948aNvPLKMq686opuffP3wZvMUl1fRCgqLmbwEeihUIjCwkKyszufTVtbG08//SznnHM2oVCYIUMKyMpK/dMLh8Pk5+cTCoVT0kOh1GPL395PX7HiNTZs2MjUqVM77u9oY7O/xl4i0c6zzz7L9OnTfXQbe0cbW/0x9l57bSXNzQcZPHiw/wRQKCgY4u4x1fM9vrG3b9+bLFu2jOnTrz7C2ITsrCxKS0uJx+OUjjTH3/1vvklLyyFGjR4FQMOBAzQ2NjHmtDEANDU2sn//AUaPGU0oFKL54EHefHM/pSNLiUQitDQ3s2XLVuLxOMC3RORbKZkDqpr6xgx9yqL0loS3Kj1V/7DsA0sxV+RhGeZ9M+Y2PSTDfEsxddCHT8Dzvh/zTItkmO+5mNfntAzzFSzu7RGcRiSDvGdgnohnZJjvIMx78muZ5Ot434ips4dmmG8xsAb4lxPQ569gnsVjjvM6ESyp9QTMc/ZV4LxM9+d4/k7pHZqzn/0Sizl6UN1bzRDvMixdz9tUtTGDfLOxDB+P6lGcXtLE+wassOJl2kMoRD/zLcJyan5RVdPqmJACd2HZJ2ZkeHyNB34H3Kiq63o6vx/5egVkd2MhDRmDiFyC2bZnaQ/eyf3MN4zZyf6kqv3qot8L3u/DyjhdrseZCktV4yJyK+Z5GgZ+qaoZsTP3F05pG5qIfBGbYMtUtb/qaPWG7zgsludfVLVf3Xl7wfunWD7HD6ml48oU36nYD2W2qvYq8W4/8Q1jMYJrVTWjpTdE5D1YnsLLVXVbT+f3I99cLJzhN6r6w0zxdbxvxpylrszwQq0UWAjcfgIWavdhQdzXZnihdi6W7OB9J2Ch9pbEKSvQRGQypvq6XFW39nR+P/N+Bpirqt/PMN9rsRXsNLWg7kzxDWG74K+p6hHzP6aJ96ex/ITXaAaLI4pIPhaAXamqGfHw8/H+LhYf+M8nYFe4ENM6HFNJmePg/Tjwuh7FOzlNfK/EdmeXqWq3/KRp5CtYFpafaVBOpgNpE2hiFaQ3AFmZXLX0BSIyQlVTVoxNM9/hwN5MTjaOr2B2woyXcj+BzzobyFPVtMebpeB9ovo8BEscnDGtg4/3ierzMGBfJrUOjq8Aw09Qn0/Is+4viEgTcKGqru+va6Z20+vK9GoReUFEDojIPhGZLyKX9dcNHC9E5BERufdY2p6owaCqezItzBxfPRHCzPE+Uc+69UQIM8f7RPW58UQIM8f7RPV5b6aFmeOrJ7DPaeMrIjeIyMsi0iQiO0TkGRHpKXF1n6Cq+f0pzKAHgSYiBVjC2RhWS2gMlpg2KFkQIECAAAMQInIHZpr4NuYRPQ74CZbTta/X6uZ4mOrY8V7TQ087tElgecNUNaGqLar6F8+oLyIhEfmKiGwSkd0i8isRKUxxA9eLyMtJx24XkT+6z4NE5PsisllEdonIz8Sy4SMiM0Vkq4h8zvHYISIfc7RPYW66X3Ariaf68FwCBAgQIIAPbv7+BlClqk+o6kFVbVPVp1T18+6cy0VkgYjsd/Pxj51q37uGikiViKzBQhmOduxM97k3MuCLIrKTztp93XE0n34se/wbWI2vdwHFSfSPYwlqJ2LVXp8Afu1op2OZ4CNYrFcjcJav7SLgevf5QaxkyFAsBdVTwHccbSaWbfsbWEmTd2PpdIod/RHg3qP04TrMOL8WuOtYYht6+4eFAOwGVviODQX+6l7iX5OfYT/xHYtlJl+FlfP4bCZ4Y1UAFmLxKq8BX3fHJ2BenGuw2mfZaXzmYSyO8H8yxRtLmLwcS5z7cqbes+NThIVd/N297ysz8J7Pdn31/hqA2zI0tm93Y2sF8JgbcxkZX1gVhxWO/23pfM99mTuw2MaH3Jy2DJjaj32+DptvjxgnClyC1amLYPP8Ku/5OLq6+x0K5PRw7Ez3uTcy4D4sxjHniPfWiw6eiwmNre6if8QqMAPMBW5JGvhtvo6q92CwSstfdZ/PwgRcrns5B/EFf7of6QZfZ1r8D9i9+Cvc50c4gkDDJrt1mMD1AgUnp2PwO35lwNSkQXk/TpBiMUn3pYHvKG9Qu8GwGpicbt7u3eW7z1nYJHMFFkjsLVZ+Btycxmd+B+Zl5gm0tPPGBNqwpGNpf8/u2o8CN7nP2ZiAywhvd/0wVktufAbG1xjMscybAH8P/GuG3vH5mDDLdfPZ39y8lZY+92XuwBb1z7jf3xXAS/3Y7xuBnX1scxvwpO+7Am9POudIx86kdzKgFRjc47308cbPwepwPea+rwLe46MPdjc5hu4C7d3AKvf5a3Tu5Ea48/b7/g4ATb7ObE26j43AO9znRziyQLsS+LPv+5eAL/X34E/ieXrSoHwdGOU+j8Jci9PG3/H5b6wERMZ4ux/+Eqwo417fe+/yDvqZ52nYourtmK1XMsGb1AIt7c8a05hsICnjSIbf8zXA/EzwdfPIFmzVHnHv+NoMveNKrB6Y9/3/YbXf0tbn3s4dWNXyf0x1Xj/cQ292aJPcu9iJ7dabged9dMWnjevh2Jn0TgZs68399+jl6Ieq/h0TIF6Npu3YSs3DOPcwUpUa/wswTEQuxsq1/NYd34vtwM5T1SL3V6iq+b29raPQvB+Eh63uWCZRqqo7ANz/ET2cf1xw4RJTsN1S2nmLSFhEXsF2zX/FdsT7tTNUI53P/EFskvG820oyxFuBv4jIYmfHhcy854lYcc//FJGlIvILEcnLEG8P12OqP9LNVy0Y/ftYcdwd2CS3mMy84xVAmYiUuED1d2Oq/Uw+6yPxSue8tgArLPzBo5zzU0zlfZaqFmBFbJNzRKaal480V/dGBhxtnu9AT16O5zhnjNPc97GYMPLKqj8G3C4iE1wg6beBxzVF3Jk7VosVBPR0w6i52v4ceEBERjg+Y1wQcG+wC/uhp+xCimO9ejAnI9w7mIPpszMSOK3mLHQxtlu6HFNRdzutv/mKyHuB3aq62H84E7yxwOGpmF25SiyNWSYQwdRSP1XVKZia5q4M8fZi+t6PVUTPBL9izLNuApbdJg975sno93esqqswm81fgWcxc8VbJZ42beNcVQ8AXwWqReSDIpIrIlki8i4Rud+dNgTbmTWJyDlYTtrj4Xm8MqADPe3QGjEV0ksichATZCuAzzn6L7FaYvMwVcgh4GilBn4LvAOoSRJ6X8QMnC+KSAOmrz67l314GJjsPG7+kER7K2SP3iUiowDc/7TEjohIFibM/ks7U/9khDeAqu7HKktfART5XGvT9czfBrxfRDZiOQvfju3Y0s5bXc48tTigJzFBnolnvRVTv3tpjmoxAZep9/wuYImqehqYdPN9B2ZH2aOW5eUJrMZZJsYXqvqwqk5V1TJgH+ackbHf1FF4pXVeU0uXdgeW9HgPthu8FcuHClbF/AZMPvwcc8w5XhyPDOhEf+l/34p/nIDs0XTXg3+Probd+9PAU7BS9w8mHU8rb2A4UOQ+5wDPY4UMa+hqtL+lP/mmuI+ZdDqFpJU3tksY4vv8AmZ3SPt7dtd+Hjjbfb7H8c0U798BH8vg+JqGeRh6zmOPYgvmjIwvYIT7Pw5TsRWns8+9nTuA99DVKWRhOvp/Mv6d8BtIewdN970as+3cnWZej2G6/jZsFfUJzK4zF1vdzSUNZS2AqzGVwzI6XavfnW7ewIWYy/wybOfuebFOxNz517rJZ1Can7tfoKWVt7v+q3SGKtztjqf9PTs+F2OOWcuwFXNxhsZYLhbCU+g7lgm+X3fCZAWmDRqUqfGFLR5Wunc9O5197svc4QRZtZvTlgOXpqP/J+PfKZucOECAAAECDCz0ycsxQIAAAQIEeKsiEGgBAgQIEGBAIBBoAQIECBBgQCAQaAECBAgQYEAgEGgBAgQIEGBAIBBoAQIECBBgQCAQaAECBAgQYEAgEGgBAgQIEGBAIBBoAU4aiMiXROTppGNrjnDs+l5c7x4R+U1/32dvICKPiMi9fTh/rIi8KCL7ROQHSbRnReTS/r/LAAFOLgQCLcDJhHnA20QkDCAiI7HColOTjp3pzk0rfAlyM4EvYbkMJwAf9ASYiHwEWK+qL2fwXgIEeEsiEGgBTiYswgTYxe57GfAcVuDQf2yduoz4IvIjEdkiIg2uftl0d/w6rI7TR0SkSURedccLReRhEdkhIttE5F6fsPxXEZkvIg+IyD4sOXAHRGSwiLSIyDD3/SsiEheRAvf9XhF50NVQuxH4guP9VC/6PgH4X7XyHouAie66d7l+BAhwyiMQaAFOGqhqK1a41Ks/VoYlkP2/pGP+3dkiTNgNxcoX1YjIYFV9ls76ffmqepE7/1Gs7tWZWKHUa4CbfNebhlVwGAF8K+n+Djl+M3z3sgkrdeN9r1fV/wD+C8uenq+q7+tF91cA7xSRIuBSLGnuN7EKC/t70T5AgAGPQKAFONlQT6fwmo4JtOeTjtV7J6vqb1T1DVWNq+oPsGztKessiUgpVvPrNlU9qFbv7AGsSrOH7aoac9drOcL9zXDqyAuBh9z3wcBl7l6PBd/x9a0a26leCDwlIr8VkXkicusxXjtAgAGBTNoAAgToD8zDqkQXA8NVdY2I7AIedcfOx7dDE5HPYTus0ViJnQJg2BGuPR4TFDtEOooCh+ha7n5LcqMk1AM/xApvLscqHj+M1a1aq6p7e9nPLlDVfcBHAEQkhPXx05jKcQXwr8ASEflfVV15LDwCBDjZEezQApxsWAAUAp8C5gOoagNWsfdT2A5qA4Czl30R+AegWFWLgAN0lrBPrp20BTgMDFPVIvdXoKrn+c7pqd7SC9gO8EOYenElViDyPfh2jr24ztHwKeBFVV0BXAC87NSxyzGBHiDAKYlAoAU4qeDUfC9jJeL96rv/c8f89rMhmD1sDxARka9iOzQPu4DT3Y4HVd0B/AX4gYgUiEhIRM4QkRn0EqraDCwGqugUYC8A/0ZXgbYLK1TZJ4jICHfte9yhDcAsEcnHbGvr+3rNAAEGCgKBFuBkRD3mlPF/vmPPu2N+gfZnrFT9asw54xBdVYY17v8bIrLEff5nIBtzungTqAVGHcP9ZWFVlb3vQ5Lu7WFgsojsF5E/AIjIMyLSk8fi94FvqGqT+/4d4O2uX38M3PcDnMoIKlYHCBAgQIABgWCHFiBAgAABBgQCgRYgQIAAAQYEAoEWIECAAAEGBAKBFiBAgAABBgQCgRYgQIAAAQYEAoEWIECAAAEGBAKBFiBAgAABBgQCgRYgQIAAAQYEAoEWIECAAAEGBP4/vGMrkQm6SSUAAAAASUVORK5CYII=n”, “text/plain”: [
“<Figure size 432x288 with 1 Axes>”
]
}, “metadata”: {}, “output_type”: “display_data”
}
], “source”: [
“# This one will take like 30 secondsn”, “# Thermosteam’s LLE algorithm is stochastic,n”, “# so its much slower than the VLE algorithm.n”, “# You’ll need to "pip install python-ternary" to run this linen”, “eq.plot_lle_ternary_diagram(‘Water’, ‘Ethanol’, ‘EthylAcetate’, T=298.15)n”, “plt.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Vapor-liquid equilibrium”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Vapor-liquid equilibrium can be performed by setting 2 degrees of freedom from the following list: T (Temperature; in K), P (Pressure; in Pa), V (Vapor fraction), and H (Enthalpy; in kJ/hr).n”, “n”, “For example, set vapor fraction and pressure:”
]
}, {
“cell_type”: “code”, “execution_count”: 70, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“MultiStream: s_eqn”, ” phases: (‘g’, ‘l’), T: 353.88 K, P: 101325 Pan”, ” composition: (g) Water 0.3861n”, ” Ethanol 0.6139n”, ” ——- 10 kmol/hrn”, ” (l) Water 0.6139n”, ” Ethanol 0.3861n”, ” ——- 10 kmol/hrn”
]
}
], “source”: [
“s_eq = tmo.Stream(‘s_eq’, Water=10, Ethanol=10)n”, “s_eq.vle(V=0.5, P=101325)n”, “s_eq.show(composition=True)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Note that the stream is a now a MultiStream to manage multiple phases. Each phase can be accessed separately too:”
]
}, {
“cell_type”: “code”, “execution_count”: 71, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: n”, ” phase: ‘l’, T: 353.88 K, P: 101325 Pan”, ” flow (kmol/hr): Water 6.14n”, ” Ethanol 3.86n”
]
}
], “source”: [
“s_eq[‘l’].show()”
]
}, {
“cell_type”: “code”, “execution_count”: 72, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“Stream: n”, ” phase: ‘g’, T: 353.88 K, P: 101325 Pan”, ” flow (kmol/hr): Water 3.86n”, ” Ethanol 6.14n”
]
}
], “source”: [
“s_eq[‘g’].show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Note that the phase of these substreams cannot be changed:”
]
}, {
“cell_type”: “code”, “execution_count”: 73, “metadata”: {}, “outputs”: [
- {
“ename”: “AttributeError”, “evalue”: “phase is locked”, “output_type”: “error”, “traceback”: [
“u001b[1;31m—————————————————————————u001b[0m”, “u001b[1;31mAttributeErroru001b[0m Traceback (most recent call last)”, “u001b[1;32m<ipython-input-73-ed0136a78442>u001b[0m in u001b[0;36m<module>u001b[1;34mu001b[0mnu001b[1;32m—-> 1u001b[1;33m u001b[0ms_equ001b[0mu001b[1;33m[u001b[0mu001b[1;34m’g’u001b[0mu001b[1;33m]u001b[0mu001b[1;33m.u001b[0mu001b[0mphaseu001b[0m u001b[1;33m=u001b[0m u001b[1;34m’l’u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0m”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\_stream.pyu001b[0m in u001b[0;36mphaseu001b[1;34m(self, phase)u001b[0mnu001b[0;32m 589u001b[0m u001b[1;33m@u001b[0mu001b[0mphaseu001b[0mu001b[1;33m.u001b[0mu001b[0msetteru001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 590u001b[0m u001b[1;32mdefu001b[0m u001b[0mphaseu001b[0mu001b[1;33m(u001b[0mu001b[0mselfu001b[0mu001b[1;33m,u001b[0m u001b[0mphaseu001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[1;32m–> 591u001b[1;33m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0m_imolu001b[0mu001b[1;33m.u001b[0mu001b[0mphaseu001b[0m u001b[1;33m=u001b[0m u001b[0mphaseu001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0mu001b[0;32m 592u001b[0m u001b[1;33mu001b[0mu001b[0mnu001b[0;32m 593u001b[0m u001b[1;33m@u001b[0mu001b[0mpropertyu001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\indexer.pyu001b[0m in u001b[0;36mphaseu001b[1;34m(self, phase)u001b[0mnu001b[0;32m 223u001b[0m u001b[1;33m@u001b[0mu001b[0mphaseu001b[0mu001b[1;33m.u001b[0mu001b[0msetteru001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 224u001b[0m u001b[1;32mdefu001b[0m u001b[0mphaseu001b[0mu001b[1;33m(u001b[0mu001b[0mselfu001b[0mu001b[1;33m,u001b[0m u001b[0mphaseu001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[1;32m–> 225u001b[1;33m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0m_phaseu001b[0mu001b[1;33m.u001b[0mu001b[0mphaseu001b[0m u001b[1;33m=u001b[0m u001b[0mphaseu001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0mu001b[0;32m 226u001b[0m u001b[1;33mu001b[0mu001b[0mnu001b[0;32m 227u001b[0m u001b[1;32mdefu001b[0m u001b[0m__format__u001b[0mu001b[1;33m(u001b[0mu001b[0mselfu001b[0mu001b[1;33m,u001b[0m u001b[0mtabsu001b[0mu001b[1;33m=u001b[0mu001b[1;34m""u001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\_phase.pyu001b[0m in u001b[0;36m__setattr__u001b[1;34m(self, name, value)u001b[0mnu001b[0;32m 68u001b[0m u001b[1;32mdefu001b[0m u001b[0m__setattr__u001b[0mu001b[1;33m(u001b[0mu001b[0mselfu001b[0mu001b[1;33m,u001b[0m u001b[0mnameu001b[0mu001b[1;33m,u001b[0m u001b[0mvalueu001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 69u001b[0m u001b[1;32mifu001b[0m u001b[0mvalueu001b[0m u001b[1;33m!=u001b[0m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0mphaseu001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[1;32m—> 70u001b[1;33m u001b[1;32mraiseu001b[0m u001b[0mAttributeErroru001b[0mu001b[1;33m(u001b[0mu001b[1;34m’phase is locked’u001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0mu001b[0;32m 71u001b[0m u001b[1;33mu001b[0mu001b[0mnu001b[0;32m 72u001b[0m u001b[0mNoPhaseu001b[0m u001b[1;33m=u001b[0m u001b[0mLockedPhaseu001b[0mu001b[1;33m(u001b[0mu001b[1;32mNoneu001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;31mAttributeErroru001b[0m: phase is locked”
]
}
], “source”: [
“s_eq[‘g’].phase = ‘l’”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Again, the most convenient way to get and set flow rates is through the get_flow and set_flow methods:”
]
}, {
“cell_type”: “code”, “execution_count”: 74, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.0”
]
}, “execution_count”: 74, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Set flow of liquid watern”, “s_eq.set_flow(1, ‘gpm’, (‘l’, ‘Water’))n”, “s_eq.get_flow(‘gpm’, (‘l’, ‘Water’))”
]
}, {
“cell_type”: “code”, “execution_count”: 75, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([10., 20.])”
]
}, “execution_count”: 75, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Set multiple liquid flowsn”, “key = (‘l’, (‘Ethanol’, ‘Water’))n”, “s_eq.set_flow([10, 20], ‘kg/hr’, key)n”, “s_eq.get_flow(‘kg/hr’, key)”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Chemical flows across all phases can be retrieved if no phase is given:”
]
}, {
“cell_type”: “code”, “execution_count”: 76, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([ 89.565, 292.793])”
]
}, “execution_count”: 76, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Get water and ethanol flows summed across all phasesn”, “s_eq.get_flow(‘kg/hr’, (‘Water’, ‘Ethanol’))”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“However, setting chemical data of MultiStream objects requires the phase to be specified:”
]
}, {
“cell_type”: “code”, “execution_count”: 77, “metadata”: {}, “outputs”: [
- {
“ename”: “IndexError”, “evalue”: “multiple phases present; must include phase key to set chemical data”, “output_type”: “error”, “traceback”: [
“u001b[1;31m—————————————————————————u001b[0m”, “u001b[1;31mIndexErroru001b[0m Traceback (most recent call last)”, “u001b[1;32m<ipython-input-77-d6cf98178f52>u001b[0m in u001b[0;36m<module>u001b[1;34mu001b[0mnu001b[1;32m—-> 1u001b[1;33m u001b[0ms_equ001b[0mu001b[1;33m.u001b[0mu001b[0mset_flowu001b[0mu001b[1;33m(u001b[0mu001b[1;33m[u001b[0mu001b[1;36m10u001b[0mu001b[1;33m,u001b[0m u001b[1;36m20u001b[0mu001b[1;33m]u001b[0mu001b[1;33m,u001b[0m u001b[1;34m’kg/hr’u001b[0mu001b[1;33m,u001b[0m u001b[1;33m(u001b[0mu001b[1;34m’Water’u001b[0mu001b[1;33m,u001b[0m u001b[1;34m’Ethanol’u001b[0mu001b[1;33m)u001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0m”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\_multi_stream.pyu001b[0m in u001b[0;36mset_flowu001b[1;34m(self, data, units, key)u001b[0mnu001b[0;32m 308u001b[0m u001b[0mnameu001b[0mu001b[1;33m,u001b[0m u001b[0mfactoru001b[0m u001b[1;33m=u001b[0m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0m_get_flow_name_and_factoru001b[0mu001b[1;33m(u001b[0mu001b[0munitsu001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 309u001b[0m u001b[0mindexeru001b[0m u001b[1;33m=u001b[0m u001b[0mgetattru001b[0mu001b[1;33m(u001b[0mu001b[0mselfu001b[0mu001b[1;33m,u001b[0m u001b[1;34m’i’u001b[0m u001b[1;33m+u001b[0m u001b[0mnameu001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[1;32m–> 310u001b[1;33m u001b[0mindexeru001b[0mu001b[1;33m[u001b[0mu001b[0mkeyu001b[0mu001b[1;33m]u001b[0m u001b[1;33m=u001b[0m u001b[0mnpu001b[0mu001b[1;33m.u001b[0mu001b[0masarrayu001b[0mu001b[1;33m(u001b[0mu001b[0mdatau001b[0mu001b[1;33m,u001b[0m u001b[0mdtypeu001b[0mu001b[1;33m=u001b[0mu001b[0mfloatu001b[0mu001b[1;33m)u001b[0m u001b[1;33m/u001b[0m u001b[0mfactoru001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0mu001b[0;32m 311u001b[0m u001b[1;33mu001b[0mu001b[0mnu001b[0;32m 312u001b[0m u001b[1;31m### Stream data ###u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\indexer.pyu001b[0m in u001b[0;36m__setitem__u001b[1;34m(self, key, data)u001b[0mnu001b[0;32m 484u001b[0m u001b[0mindexu001b[0m u001b[1;33m=u001b[0m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0mget_indexu001b[0mu001b[1;33m(u001b[0mu001b[0mkeyu001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 485u001b[0m u001b[1;32mifu001b[0m u001b[0misau001b[0mu001b[1;33m(u001b[0mu001b[0mindexu001b[0mu001b[1;33m,u001b[0m u001b[0mChemicalIndexu001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[1;32m–> 486u001b[1;33m raise IndexError("multiple phases present; must include phase key "nu001b[0mu001b[0;32m 487u001b[0m "to set chemical data") nu001b[0;32m 488u001b[0m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0m_datau001b[0mu001b[1;33m[u001b[0mu001b[0mindexu001b[0mu001b[1;33m]u001b[0m u001b[1;33m=u001b[0m u001b[0mdatau001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;31mIndexErroru001b[0m: multiple phases present; must include phase key to set chemical data”
]
}
], “source”: [
“s_eq.set_flow([10, 20], ‘kg/hr’, (‘Water’, ‘Ethanol’))”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Similar to Stream objects, all flow rates can be accessed through the imol, imass, and ivol attributes:”
]
}, {
“cell_type”: “code”, “execution_count”: 78, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“MolarFlowIndexer (kmol/hr):n”, ” (g) Water 3.861n”, ” Ethanol 6.139n”, ” (l) Water 1.11n”, ” Ethanol 0.2171n”
]
}
], “source”: [
“s_eq.imol # Molar flow rates”
]
}, {
“cell_type”: “code”, “execution_count”: 79, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“1.1101687012358397”
]
}, “execution_count”: 79, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Index a single chemical in the liquid phasen”, “s_eq.imol[‘l’, ‘Water’]”
]
}, {
“cell_type”: “code”, “execution_count”: 80, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([0.217, 1.11 ])”
]
}, “execution_count”: 80, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Index multiple chemicals in the liquid phasen”, “s_eq.imol[‘l’, (‘Ethanol’, ‘Water’)]”
]
}, {
“cell_type”: “code”, “execution_count”: 81, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([3.861, 6.139, 0. ])”
]
}, “execution_count”: 81, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Index the vapor phasen”, “s_eq.imol[‘g’]”
]
}, {
“cell_type”: “code”, “execution_count”: 82, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([6.356, 4.972])”
]
}, “execution_count”: 82, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“# Index flow of chemicals summed across all phasesn”, “s_eq.imol[‘Ethanol’, ‘Water’]”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Because multiple phases are present, overall chemical flows in MultiStream objects cannot be set like in Stream objects:”
]
}, {
“cell_type”: “code”, “execution_count”: 83, “metadata”: {}, “outputs”: [
- {
“ename”: “IndexError”, “evalue”: “multiple phases present; must include phase key to set chemical data”, “output_type”: “error”, “traceback”: [
“u001b[1;31m—————————————————————————u001b[0m”, “u001b[1;31mIndexErroru001b[0m Traceback (most recent call last)”, “u001b[1;32m<ipython-input-83-fcb482ddb0a2>u001b[0m in u001b[0;36m<module>u001b[1;34mu001b[0mnu001b[1;32m—-> 1u001b[1;33m u001b[0ms_equ001b[0mu001b[1;33m.u001b[0mu001b[0mimolu001b[0mu001b[1;33m[u001b[0mu001b[1;34m’Ethanol’u001b[0mu001b[1;33m,u001b[0m u001b[1;34m’Water’u001b[0mu001b[1;33m]u001b[0m u001b[1;33m=u001b[0m u001b[1;33m[u001b[0mu001b[1;36m1u001b[0mu001b[1;33m,u001b[0m u001b[1;36m0u001b[0mu001b[1;33m]u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0m”, “u001b[1;32m~\OneDrive\Code\thermosteam\thermosteam\indexer.pyu001b[0m in u001b[0;36m__setitem__u001b[1;34m(self, key, data)u001b[0mnu001b[0;32m 484u001b[0m u001b[0mindexu001b[0m u001b[1;33m=u001b[0m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0mget_indexu001b[0mu001b[1;33m(u001b[0mu001b[0mkeyu001b[0mu001b[1;33m)u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0;32m 485u001b[0m u001b[1;32mifu001b[0m u001b[0misau001b[0mu001b[1;33m(u001b[0mu001b[0mindexu001b[0mu001b[1;33m,u001b[0m u001b[0mChemicalIndexu001b[0mu001b[1;33m)u001b[0mu001b[1;33m:u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[1;32m–> 486u001b[1;33m raise IndexError("multiple phases present; must include phase key "nu001b[0mu001b[0;32m 487u001b[0m "to set chemical data") nu001b[0;32m 488u001b[0m u001b[0mselfu001b[0mu001b[1;33m.u001b[0mu001b[0m_datau001b[0mu001b[1;33m[u001b[0mu001b[0mindexu001b[0mu001b[1;33m]u001b[0m u001b[1;33m=u001b[0m u001b[0mdatau001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mn”, “u001b[1;31mIndexErroru001b[0m: multiple phases present; must include phase key to set chemical data”
]
}
], “source”: [
“s_eq.imol[‘Ethanol’, ‘Water’] = [1, 0]”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Chemical flows must be set by phase:”
]
}, {
“cell_type”: “code”, “execution_count”: 84, “metadata”: {}, “outputs”: [], “source”: [
“s_eq.imol[‘l’, (‘Ethanol’, ‘Water’)] = [1, 0]”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“One main difference between a [MultiStream](../MultiStream.txt) object and a [Stream](../Stream.txt) object is that the mol attribute no longer stores any data, it simply returns the total flow rate of each chemical. Setting an element of the array raises an error to prevent the wrong assumption that the data is linked:”
]
}, {
“cell_type”: “code”, “execution_count”: 85, “metadata”: {}, “outputs”: [
- {
- “data”: {
- “text/plain”: [
“array([3.861, 7.139, 0. ])”
]
}, “execution_count”: 85, “metadata”: {}, “output_type”: “execute_result”
}
], “source”: [
“s_eq.mol”
]
}, {
“cell_type”: “code”, “execution_count”: 86, “metadata”: {}, “outputs”: [
- {
“ename”: “ValueError”, “evalue”: “assignment destination is read-only”, “output_type”: “error”, “traceback”: [
“u001b[1;31m—————————————————————————u001b[0m”, “u001b[1;31mValueErroru001b[0m Traceback (most recent call last)”, “u001b[1;32m<ipython-input-86-632093460ce3>u001b[0m in u001b[0;36m<module>u001b[1;34mu001b[0mnu001b[1;32m—-> 1u001b[1;33m u001b[0ms_equ001b[0mu001b[1;33m.u001b[0mu001b[0mmolu001b[0mu001b[1;33m[u001b[0mu001b[1;36m0u001b[0mu001b[1;33m]u001b[0m u001b[1;33m=u001b[0m u001b[1;36m1u001b[0mu001b[1;33mu001b[0mu001b[1;33mu001b[0mu001b[0mnu001b[0m”, “u001b[1;31mValueErroru001b[0m: assignment destination is read-only”
]
}
], “source”: [
“s_eq.mol[0] = 1”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Note that for both Stream and MultiStream objects, get_flow, imol, and mol return chemical flows across all phases when given only chemical IDs.”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“### Liquid-liquid equilibrium”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Liquid-liquid equilibrium (LLE) only requires the temperature. Pressure is not a significant variable as liquid fungacity coefficients are not a strong function of pressure. “
]
}, {
“cell_type”: “code”, “execution_count”: 87, “metadata”: {}, “outputs”: [
- {
“name”: “stdout”, “output_type”: “stream”, “text”: [
“MultiStream: liquid_mixturen”, ” phases: (‘L’, ‘l’), T: 300 K, P: 101325 Pan”, ” flow (kmol/hr): (L) Water 98.54n”, ” Butanol 1.209n”, ” Octane 0.001977n”, ” (l) Water 1.458n”, ” Butanol 3.791n”, ” Octane 100n”
]
}
], “source”: [
“tmo.settings.set_thermo([‘Water’, ‘Butanol’, ‘Octane’])n”, “liquid_mixture = tmo.Stream(‘liquid_mixture’, Water=100, Octane=100, Butanol=5)n”, “liquid_mixture.lle(T=300)n”, “liquid_mixture.show()”
]
}, {
“cell_type”: “markdown”, “metadata”: {}, “source”: [
“Compared to VLE, LLE is several orders of magnitude times slower. This is because differential evolution, a purely stochastic method, is used to find the solution that globally minimizes the gibb’s free energy of both phases. For now, the LLE algorithm may not present completely accurate results and is subject to change in the future.”
]
}
], “metadata”: {
- “kernelspec”: {
“display_name”: “Python 3”, “language”: “python”, “name”: “python3”
}, “language_info”: {
- “codemirror_mode”: {
“name”: “ipython”, “version”: 3
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