CoCalc -- n-d-M.ipynb

Jupyter notebook n-d-M.ipynb

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Kernel: Python 3

In [1]:

import numpy as np
from numpy import exp, log, abs
import matplotlib.pyplot as plt

def M(SG, t):
    T=t+273.15
    return 42.965*exp(2.097E-4*T-7.78712*SG+2.08476E-3*T*SG)*(T**1.26007)*(SG**4.98308)

def ndM(n,d,M,S=0):
    w = (d - 0.851) - 1.11*(n - 1.475)
    v = 2.51*(n - 1.475) - (d - 0.851)
    if v > 0:
        a, b = 430, 0.055
    else:
        a, b = 670, 0.080
    if w > 0:
        CR = 820*w - 3*S + 10000/M
        RT = 1.33 + 0.146*M*(w - 0.005*S)
    else:
        CR = 1440*w - 3*S + 10600/M
        RT = 1.33 + 0.180*M*(w - 0.005*S)
    RA = 0.44 + b*v*M
    RN = RT - RA
    CA = a*v + 3660/M 
    CN = CR - CA 
    CP = 100 - CR
    return CP,CN,CA, RT,RA,RN

def TBPs(x, A, B, To):
    To+=273.15
    T=To*(A/B*log(1/(1-x)))**(1/B)+To
    return T-273.15

TBP = np.vectorize(TBPs)

In [46]:

x = np.arange(0, 1, 0.01);
#y = [TBP(xx, 0.0125, 1.80881, 525) for xx in x]
y1 = TBP(x, 0.5, 1.8, 50)
y2 = TBP(x, 0.5, 3.0, 50)
#plt.xkcd()
plt.grid()
plt.xlabel("vol. %")
plt.ylabel("t, °C")
plt.plot(x*100, y1)+plt.plot(x*100, y2)

Out[46]:

[<matplotlib.lines.Line2D at 0x7fc27201e320>,
 <matplotlib.lines.Line2D at 0x7fc271df8b38>]

In [26]:

Ant_db=[
    ["C5H12",          309.22, 9.159361, 2451.885,  -41.136],
    ["C6H14",           341.88, 9.213541, 2696.039,  -48.833],
    ["C7H16",          371.57, 9.256922, 2910.258,  -56.718],
    ["C8H18",           398.82, 9.327197, 3123.134,  -63.515],
    ["C9H20",           423.97, 9.379719, 3311.186,  -70.456],
    ["C10H22",           447.3,  9.368137, 3442.756,  -79.292],
    ["C11H24",         469.08, 9.433921, 3614.068,  -85.450],
    ["C12H26",         489.48, 9.493213, 3774.559,  -91.310],
    ["C13H28",        508.63, 9.515341, 3892.912,  -98.930],
    ["C14H30",      526.76, 9.527867, 4008.524, -105.430],
    ["C15H32",      543.83, 9.552251, 4121.512, -111.770],
    ["C16H34",       559.98, 9.563948, 4214.905, -118.700],
    ["C17H36",      574.56, 9.53086,  4294.551, -123.950],
    ["C18H38",       588.3,  9.502999, 4361.787, -129.850],
    ["C19H40",       602.34, 9.533163, 4450.436, -135.550],
    ["C20H42",         616.84, 9.848387, 4680.465, -141.050],
    ["Циклопентан",     322.38, 9.366525, 2653.900,  -38.640],
    ["Метилциклопентан",344.98,9.629388, 2983.098,  -34.760],
    ["Этилциклопентан",376.59, 9.219735, 2978.882,  -53.030],
    ["Циклогексан",      353.93, 9.049205, 2723.438,  -52.532],
    ["Метилциклогексан",374.09, 9.169631, 2972.564,  -49.449],
    ["Бензол",          353.24, 9.176331, 2726.813,  -55.578],
    ["Толуол",          383.79, 9.32646,  3056.958,  -55.525],
    ["Этилбензол",     409.36, 9.368321, 3259.931,  -60.850],
    ["Пропилбензол",    432.35, 9.38681,  3434.996,  -65.900],
    ["Бутилбензол",     456.42, 9.448543, 3627.654,  -71.950],
    ["о-Ксилол",         417.59, 9.43574,  3358.795,  -61.109],
    ["м-Ксилол",         412.34, 9.533877, 3381.814,  -57.030],
    ["п-Ксилол",         411.53, 9.451974, 3331.454,  -58.523]
]

def ant(t, A, B, C):
    """Уравнение Антуана для расчета давления насыщенного пара (бар)"""
    return exp(A-B/(t+273.15+C))

antl = np.vectorize(lambda t, n: ant(t, Ant_db[n][2], Ant_db[n][3], Ant_db[n][4]), excluded=["A", "B", "C"])
#antl = lambda t, n: ant(t, Ant_db[n][2], Ant_db[n][3], Ant_db[n][4])
def bt(t, n1, n2, x):
    """Расчет давления насыщенного при температуре t пара двухкомпонентной смеси жидкостей n1 и n2 по состава x"""
    return x*antl(t, n1)+(1-x)*antl(t, n2)

In [27]:

from scipy import optimize
P = 1.01325
while True:
    n1,n2=np.random.choice(len(Ant_db),2)
    if abs(Ant_db[n1][1]-Ant_db[n2][1])>10:
        break
xi=np.linspace(0, 1, 201)
print(Ant_db[n1][1], Ant_db[n2][1])
# Температуры кипения (Bubble Point)
bp=np.array([optimize.fsolve(lambda t: bt(t, n1, n2, x)-P, Ant_db[n1][1]*x+(1-x)*Ant_db[n2][1]) for x in xi]).flatten()
yi=xi*antl(bp, n1)/P
plt.grid()
plt.xlim([0,100])
plt.xlabel("Mol. %% %s"%(Ant_db[n1][0]))
plt.ylabel("t, °C")
plt.title("%s - %s"%(Ant_db[n2][0], Ant_db[n1][0]))
plt.plot(xi*100, bp)+plt.plot(yi*100, bp)
plt.

Out[27]:

469.08 374.09

[<matplotlib.lines.Line2D at 0x7f2173f71c18>,
 <matplotlib.lines.Line2D at 0x7f216988da58>]

In [18]:

bp[-1]

Out[18]:

183.27354476658181

In [ ]:

def f(x):
    return x**3

y = 2.0
a, b = 0.0, 2.0
xmin, rmin = a, (f(a)-y)**2
for s in range(30):
    for i in range(1,11):
        x = a + i*(b-a)/10.0
        r = (f(x)-y)**2
        if r<rmin: xmin, rmin = x, r
    a, b = xmin-(b-a)/10.0, xmin+(b-a)/10.0
    print(xmin)

In [ ]:

2**(1/3.0)

In [10]:

bt(, n1, n2, x)-P

Out[10]:

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-10-c070dbc47821> in <module>()
----> 1 bt(t, n1, n2, x)-P

NameError: name 't' is not defined

In [41]:

yi.reshape()

Out[41]:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-41-2982e7b2205e> in <module>()
----> 1 yi.reshape()

TypeError: function takes exactly 1 argument (0 given)

In [43]:

antl(bp,1).flatten()*xi

Out[43]:

array([ 0.        ,  0.21057646,  0.41025249,  0.59968927,  0.77951167,
        0.95030918,  1.112637  ,  1.26701726,  1.41394036,  1.55386632,
        1.68722625,  1.81442379,  1.93583658,  2.05181768,  2.16269701,
        2.26878273,  2.37036259,  2.46770519,  2.56106125,  2.65066475,
        2.73673407,  2.81947304,  2.89907191,  2.97570832,  3.04954816,
        3.12074638,  3.18944778,  3.25578769,  3.31989268,  3.38188115,
        3.44186389,  3.49994466,  3.55622068,  3.61078303,  3.66371716,
        3.71510323,  3.7650165 ,  3.81352766,  3.86070316,  3.90660546,
        3.95129338,  3.99482225,  4.03724422,  4.07860845,  4.11896129,
        4.15834649,  4.19680536,  4.23437693,  4.27109811,  4.3070038 ,
        4.34212702,  4.37649907,  4.4101496 ,  4.4431067 ,  4.47539704,
        4.50704592,  4.53807738,  4.56851426,  4.59837826,  4.62769003,
        4.65646923,  4.68473458,  4.7125039 ,  4.73979418,  4.76662164,
        4.79300174,  4.81894924,  4.84447824,  4.86960222,  4.89433405,
        4.91868605,  4.94267002,  4.96629722,  4.98957847,  5.01252413,
        5.03514412,  5.05744796,  5.07944478,  5.10114334,  5.12255207,
        5.14367904,  5.16453202,  5.18511849,  5.20544562,  5.22552032,
        5.24534925,  5.2649388 ,  5.28429514,  5.30342422,  5.32233177,
        5.34102329,  5.35950412,  5.3777794 ,  5.39585409,  5.41373298,
        5.43142068,  5.44892167,  5.46624027,  5.48338064,  5.50034684,
        5.51714275,  5.53377216,  5.55023873,  5.566546  ,  5.58269739,
        5.59869622,  5.61454573,  5.63024901,  5.64580911,  5.66122895,
        5.67651137,  5.69165915,  5.70667495,  5.72156138,  5.73632097,
        5.75095616,  5.76546935,  5.77986285,  5.79413891,  5.80829971,
        5.82234739,  5.83628402,  5.85011161,  5.86383212,  5.87744746,
        5.89095949,  5.90437   ,  5.91768077,  5.93089352,  5.9440099 ,
        5.95703155,  5.96996006,  5.98279698,  5.99554381,  6.00820202,
        6.02077306,  6.03325832,  6.04565918,  6.05797695,  6.07021296,
        6.08236846,  6.09444471,  6.1064429 ,  6.11836424,  6.13020987,
        6.14198093,  6.15367852,  6.16530373,  6.17685761,  6.18834119,
        6.19975548,  6.21110148,  6.22238015,  6.23359244,  6.24473926,
        6.25582154,  6.26684015,  6.27779597,  6.28868983,  6.29952259,
        6.31029505,  6.32100801,  6.33166225,  6.34225855,  6.35279765,
        6.36328029,  6.37370719,  6.38407906,  6.3943966 ,  6.40466048,
        6.41487137,  6.42502993,  6.43513679,  6.4451926 ,  6.45519797,
        6.4651535 ,  6.47505979,  6.48491742,  6.49472697,  6.50448901,
        6.51420407,  6.52387272,  6.53349548,  6.54307287,  6.55260542,
        6.56209362,  6.57153797,  6.58093896,  6.59029707,  6.59961278,
        6.60888654,  6.61811881,  6.62731004,  6.63646067,  6.64557113,
        6.65464185,  6.66367325,  6.67266573,  6.68161971,  6.69053559,
        6.69941374])

In [24]:

Ant_db={
    "C5H12":            [309.22, 9.159361, 2451.885,  -41.136],
    "C6H14":            [341.88, 9.213541, 2696.039,  -48.833],
    "C7H16":            [371.57, 9.256922, 2910.258,  -56.718],
    "C8H18":            [398.82, 9.327197, 3123.134,  -63.515],
    "C9H20":            [423.97, 9.379719, 3311.186,  -70.456],
    "C10H22":           [447.3,  9.368137, 3442.756,  -79.292],
    "C11H24":           [469.08, 9.433921, 3614.068,  -85.450],
    "C12H26":           [489.48, 9.493213, 3774.559,  -91.310],
    "C13H28":           [508.63, 9.515341, 3892.912,  -98.930],
    "C14H30":           [526.76, 9.527867, 4008.524, -105.430],
    "C15H32":           [543.83, 9.552251, 4121.512, -111.770],
    "C16H34":           [559.98, 9.563948, 4214.905, -118.700],
    "C17H36":           [574.56, 9.53086,  4294.551, -123.950],
    "C18H38":           [588.3,  9.502999, 4361.787, -129.850],
    "C19H40":           [602.34, 9.533163, 4450.436, -135.550],
    "C20H42":           [616.84, 9.848387, 4680.465, -141.050],
    "Циклопентан":      [322.38, 9.366525, 2653.900,  -38.640],
    "Метилциклопентан": [344.98, 9.629388, 2983.098,  -34.760],
    "Этилциклопентан":  [376.59, 9.219735, 2978.882,  -53.030],
    "Циклогексан":      [353.93, 9.049205, 2723.438,  -52.532],
    "Метилциклогексан": [374.09, 9.169631, 2972.564,  -49.449],
    "Бензол":           [353.24, 9.176331, 2726.813,  -55.578],
    "Толуол":           [383.79, 9.32646,  3056.958,  -55.525],
    "Этилбензол":       [409.36, 9.368321, 3259.931,  -60.850],
    "Пропилбензол":     [432.35, 9.38681,  3434.996,  -65.900],
    "Бутилбензол":      [456.42, 9.448543, 3627.654,  -71.950],
    "о-Ксилол":         [417.59, 9.43574,  3358.795,  -61.109],
    "м-Ксилол":         [412.34, 9.533877, 3381.814,  -57.030],
    "п-Ксилол":         [411.53, 9.451974, 3331.454,  -58.523]
}
np.random.choice(list(Ant_db.keys()))

Out[24]:

'C9H20'

In [ ]:

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