The Kitchin Research Group

We start with \(G=\sum\limits_j n_j \mu_j\). Recalling that we define \(\mu_j = G_j^\circ + RT \ln a_j\), and in the ideal gas limit, \(a_j = y_j P/P^\circ\), and that \(y_j = \frac{n_j}{\sum n_j}\). Since in this problem, P = 1 atm, this leads to the function \(\frac{G}{RT} = \sum\limits_{j=1}^n n_j\left(\frac{G_j^\circ}{RT} + \ln \frac{n_j}{\sum n_j}\right)\).

import numpy as np

def func(nj):
    nj = np.array(nj)
    Enj = np.sum(nj);
    G = np.sum(nj * (Gjo / R / T + np.log(nj / Enj)))
    return G

The total number of each type of atom must be the same as what entered the reactor. These form equality constraints on the equilibrium composition. We express these constraints as: \(A_{eq} n = b\) where \(n\) is a vector of the moles of each species present in the mixture. CH4 C2H4 C2H2 CO2 CO O2 H2 H2O C2H6

Aeq = np.array([[0,   0,    0,   2,   1,  2,  0,  1,   0],      # oxygen balance
                [4,   4,    2,   0,   0,  0,  2,  2,   6],      # hydrogen balance
                [1,   2,    2,   1,   1,  0,  0,  0,   2]])     # carbon balance

# the incoming feed was 4 mol H2O and 1 mol ethane
beq = np.array([4,  # moles of oxygen atoms coming in
                14, # moles of hydrogen atoms coming in
                2]) # moles of carbon atoms coming in

def ec1(n):
    'equality constraint'
    return np.dot(Aeq, n) - beq

def ic1(n):
    '''inequality constraint
       all n>=0
    '''   
    return n

Now we solve the problem.

# initial guess suggested in the example
n0 = [1e-3, 1e-3, 1e-3, 0.993, 1.0, 1e-4, 5.992, 1.0, 1e-3] 

n0 = [0.066, 8.7e-08, 2.1e-14, 0.545, 1.39, 5.7e-14, 5.346, 1.521, 1.58e-7]

from scipy.optimize import fmin_slsqp

X = fmin_slsqp(func, n0, f_eqcons=ec1,f_ieqcons=ic1, iter=300, acc=1e-12)

for s,x in zip(species, X):
    print '{0:10s} {1:1.4g}'.format(s, x)

# check that constraints were met
print np.dot(Aeq, X) - beq
print np.all( np.abs( np.dot(Aeq, X) - beq) < 1e-12)

>>> >>> >>> >>> >>> >>> Optimization terminated successfully.    (Exit mode 0)
            Current function value: -104.403951524
            Iterations: 16
            Function evaluations: 193
            Gradient evaluations: 15
>>> ... ... CH4        0.06644
C2H4       9.48e-08
C2H2       1.487e-13
CO2        0.545
CO         1.389
O2         3.096e-13
H2         5.346
H2O        1.521
C2H6       1.581e-07
... [  0.00000000e+00   0.00000000e+00   4.44089210e-16]
True

I found it necessary to tighten the accuracy parameter to get pretty good matches to the solutions found in Matlab. It was also necessary to increase the number of iterations. Even still, not all of the numbers match well, especially the very small numbers. You can, however, see that the constraints were satisfied pretty well.

Interestingly there is a distribution of products! That is interesting because only steam and ethane enter the reactor, but a small fraction of methane is formed! The main product is hydrogen. The stoichiometry of steam reforming is ideally \(C_2H_6 + 4H_2O \rightarrow 2CO_2 + 7 H2\). Even though nearly all the ethane is consumed, we do not get the full yield of hydrogen. It appears that another equilibrium, one between CO, CO2, H2O and H2, may be limiting that, since the rest of the hydrogen is largely in the water. It is also of great importance that we have not said anything about reactions, i.e. how these products were formed.

The water gas shift reaction is: \(CO + H_2O \rightleftharpoons CO_2 + H_2\). We can compute the Gibbs free energy of the reaction from the heats of formation of each species. Assuming these are the formation energies at 1000K, this is the reaction free energy at 1000K.

G_wgs = Gjo[3] + Gjo[6] - Gjo[4] - Gjo[7]
print G_wgs

K = np.exp(-G_wgs / (R*T))
print K

-0.638
>>> >>> 1.37887528109

One normally uses activities to define the equilibrium constant. Since there are the same number of moles on each side of the reaction all factors that convert mole numbers to activity, concentration or pressure cancel, so we simply consider the ratio of mole numbers here.

print (X[3] * X[6]) / (X[4] * X[7])

1.37887525547

This is very close to the equilibrium constant computed above.

Clearly, there is an equilibrium between these species that prevents the complete reaction of steam reforming.

This is an appealing way to minimize the Gibbs energy of a mixture. No assumptions about reactions are necessary, and the constraints are easy to identify. The Gibbs energy function is especially easy to code.