Nonlinear Optimization Examples

Example 11.1: Chemical Equilibrium

The following example is used in many test libraries for nonlinear programming. It appeared originally in Bracken and McCormick (1968).

The problem is to determine the composition of a mixture of various chemicals that satisfy the mixture's chemical equilibrium state. The second law of thermodynamics implies that at a constant temperature and pressure, a mixture of chemicals satisfies its chemical equilibrium state when the free energy of the mixture is reduced to a minimum. Therefore, the composition of the chemicals satisfying its chemical equilibrium state can be found by minimizing the free energy of the mixture.

The following notation is used in this problem:

mnumber of chemical elements in the mixture
nnumber of compounds in the mixture
x_jnumber of moles for compound j, j=1, ... ,n
stotal number of moles in the mixture, s=\sum_{i=1}^n x_j
a_{ij}number of atoms of element i in a molecule of compound j
b_iatomic weight of element i in the mixture i=1, ... ,n

The constraints for the mixture are as follows. Each of the compounds must have a nonnegative number of moles.

x_j \geq 0 ,  j=1, ... ,n
There is a mass balance relationship for each element. Each relation is given by a linear equality constraint.
\sum_{j=1}^n a_{ij} x_j = b_i ,  i=1, ... ,m
The objective function is the total free energy of the mixture.
f(x) = \sum_{j=1}^n x_j [c_j +    \ln ( {x_j \over s} ) ]
where
c_j = ( {f^0 \over rt} )_j + \ln(p)
and ( f^0/rt )_j is the model standard free energy function for the jth compound. The value of ( f^0/rt )_j is found in existing tables. p is the total pressure in atmospheres.

The problem is to determine the parameters x_j that minimize the objective function f(x) subject to the nonnegativity and linear balance constraints. To illustrate this, consider the following situation. Determine the equilibrium composition of compound \frac{1}2 n_2 h_4 + \frac{1}2 o_2 at temperature t = 3500^{\circ} k and pressure p = 750  {psi}. The following table gives a summary of the information necessary to solve the problem.

  a_{ij}
  i=1 i=2 i=3
jCompound(f^0/rt)_jc_jHNO
1h -10.021-6.0891  
2h_2-21.096-17.1642  
3h_2o-37.986-34.0542 1
4n-9.846-5.914 1 
5n_2-28.653-24.721 2 
6nh-18.918-14.98611 
7no-28.032-24.100 11
8o-14.640-10.708  1
9o_2-30.594-26.662  2
10oh-26.111-22.1791 1

The following statements solve the minimization problem:

  
    proc iml; 
       c = { -6.089 -17.164 -34.054  -5.914 -24.721 
             -14.986 -24.100 -10.708 -26.662 -22.179 }; 
       start F_BRACK(x) global(c); 
          s = x[+]; 
          f = sum(x # (c + log(x / s))); 
          return(f); 
       finish F_BRACK; 
  
       con = { .  .  .  .  .  .  .  .  .  .    .   .  , 
               .  .  .  .  .  .  .  .  .  .    .   .  , 
               1. 2. 2. .  .  1. .  .  .  1.   0.  2. , 
               .  .  .  1. 2. 1. 1. .  .  .    0.  1. , 
               .  .  1. .  .  .  1. 1. 2. 1.   0.  1. }; 
       con[1,1:10] = 1.e-6; 
  
       x0 = j(1,10, .1); 
       optn = {0 3}; 
  
       title 'NLPTR subroutine: No Derivatives'; 
       call nlptr(xres,rc,"F_BRACK",x0,optn,con);
 

The F_BRACK module specifies the objective function, f(x). The matrix CON specifies the constraints. The first row gives the lower bound for each parameter, and to prevent the evaluation of the \log(x) function for values of x that are too small, the lower bounds are set here to 1E-6. The following three rows contain the three linear equality constraints.

The starting point, which must be given to specify the number of parameters, is represented by X0. The first element of the OPTN vector specifies a minimization problem, and the second element specifies the amount of printed output.

The CALL NLPTR statement runs trust-region minimization. In this case, since no analytic derivatives are specified, the F_BRACK module is used to generate finite-difference approximations for the gradient vector and Hessian matrix.

The output is shown in the following figures. The iteration history does not show any problems.

Optimization Start
Active Constraints 3 Objective Function -45.05516448
Max Abs Gradient Element 4.4710303342 Radius 1

Iteration   Restarts Function
Calls
Active
Constraints
  Objective
Function
Objective
Function
Change
Max Abs
Gradient
Element
Lambda Trust
Region
Radius
1   0 2 3 ' -47.33413 2.2790 4.3613 2.456 1.000
2   0 3 3 ' -47.70051 0.3664 7.0044 0.908 0.418
3   0 4 3   -47.73117 0.0307 5.3051 0 0.359
4   0 5 3   -47.73426 0.00310 3.7015 0 0.118
5   0 6 3   -47.73982 0.00555 2.3054 0 0.0169
6   0 7 3   -47.74846 0.00864 1.3029 90.184 0.00476
7   0 9 3   -47.75796 0.00950 0.5073 0 0.0134
8   0 10 3   -47.76094 0.00297 0.0988 0 0.0124
9   0 11 3   -47.76109 0.000155 0.00447 0 0.0111
10   0 12 3   -47.76109 3.385E-7 0.000011 0 0.00332

Optimization Results
Iterations 10 Function Calls 13
Hessian Calls 11 Active Constraints 3
Objective Function -47.76109086 Max Abs Gradient Element 7.3901293E-6
Lambda 0 Actual Over Pred Change 0
Radius 0.0033214552    



The output lists the optimal parameters with the gradient.

Optimization Results
Parameter Estimates
N Parameter Estimate Gradient
Objective
Function
1 X1 0.040668 -9.785055
2 X2 0.147730 -19.570111
3 X3 0.783154 -34.792170
4 X4 0.001414 -12.968920
5 X5 0.485247 -25.937841
6 X6 0.000693 -22.753976
7 X7 0.027399 -28.190992
8 X8 0.017947 -15.222060
9 X9 0.037314 -30.444119
10 X10 0.096871 -25.007115

Value of Objective Function = -47.76109086



The three equality constraints are satisfied at the solution.

Linear Constraints Evaluated at Solution
1 ACT -3.053E-16 = -2.0000 + 1.0000 * X1 + 2.0000 * X2 + 2.0000 * X3 + 1.0000 * X6 + 1.0000 * X10
2 ACT -1.735E-17 = -1.0000 + 1.0000 * X4 + 2.0000 * X5 + 1.0000 * X6 + 1.0000 * X7        
3 ACT -1.527E-16 = -1.0000 + 1.0000 * X3 + 1.0000 * X7 + 1.0000 * X8 + 2.0000 * X9 + 1.0000 * X10



The Lagrange multipliers and the projected gradient are also printed. The elements of the projected gradient must be small to satisfy a first-order optimality condition.

First Order Lagrange Multipliers
Active Constraint Lagrange
Multiplier
Linear EC [1] -9.785055
Linear EC [2] -12.968922
Linear EC [3] -15.222061

Projected Gradient
Free
Dimension
Projected
Gradient
1 0.000000328
2 -9.703359E-8
3 3.2183113E-8
4 -0.000007390
5 -0.000005172
6 -0.000005669
7 -0.000000937



Previous Page | Next Page | Top of Page