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Formulating the Optimization Problem

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Formulating the Optimization Problem Powered By Docstoc
					  FORMULATING THE OPTIMIZATION
           PROBLEM
  “If do not know where we are going, any path will do.”
  •   The importance of formulation
  •   The standard formulation
  •   Degrees of Freedom
  •   The operating window


DOFASCO: We find it useful to formulate the optimization
problem completely, even if we cannot solve it.
     FORMULATING THE OPTIMIZATION
              PROBLEM

While walking home after the first Chem. Eng. 4G03 class,
you began to worry about passing the course.

Fortunately, you notice a magic
lantern, which you rub. A Genie
appears and grants you ONE wish.
You wish for the highest grade in
4G03.
What happens?
What did the Genie do to grant your wish?
                                     Your classmates
                  you




Oh no! The Genie tricks the class,
minus you, into a bus that runs over a
cliff. Everyone in the bus perishes;
but you receive the highest grade in
4G03.
You are very mad at the Genie, but he
says that he only did what you asked.
      FORMULATING THE OPTIMIZATION
               PROBLEM
LESSONS LEARNED
•   The Genie gave you what you asked for - not what you
    intended.
•   An Optimizer is like the Genie. Therefore, we must
    formulate the problem carefully and check the results for
    surprises!
•   If we maximize profit, can we
    - pollution the environment?
    - endanger workers?
    - produce products that are shabby or dangerous?
    - design a plant that only functions for six months?
 FORMULATING THE OPTIMIZATION
          PROBLEM
This is the general formulation that we will be using
               throughout the course

 max P                           Objective function
    x

 s.t .
 h( x )  0                      Equality constraints
                                 Inequality constraints
 g( x )  0
 x min  x  x max               Variable Bounds
                                                         max P
                                                            x


 FORMULATING THE                                         s.t .
                                                         h( x )  0
 OPTIMIZATION PROBLEM                                    g( x )  0
                                                         x min  x  x max

Objective   This is the goal or objective, e.g.,
Function      - maximize profit (minimize cost)
              - minimize energy use
max P         - minimize polluting effluents
  X           - minimize mass to construct a vessel
            We will formulate most problems with a scalar objective function,
            i.e., a single value.
min P       This should represent the full effect of x on the objective. For
  X
            example, $/kg is not a good objective unless kg is fixed. When
            needed, include time-value of money.

            Also, we need a quantitative measure, not “good” or “bad”.

            The value of the material may depend on the composition,
            enthalpy, flow rate, etc.

            The symbol “x” represents the variables. It is a vector.
                                             max P
                                                x


FORMULATING THE                              s.t .
                                             h( x )  0
OPTIMIZATION PROBLEM                         g( x )  0
                                             x min  x  x max


Some comments on the objective function
•   A scalar is preferred for solving. However, multiple
    objectives are typical in real life.
•   Note that Max (P) is the same as Min (-P)
•   Sometimes we use a simple, physical variable, such as
    yield of a key product. This assumes that max (profit) is
    the same as Max(yield), which might not always be true.
                                            max P
                                               x


FORMULATING THE                             s.t .
                                            h( x )  0
OPTIMIZATION PROBLEM                        g( x )  0
                                            x min  x  x max


Some comments on the objective function (continued)
•   We have difficulty when the models are inaccurate, for
    example, the tradeoff between current reactor operation
    and long-term catalyst activity.
•   Modelling the market response to improved product
    quality, etc is difficult.
•   We want a “smooth” function.
                                                            max P
                                                               x

                                                            s.t .
   FORMULATING THE                                          h( x )  0
   OPTIMIZATION PROBLEM                                     g( x )  0
                                                            x min  x  x max



s.t.          This means “subject to”. The expressions below limit (or
              constrain) the allowable values of the variables x. They define the
              feasible region.
Equality      These are equality constraints, e.g.,
Constraints    - material, energy, force, current, … BALANCES
               - equilibrium
h(x) = 0       - decisions by the engineer ( F1 - .5 F2 = 0 )
               - behavior enforced by controls TC set point = 231 C
               - sub-sections of a model used in other parts of the model,
                 rate = - kCA

              By convention, we will write the equations with a zero rhs (right
              hand side).

              There can be many of these equations, so that h(x) is a vector.
                                            max P
                                               x


FORMULATING THE                             s.t .
                                            h( x )  0
OPTIMIZATION PROBLEM                        g( x )  0
                                            x min  x  x max


Some comments on equality constraints
•   The key balances must be strictly observed. If we do not
    ensure that they are “closed”, the optimizer will find a
    way to create mass and energy!
•   The models may change. For example, a heat exchanger
    could have either one or two phases, with the number of
    phases depending on the optimization decisions.
                                                                max P
                                                                   x

                                                                s.t .
FORMULATING THE                                                 h( x )  0
OPTIMIZATION PROBLEM                                            g( x )  0
                                                                x min  x  x max
Inequality    These are “one-way” limits to the system, e.g.,
constraints    - maximum investment available
               - maximum flow rate due to pump limit
g(x)  0       - minimum liquid flow rate on tray # 24
               - minimum steam generation in a boiler for stable flame
               - maximum pressure of a closed vessel
               - maximum region within which we think that the model is
                 acceptable

              These are essential for optimization. We have not formulated
              inequalities in previous course, although we have learned the
              underlying technologies.

              By convention, we will write the equations with a zero rhs (right
              hand side).

              We must be careful to prevent defining a problem incorrectly with
              no feasible region.

              By multiplying by (-1), we can change the inequality to g(x)  0.
              So, these two forms are equivalent.
                                                         max P
                                                            x

                                                         s.t .
  FORMULATING THE                                        h( x )  0
  OPTIMIZATION PROBLEM                                   g( x )  0
                                                         x min  x  x max
Variables   Variables can be grouped into two categories
and their    Some are “decision” or “optimization” variables. These are
Bounds        the variables in the system that are changed independently to
              modify the behavior of the system.
xmin  x     Some are dependent variables whose behavior is determined
x  xmax      by the values selected for the independent variables.

            Although they can be grouped this way to help understanding, the
            solution method need not distinguish them. We need to solve a set
            of equations involving many variables.

            Examples of variables are
            DESIGN: reactor volume, number of trays, heat exch. area, …
            OPERATIONS: temperature, flow, pressure, valve opening, …
            MANAGEMENT: feed type, purchase price, sales price, ..

            These bounds limit the values of the variables. Note that setting
            the min and max values equal sets the variable to a constant value.
                                              max P
                                                 x


FORMULATING THE                               s.t .
                                              h( x )  0
OPTIMIZATION PROBLEM                          g( x )  0
                                              x min  x  x max


Some comments on variables
•   Many variables are continuous, but some are discrete or
    integer. Give some examples of each.
•   Typically, we do not define the “decision” variables.
    Since we solve a set of simultaneous equations, are
    variables are determined together.
•   We should always place bounds on variables. Why?
        FORMULATING THE OPTIMIZATION
                 PROBLEM

                    When modelling, we always encounter the issue
max P               of Degrees of Freedom (DOF). How do we
   x                determine the ODF for an optimization
s.t .               problem using the relationship below?
h( x )  0          DOF = (# variables) - (# equations)
g( x )  0          # variables =
x min  x  x max

                    # equations =
           FORMULATING THE OPTIMIZATION
                    PROBLEM

                    In 3G03 and 3P03, we required the models to
max P               have DOF=0. Why?
   x

s.t .
h( x )  0          For optimization, what value(s) do we expect
g( x )  0          for the DOF?
x min  x  x max


       The answer explains why optimization is so widely applied!
        FORMULATING THE OPTIMIZATION
                 PROBLEM

                    Often, we will think of the problem as having
max P               #Opt Var = # var - #equality constr.
   x

s.t .               We can plot this if only two dimensions.
h( x )  0
g( x )  0                                           What about points
                     Opt Var2




                                                     inside?
                                     feasible
x min  x  x max                    region          Which is the best?




                                Opt Var1
           FORMULATING THE OPTIMIZATION
                    PROBLEM
We can plot values of the objective function as contours.
Where is the optimum for the two cases shown below?
Opt Var2




                               Opt Var2


            Opt Var1                      Opt Var1
             Case A                       Case B
FORMULATING THE OPTIMIZATION
         PROBLEM
Variables and objective function can be plotted in 3D




       T                            FA
        FORMULATING THE OPTIMIZATION
                 PROBLEM


                    How do we select the appropriate
max P               “system” for a specific problem?
   x

s.t .
h( x )  0
g( x )  0
x min  x  x max
        FORMULATING THE OPTIMIZATION
                 PROBLEM
                    How do we select the appropriate
                    “system” for a specific problem?
max P                       Marlin’s Rule 1
   x

s.t .               We must consult people with a
                    broader responsibility/knowledge
h( x )  0          than “system S” to determine the
                    true objectives of “system S”.
g( x )  0
x min  x  x max
        FORMULATING THE OPTIMIZATION
                 PROBLEM

                      How do we define a scalar that
                      represents performance,
max P                 including
   x
                     • Economics
s.t .
                     • Safety
h( x )  0           • Product quality
g( x )  0           • Product rates (contracts!)

x min  x  x max    • Flexibility
                     • …...
        FORMULATING THE OPTIMIZATION
                 PROBLEM



max P                 How accurately must we model
   x                  the physical process?
s.t .                • Macroscopic

h( x )  0           • 1,2 3, spatial dimensions
                     • Steady-state or dynamic
g( x )  0
                     • Physical properties
x min  x  x max    • Rate models (U(f), k0e-E/RT, ..
        FORMULATING THE OPTIMIZATION
                 PROBLEM



max P                 What limits the possible
   x                  solutions to the problem?
s.t .                • Safety

h( x )  0           • Product quality
                     • Equipment damage (long
g( x )  0             term)

x min  x  x max    • Equipment operation
                     • Legal/ethical considerations
     FORMULATING THE OPTIMIZATION
              PROBLEM

No one solution or          • More to learn
approach is suitable for
                            • Must have
optimization!
                              toolkit of models
   max P                      and solvers
      x

   s.t .
   h( x )  0              • Lots of
                             opportunity for
   g( x )  0                ingenuity
   x min  x  x max       • Makes engineers
                             valuable

				
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