Chemical Engineering November 2014 - 53

Feature Report
Define the aim
Deduce improvements
Determine boundary
conditions
Fit and evaluate models
Analyze available data
Prepare and do the
actual experiments
Experimental design
Define output variables
Collect input variables
it is known to be important) and
therefore does not vary much, no
correlation with outputs will be
found and it looks unimportant
* Even strong correlations may be
spurious. For example, just imagine
that occasionally your starting
material is contaminated,
and this contamination causes
foaming and a low yield. To suppress
the foaming, you then increase
the pressure. If now you
plot yield versus pressure, you
will find low yield at high pressure
- but it is no cause-andeffect
relationship. Only if you
decide on the pressure before the
experiment (as you would do in
a designed experiment) can you
be sure that the observed dependence
is cause-and-effect
Determine factor ranges
Select factors
FIGURE 5. The experimental cycle, described in the text, is summarized here
The experimental cycle
Learning from experiments is done
in cycles, as shown in Figure 5.
Each cycle starts with a problem
to be solved or with questions to be
answered - the aim, which should
be as quantitative as possible. After
careful preparation, the factors
and their ranges are chosen. These
steps are crucial for the success of
designed experiments. Then the
actual design is set up, the experiments
are done, the models fitted
and the conclusions drawn. Several
such cycles may be required.
Define the aim. What improvement
is wanted or what problem is
to be solved? A good aim has the following
properties:
* Specific and precise
* Measurable
* Accepted by everybody involved
* Realistic
A good starting point is the customer
(internal or external). Leading
questions are: What is his or
her problem with the present product
or process? What does he or she
need? How valuable is the expected
improvement to him or her?
Determine the boundary conditions.
When choosing the boundary
conditions, the following questions
should be considered:
* How much time and money are
available for the experiment?
* Is the necessary equipment (both
for the experiments and for the
measurement of the results)
available?
* How many components are involved,
or how much material is
available? Is it homogeneous?
Often these boundary conditions
impose serious limitations on what
is possible. They may require adjustment
of the aim.
Analyze available data (if there
are any). The following questions
are asked regarding available data:
* What are the present settings for
the process parameters? What
results are achieved with these
settings?
* How much do the results vary?
(The standard deviation is a good
measure for variation.)
If process parameters, material
properties or environmental conditions
vary in the available data, it
is useful to look for correlations between
all of these parameters and
all important outputs (responses).
Strong correlations may indicate
that a parameter is important. But
beware of the following:
* If a process parameter is tightly
controlled (for example, because
Define the output variables. The
output variables should represent
the aim (that is, from their value
you can see whether the problem
has been solved and the intended
improvement reached or not). Furthermore,
all quantities that are important
to the customer should be
included as outputs. They may be directly
measured or calculated from
one or several measured quantities.
Outputs should be quantitative (as
conversion or thermal activity are in
the introductory example), rather
than just a yes or no, because numbers
contain much more information.
If no measurement is possible
(for instance, taste), a grade is better
than just yes or no, or good or bad.
Collect input variables. Collect
all variables that may influence the
outputs, such as process variables,
environmental conditions, material
properties and so on. Use your
experience and also brainstorm in
a small team, to make sure that
nothing is forgotten. For a particular
process, this list only has to be
compiled once (possibly extended
later with new experience). It may
be very long.
The aim of this list is to not to
forget important influences and
ensure comparability of the results
with later experiments.
Select factors. Only after the complete
list of input variables is compiled
are the factors for the present
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