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Is Counting the Number of Control Loops a Valid Technique for
Estimating Operator Workload?
Many control system manufacturers have assumed that there is a
direct correlation of the number of
control loops of a process and the resulting workload of the
operator controlling the process. Duringreinstrumentation projects,
control system engineers exert a tremendous amount of effort in
balancing thenumber of control loops between control board
operators. Intuitively, from a hardware standpoint, this
line of reasoning makes sense; as the operators instrumentation
responsibilities increases, so should the
number of things the operator must monitor and adjust. The more
things the operator must monitor andadjust, the more time he must
spend doing so, and the higher his workload. Similar to adding
weight to a
laborer to increase his physical workload, adding
instrumentation to a control board operator will increasehis mental
workload. If this line of reasoning is correct, then there should
be some way to objectively
measure this effect. For example, one would expect that as the
number of control loops increases, theamount of time the operator
spends monitoring and adjusting the control system would also
increases by a
proportional amount. The question is, what parameters of
operator performance should be measured to
find this effect, and also, what parameters should be used to
gauge operator workload?
Since 1983 Beville Engineering has been investigating factors
that influence workload of petrochemical
plant operators. During this period of time, Beville Engineering
has developed a database of operatorperformance, which is a
collection of observations of petrochemical plant operators
performing their
duties. The database has recently been used to investigate
whether or not any relationship exists betweenthe number of
feedback control loops under a distributed control system operators
control and control
board operator activities. The relationships which were
investigated were the number of control loopsversus: 1) # of alarms
per hour, 2) communication contacts per hour, 3) # of display
changes per hour, 4)
% of operators time engaged in job activities (direct time), 5)
% of time operator spent interacting with
DCS, and 6) # of controller adjustments per hour. All of the
process unit data used in the comparisonswas taken during normal,
steady-state operating conditions.
Linear regression analysis was used to investigate whether of
not relationships existed between thenumber of control loops under
an operators control and the board operator activities. The measure
of a
relationship between two variables is R-squared. A direct 1-to-1
relationship would produce an R-squaredvalue of 1.0, or -1.0 for a
negative relationship. An R-squared value of 0.0 would indicate
that no
relationship existed, and a graph of the two variables would be
a straight line. For a relationship to beuseful for making
predictions, most statisticians would agree that an R-squared value
of 0.6 or higher is
needed.
Figure 1 is the first correlation between the number of control
loops and the number of alarms per hour. Arelationship between
these two parameters indicates that as the number of control loops
increases, the
number of alarms per hour the operator must respond to also
increases. The R-squared value of 0.34 does,
in fact, indicate that relationship exists, though the
relationship is moderate.
Figure 2 contains a comparison between the number of control
loops and the number of communicationcontacts the control board
operator made during the sampling. This indicates that as process
complexityincreases (represented by the number of control loops),
the control operator communications also
increase. In this comparison also, the R-squared value of 0.31
indicates that a moderate relationshipexists.
Figure 3 is a comparison between the number of control loops and
the number of display changes per
hour the board operator made. This correlation would indicate
that as the number of control loops
increases, the operator must make more display changes to access
the instruments. The analysis obtainedan R-squared value of 0.2,
indicating that a relationship does exist, but the relationship is
weak.
Figure 4 is a comparison between the number of control loops and
the percentage of time the operator wasobserved engaged in job
related activities (direct time). A correlation between these two
variables would
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indicate that as the number of control loops increases, the
amount of time the operator is engaged in job
related activities also increases. The R-squared value of 0.15
indicates that it is an extremely smallrelationship.
Figures 5 and 6 are comparisons of the number of control loops
versus % of time the operator wasobserved interacting with the DCS
and, number of control loops versus the number of
controlleradjustments per 4 hours. With R-squared values of 0.07
and 0.05 respectively, neither comparison
contained a relationship. In short, there is no relationship
between the number of control loops and theamount of time the
operator spends on the control system, nor is there a relationship
between the numberof control loops the operator is controlling and
the number of controller adjustments the operator makes
per hour.
From the data gathered by Beville Engineering, the number of
control loops does appear to be correlated
to some parameters of operator performance which contribute to
operator workload, but that all of the
correlations are very weak. In the absence of other objective
data, using the number of control loops as anestimator of operator
workload is probably better than nothing at all. However, there are
other ways toobtain objective workload estimations.
New techniques to measure workload which have been developed by
the aerospace community hold
much promise. The two most prominent techniques are the USAFs
Subjective Workload AssessmentTechnique (SWAT), and the National
Aeronautical and Space Administrations Task Load Index (NASA-TLX).
Beville Engineering has used these techniques in the petrochemical
industry to gauge control boardoperator workload in combination
with the observational techniques described above.
There are probably numerous reasons why the number of control
loops is a poor estimator of the factorswhich contribute to and are
an indication of operator workload. Operator workload is a
multifacetedconstruct which is influenced by numerous variables.
Some additional factors that influence operator
workload include: process stability, level of process
automation, training/experience of the operator,
quality of control system-human interface (displays/alarms), and
crew interactions/communicationactivity. To attain a valid estimate
of control board operator workload, the best approach is to measure
a
number of parameters of operator performance and employ the new
workload assessment techniques.
