Hydrocarbon Processing - November 2021 - 25

Process Control, Instrumentation and Automation
services engineers, control system engineers
and reliability engineers.
A rigorous AR exercise
through P&IDs one-by-one and
systematically covers all tags. This
approach is effective if the AR is
carried out for the first time for a
proceeds
Ensuring the quality of the master
data set. One way to ensure that the
master data set has been improved is to
and use it for reference or audits. Operating
limit sets are configured directly
into the DCS. When a product grade
grassroots plant. For a plant that has
been in operations, with adequate
knowledge about plant behavior,
a system-by-system approach may
be taken-e.g., in a crude distillation
plant, the crude column can
be considered (typically approximately
50-60 tags) and rationalized, and
the same can be done for the vacuum
column, and so on. As a best practice, the
team should yellow-line the tags that have
been rationalized on the P&ID sheet so it
can be verified that no tag is missed.
Rationalized limit sets may be updated
Operating limits are not cast in stone and must be constantly
reviewed to derive maximum benefit with respect to production,
quality and cost. Often, undesirable events occur during
shutdown and startup because the safe operating limits for
these modes of operations were either not defined or ignored.
into the DCS using the company's change
management process. Since the changes
implanted with respect to the measurement
points (adding/deleting tags) or
limits have an impact on the process safety
aspects of the plant, the updating of the
DCS with rationalized data should be
done following proper communication
and training of the relevant operating staff.
Two types of risks must be considered
while making the change: the risks associated
while implementing the change (i.e.,
writing new limits over the old limits,
which entails human errors); and continued
operation with new limits, the actual
" toll " on the plant and equipment that
can only be determined over time. The
updating itself may be done in many ways
(a system-by-system approach, updating
all revised data and allowing operators to
adapt to the new limits, etc.). Another approach
is more global: rationalization for
the entire plant is completed and its entire
data set is updated simultaneously. All approaches
have their own pros and cons-
plants should manage the changes as per
their convenience and practice.
Limit fixation. FIG. 1 shows a typical consideration
for fixing the standard high and
low limits for a furnace feed flow. Various
considerations (design rates, upstream
and downstream system constraints, physical
conditions of the coils, etc.) have their
own minimum/maximum limits. During
the rationalization exercise, all inputs are
considered and the most favorable or logical
high or low alarm limits are set.
compare the alarm key performance indicators
(KPIs) before and after the change.
One of the simplest alarm KPIs is the
average alarm count, which can be 6 per
hour per console. However, this only indicates
the short-term effectiveness and
at the operator level. To determine longterm
effectiveness, close monitoring of
the process performance under various
product grades and modes of operations
is required. An effective KPI to track this
is the peak alarm count, which can be 60
per hour per console.
Another way to check if the changes
have added value is to test them on an
operator simulator, provided an operator
simulator has been maintained with upto-date
plant information or replicates all
DCS graphics.
Of course, in the medium and long
term, the continuous reduction in equipment
failures, loss of containment due to
internal corrosion and leaks, throughput
loss due to fouling, etc., are evidence of a
good operating limit set.
Operating companies may set their
own leading/lagging KPIs to track the effectiveness
of a good limit set.
Note: Alarm KPIs should be defined
in the company's alarm philosophy document;
if this is unavailable, all petrochemicals
and refining facilities may refer to International
Society of Automation (ISA) 18.2.
Change management. As mentioned
here, all changes should follow the company's
change management process, which
should be audited periodically, simplified
and made more effective. A best practice
is to carry out a complete AR of the whole
plant every 5 yr or every time a major turnaround
is executed.
Enforcement. Most plants maintain
their master data in a standalone system
change or operating mode change happens,
companies use one of the following
two methods to change the alarm limits
in the DCS: allow the operator to change
the limit sets through an SOP on the operating
panel itself or ask the control system
engineer or shift supervisor to make
changes from the engineering workstation/supervisory
system. In these two
cases, human intervention is involved. It
is advisable to follow a doer/checker approach
for such critical changes, although
such changes happen frequently and a
risk of laxity can creep in.
An advanced management practice
to ensure the right limit sets are used by
a shift operator is to deploy digital tools
and empower the operator to enforce the
right limits into the DCS, as per the product
grade or operating mode. The digital
tool should allow the shift panel operator
to check at the beginning of each shift that
all alarm set points in the DCS are consistent
with the master limit set, ensuring
no discrepancies. In this case, the DCS is
wired with an external master alarm data
set that requires the installation of stringent
security architecture to prevent undesirable
protocols into the DCS process
control system by external sources.
Both methods discussed here have
their own inherent advantages and disadvantages-the
operating company should
determine the best fit.
M. ANANTH BALIGA is a chemical
engineer with more than 40 yr
of experience in refinery and
petrochemical plant operations
and projects implementation.
As Senior Vice President,
Operating Management Systems
(OMS) Assurance, Mr. Baliga is engaged in defining
and implementing OMS and Operational Excellence
(OE) programs and contributes to the digital
initiatives of manufacturing operations for Reliance
Industries Ltd. in Mumbai.
Hydrocarbon Processing | NOVEMBER 2021 25
Hydrocarbon Processing - November 2021

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