Chemical Engineering May 2021 - 37

Opportunities to improve safety
Advanced analytics offers a number
of opportunities for improved safety,
including the following:
Efficient process hazard studies.
Process
hazard
analysis
(PHA)
studies are required by Occupational
Safety and Health Administration's
(OSHA; Washington,
D.C.) Process Safety Management
(PSM) and the U.S. Environmental
Protection Agency's (EPA; Washington,
D.C.)
Risk
Management
Plan (RMP) regulations in the U.S.
and serve as the foundation for
process safety and risk management
programs within chemical
plants. PHAs also help protect
against product quality issues,
process downtime, and property
damage. During a PHA session,
the process experts often need to
locate specific information or process
parameters at a given time
within the past 5 or 10 years to assess
whether a particular process
safety scenario is valid. Using advanced
self-service analytics, the
user can quickly pull up statistical
information on and between different
process tags and also layer
events on top of each other, which
facilitates the speed and increases
effectiveness
of PHAs. Another
common task during PHAs is to
identify how many times a certain
temperature or pressure exceeded
a given threshold. Traditionally, this
could take hours if not longer to
accomplish with multiple people
looking at a span that could be
5 to 10 years. However, with advanced
self-service analytics, this
search can be completed in seconds.
Furthermore, advanced
analytics can help ensure that no
event of interest is missed when
assigning risk categories, which
assists in the effectiveness and
accuracy of PHAs.
Improved asset reliability. Picture
a classic asset-reliability use
case with two critical ethanol
pumps in parallel, which deliver a
high-pressure recycle stream to
a packed-bed reactor (Figure 1).
The primary indication of a leak is
a decrease in flow, which sets off
a distributed control system (DCS)
alarm that alerts operators to a
potential leak upstream or a pressure
safety valve (PSV) release.
However, the outlet flow meters
occasionally drift, so operators are
not always able to respond quickly
enough to swap pumps before a
large spill (that is, a process safety
incident) occurs.
Using advanced self-service
analytics,
a
realtime
theoretical
flow can be easily determined,
and a deviation alarm can be created
to alert production engineers
and operators whenever the flowmeter
needs to be recalibrated.
Furthermore, it can be used to
monitor the relationship between
differential pressure and flow rate
across the pump in real time,
which serves as a secondary safeguard
to ensure that the pump is
operating on the pump curve. As
a result, self-service analytics can
help mitigate the severity of future
process safety incidents related to
these two ethanol pumps and also
improve asset effectiveness.
Root-cause analysis for an ammonia
PSV relief. Another common
use case is performing root
cause
analysis
to
troubleshoot
events that can lead to an ammonia
PSV relief (Figure 2). Typically,
production engineers would manually
search through tens, potentially
hundreds of process tags in
the historian or DCS looking for a
cause that led to a high-pressure
relief event. This can take a considerable
amount of valuable time
and resources.
With advanced self-service analytics,
a single engineer or operator
can quickly conduct a pattern recognition
search on historical data
to capture all instances of pressure
transmitter reading spikes that led
to the PSV relieving. Furthermore,
advanced self-service analytics
can analyze correlations between
the pressure process tag and all
process tags available in the historian,
and account for time shifts
between process tags. Once the
process expert selects a process
tag of interest as the root cause,
it can be visualized and monitored
in the future to minimize the potenCHEMICAL
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Chemical Engineering May 2021

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