Hydrocarbon Processing - April 2022 - GP-18

SPECIAL FOCUS: GREEN TECHNOLOGIES
ing, while promoting better knowledge
sharing between the different teams. By
this approach, streaming analytics in the
operational twin integrate bidirectionally
with higher-level analytics that use modeling
and simulation, machine learning and
other advanced digital tools to provide an
accurate, single source of truth.
Reduced process variability for increased
energy efficiency. Once the
process twin is created for the engineering
planning stage, construction is completed,
and startup commences. Then, the engineering
teams can hand that process twin
over to the operations team to integrate
with the operational twin to continue refining
processes and proactively manage
equipment to prevent unexpected shutdowns
and equipment failures. The digital
twin does this by reducing process variability,
using predictive analytics to monitor
equipment health and conduct overall
process performance monitoring. For
example, the operational twin can access
pump curve data from the process twin
and compare it against real-time pump
efficiency. When the system detects a deviation
from the best efficiency point, it issues
an alert to operators so that they can
address the issue immediately.
Advanced process control (APC) is
used in multiple cryogenics operations to
stabilize throughput and reduce variability
from different feedstock compositions,
ambient temperatures or equipment conditions.
APC adjusts conditions and processes
to manage changes in feedstock rate
and quality, while also keeping plant operations
within safe operating conditions
and horsepower constraints.
At most LNG plants, implementing
APC can deliver a 3%-4% increase in
throughput and yield and a 2%-5% drop
in energy consumption as measured by
MMBtu/t. APC also reduces the need
for operator interventions and improves
plant stability.
A more proactive approach to maintenance
with layers of analytics. Along
with the increasing sophistication of digital
twin capabilities, improvements in predictive
analytics are being made, as well. This
enables more precise recommendations
earlier and requires less historical data to
accurately anticipate failures. If a piece of
equipment fails in production, it can cost
tens to hundreds of thousands of dollars
18 MARCH/APRIL 2022 | GasProcessingNews.com
to replace the asset. Unplanned plant shutdowns
can cost upwards of $1 MM/d in
lost production revenue. However, plant
overruns can quickly add unexpected
costs at an even faster rate, to say nothing
of possible safety implications.
Unplanned shutdowns can hit the natural
gas industry especially hard because
LNG trains are extremely cold during
operation. If a shutdown occurs, it will require
more time to restore full production
than it would for operations near ambient
temperatures. A layers-of-analytics approach
enables the modeling of rotating
equipment using machine learning and
advanced pattern recognition with streaming
and predictive analytics integration.
Advanced predictive analytics compares
real-time operations data to the
digital twin models and flags any subtle
deviations from normal equipment behavior.
This early warning system means
that reliability and maintenance teams can
identify, evaluate and address problems
before a major breakdown takes place. It
also means teams can make an informed
assessment when a particular piece of
equipment starts showing signs of wear.
TransCanada-one of the largest
LNG pipeline operators in the world-
calls this 'fixing little things; " things that
can have million-dollar impacts if they are
neglected. TransCanada manages 31,000
mi of pipeline across North America with
a very diverse fleet, ranging from small
reciprocating 300-hp units to 35,000-hp
turbines, while tracking more than 16,000
streams of data.
Like Shell's Prelude FLNG, TransCanada
has also adopted a layers-of-analytics
approach. The company's analytics journey
began with relatively modest objectives:
digital asset construction and basic
anomaly detection. Once TransCanada
had built a solid foundation of operational
data management and streaming analytics,
it began to evolve its analytics overlay
with more advanced algorithms and
event frame generation to monitor key
performance indicators (KPIs) and performance
metrics. By 2017, these layers
of analytics had detected 129 anomalies
and netted more than $10.65 MM in prevented
expenses.
Maximizing throughput with real-time
performance monitoring. There is one
other key area where digital twins can reduce
the carbon footprint for LNG operations:
process performance monitoring.
From the beginning, an LNG plant is designed
using APC to operate at maximum
safe efficiency based on the characteristics
of the assets, plant layout and production
pipeline. However, as soon as operations
start, variations in feedstock and fuel gas
composition or changes in ambient conditions
inevitably impact throughput, which
can then affect operating profits.
Performance monitoring studies live
operations to resolve non-linear relationships
that may impact plant throughput,
including process constraints, feedstock
and mixed-refrigerant composition, to
reconcile material use and look at energy
and equilibrium balances around a piece
of equipment. With better tracking of realtime
performance, pipeline companies
can also identify inaccurate instrument
readings. This acts as a data validation
layer, while also pinpointing material and
energy loss locations so operators can
quickly find the source of any fugitive
emissions that occur.
More-efficient LNG, a more sustainable
future. LNG producers and pipeline
operators are facing demands to make
their operations greener and more sustainable.
Simultaneously, they must maximize
performance, minimize costs and delays,
and ensure efficient organizational operations.
To achieve all these aims at once,
natural gas companies need to synchronize
every facet of their business, from
planning and engineering to operations
and onward. By reducing emergency flareups
and unplanned shutdowns, a holistic
digital twin enables producers to thread
that needle-reducing energy usage and
emissions while increasing performance
and efficiency all at the same time. By
embracing a layers-of-analytics approach,
LNG companies can help ensure efficient
use of the world's precious resources. GP
CRAIG HARCLERODE is a Global Oil and Gas Industry
Principal at AVEVA, where he consults with companies
on how they can add value to their organizations
leveraging the portfolio of AVEVA offerings, including
the PI System, in support of their business strategies
and digital transformation programs. His career has
included engineering, operations and automation in
supervisory, executive management and consultative
roles at Amoco Oil, Honeywell IAC, AspenTech and
OSIsoft (now part of AVEVA). He earned a BS degree
in chemical engineering from Texas A&M University,
an MBA from Rice University and is a PMP. Mr.
Harclerode has numerous publications and is a regular
thought leader presenter at conferences and events
globally on digital strategy and transformation.
http://www.GasProcessingNews.com
Hydrocarbon Processing - April 2022

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