POWER September 2022 - 14

O&M
How Digital Technology
Can Help Lower Carbon
Footprint
As owners and operators of fossil fuel
plants navigate through the energy
transition, digital technology offers the
opportunity to perform while transforming,
balancing the energy trilemma of
providing a secure, sustainable, and equitable
energy system each step along
the way.
That being said, not just any digital
technology applied will result in operations
and maintenance (O&M) benefits.
You may not achieve your intended goals
if the importance of technology adaptability,
user experience, and equipment
know-how are not addressed from the beginning.
Without tailoring the approach to
the teams, it may be hard to connect the
dots between and among systems. If the
solution is too difficult to use, adoption is
at risk. And if the expertise of the developers
isn't behind the models, there's a
risk to the accuracy of data.
However, with the right solution, O&M
leaders are finding that digital solutions
allow them to progress toward a more
sustainable future, reducing their plants'
carbon footprints, but not at the expense
of cost or reliability. And in many cases,
they find that by bringing greater visibility
to reliability risks and performance shortfalls,
and increasing automation, they
can reduce their cost to generate, and
improve reliability and availability, while
simultaneously reducing greenhouse
gas (GHG) emissions.
Operationalizing the Asset with
Digital Technology
Starting at the asset level, when industry
and equipment knowledge are coupled
with AI/ML (artificial intelligence/
machine learning) technology, it can be
a powerful tool to unlock performance,
improve fuel consumption and availability,
and reduce heat rate and emissions.
Whether applied to a gas or steam
plant, such technology delivers real outcomes
in O&M.
Gas Turbine Combustion Optimization.
Typically gas turbines require seasonal
adjustment, tuning, or mapping
of flame temperatures and fuel splits to
ensure reliable and emissions-compliant
operations as weather patterns change
by the season. This can be a manual pro14
cess
performed by an expert onsite and
more often than not requires an outage,
which impacts availability. In addition,
manual seasonal tuning is only effective
for the precise conditions in which it was
completed, and does not enable the gas
turbine to efficiently respond to ambient
temperature or fuel property changes
between tunings.
By utilizing AI/ML technologies to
continuously optimize combustion in
closed-loop control in place of manual
seasonal adjustments, aeroderivative
gas turbine operators could achieve the
following benefits:
■ 0.5% to 1% reduction in CO2
sions/fuel consumption/heat rate.
emis■
Up to 14% reduction in CO emissions.
■ Up to 12% reduction in NOx
emissions.
■ Improved availability with zero manual
tunings or associated downtime.
AI-enabled tuning software that is deployed
in a supervisory control system,
which is fully bounded by the control
system safety-critical programming, can
safely use machine learning to find the
ideal flame temperatures and fuel splits
continually and autonomously for optimal
combustion. This is true as critical
variables such as ambient conditions and
fuel quality change.
Two aeroderivative power plant case
studies exemplify the types of benefits
such technology can deliver. In the first
case study, a peaking combined cycle
power plant was subject to fluctuating
natural gas composition that caused
emissions and operability issues, and
required frequent technical intervention,
leading to downtime. Remote tuners
were often called to make manual adjustments
to avoid stage down for high
acoustics or blowout, and to address
problems with NOx
at baseload operations
or CO at low-load operations. With
the implementation of AI-enabled combustion
optimization, the following results
were achieved:
■ CO emissions were reduced by 14%
when operating with low specific
gravity composition that tends to increase
CO emissions.
■ NOx
emissions were reduced by 12%
when operating with high specific
gravity composition that tends to increase
NOx
emissions.
www.powermag.com
1. A digital twin, in this case of a gas turbine,
can enable integration of analytic models
for components of the power plant that
measure asset health, wear and performance
with customer defined known performance
indicators (KPIs), and business objectives.
Courtesy: GE Digital
■ Yearly or seasonal tuning events were
reduced from four to zero, while avoiding
12 days of downtime.
■ After installation, the site's high acoustics
events were reduced from six to
zero during a 12-month period.
The second case study involved a power
plant that struggled with emissions to
the point that it exhausted NOx
credits
one summer, precluding further generation
for the remainder of the year. The
months of July, August, and September
represented one-third of the total typical
generation for this site, adding to the
criticality of the problem. After a digital
solution was deployed to continuously
optimize combustion in closed-loop, the
following benefits were realized:
■ NOx
emissions were reduced by 10%,
precluding the need for a combustion
overhaul that would have cost $2 million
and resulted in a 12-week outage.
■ Software enabled the site to generate
power
throughout
its high-demand
season and beyond without exceeding
NOx
credits, generating $300,000
in revenue above the previous season.
■ Yearly or seasonal tuning events were
reduced from two to zero, while avoiding
six days of downtime.
■ Post installation, the site did not experience
high acoustics events.
Steam Plant Boiler Optimization
Traditional, schedule-based control systems
for steam plants limit the ability to
optimize combustion for heat rate and
emissions as the boiler degrades, which
can lead to unplanned downtime due to
tube ruptures from excessive sootblowing.
Several digital solutions have been
POWER | September 2022
http://www.powermag.com
POWER September 2022

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