September 2022 - 13

feature | digital twins
contain four key elements:
mathematical model describing a system or
piece(s) of equipment
model created with industry-specifi c data
that describes how the system or equipment
was built
measured values from plant instrumentation
to simulate system or asset operation and
validate the calculated results
computational and visual method to
analyze those results and gain operational
Th ere are also two key elements that are
necessary to properly support a digital twin.
First, data accuracy and completeness need
to be of high quality. Bad data in equals bad
data out. Second, the " as designed " and " as
running " process of systems and equipment
must be considered during development.
When applying artifi cial intelligence (AI)/
machine learning (ML) concepts to a digital
twin, even minor deviations can aff ect
output accuracy. While talented operating
teams can build workarounds to these conditions,
in an ML paradigm this could mean
adding an implied bias to real and lasting
By this defi nition, a 3D CAD rendering
or a static picture of systems or equipment
is not a digital twin. It's merely a visual
representation that lacks the power to act on
your data.
As in all ML concepts, the more current,
accurate, and complete the data provided to
a digital twin, the better fi delity and accuracy
you get as a result. Th at means that a new
digital twin will improve as it collects data
and matures into an accurate representation
of the systems or equipment.
For example, in the case of a single analytic,
on day one of a digital-twin activation,
assumptions have to be made regarding
expected performance. At this point, historical
data will be used to shape digital-twin
output behavior. On day two, the digital
twin begins to incorporate real-time operating
data that improves accuracy. Th e digital
twin also logs the data, benchmarks asset
performance, and identifi es anomalies.
As time goes on, the real-time data feed
continues to improve and maintain the
accuracy and fi delity of the digital-twin
results. As model maturity increases, AI can
also be deployed to turn ML outputs into
forward-thinking decision making tools
across the plant. Just as ML needs more data
and time to mature, the same can be said
for AI.
Digital twins do not have to be limited
to a single analytic or a single form of data.
To truly realize business value, digital twins
should not be narrowly focused on siloed
solutions that do not integrate and scale.
Th ese siloed solutions ultimately require
manual manipulation and aggregation of the
outputs from other siloed solutions to create
a comprehensive view and turn data into
information that drives decisions. Systems
that require this type of manual eff ort are
typically ineffi cient, error prone, hard to
maintain, and not scalable enough to keep
up with growing organizations and technology
Imagine a digital twin, or system of digital
twins, that uses data from multiple systems
such as operating rounds, instrument
calibration, anomaly dictions systems,
tribology, vibration programs, equipment
criticality, and business systems (EAM/
CMMS, fi nancial systems, and historians).
Th is twin can scale across multiple plants
and technology types (solar, wind, battery,
hydro, grid, fossil, nuclear).
If this complex digital twin is modular,
to enable deployment using a phased
approach, then possibilities are endless.
Considering an investment in assets that are
expected to run for 10, 20, and sometimes
30 years, fl eets that have mixed technologies,
or fl eets that have mixed life spans,
then a complex digital twin that integrates
and scales with a plant's needs makes sense.
Once you have identifi ed where to invest
in digital twins, determine how you should
deploy them.
Most of today's digital-twin soſt ware
solutions require manual updating. While
closed-looped AI/ML solutions allow digital
twins to be updated in real time, openlooped
solutions still remain the industry
Open-looped solutions with advanced
digital twins provide analytics that combine
critical data into actionable insights.
Th is allows operating personnel to control
the systems while empowering teams to
make data-informed decisions. On-premise
solutions without AI/ML capabilities
allow companies to own the soſt ware on
their own network, making any updates to
digital twins the customer's responsibility. If
the solutions are in the cloud, however, the
digital twin model(s) are typically updated
by either a service provided by the company
solution or in-house teams.
Whether you're part of the Energy
Transition or Industry 4.0 movement, the
development and enhancement of machine
interconnectivity, automation, ML, and
real-time data are your future. Today, there
are a large number of equipment manufacturers
that are off ering smart-equipment
packages, integrating low-cost sensor capability,
and modifying controllers to meet the
needs of digital technology.
A digital-twin mathematical model is based
on the energy balance in a pump, analyzing
the electrical energy supplied to that pump,
and converting the energy to the expected
fl uid energy leaving the pump through its

September 2022

Table of Contents for the Digital Edition of September 2022

September 2022 - Cover1
September 2022 - Cover2
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September 2022 - Cover3
September 2022 - Cover4