Chemical Engineering May 2023 - 33

Digital
Transformation
Remote &
Autonomous
Operations
IT/OT
Asset
Optimization
Data
Convergence Democratization
Sustainability
Cloud
Computer
Vision
Edge
Wireless
Cellular
AI/ML
Virtual
Reality
vices is providing companies with
access to far more data than ever
before, but only if they can harness
the data effectively.
Creating value from data is difficult
FIGURE 1. New market needs have created an inflection point, along with a corresponding demand for a
new automation paradigm
cautious about approving capital expenditures
for new plants across a
fleet. Moreover, building a new plant
requires more people to operate it -
and experienced people are becoming
harder to find.
Instead, today's successful companies
are focused on optimizing
existing assets. But because
many plants have been optimizing
their local operations for years, accomplishing
a true paradigm shift
in operations requires an expanded
vision. The most successful organizations
are optimizing from an
enterprise level to drive improvements,
with more confidence in the
strategies their teams implement to
achieve these objectives.
For example, more and more
teams are identifying a need for advanced
analytics to optimize operations
across their fleets. By leveraging
artificial intelligence (AI) and machine
learning (ML), analytics technologies
provide guidance for holistic optimization.
Data from a robust suite of
sensors, software and control technologies
enable companies to optimize
the business and sustainability
performance of their plants and enterprise
through advanced asset and
business optimization software.
Similarly, many teams are finding a
need to employ powerful simulation
software to identify, design and test
process changes to optimize operations.
Simulation software empowers
enterprise teams to use design
models to identify potential changes
and improvements across an organization's
facilities.
Many teams further improve on the
gains of simulation with digital twin
software, replicating entire plants to
see how changes to improve reliability,
sustainability and energy use will
cascade across all areas. And when
optimization is complete, those same
digital twins are used to train operators
on the new processes (Figure 2).
Typically, these new systems
integration
(and
their
associated
with business systems) also create
a need for cloud connectivity. But
letting control systems touch the
internet introduces a wide array of
cybersecurity concerns. In the past,
those concerns were alleviated by
completely isolating the control system
from the outside world. Today,
however, as cloud connectivity becomes
a need rather than a desire,
operations teams are flattening the
Purdue model to simplify and enable
external connectivity. As they
do so, they dramatically increase
their need for more cybersecure,
data-centric solutions.
Old strategies struggle
At the heart of making all
these new strategies work
are data, and operations
teams are rapidly bringing
in the technology necessary
to gather those data,
add value to it, and make
it easy to access and use.
Input/output (I/O) points
are expanding, with many
plants adding three to ten
times the number of I/O
points they had only five
or ten years ago, many of
them inputs from wireless
instruments. A vast new
array of intelligent field deCHEMICAL
ENGINEERING WWW.CHEMENGONLINE.COM MAY 2023
using legacy automation strategies.
Often, teams are hesitant to connect
intelligent field devices used outside
process control into the existing
distributed control system (DCS)
because of the cost of the DCS infrastructure.
To avoid the extra cost,
operations teams instead design
new infrastructure to collect and
move data where they need it, which
in turn increases complexity and creates
new silos.
Creating new infrastructure introduces
its own set of problems,
however. Most importantly, when
process data bypasses the control
system, it loses all the critical contextual
metadata the control system
can provide, which is often needed
to turn raw data into valuable information.
Data without context can
potentially be helpful if it is used immediately
and locally, but over time
and when aggregated, the data becomes
less valuable.
Moreover, every time the team
creates a new infrastructure for
moving data, they create a new data
silo. Such a data set may be useful
in a single reliability tool or edge
analytics package, but it is typically
not available across the enterprise
to the other users and cross-functional
teams that might otherwise
be able to use it.
Relying on these legacy strategies
puts operations teams in a noFIGURE
2. A digital twin can replicate an entire plant, empowering
operations teams to test and train on new operations strategies
to improve performance, without the risk or downtime of
using live equipment
33
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Chemical Engineering May 2023

Table of Contents for the Digital Edition of Chemical Engineering May 2023

Chemical Engineering May 2023 - Intro
Chemical Engineering May 2023 - Cover1
Chemical Engineering May 2023 - Cover2
Chemical Engineering May 2023 - 1
Chemical Engineering May 2023 - 2
Chemical Engineering May 2023 - 3
Chemical Engineering May 2023 - 4
Chemical Engineering May 2023 - 5
Chemical Engineering May 2023 - 6
Chemical Engineering May 2023 - 7
Chemical Engineering May 2023 - 8
Chemical Engineering May 2023 - 9
Chemical Engineering May 2023 - 10
Chemical Engineering May 2023 - 11
Chemical Engineering May 2023 - 12
Chemical Engineering May 2023 - 13
Chemical Engineering May 2023 - 14
Chemical Engineering May 2023 - 15
Chemical Engineering May 2023 - 16
Chemical Engineering May 2023 - 17
Chemical Engineering May 2023 - 18
Chemical Engineering May 2023 - 19
Chemical Engineering May 2023 - 20
Chemical Engineering May 2023 - 21
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Chemical Engineering May 2023 - 24
Chemical Engineering May 2023 - 25
Chemical Engineering May 2023 - 26
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Chemical Engineering May 2023 - 28
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Chemical Engineering May 2023 - 30
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Chemical Engineering May 2023 - 56
Chemical Engineering May 2023 - Cover3
Chemical Engineering May 2023 - Cover4
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https://www.nxtbook.com/accessintelligence/ChemicalEngineering/chemical-engineering-december-2018
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https://www.nxtbook.com/accessintelligence/ChemicalEngineering/chemical-engineering-december-2015
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