Chemical Engineering June 2023 - 28

FIGURE 2. Information technology (IT) and operational
technnology (OT) systems need to communicate
easiliy to enable Industry-4.0 initiatives
drive business growth.
In addition, the chemical manufacturing
industry is required to comply
with a complex set of government
regulations. An effective compliance
program is essential to prevent business
disruptions and loss of reputation.
As a result, chemical manufacturers
must maintain
strict
quality
control, detailed product information,
and a continuous integration of the
data between OT and IT systems.
Due to the drive toward digital
transformation, plus the need for
regulatory compliance and quality
control, data and analytics capabilities
in the chemical manufacturing
area have leapt forward in recent
years. The volume of available data
is growing exponentially, and more
sophisticated algorithms have been
developed and computational power
and storage have steadily improved.
Therefore, instead of intuition, the
new normal is to rely on data to drive
digital innovations and business decisions.
Indeed, data are the " most
valuable resource " for chemical manufacturing
organizations.
Industry 4.0 brings together advanced
manufacturing technologies
like AI, machine learning (ML), digital
twins, AR and virtual reality (VR) to
enable integrated, autonomous and
self-organizing manufacturing systems
that operate independent of
human intervention. Chemical manufacturing
machine and process
data can be analyzed by algorithms
and used for critical real-time business
and operational decisions that
directly help achieve higher overall
equipment efficiency (OEE), enable
remote monitoring, reduce carbon
footprint and enable product innovations.
28
The
journey from data collection
to digital maturity in chemical manufacturing
is one in which analysis,
context and insights are added to
transform raw data captured from
a device or system into information,
knowledge, and finally, actionable
wisdom for decision makers in the
facility (Figure 1).
First, data are collected from
chemical manufacturing processes
and equipment, such as boilers, heat
exchangers, tanks, furnaces and so
on. They are then normalized, digitized,
and organized as " big data. "
Next, meaning is added and data
are synthesized into knowledge via
AI. Finally, the data are transformed
into actionable wisdom attained
through the combined insights of
digital maturity.
IT-OT communication
The first and most significant frontier
to achieve digital maturity for Industry
4.0 is data collection and data movement.
Data from chemical manufacturing
machines, processes, and environment
are captured via sensors
and stored via several key technologies.
On the OT side, data are stored
with controllers, PLCs, gateways and
edge devices, and on the IT side with
a data center or enterprise cloud.
Data storage technology enables the
long-term storage of digitized data
captured from advanced sensors.
This data-rich environment enables
advanced initiatives such as ML, AI,
adaptive control and digital twins.
There are some challenges to data
collection and data movement in
chemical manufacturing. Machines
and processes in the chemical manufacturing
plant are heterogeneous
and use various protocols to communicate.
Data generated during
chemical manufacturing can vary
significantly in terms of format, quality
and completeness. This can make
it difficult to collect, integrate, and
analyze data effectively.
Data connectivity is also a major
issue due to the archaic, legacy nature
of factory systems. As a result,
typically IT and OT systems do not
have an easy way to communicate
to enable Industry 4.0 initiatives
(Figure 2).
IIoT, a subset of Industry 4.0, uses
smart sensors and actuators along
with software to consolidate data to
enhance manufacturing and industrial
processes. IIoT is a key enabler
to achieving IT-OT convergence,
and the interoperability of the various
communication protocols. It
does it by creating a data abstraction
layer in the middle and a common
data language to translate the
various communication protocols to
enable interoperability.
This IT-OT convergence is important
because success in today's connected
industrial landscape hinges
on collaboration. IIoT is changing
how manufacturers work, blurring
the lines between IT and operations.
For example, IT professionals
might now spend more time working
with equipment on the factory floor,
while OT teams will need to focus
on cybersecurity and networking
best practices. IT-OT convergence
isn't about turning IT professionals
into heavy machinery operators or
plant engineers into data scientists.
Rather, it is about creating a strategy
FIGURE 3. Data brokers are intermediary entities that enable OT annd IT cliient systems to communicate
with each other
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Chemical Engineering June 2023

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