Chemical Engineering January 2022 - 28

Feature Report
If Data is the New Gold,
Where to Start Digging?
In a data-overloaded environment, companies may struggle to fully reap the benefits
of Industry 4.0 technologies. Understanding data utilization can help transform
process datasets into decision-making assets
Edwin
Elmendorp
Kinsmen Group
IN BRIEF
WHAT DOES DATA MEAN
FOR CPI FIRMS?
THE IMPACT OF POOR
DATA
WHAT IS DATA QUALITY?
WHAT ARE MATURITY
ASSESSMENTS?
AVOIDING PROBLEMS
T
he chemical process industries
(CPI) have been realizing for some
time that information is of strategic
importance for the survival of any
organization. Colloquially, data have been
called a new natural resource [1] - every
part of an organization is making decisions
daily, based on data they collect, process,
analyze and review. The global pandemic
has further driven the awareness of digital
transformation for any company, and manufacturers
are no exception to this. The rise of
Industry 4.0 technologies has further driven
this trend, and with more and more data
available, organizations are struggling to
reap the benefits of these data. At the same
time, the industry acknowledges that poor
engineering information can have real and
significant impacts, but little research has
been done to quantify this. Among this mixture
of promising potential and data overload,
companies are struggling to define
where and how to start. This article shines
a bit of light on how an organization can
make progress in its quest to transform its
vast volume of process data into a decisionmaking
asset.
What does data mean for CPI firms?
CPI facilities collect massive amounts of
data. One way to describe the types of data
being recorded is to look at the different levels
of the manufacturing execution system
(MES). The MES is the culmination of tools
and technologies used at different levels to
manage the operations of the facility. Often,
the MES is also displayed as an automation
pyramid. The left side of Figure 1 shows an
example of an automation pyramid. Considering
each of the levels illustrated in Figure
1, let us examine the different types of data
being collected.
Level 1: Control and field. At this level, the
input from the field is generated by sensors
and equipment used in the field. Tempera28
ture,
pressure, rotational speed, valve status
and many other variables are generated at
very frequent increments. The inputs generated
at this level are provided to the second
level in the pyramid.
Level 2: Operations. The second level
provides low-level automation in the form
of programmable logic controllers (PLCs),
which ensure basic controls for individual
systems and operate and respond at very
high speeds.
Level 3: MES. The third level is the MES
level, often executed in the form of a distributed
control system (DCS) or supervisory
control and data acquisition (SCADA) system.
There are obvious differences between
these systems, but for this article, their
function is the same: to provide overarching
control of the facility across multiple systems.
Ref. 2 provides a good overview of the
systematic differences.
Level 4. Enterprise resource planning
(ERP). The ERP level provides integrated
resource planning and controls the planning
and resources required to generate the desired
production.
Each lower level can produce data that
can be used by a higher level to create a
more integrated system. Research has been
conducted on the importance of this integration.
According to these findings, to reap
the full benefits of Industry 4.0 technologies
and effectively utilize digital twins, data must
be aligned at all levels [3].
A second description of the data being
generated is the supporting data used to
ensure safe and compliant operations - for
example, piping and instrumentation drawings
(P&IDs), specifications, inspections,
environmental reports, work processes for
management of change (MOC), training records,
and many others. Where the MES
generates vast amounts of transactional
and real-time data, the supporting data are
more static. With the data being more static,
CHEMICAL ENGINEERING WWW.CHEMENGONLINE.COM
JANUARY 2022
http://WWW.CHEMENGONLINE.COM
Chemical Engineering January 2022

Table of Contents for the Digital Edition of Chemical Engineering January 2022
