Chemical Engineering October 2019 - 36

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improvement loop through data analytics
and contextualization of operational
production data.
FIGURE 6. Self-service analytics can
allow engineers to quickly find the root
cause of a process problem and adjust
batch cycle time, for example
hands of those who really need to interpret
it (Figure 4).
All four types of analytics, from descriptive
to predictive, can now be performed
by operators and engineers.
Besides visual analytics, self-service
analytics software can provide various
tools, filters and advanced queries to
find anomalies, create early warnings
and troubleshoot incidents across multiple
assets. For example, an incident
during production may seem like a single
occurrence, but with rapid-fire pattern
recognition technology, it may turn
out that the incident has happened multiple
times over the past few years.
Engineers investigating potential process
optimizations may start by searching
for specific anomalies in process
behavior. Searching for tags is as easy
as searching in Google where easy
time sliders and filters help you navigate
through years of data. The first question
is " what has happened? " directly followed
by " has this happened before? " .
If multiple occasions are found, the engineer
can start searching for root causes
using either influence factors or a recommender
engine.
Additionally, periods of best performance
can be collected and overlaid to
create fingerprints (for the process) or
best operating zones (for the assets) that
can be used as monitors. The monitors
can be used to capture events, while
events reside in other third-party business
applications, such as maintenance
management or laboratory information
management systems. All this can
lead to new starting points for process
optimization, leading to a continuous36
Building
a production cockpit
Ideally, all operational stakeholders would
have what we refer to as a " production
cockpit " to work with that is complete
with a dashboard, analytics suite and
agile communications facility. Users can
create and share complete and live overviews
of their current process statuses
and performances, enabling teams and
individuals to immediately access production
data, analyze situations at hand,
and make decisions in an instant.
When you enhance the current process
status overview with the earlywarning
capabilities that self-service
analytics solutions provide, the production
cockpit can provide operators the
opportunity to be proactive and optimize
operational performances, even before
issues arise. It also helps optimize the
flow of information between shifts, from
shift teams to engineers and between all
related actors and production stakeholders,
thereby boosting the organization's
collaborative agility. These are just a few
of the ways that self-service industrial
analytics is able to cater to the needs of
engineers today.
Real-world use cases
Predicting fouling of heat exchangers.
In a reactor with subsequent heating and
cooling phases (Figure 5), the controlled
cooling phase is the most time-consuming,
and it is almost impossible to monitor
fouling when the reactor is used for
different product grades and a different
recipe is required for each grade. Fouling
of heat exchangers increases the cooling
time, but scheduling maintenance
too early leads to unwarranted downtime
and scheduling too late leads to degraded
performance, increased energy
consumption and potential risks.
Under normal process conditions,
self-service analytics can be used to
identify a general trend for predictivemaintenance
planning, alert and support
a maintenance decision, and help
find the right process parameters to
tweak in order to lengthen the time between
maintenance cycles. However,
fouling can also be caused by other factors
- the viscosity of the process fluid
changed, the temperature of the heatCHEMICAL
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Chemical Engineering October 2019

Table of Contents for the Digital Edition of Chemical Engineering October 2019
