Chemical Engineering October 2019 - 35

sights gathered and trends produced,
and determines potential future courses
of action. Prescriptions require a good
diagnosis, so it often comes down to
pre-described suggestions that allow
the SMEs to take appropriate actions
on the notifications they receive, and
the instructions provided by the system
(Figure 3). With every new situation,
knowledge expands for fast and appropriate
action when early warnings are
sent or needed action is predicted by
the system. We call this user-driven machine
learning. In a very complex specialty
chemical environment with small
batches, this is crucial.
Within a chemical plant, any or all
of the aforementioned analytics are
necessary in order to address the issues
and goals that production teams
are facing. Traditionally, data scientists
have been required in order to solve
complex issues. Having to rely on data
scientists or central analytics teams to
create data-driven solutions or resolve
any data analysis-related questions,
however, can lead to missed opportuniCall
the
Solids Mixing
Ribbon & Cone Blenders
Fluidizing Mixers
Sigma Blade Mixers
(also for high-viscosity mixing)
Size Reduction
Wet & Dry Size Reduction
Steel & Ceramic Lined Mills
Jars & Jar Rolling Mills
Vacuum Drying
Dryers & Complete Systems
ties that can be costly in terms of time
and money.
TrendMiner
So how exactly does the plant engineer,
maintenance engineer, safety
engineer or production engineer go
about analyzing data without traditional
models? This can be done by enabling
the masses, so that not only analytics
specialists, but the vast majority of a
company's employees can utilize the
data. That means making business
intelligence (BI) and analytics tools accessible
to business users and SMEs
so they can analyze the data and derive
new insights just by using their particular
expertise, without needing to acquire
sophisticated analytics skills.
This is best described as the democratization
of analytics. And there is actually
no better way to implement that
than using self-service analytics.
Self-service analytics explained
Self-service analytics leverages smart
data combined with the tactical expertise
of those at the production site.
It puts the power of the data into the
for all your solids processing
Experts
Batch & Continuous Mixing That
Delivers More
Applications:
APIs ∙ Ag-Chemicals
Biologics ∙ Catalysts
Ceramics ∙ Chemicals
Food Ingredients
Herbicides ∙ Minerals
Nutraceuticals ∙ Pesticides
Pharmaceuticals ∙ Pigments
Polymers ∙ Powdered Metals
Proteins ∙ Resins ∙ Vitamins
* Easy to clean and
maintain
* Excellent heat
transfer
* Parts and service
available for life
* Vacuum- and
pressure-rated
CW
LS
FIGURE 5. Self-service analytics helps
predict heat exchanger fouling, and control
the process to avoid it
Innovation
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OCTOBER 2019
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Since
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Chemical Engineering October 2019

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