Digital Twin + Industrial Internet for Smart Manufacturing: A Case Study in the Steel Industry
apps in the platform that focus on optimizing
a number of sub-processes in the plant.
In this article, we describe the smart apps as
depicted in Figure 8, which include:
Sintering Smart App: sintering
machine terminal temperature
prediction
and
operation
recommendation;
Gas Boiler Smart App: gas boiler
thermal efficiency optimization; and
Oxygen Pipeline Smart App: oxygen
pipeline supply and consumption
balance optimization.
Though each of these apps addresses a
different
problem
across
different
production sub-processes, they share a
common theme, which includes:
They are built on the same digital
twin system covering the sub-
processes that are involved. Once
being defined and configured, the
digital twin system supports various
analytics models and applications
across these sub-processes.
Predictive and prescriptive analytics
are performed on data collected
from equipment in the relevant sub-
processes.
Analytic outcomes are combined
with business logics to arrive at role-
based operational recommendations
targeted toward specific operators.
The data analytics run continuously
with internal data collection in order
of seconds, dynamically reflecting
the
real-world
condition.
Operational recommendations are
provided to specific operators as
necessary.
While the first two apps (sintering
and gas boiler) focus on optimization
in a single sub-process, the oxygen
Figure 8: Apps and Platform Deployment Architecture
IIC Journal of Innovation
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