Plant optimization through automated
controller analysis in the glass industry
Control Performance
Analytics (CPA)
Plant optimization through automated
controller analysis in the glass industry
The complexity of industrial process applications is
continually increasing. At the same time, the demands
placed on production targets are increasing in terms of
process reliability, flexibility, product quality, energy
consumption and emissions. Professional handling of
this situation requires additional transparency in order
to identify potential and starting points for optimal
adjustment of the process parameters.
In the glass industry, the control quality is decisive for
achieving production targets. The individual control
loop is the original core of the production process,
especially in the hot area of glass production. However,
studies show that half of all control loops are not
operated satisfactorily. This may be due to less than
optimal parameters, controls in manual mode,
oscillating behavior of the controlled systems or
mechanical problems with the control valves.
A measurement and control engineer in large plants is
today responsible for hundreds of control loops. The
evaluation of the control quality in the process steps,
such as the mixture composition, melting, shaping or
cooling, in connection with the associated alarms
requires both time and a high degree of experience.
Identifying improvement potential and optimizing
control loops are not one-off tasks; changes in
production processes and wear and tear mean that
these challenges remains constant.
CPA is a cloud-based managed service that increases
the transparency of process data and optimizes control
loops. By collecting and analyzing information on the
plant level, the customer gains full control over the
data. The identification of the control loop states is
based on an automatic KPI calculation (Key
Performance Indicators), which enables detection of
setpoint jumps, steady state problems and even static
and sliding friction in control valves. CPA enables
automatic calculation of new parameter sets to tune
control loops without affecting plant operation.
Better product quality due to less variation of process variables
Maximized device run times due to reduced actuator variability
More stable setpoints increase throughput by bringing it closer to the plant limits
Resource savings thanks to improved setpoint jumps (e.g. energy, raw materials, etc.)
Fewer manual interventions in the control loops, which means optimized control such as Advanced Process Control can be superimposed.
Fewer alarms and fewer operator actions reduce operator workload