Siemens References
Only 50% of control loops in the process industry are well tuned and non optimized control loops lead to:
If customers are aware, that they loose money with non optimized control loops, why don't they optimize?
CPA add a new layer of transparency to process data to support an efficient optimization process
Main building blocks:
Detailed Analytics to identify cause: Detailed information on control performance of each single loops
Optimization (Quality/Process)
High level architecture:
Preferred Customer Profile:
General:
Collector software is deployable without influencing operations
Identification of the bad control loop is done within minutes instead of hours
Our managed service model can be realized via operational costs (OPEX), avoiding add. CAPEX and requires no resources from customer
Important Note: Detecting of possible areas for optimization and tuning of control loops is not a one-time job, due to continuous process changes and wear and tear on equipment!
Process Performance:
Reduced variability in process improves product quality
Reduced variability in actors improves equipment life cycle: e.g. generate transparency about actuators like pumps which are switching on/off in high frequency and so reducing lifetime significantly
Improved set points increases throughput
Lowers resource input. Examples for energy cost savings: heating system at a pasteurization process up to 30 TEUR p.a., clinker cooling fan at cement mill up to 4 TEUR p.a.
Reduced alarms and fewer operator interaction reduce operators' work load
Base for Advanced Process Control
