Tools: Production Operations

Home Tools: Production Operations

'The human brain is an incredible pattern-matching machine.'

- Jeff Bezos

Geometric Process Control (GPC)

Geometric Process Control (GPC) is an innovative approach to process analysis and control based on n-Dimensional Geometry from our business partner, Process Plant Computing Ltd. GPC consists of three modules: C-Visual Explorer, Response Surface Visualizer and C-Process Modeler.

GPC provides insights into complex processes, revealing relationships among data across multiple process phases that are otherwise unobservable. Applications include:

  • Process Analysis and Troubleshooting
  • Production Reporting,
  • Process Unit Alarm Rationalization
  • Operator Console Display Limit Setting
  • Condition Monitoring and Fault Prediction
  • Operator Guidance for Process Management and Control

Any complex, multivariate dataset is fair game for analysis.

GPC has been successfully applied in a wide range of applications, from continuous to batch process to discrete manufacturing, and in industries ranging from clinical trials to aerospace, building materials, pharmaceuticals, chemicals, oil extraction and refining.

Link to recorded webinar: “Capturing and Replicating Optimal Process Operation

Ultramax®: Sequential Optimization and Adaptive Control

Ultramax® is an innovative, powerful and easy-to-use process optimization tool; one only needs to know the process inputs, outputs, and external influences to run it.

Ultramax® operates by devising models centered around a perceived local optimum. Data is then acquired in a sequential fashion and trials are conducted, one at a time. Results from these tests are fed back into the sequential optimizer. This is followed by generation of new advice designed to step toward the optimum. Hence a “hill climbing” approach is followed as ideal performance is sought.

Ultramax® acts as a multiple input / output adaptive supervisory controller, adjusting for changes to optimization requirements and external conditions while learning to optimally control a process in the act of optimizing it. Results for a power plant boiler optimization are shown to the right.