What GPC can do

	Increase profitability
	Reduce operating costs
	Optimise Operating Windows and Operating Envelopes
	Predict and avoid plant trips and process events
	Rationalise alarms scientifically
	Condition Monitoring of equipment for early alerting and mitigation
	Reduce energy consumption
	Improve product consistency through reduced variability
	Investigate and implement multi-mode alarming and operation
	Increase safety through better operator alarms
	Improve process understanding
	Increase right-first-time and golden batches in batch processing
	Improve site-wide implementation of Operational Excellence

Geometric Process Control (GPC) is a patented graphical technology which has been applied to the process industries to enable new levels of operational process control which were previously not achievable. Process engineers can directly leverage their process knowledge without mathematical intervention. The method can be applied to both batch and continuous processes.

GPC uses n-dimensional geometry to take a very large spreadsheet of data (either input manually or directly from your OSI PI or Honeywell PHD plant historian) to create visual outputs which describe production for a given period. Because the outputs are visual you gain new insights into your process. GPC technology implements an Operating Envelope to reduce operational variability, prevent waste, reduce recycles and lower energy consumption – which have a substantial impact on your bottom line.

GPC technology provides a wholly new method for rationalizing operator alarms by positioning alarm limits at the boundary of a multi-variable Operating Window related to an Operating Envelope. Alarm performance is predictable and dramatically improved, leading to much greater operator belief in alarms and faster response - which in turn contribute to better process economics, fewer plant trips and increased process safety.

The new GPC alarm limits can be loaded into your existing Master Alarm Database, if you have one, or into your DCS if you don’t so there is minimum change to control room procedures. GPC is the first method to set alarms scientifically and it avoids the many day-long, multi-discipline meetings which previously characterized alarm rationalization. It is a much faster and lower-cost way to rationalize.

GPC uses Operating Envelopes as very sensitive detectors of change enabling it to predict most avoidable process events (eg. compressor surge, column flooding) well before they happen and in time for operators to make process changes to avoid them. For unavoidable equipment events GPC will predict deteriorating equipment performance usually in good time before equipment failure allowing operators to take mitigating action such as switching in a standby pump or fin-fan, thus reducing equipment damage and minimising process disturbance.

GPC applies to batch, continuous processes and to multi-phase and multi-mode processes such as those used in polymer manufacturing.

The benefits of GPC

GPC is much easier to understand and use than data analysis techniques based on algebra or calculus, AI or ML because its operation as far as the user is concerned is almost entirely visual. Anyone in your organisation can understand how and why the process improvements will work and they don’t need to be a scientist, engineer or mathematician. They don’t need to write programs either as all work is done by the CVE and CPM products.

GPC works by replacing the fundamental assumption of orthogonality between dimensions. The uniqueness of the parallel coordinate transformation opens up n-dimensional geometry to non-mathematicians by making it possible to see a multi-variable graph containing a thousand or more variables (such as temperatures, pressures, flows and product qualities) and hundreds of thousands of different observations in a single picture.

For the last several thousand years, the world has been restricted to graphs that could show at most half a dozen variables. This has severely limited the understanding of multi-variable processes by forcing over-simplification, resulting in an artificially constrained understanding of complex process interactions.

With the advent of GPC, users can interact with the full extent of the data quickly and easily, instead of working within an artificially constrained number of variables.