ConnoisseurTM delivers significant cost and management benefits.
Connoisseur is comprehensive, advanced process control software that improves process profitability by enhancing quality, increasing throughput, and reducing energy usage, It works using modern, state-of-the art technology to provide automatic control systems capable of releasing latent process potential.
Connoisseur is compatible with all major computer platforms. A unique feature even permits application of model predictive control to batch processes with multiple process models.
Connoisseur has been proven to reduce standard deviations in product quality by a factor of two or more. This improvement is achieved by simultaneously controlling a number of process parameters and orchestrating them to maintain the product within precise specifications. That, in turn, releases additional process potential. Payback typically occurs in three to twelve months.
Benefits:
- Increase throughput by 1% - 5%
- Improve process yields by 2% - 10%
- Reduce specific energy consumption by 3% - 10%
- Enable quicker, more effective start-ups
- Reduce waste, reworking, and recycling costs
Features:
- Easier Identification - On-line collection and analysis - statistical techniques applied to past dynamic data identify cause and effect relationships, giving important insight into process characteristics; Pseudo random binary signal testing allows minimum process perturbation
- Process modeling - Quantifies cause and effect relationships, accurately representing process behavior to provide better understanding of problems and assist in controlling them
- Controller generation - Allows the system to automatically generate a robust and accurate multivariable controller
- Real-time adaptive control - Enables the control system to be adapted to prevailing process conditions on-line
- Constrained linear econonmic optimization - Permits operation within the physical constraints of the process, allowing Connoisseur to maximize process potential
- Non-linear control - Model scheduling is an integral feature; Piece-wise linear approximation addresses the majority of non-linear behavior; A neural network model is available; The fundamental dynamic model is linkable for piece-wise linear model generation
