Advanced process control

From Wikipedia, the free encyclopedia

In control theory Advanced process control (APC) is a broad term composed of different kinds of process control tools, often used for solving multivariable control problems or discrete control problem.

1 Overview
2 Advanced process control: Topics
3 References
4 External links

[edit] Overview

Advanced process control is composed of different kinds of process control tools, for example:

Model predictive control (MPC),
Statistical process control (SPC),
Run2Run (R2R),
Fault detection and classification (FDC),
Sensor control and
Feedback systems.

APC are often used for solving multivariable control problems or discrete control problem.

Normally an APC system is connected to a distributed control system (DCS). The APC application will calculate moves that are send to regulatory controllers. Historically the interfaces between DCS and APC systems were dedicated software interfaces. Nowadays the communication protocol between these system is managed via the industry standard OLE for process control (OPC) protocol.

[edit] Advanced process control: Topics

[edit] APC industries

APC can be found in the (petro)chemical industries where it makes it possible to control multivariable control problems. Since these controllers contain the dynamic relationships between variables it can predict in the future how variables will behave. Based on these predictions, actions can be taken now to maintain variables within their limits. APC is used when the models can be estimated and do not vary too much.

In the complex semiconductor industry where several hundred steps with multiple re-entrant possibilities occurs, APC plays an important role for control the overall production.

APC is more and more used in other industries. In the mining industry for example, successful applications of APC (often combine to Fuzzy Logic) have been successfully implemented. In the mining industry, the models change and APC implementation is more complex.

[edit] Terminology

Manipulated Variables (MVs) are variables where advanced controllers send setpoints to. Controlled variables (CVs) are variables that normally need to be controlled between limits. Disturbance variables (DVs) are only used as an input to the controller, they cannot be influenced, but when measured contribute to the predictability of the CV.

[edit] Commercial products

Popular commercial multivariable control applications include DMCplus from Aspen Technologies,SSE DNP server from Imperious Technology, RMPCT from Honeywell, P&C from ABB (Asea Brown Boveri), Process Perfecter from Pavilion Technologies and Connoisseur from Invensys, INCA from IPCOS (also for batch application), ProfitNLC from Honeywell, GMAXC from Intelligent Optimization, Expert System ES III from Glass Service, eMPC from eposC Process Optimization, ControlPerformanceOptimizer from Matrikon Matrikon and ControlSoft Inc. but there are many more.

[edit] References

This article does not cite any references or sources. (September 2007)
Please help improve this article by adding citations to reliable sources. Unverifiable material may be challenged and removed.