Control designs in the process industry are almost exclusively based on PID controllers these days. Even though they are simple to implement and easily integrated into the control system, they quickly reach their limits when more complexity is involved. Advanced Process Control (APC) opens new opportunities. With APC, even complex situations can be mathematically described with process parameters or variables – and used for automatic, flexible plant operation. APC provides process management that can significantly reduce the consumption of energy and raw materials, consistently maintain high quality standards and contribute to more flexible production.

Simple, economical, ready for use

In the past, APC had a reputation for being expensive and complicated to use. With its innovative process control system, SIMATIC PCS 7, Siemens shows that there is another way – one that enables easy and cost-effective implementation of even the most advanced APC applications. The standard library of SIMATIC PCS 7 contains numerous high-level controls for APC functions, which cover a large part of the sophisticated control tasks for the process industry. This provides you, the user, with easy access to Advanced Process Control – at no extra cost and with little design effort.

We also offer numerous sophisticated control functions for special requirements: These
 add-ons can be purchased separately and integrated seamlessly into SIMATIC PCS 7.

Features

In addition to numerous types of standard measurement for technological functions, such as monitoring of measurement values or the control of valves or engines, the standard library also includes control modules (templates) for essential control functions, such as:

  • PID control

  • Cascade loop control

  • Split-range control

  • Ratio control

What’s more, the standard library also provides these high-level control functions at no extra charge:

  • Gain scheduling

  • Override control

  • Lead-lag feed-forward control

  • PID tuning

  • Control performance monitoring

  • Smith predictor

  • Model-based predictive control