Process optimization is key in food and beverage manufacturing and control loops are the critical components. “Out of tune” loops can affect the quality of the product, the material and energy consumption, and ultimately increase the risk of contamination. This article demonstrates how Industrial AI and Machine Learning can be used for PID loop tuning to improve and optimize control loops to generate big savings and reduce risks.
What is a Control Loop?
A simple form of a process controller is the thermostat which maintains the temperature of a room according to a given setpoint. It operates as a closed loop control device, trying to minimize the difference between the room temperature and the desired one.
The industrial version is the PID (Proportional-Integral-Derivative) control loop - an essential part of every process application. PID loops have been around for a very long time. The first pneumatic instruments featuring a proportional controller were developed by Taylor Instrument Companies at the beginning of the 20th century,
Nowadays, loop controllers are available as standalone devices called single loop controllers, but the most common version is a piece of code that resides in a PLC (Process Logic Controller) or a DCS (Distributed Control System). It makes it easier to combine them to create advanced control diagrams like cascade or feed-forward control, or split range required for the complex control of food & beverage, chemical, oil & gas operations and more.