MACHINERY | PROCESS CONTROL
Right: An operator at the control panel of an Entek twin-screw extruder
large-scale aggregation, cleaning, enriching, and contextualising of semi-structured process and pro- duction data in real time. It says such tasks can be expensive, complicated and resource-intensive if done within a Data Warehouse or pulled from a Data Lake.
Smart integration
US-based compounding machinery maker Entek highlights increased use of IoT and IIoT devices, such as smart flowmeters, drives and level transmit- ters. “This is becoming more common, where devices are no longer single variables, but a whole set of variables with a custom drop-in interface for a control system. This can also add features like parameterisation, calibration, alarming, status track- ing and health monitoring, for example,” says Ben Burton, Controls Supervisor at the company. “We have also developed a new temperature
control algorithm. This new temperature control has some aspects of machine learning, particularly where the auto-tune comes into play. The system warms up and records heating and cooling percentages, and other bias terms, to come up with a set of gains automatically. Entek is rolling this new
temperature control out to all new extruder projects soon,” he says. “In addition, we are making our plant operations
collaborative and interconnected. We track our runtime and downtime and share statistical data from the lab using GEMs (Generic Equipment Models) to correct process variables on the line. Some of this data is entered manually, either from process data or lab tests, and some of it is logged automatically by the control system in GEMs. Extending the function-
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IMAGE: ENTEK
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