The food industry has improved product quality while reducing production time and cost by automating production using programmable logic controllers (PLC) over the last several decades. However, many production plants still require some level of manual expert interaction, mainly because the production processes are not 100% under control. Operators are often still present to take quality samples, re-tune unit operation controls or resolve failures.
The use of a physics-based “Digital Twin” is getting more and more traction to develop the equipment virtually due to the improvements in prediction accuracy and speed of computation. Digital twins allow engineers to find the optimal design before the unit goes into production. However, these digital twins can’t be deployed at the operational level because they can be complex or too slow to react at the speed of operation.
In this contribution a new set of solutions that lowers the barrier in executing the digital twins on the production floor is explain based on a few examples. This will deliver substantial return on investment (ROI) for the food production industry. They include technologies such as:A machine learning based methodology to perform Model Order Reduction (MOR) on the digital twin in order to get real time response based on production information.
A machine learning based methodology to convert the reduced model into a virtual sensor for online quality predictions or predictive maintenance scheduling as well as to use it for creating an optimal controller of the unit based on the product requirements.
Fast edge computing hardware that can collect data from sensors and run the Executable Digital Twin (xDT) to suggest corrective action to the operator, in real time, or ultimately run in closed loop control.