Optimizing a Continuous mixing process with discrete element modeling and machine learning | EDEM webinar series
Altair Employee
https://www.youtube.com/watch?v=UM_4bXs9J98
Achieving reliability in continuous bulk solids mixing processes is key to meeting product quality requirements in a wide range of industries but physical trial-and-error optimization is time consuming and expensive. This webinar demonstrates an efficient virtual optimization methodology that combines high-fidelity physics-based simulation in Altair EDEM, High Performance Computing (HPC) in Altair Unlimited and machine learning and automation in Altair HyperStudy to rapidly identify the optimal mixer design and operation in-silico.
A live presentation will be hosted by Elis Bright and Stefan Pantaleev with plenty of time for questions!
The presented methodology will cover parameterizing the equipment geometery, automatically generating and running EDEM simulations with well distributed sample set to drive machine learning. A multi-objective genetic algorithm, MOGA, is then utilized to rapidly estimate the optimal parameter set from a fitted response surface. All of this is achieved through an automated, easy to use, GUI-based workflow that combines all of the Altair tools together.