Using Machine Learning and Simulation for the Optimization of Industrial Bulk Handling Processes – a Case Study on Bin Blending

When it comes to simulating bulk solids handling processes the computational expense of high-fidelity simulations can be significant. In this webinar, Stefan Pantaleev, Senior Application Engineer at Altair, presents an efficient virtual optimization methodology for industrial processes which combines Discrete Element Modelling (DEM), Design of Experiments (DoE), machine learning and optimization methods to significantly reduce the computational expense of optimization relative to a purely simulation-driven approach.

A case study of a generic bin blender is used to demonstrate how this methodology can be applied to rapidly optimize the blender operational parameters.