Using Machine Learning and Simulation to Optimize a Rotary Dryer

Harshal Maradia, Mechanical Engineering Student at the University of Edinburgh (Formerly Engineering Intern at Altair) presents at the 2024 ATCx DEM.

This work explores the use of machine learning and simulation tools to optimize the performance of a rotary dryer using a new CFD-DEM coupling workflow. The study identifies key operational parameters influencing drying efficiency and machine learning algorithms are employed to develop predictive models, allowing for the optimization of dryer performance while reducing energy consumption. The results demonstrate the potential of integrating simulation and machine learning to enhance industrial processes, offering insights into the complex dynamics of rotary dryers.