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Bayer uses Rocky DEM simulations for automated design optimization of seed treaters

Rocky DEM and optiSLang helped to optimize Bayer’s seed processing equipment and significantly accelerate product design cycles

Bayer AG’s Crop Science Division constantly deals with seed treating processes. These processes involve challenges such as: How well is the batch mixed? How do we detect the process endpoint? How do we avoid build-up of treating product on the equipment? The lack of suitable sensor and process analytical technology implies the inability to access these important metrics experimentally. 

Combining simulation software Ansys SpaceClaim, Ansys optiSLang, and Rocky DEM, Bayer engineering team obtained insights and model-based design optimization to deal with those issues. Over 200 designs were digitally screened, and a single design was physically prototyped and validated experimentally, in a short project timeline of only 4 months.

The effort for tests and prototypes could be reduced to an unprecedented extent, paving the way for a completely novel and accelerated approach to process design and optimization at Bayer.

“Using Rocky DEM and Ansys optiSLang allowed us to digitally prototype a seed processing equipment resulting in significant acceleration of product design cycles. This represents a significant milestone towards our ambition of Right-First-Time in designing solids processing equipment.”

Dr. Rakulan Sivanesapillai

Expert Modeling Formulation Processes - Bayer AG

Bayer AG’s Crop Science Division needed to optimize its seed treaters taking into account key elements involving bulk solids, an operation that typically requires time-consuming experimentation – all too often involving trial and error approaches.

Bayer engineers turned to simulation as an engineering solution to digitally prototype a seed processing equipment, using a combination of Ansys SpaceClaim, Ansys optiSLang, and Rocky DEM. The team defined quantitative optimization metrics and an optimization design space including geometric parameters of interest. The next steps involved the creation of a parametric CAD model and the automated digital evaluation of almost 200 design variants, and the training of a surrogate model and design optimization using the model. Lasty, the team proceeded to physical prototyping of optimal design and experimental validation.

Simulation allowed Bayer to virtually test 200 designs and produce a physical prototype in only 4 months. The technology gave Bayer’s engineers the capability to look inside “black boxes” of the seed treating process, enabling an innovative and faster way to optimize solids processing equipment, and resulting in significant acceleration of product design cycles.

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