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CWA uses Discrete Element Method (DEM) modelling to optimize bulk materials transfers, reducing dust emissions and product degradation, and resulting in a more efficient throughput.

We use DEM modeling for analysis of material flows through transfer chutes and hoppers before they are built, allowing us to create designs with optimal performance. We also use this type of analysis to improve the performance of existing transfer chutes.

Aside from addressing the issue of dust management, we frequently use DEM modeling to design transfer chutes, reduce impact angles, and to make sure that the material is being loaded centrally on the receiving conveyor, which provides the client with better belt tracking and helps to reduce product spillage and waste.

We also look to reduce wear on the walls of the chute and to reduce particle breakage. These are all issues that we address with DEM modelling, which enables us to optimize a conveyor system's chute transfers during the design phase.

DEM modeling can also be used to protect against material degradation which is very important to some clients, such as when they are selling a product based on the particle sizes. This would include products like potash, where buyers of the product require a particular size, or certain agricultural products such as grain, where the integrity of the product needs to be maintained. These are all cases where the client is looking to protect the materials against degradation.

Additionally, reducing the amount of wear on the chute walls results in savings to the clients by reducing both expenditures on equipment, such as liner replacements, and the need for downtime to their operations for equipment repair and replacement.

Our database of material flow behavior is quite extensive. Because of this experience we can design, for example, a new potash conveyor system with confidence, knowing that the transfer chutes that we design will operate efficiently once they are built, even if a client cannot supply us with a sample of potash or doesn’t have an operating chute for us to use to validate our simulation.

Similarly, if we are designing a greenfield system, we have enough historical information in our database to design a highly efficient operating system.