Artificial neural networks for greater flexibility
The performance of deep learning technologies is based on artificial neural networks. Trained with thousands of images of objects that can usually be found in the waste stream, artificial neural networks hold a pool of object information they draw on when detecting and separating materials. Based on the extensively annotated data, the system recognizes patterns and properties of individual wood chips and instantly connects this information with data scanned by sensors. Wood chips are then categorized by material type and separated according to the customer-defined sorting task.
Deep-learning-based systems offer significantly more flexibility to the operator, allowing them to choose the types of materials they would like to target in the sorting process. For example, MDF and plywood can be recovered from processed wood. Operators can take advantage of the sorting flexibility to recover more types of recyclable materials and create new revenue streams.
In conclusion, combining advanced NIR systems and Deep Learning gives particleboard manufacturers and wood recyclers a competitive edge in optimizing their operation and reducing costs of limited and expensive primary materials.