The company has combined its industry-leading AUTOSORT® technology with its deep learning-based sorting add-on, GAIN, to create a solution that can distinguish between and sort different types of wood-based materials, significantly enhancing customers’ sorting and manufacturing processes.
The primary application for TOMRA Recycling’s new solution is sorting Wood A – non-processed wood – from Wood B – processed wood products such as MDF (medium-density fiberboard), HDF (high-density fiberboard), oriented strand board (OSB) and chipboard.
TOMRA Recycling has been a frontrunner in the global wood recycling sector for more than 10 years. The company’s X-TRACT solution quickly became popular with chipboard manufacturers to produce a clean recycled woodchip fraction by sorting and separating out the inert material (glass, stones, ceramics, etc.) and metals. Once the X-TRACT unit has removed these impurities, the recovered woodchip is of sufficiently high quality to be used in the production of standard chipboards.
In recent years, however, TOMRA Recycling has been approached by an increasing number of customers who are looking to use recycled wood of a much higher purity level in their production processes. To achieve these specific purity requirements, in addition to removing the inert material and metals in the infeed stream, other impurities including engineered wood composites as well as polymers, would have to be removed.
As these materials are not distinguishable using x-ray technology, the X-TRACT unit was unsuited to this sorting task. Determined to help these customers and recognizing a potential gap in the market for a solution which would allow companies in the wood recycling sector to optimize their wood sorting processes, TOMRA Recycling’s deep learning experts developed an application that combines TOMRA’s industry-leading AUTOSORT® unit with its deep learning-based sorting add-on, GAIN.
TOMRA’s Wood A vs Wood B application uses deep learning technology to sort and extract impurities that couldn’t previously be detected, making it possible for the first time to detect, analyze and sort every different wood type, therefore cleaning up the real wood fraction.