TOMRA AUTOSORT combined with GAIN deep learning first to use AI to sort wood waste
AI-based technology to reach higher purity levels compared to X-ray based systems
TOMRA Recycling has become the first in the world to use deep learning, a subset of artificial intelligence, in wood recycling applications.
The company has combined its 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 fibreboard), HDF (high-density fibreboard), oriented strand board (OSB) and chipboard.
Achieving higher purity levels
In recent years, 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. 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 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.
Philipp Knopp, Product Manager at TOMRA Recycling, comments: "Wood recycling is a fast-evolving market, with increasingly stringent legislation being introduced in a number of regions globally to move towards a more circular economy model. Our AUTOSORT with GAIN solution uses deep learning technology to create a robust and flexible solution which we are confident will be welcomed by wood goods producers across to globe. It will also enable our customers to future-proof their operations as they will be better equipped to adapt and react to any future changes in the global wood recycling market such as new legislation. We are delighted to be the first in the market to offer this artificial intelligence-based solution."
Traditionally, waste management companies have operated using a simple "management of waste" approach to operating a MRF. Throughput targets and continuous operation (minimal downtime) were the main driving forces. The industry has changed however, and the focus moving forward is now on optimizing system performance and reliability, in conjunction with increasing recycling rates and a drive for a "greener" and more sustainable tomorrow.
When considering the addition of, or upgrade to, an "intelligent" MRF, for municipalities or private operators, the main factors should always be the client's (operator) current requirements, and evolving market needs, which include throughput, reliability, output quality, and adaptability. Equally important is a full understanding of what is really expected from any proposed system. Having an engaged and focused mindset for the project with the client from the beginning, will impact and drive the entire design process. This then impacts the overall project result, through to the productive, efficient, ongoing operation of the facility itself.