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Understanding how AI can help the sortation process

Published: February 19, 2024




This article appeared in the December 2023 issue of Resource Recycling. Subscribe today for access to all print content.

Artificial intelligence has taken the recycling industry by storm – and for good reason. When applied correctly, today’s emerging AI technologies have transformative powers, driving automation and efficiently enabling more granular sorting of complex material fractions. But they can also be a waste of both time and financial investment if not considered as part of a holistic solution.

Much of the AI buzz originates from optical sorters using robotic arms rather than valve blocks. The valve block optical sorters employed in thousands of recycling facilities for decades use precisely timed pulses of air in a drop-eject method to sort materials at rates reaching 8.0 tons per hour or higher. Conversely, the new robotic arm optical sorters more closely mimic a human’s picking motion of multiple fractions at rates slightly higher than that of a person: about 0.5 tons per hour.

To properly implement these technologies, recyclers need to fully understand what AI is and what it’s designed to do. Fully understanding how AI works and how it will improve the sort is the key to optimizing product throughput and purity, not to mention profitability.

AI is not new

Recyclers must keep in mind that AI is nothing new to the industry, and that most recycling facilities are currently using some form of it. Valve block optical sorters have for decades leveraged traditional AI to drive sorting circuit automation and recycled product purity. Plant builders and technology suppliers have worked with customers to design material flows to improve yield, purity and throughput with fewer manual sorters.

AI consists of software and algorithms, not hardware. Traditional AI is used in combination with a variety of sensors – RGB cameras, near-infrared (NIR), laser, EM (electromagnetic) and X-Ray – to automatically detect and process fractions. Traditional AI is a manual process whereby a technician engineers the classifiers and deploys them into the house-developed sorting software.

Every sensor-based sorting machine consists of numerous components, all of which play their roles and are chosen depending on the sorting task. How well, or poorly, material fractions are sorted depends on how the core components – the sensor system, data processing (software), conveyor system and ejection system – work together.

Advancements in sensors and traditional AI have already led to many automated sorting improvements. Throughout the years, optical sorter designs have evolved with greater computing power and improved algorithms to leverage traditional AI and greatly improve color sorting. This is why the MRF can now sort out higher-value clear and light blue PET with higher yields and less contamination, meaning bales can be sold to secondary plastics sorters as Grade A or B versus Grade C or D.

Deep learning: a game changer

AI’s deep learning subset is the current game-changing technology that further advances sorting accuracy and adaptability to material streams. Large datasets of trained neuronal networks enable deep learning technologies to recover materials that are difficult or even impossible to distinguish using traditional technologies.

For deep learning to work, software engineers train the network using thousands of images to create a pool of object information. The network recognizes patterns in the data and connects this information pool to the sorting task.

Deep learning advancements in AI are application-specific, so the supplier must provide the recycler with material testing, training and optimization. The supplier cannot just drop AI-based equipment into the circuit and leave. Full sorting potential is only achieved through a well-optimized and well-positioned machine with workers trained on how to get the most out of it.

Optimization leads to a much more granular sort. Whereas conventional optical sorters accurately identify and sort aluminum from the material stream, trained deep learning systems take the next step to detect and sort items such as used beverage containers (UBCs) from other aluminum in the stream. Deep learning enables recyclers to selectively target a specific value stream and increase purity of that product.

There are several areas where deep learning AI can play a part in improving the sort. We have already launched a deep learning wood sorting application with our GAIN technology and are currently developing an application that focuses on UBC cleaning. There is also value to be found in PET cleaning and the ability to detect PET food grade containers from the material stream using deep learning.

Because of its ability to be trained to see objects on the belt like a human sorter, AI’s deep learning technology, when combined with conventional sorting technologies, can bring final product qualities and yield to the next level.

Ty Rhoad is TOMRA’s regional director of recycling sorting for the Americas. He can be reached at [email protected].

The views and opinions expressed are those of the author and do not imply endorsement by Resource Recycling, Inc. If you have a subject you wish to cover in an op-ed, please send a short proposal to [email protected] for consideration.

This article appeared in the December 2023 issue of Resource Recycling. Subscribe today for access to all print content.

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Women in Circularity: Karen Jayne

Published: January 29, 2024


A warm welcome back to “Women in Circularity,” where we shine a light on women moving us toward a circular economy. This month, I connected with a dedicated nonprofit professional: Karen Jayne. Karen is the Chief Executive Officer of Stardust in Phoenix, Arizona—an innovative nonprofit organization that operates two reuse centers and offers deconstruction services that diverts reclaimed building materials from landfills and supports economic and environmental sustainability in their community. She has more than 13 years of expertise in building materials reuse.  Continue Reading

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Ottawa grocers, other partners plan reusable packaging pilot

Published: January 22, 2024


By mid-2024, Ottawa, Canada grocers hope to have a reusable packaging system going for customers. | Deemerwha Studio/Shutterstock

Major grocery retailers and other foodservice businesses in Ottawa, Ontario will start selling foods in reusable, returnable containers later this year through a first-of-its-kind pilot program to help reduce single-use plastic packaging, according to a press release from the pilot’s organizers. Continue Reading

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Curbside fiber grades continue to climb in value

Published: January 16, 2024


The new year kicked off with good news for MRF operators, who saw OCC and mixed paper fetching higher prices and curbside plastic grades mostly holding steady. Market graph icon

The national average price for corrugated containers (PS 11) is up again this month, from $80 in December to $87 per ton this month. This compares to $29 per ton this time last year. It’s the highest OCC prices have reached since falling sharply in September 2022.

Mixed paper (PS 54) is also up by $10 this month, and is now trading at $48 per ton. This compares to $0 per ton this time last year.

Sorted residential papers (PS 56) are up by $10, now trading at an average $61 per ton. This compares to $23 per ton one year ago.

Sorted office papers (PS 37) remain unchanged at $132 per ton. This compares to an average $221 per ton one year ago.

In the plastics space, grades had marginal or no movement in price.

The national average price of post-consumer PET beverage bottles and jars is up marginally this month, now averaging 11.47 cents per pound, compared to 11.02 cents per pound this time last month. Some regional contracts are still yielding 13 to 15 cents per pound. One year ago this grade was trading at 12.02 cents per pound.

Post-consumer natural high-density polyethylene (HDPE) dropped marginally, now at an average 28.50 cents per pound. This compares to 29.50 cents last month and 61.59 cents this time last year.

Color HDPE remained unchanged this month, now at an average 18.81 cents per pound. It was 9 cents one year ago.

Post-consumer polypropylene (PP) also remains unchanged at 4.94 cents per pound. PP was 5.38 cents one year ago.

The national average price of Grade A film remains unchanged at 13.88 cents per pound. It traded at 15.63 cents one year ago.

Grade B film remains unchanged at 6.88 cents.

Grade C film remains at a nominal 0.13 cents per pound.

Aluminum cans remain steady, at 61 cents per pound. These used beverage cans (UBCs) traded for 65 cents per pound this time last year.

Sorted, baled steel cans remain constant at $212 per ton. The price was $156 per ton one year ago.

These prices are as reported on the Secondary Materials Pricing (SMP) Index. This pricing represents what is being paid for post-consumer recyclable materials in a sorted, baled format, picked up at most major recycling centers.

For a free trial to SMP’s Online Post-Consumer Pricing Index, visit the Recycling Markets website ( You can also contact Christina Boulanger-Bosley at [email protected] or call 330-956-8911.

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