By Rick Zettler, owner, Z-Comm LLC
Talk of artificial intelligence (AI) 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.
Much of this buzz originates from the type of ejection method for optical sorters – a robotic arm rather than valve blocks. The traditional valve block optical sorter employed in thousands of recycling facilities for decades uses precisely timed pulses of air in a drop-eject method to sort materials at rates reaching eight tons per hour or even higher, depending on the sorting task. Conversely, the new robotic arms more closely mimic a human’s picking motion of multiple fractions at rates slightly higher than that of a person, roughly 0.5 tons per hour.
For this reason, Eric Olsson, plastics segment manager for TOMRA Recycling Sorting, advised recyclers they need to fully understand exactly what makes up AI and what it’s designed to do. He stressed that AI is not the machinery – either the robotic arm or the valve block – and redirected the focus to highlighting how AI can be leveraged at the facility by incorporating it as part of a holistic solution.
“Right now, it’s like AI is the must-have technology for customers,” he said. “Recyclers must fully understand how it works and how it will improve the sort. Otherwise, final product and investment expectations may not be fully realized.”
AI Is Not New
The good news: AI is nothing new to the industry, and most recycling facilities already are using some form of it. Optical sorters with valve block ejection have leveraged traditional AI for decades 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.
The heart of AI is software and algorithms, not the 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. However, traditional AI uses a manual process where a technician engineers the classifiers and deploys them into the in-house-developed sorting software.”
Every sensor-based sorting machine consists of numerous components, all of which play their role and are chosen according to the sorting task. How well, or poorly, material fractions are sorted depends on how the core components – namely the sensor system, data processing (software), conveyor system and ejection system – work together. “This is why TOMRA invests roughly 8% of our revenues on research and development,” offered Olsson. “We create the software internally, so we can optimize sorting performance for a range of material streams, from plastics and waste to metals and wood.”
Advancements in sensors and traditional AI already have 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 allows the MRF to sort out higher-value clear and light-blue PET with higher yields and less contamination for secondary plastics sorters, so bales can be sold as Grade A or B vs. Grade C or D.
Five years ago, optical sorters had a difficult time sorting PET bottles from thermoforms. The ability to separate the two results in higher quality rPET being cycled back into new PET bottles. “We developed a sensor and software system to give AUTOSORT™ the ability to distinguish the small differences between PET bottles and thermoforms, so they can be separated for equivalent product recycling,” added Olsson.
He also pointed to recent advancements in flake sorting and the company’s work to develop a mechanical sorting process for consistently separating mixed polyolefins by polymer and color to close the loop on food-grade, cap-to-cap recycling. “Advancements made in NIR sensors, two-sided color cameras and sorting software ensure classification of PE and PP materials and position the industry at the beginning of mixed polyolefins going
closed-loop,” Olsson said.
Deep Learning – A Game Changer
Today’s game-changing technology for the recycling industry is AI’s deep learning subset, which 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 impossible to distinguish using traditional technologies.
For deep learning to work, software engineers train the network with thousands of images that hold a pool of object information. The network recognizes patterns in the data and connects this information pool to the sorting task.
Since these most recent advancements in AI are application-specific, the technology supplier must provide the customer with material testing, training and optimization. A supplier simply cannot just drop AI-based equipment into the circuit and leave. Full potential only is achieved through a well-optimized and positioned machine with workers trained on how to get the most out of it.
Olsson explained that a well-optimized circuit leveraging deep learning technology can lead to a much more granular sort. “We recently launched an application that offers high-accuracy sorting of opaque white packaging, textile and foils from PET, and the system identifies and removes over 92% of opaque objects with titanium dioxide,” he said. “Deep learning enables recyclers to selectively target a specific value stream and increase purity of that product.”
He is optimistic about the industry’s future with deep learning and sees several areas where AI can play a part in improving the sort.
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. Because deep learning is application-specific, however, recyclers need to work with trusted technology suppliers to ensure that they get the most out of it.
President of Z-Comm LLC, Rick Zettler is a writer, photographer and award-winning PR and marketing consultant specializing in the recycling, mining, construction and road building industries.
TOMRA Recycling, a division of TOMRA Group, designs and manufactures sensor-based sorting technologies for the global recycling and waste management industry to transform resource recovery and create value in waste. TOMRA was founded on an innovation in 1972 that began with the design, manufacturing and sale of reverse vending machines for automated collection of used beverage containers. Today, TOMRA is transforming how the planet’s resources are obtained, used and reused to enable a world without waste.
More information: www.tomra.com
