Welcome to Artificial Intelligence in Recycling

“Looking at the current world situation, the amount of waste production is increasing with population growth,” notes Tanner Cook, CleanRobotics co-founder and senior vice president of engineering.

“Regular methods of waste collection are inefficient and costly. Smart waste management using AI is a more efficient and cost-effective way to manage trash, help with recycling, and reduce the negative impact of waste on the environment.” 

Indeed, artificial intelligence (AI) is becoming more commonplace at solid waste facilities to achieve those goals. Options abound in doing so.

Glacier is building what is designed to be the highest-ROI, smallest-footprint robotic sorter and AI technology for MRFs, with a typical payback period of fewer than two years, notes Rebecca Hu, founder.

Glacier's team comes from cutting-edge engineering and data science programs at Google, Facebook, MIT, and Stanford.

With Glacier's AI technology, MRFs can detect real-time trends and aberrations in the material stream to make better business and operational decisions, says Hu.

Glacier's AI data can help answer questions such as how much value is being lost to the landfill each minute; from where the leakage is coming; which lines need more staffing or equipment upgrades, and what inbound routes have the most contaminated loads.

Other questions the technology addresses include how the month, day of week, or hour of day impacts the composition of material being received; how can the facility react effectively to those fluctuations; the purity rate of each of the bales, and what bales need to be pulled for further quality control.

“Our robot supplements our AI technology by offering reliable, high-ROI sorting power on most material streams,” says Hu.

AMP Robotics provides a portfolio of recycling solutions powered by its neural network, which has recognized more than 50 billion containers and packaging types in real-world conditions.

“AMP Cortex is our high-speed robotic sorting system guided by our AI technology,” notes Amanda Marrs, AMP's senior director of product. “Our robots intelligently perform physical tasks of sorting, picking, and placing material to achieve up to 99 percent accuracy and 80 to 120 picks per minute.”

AMP Vision is a modular computer vision system designed to drop into key stages of recycling operations to better understand material flow from inbound processing to bale quality control to the end of the line.

“AMP Clarity is our material-characterization and robot-performance software solution that allows users to monitor real-time material composition and performance measurement throughout a facility,” says Marrs, adding recent Clarity features include mass estimation, robot pick assignments, alerts, status tracking, and expanded reporting capabilities.

AMP Vortex is an AI-powered automation system for the recovery of film and flexible packaging designed for MRFs to “tackle the persistent challenge of film contamination and ultimately to target and recover film and flexible packaging for baling and selling,” says Marrs.

AMP technology has proven its effectiveness in installations such as Emmet County, a rural area in the northern tip of Michigan’s Lower Peninsula.

The county’s Department of Public Works operates comprehensive recycling, resource recovery, and solid waste transfer services—its MRF processing and marketing about 8,000 tons of material for recycling.

Emmet County had staffing challenges against the backdrop of seasonal issues in which tourists to the area led to a solid waste increase, forcing the facility to send material to a different MRF in summer 2020 due to a processing backlog.

As part of the solution, managers installed three AMP Cortex high-speed intelligent robotics systems to sort PET, HDPE, cartons, aluminum, and mixed plastics on its container lines.

Among the many benefits, robots boosted labor efficiency by 60 percent and capture of recyclables by 11 percent.

At a pace close to 90 picks per minute, robots are accomplishing in three hours what would normally take human sorters a full eight-hour day. The operation is not only saving $15,000 by not having to send materials to another MRF, but is accepting material from a nearby MRF that cannot process everything.

During COVID, with robots, the county was able to adjust its operations to continue processing as normal with half of its previous staffing and now has brought on all of its long-term temps as full-time county employees with benefits.

“It’s amazing how well and efficiently the surge hopper works. The more we run it, the less time we’re actually spending with it. It fills up, our people walk away, and we let the robots do the work. Whether it be minutes or seconds, we empty it sooner every week. There are more picks per minute, and it seems like it’s only getting faster,” notes Joshua Brubacher, Emmet County Operations Manager.

EverestLabs, provider of RecycleOS, uses deep learning AI combined with robotics to make residential and commercial recycling economically viable by drastically increasing material recovery at a MRF.

“Through close collaboration with recyclers, RecycleOS was built to be the most accurate and scalable AI system in real-world facilities, iterating on computer vision models and robotics designs that beat industry benchmarks,” says Jagadeesh “JD” Ambati, CEO of EverestLabs, adding the systems can be installed anywhere in a recycling line without expensive retrofits and can guarantee successful picks over its lifetime for more than 100 classes and subclasses of recyclable materials. 

RecycleOS AI models are designed to accurately identify nearly indistinguishable objects and guarantee successful picks at rates twice that of manual sorters, Ambati notes.

“MRFs realize profits within months of installing RecycleOS without huge upfront costs of retrofits,” he says. “RecycleOS also provides benefits of 24/7 monitoring of robot performance, minute-by-minute visibility of plant operations, and precise quantification of what goes in and out of a facility with the ability to aggregate sustainability data across plants, packaging brands and regions.”

RecycleOS not only provides MRFs a solution to run effectively and realize higher revenue from recovered materials, but it also positively benefits the global ecosystem, notes Ambati.

“Preventing materials from being landfilled saves energy used in—and GHG emissions from—mining and production of virgin materials,” he adds. “RecycleOS tracks all materials entering and exiting recycling facilities to provide granular analytics based on EPA-approved methodology on the environmental impact of a recycling facility. Data from RecycleOS also can be used to validate EPR [Extended Producer Responsibility] goals of CPG brands and provide much-needed end of line data to improve packaging designs.”

EverestLabs recently entered into a partnership with Sims Municipal Recycling (SMR) Sunset Park Materials Recovery Facility in Brooklyn, New York—North America’s largest commingled recycling facility—to install up to eight RecycleOS-powered robotics cells.

New York City operates the largest curbside recycling program in the U.S., with SMR processing more than 300,000 tons of glass, metal, and plastic produced by NYC and several other New York and New Jersey municipalities.

Tom Ferretti, SMR general manager, notes “These installations allow us and our partners to stay committed to sustainability while also saving us on costs, and enable moving SMR’s key personnel into higher-priority positions across the plant. It is a win for New York City recycling (as we are recovering more), for our team members, and plant safety and efficiency.”

CleanRobotics has developed a solution designed to allow recycling programs to be effective and drive substantial environmental impact by separating waste items at the time of disposal and eliminating human error.

TrashBot, by CleanRobotics, is a smart recycling bin that sorts waste with an accuracy of 95 percent, using recycling AI, robotics, computer vision, and machine learning. TrashBot also is designed to generate high-quality data for on-demand waste audits. It triggers fullness alerts and features a large display for custom video and educational content.

TrashBot is designed for areas such as airports, hospitals, stadiums, and other high-traffic facilities with the goal of improving diversion, leading to affordable recycling and cost-effective environmental programs.

CleanRobotics cites U.S. Environmental Protection Agency statistics that sporting event attendees can generate up to 39 million pounds of trash per year, with most of it being containers for food and drink consumed in stadiums and parking lots.

Waste management and recycling in stadiums presents challenges in that contamination of recyclables reduces the ability to divert waste at stadiums; educating the transient public is difficult because of varying recycling rules, and granular waste data is unreliable, expensive, seasonal, and changes often.

In stadiums with transient populations, this leads to large-scale contamination, low recycling yields, and poor diversion.

TrashBots were piloted for one year at a Pittsburgh multi-purpose indoor arena with a capacity of more than 18,000 spectators. In the pilot program, 30,000 items were sorted with 90 percent accuracy compared to conventional sorting with 30 percent accuracy.

Some 1,800 pounds of recyclables were collected compared to 650 pounds with conventional bins.

MSS Optical Sorters offers a full range of NIR, color, metal, and AI-based sensor sorters using either air jets or robotic arms for extraction technologies. Various combinations of sensor and extraction configurations are available depending on the specific application and particle size.

Why this is important

Better data and data-capture technology provide opportunities for consumer-packaged goods companies, retailers, and packaging manufacturers to understand the quality, flow, and recovery of their specific containers and packaging, says Marrs of the importance of AI in recycling facilities.

“Our technology can help producer initiatives to increase recycling rates and create new value streams for recyclables, ultimately aiding their pursuit of recycled content goals,” she says. “As Extended Producer Responsibility (EPR) schemes emerge and mature, sensors growing in the fleet of MRFs can help satisfy the demand for reporting recovery rates.

“Data collection, measurement, and material characterization for recycling also create a mechanism to support federal, state, and local government programs focused on landfill diversion goals and recycled content standards to advance a more circular economy.”

Ambati concurs, noting not only does AI strengthen the circular economy, but it also ensures more valuable recyclables such as widely used metals like aluminum, that are facing deep shortages, can be recovered and reused.

It also plays a key role in helping reduce GHG emissions and decarbonizing the planet for a more sustainable future, Ambati adds.

MRFs are faced with increasing pressure to produce more and higher-quality bales, while dealing with labor shortages and commodity price volatility, notes Hu, adding AI is a new tool to combat the mounting challenges of running MRFs and enable more data-driven decision-making to ensure their 24/7 peak operation.

“Differentiating between food-contact and non-food-contact materials is important in order to prevent health risks using recyclates that do not meet quality demands,” says Husam Taha, Deep Learning Solution Manager at TOMRA Recycling Sorting, adding that the U.S. Food and Drug Administration and the European Food and Safety Authority provides clear guidance and regulations for the safety assessment and the determination of the quality levels of recyclates.

“With the possibility to recover more material at higher purity levels, recycling rates can be increased, and materials previously thought hard or impossible to recycle be recycled,” he adds. “Thus, we save primary resources due to high-quality recyclates on the market to meet demands—including legislation standards—and even offer new business opportunities in new material streams.”

On the paper and plastics packaging side, new materials and combinations of materials and structures are coming to market at an increasingly faster rate, notes Felix Hottenstein, MSS Optical Sorters sales director.

“Conventional as well as AI-based optical sorting technologies need to be able to adapt quickly and without hardware upgrades,” he adds. “A combination of sensor technologies and extraction methods may be required to optimize certain sorting functions.”

AI can serve to augment human labor along the solid waste collection stream, addressing such issues as health and safety.

The AI technology is able to identify material stream trends that allow for smarter staffing decisions, says Hu.

“Everyone in the waste and recycling industry is working with a limited labor pool, so it's important to staff workers where they can add the most unique value,” she says. “Furthermore, real-time tracking allows AI technology to flag issues to operators that may present a health or safety issue to workers such as detecting propane tanks or batteries on the line to reduce fire hazards.

“By installing Glacier's robot in lower-value, more arduous, or higher-risk sorting positions, MRFs are able to staff their workers to locations where humans are uniquely capable of adding value.”

“Robotics are perfect for replacing dirty, dull, and dangerous labor,” says Cook. “Offloading tasks to robots has already become commonplace in our industry with noticeably positive results.”

 TrashBot, for instance, is designed to do the work of sorting waste at the source, eliminating the need for the painstaking process of human sorting, Cook adds.

“TrashBot also delivers on-demand waste audits which are otherwise done by humans manually going through the trash,” says Cook. “As policy and technologies improve, we expect waste-related workplaces to become significantly safer and more efficient.”

Automation in recycling drives consistency, as robots can work 24/7, Marrs points out.

“They don’t tire, nor do they need breaks,” she says. “Plus, they can work on faster-moving belts than humans. Their consistency also results in higher-quality recovered commodities.

“Robots are flexible. Our systems can be adjusted to reflect material stream changes, commodity prices, and more. Robots can work in areas and on materials where volumes don’t warrant a human, because they can multitask and target a variety of materials in lieu of just one or two.”

For many end-users, the addition of robots has allowed them to shift staff to higher-skilled positions with the facility, such as roles in maintenance, as an equipment operator, or as a route driver, she adds.

AI and robotics technologies address labor challenges by helping MRFs reduce time and money spent on high-turnover manual sorting positions, says Marrs.

“Automation isn’t eliminating jobs, but helping facilities run fully staffed and run additional shifts,” she says. “AI and robotics also help improve facility safety by reducing contact with harmful, hazardous materials in the stream and lowering training overhead.

“MRFs weren’t designed with social distancing in mind and robots create natural barriers between humans—a feature that took on greater importance in the early days of the COVID-19 pandemic.”

Ambati notes that EverestLabs spent more than three years working with MRFs of different sizes to iterate on a design that works with the way MRFs are currently designed to vastly improve the operations.

“The design thinking behind our systems is that our robotics can go anywhere people can, but more importantly go in places where it's too difficult for other robotic systems to be placed or where it's too dangerous for people,” he adds.

“Most MRFs are a complex maze of conveyor belts, heavy equipment, walkways, and forklift paths. Installing a new piece of sorting equipment can take weeks of work and costly downtime due to the required retrofitting and reorganizing.”

The same is true for most other robotic systems, because they utilize large, belt-encompassing structures, Ambati says, adding, “Human sorters fit in more places, but with moving equipment and belts overhead, safety is a limiting factor.”  

Ambati notes that EverestLabs has recognized that if the objective is to pick bottles, papers, and boxes one-by-one, “then our solution does not need a huge robot or piece of equipment.

“The robotic cell, end effector, suction and grabbers, and mountings were all designed with the end goal of easily placing a robotic system anywhere in the MRF. These robots can ultimately work in cells to coordinate on a task or can work individually to augment humans or other sorters.”

Additionally, vision and AI is modular and can go anywhere on the waste stream inside of a MRF, informing and optimizing processes, Ambati says, adding AI can produce data that can evaluate material input to the facility to predict the outcome for the facility, but also be used to optimize collection schemes.

Hand-picking is not a preferred job at the plant, and it can pose safety challenges, notes Taha.

“For instance, during the pandemic, regions saw an increased rate of medical waste sorted. Automating the process can free these people from uncomfortable work and make them available for other, safer and more engaging jobs within the plant,” Taha says.

“The use of deep learning technology will not entirely replace manual sorting. It does create other types of jobs to make use of this technology.”

Hottenstein notes that “the trend in the waste and recycling industry for many years—and it has only sped up during and since the pandemic—has been to shift the function of human labor to more supervisory and maintenance roles versus actual sorting positions.

“The newest overall facility designs by CP Group, as well as the latest generation of MSS optical sorters, offers a chance to completely automate the container and paper lines, with some manual sorting labor only required for oversized items. This approach increases the health and safety rating of a recycling facility by default.”

Return on investment

An AI investment leads to an ROI in multiple areas for MRFs, notes Ambati.

“Operationally, it is more cost-effective to run robots in areas where manual sorting is difficult or impossible due to labor shortages, difficult working conditions, and impractical locations,” he adds. “Robots can also do this job more accurately and efficiently around the clock.

“Additionally, AI-driven automation prevents safety and line down situations which are extremely costly. If small dangerous items are identified early on, it can prevent expensive damage.” 

Ambati notes that on the revenue side, AI and robotics can improve the volume of valuable recyclables that would otherwise have been landfilled, as well as the quality of the yield, which fetches higher prices. 

Case in point: in one month, EverestLabs’ robots deployed in end users’ facilities successfully picked more than 4.2 million objects.

“Of those, more than 1,400,000 were recyclable objects of value sorted by only three robotic cells, totaling around $106,000,” Ambati says.

AI data yields strong ROI in many ways, including commodity revenues, reducing landfill fees, or strategically investing in better staffing and equipment upgrades to increase throughput where it's most needed, notes Hu. 

“As an example, losing even a few dozen containers to landfill per minute is very costly,” she notes. “Over the course of a year, this leakage adds up to nearly $1 million in lost commodity revenue and almost $100,000 in additional landfill fees. If AI data can help a MRF owner make decisions to recoup even a small fraction of that cost, the AI data pays for itself in only a few months.”

For AMP robots, while it depends on the facility and how many shifts it runs, costs can drop by as much as 50 to 70 percent by replacing hard-to-secure human labor in various sorting roles, notes Marrs, adding AI-driven automation also produces a higher-quality end product, increasing the facility’s revenue opportunities.

“Moreover, AI significantly decreases the cost of measuring what’s happening in a facility,” she adds. “Each AI sensor can identify nearly all the different material types that are of interest. With its software-focused approach, the cost to do waste characterization within a facility drops from thousands of dollars per ton to only several dollars (or in some applications less than a dollar) per ton. This is a several-orders-of-magnitude reduction in cost of understanding what’s really happening to material flows in the industry.”

“While economic conditions vary, generally facilities save money by recycling more,” notes Zak Wehman, CleanRobotics associate director of business development. “Instead of paying for landfill collections, they can earn recycling rebates.

“The value is not only in identifying and sorting waste properly before it goes to recycling facilities, but since computer vision is used to identify various objects within the waste stream, it generates a considerable amount of useful data. With this data, facilities can reach their goals faster, eliminating overspending.”

Forms of AI have been used in the recycling industry for decades, with investments in AI/deep learning as a powerful subset of AI can lead to higher recycled product quality, which can allow the recycler to receive more money for the material, says Taha.

“It’s advantageous given the energy crises, as it reduces the carbon footprint and labor,” he adds. “Skilled labor is in short supply and hand-picking is not a preferred job. Automating the process reduces the need for manual sorters and allows people to be relocated to other positions in the plant.”

In one example, while not using AI�%