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Two Glacier systems at work in an MRF in Chicago. The robots can sort up to 45 items per minute. | Source: Areeb Malik, co-founder of Glacier, on LinkedIn
Glacier, an innovator in the realm of artificial intelligence and robotics devoted to improving recycling efficiency, has announced the successful acquisition of $16 million in Series A funding. This investment comes alongside the deployment of a fleet of AI-driven recycling robots at Recology’s King County Material Recovery Facility (MRF) in Seattle.
Based in San Francisco, Glacier revealed that this funding round marks a significant milestone in its expansion across various key markets, including San Francisco, Los Angeles, Chicago, Detroit, and Phoenix.
“This funding serves as a robust affirmation of our growth journey as we strive towards fostering a circular manufacturing environment,” stated Rebecca Hu-Thrams, co-founder and CEO of Glacier, in an interview with The Robot Report. “Our primary focus will be on enhancing our hardware, including our robots and vision systems, making them faster to deploy and more scalable.”
Glacier’s robots are designed to handle the complexities of recycling streams. Utilizing computer vision and AI, the technology is capable of identifying and sorting more than 30 distinct materials, ranging from typical recyclables like PET plastics and aluminum cans to less common items such as toothpaste tubes and cat food tins.
Glacier Builds Out Data Flywheel
According to Hu-Thrams, Glacier is increasingly viewing its recycling robots and AI data as integral components of a unified system. The data enhances the accuracy and efficiency of the robots, which in turn generate more data with every sorting operation.
“Our current objective is to develop this data flywheel while exploring how to leverage this information to tackle challenges that may extend beyond the robots’ immediate application,” Hu-Thrams explained. “Our vision is to position Glacier as an operational framework for recycling facilities.”
The Ecosystem Integrity Fund (EIF) led the funding round, alongside contributions from existing investors such as New Enterprise Associates (NEA), AlleyCorp, Overture Climate VC, the Amazon Climate Pledge Fund, and VSC Ventures.
New participants in Glacier’s funding journey included Elysium, Overlap Holdings, Cox Exponential, Alumni Ventures, One Small Planet, and Working Capital Fund.
Closing the Circular Manufacturing Loop
To grasp Glacier’s long-term aspirations, it’s essential to understand the recycling value chain. This process begins with placing recycling bins curbside, followed by collection and transport to an MRF, according to Hu-Thrams.
“The primary responsibility of an MRF is to transform a large heap of mixed recyclables—often interspersed with waste—into distinct commodities ready for resale,” she elaborated. “In an ideal sorting scenario, one would yield clean bales of paper, cardboard, plastics, and aluminum for manufacturers to reprocess into new products. However, the sorting stage is notoriously labor-intensive and expensive, with much of the work still performed by hand.”
MRFs often experience high employee turnover, and a shortage of workers can hinder their ability to effectively sort materials. Hu-Thrams articulated Glacier’s aim to bridge the gap in circular manufacturing.
Ultimately, materials collected from recycling bins should be processed in a facility to extract individual components, which can then be repacked and sent to manufacturers for repurposing. However, this current cycle faces disruption primarily at the MRF stage.
“Consider the role of MRFs: they are tasked with processing incoming materials while ensuring optimal yields and quality—a duty akin to that of a manufacturing plant,” Hu-Thrams noted. “Yet, they operate with far fewer tools compared to those found in specialized plants.”
“Their dependence on manual labor remains significant,” she added. “Moreover, they often lack comprehensive data to effectively audit and enhance their processes. This is the critical void that Glacier aims to address.”
Recycling Poses Sortation Challenges
Glacier states that its robot costs significantly less than traditional alternatives and requires minimal conveyor space. | Source: Glacier
MRFs deal with a highly diverse array of materials that are often contaminated, posing substantial challenges to automation. Prior to Glacier’s innovations, existing recycling sorting systems predominantly employed generic robots that were frequently too costly for MRFs, according to Hu-Thrams.
“Additionally, many of these robots were physically large and cumbersome, taking up considerable space in environments where every square foot matters,” she explained. “Envision a robot occupying the footprint of two or three operators on a sorting line yet functioning as a single unit.”
Furthermore, MRFs, like any manufacturing facility, depend heavily on their inputs. Reliable operational systems are essential to maintaining productivity despite fluctuating material streams.
“These facilities aren’t just sorting simple recyclables; they also encounter items like holiday lights, surfboards, microwaves, and even hazardous materials,” she explained. “Designing a system capable of effectively sorting such a varied material stream necessitated a purpose-built approach.”
Utilizing off-the-shelf components from trustworthy manufacturers, Hu-Thrams noted, allowed Glacier to develop a compact robot that boasts high operational uptime and a faster return on investment (ROI). Indeed, many of Glacier’s clients have achieved their ROI in under a year, whereas a three-year return is typically viewed as commendable in the recycling sector.
Glacier Helps MRFs Maximize Data Utilization
Throughout the dialogue, Hu-Thrams underscored Glacier’s commitment to prioritizing customer needs, addressing aspects such as pricing models, uptime solutions, service rights, and crucially, the role of AI.
“We actively engage with our recycling facility clients to determine which commodities or items matter most to them,” she stated. “This inquiry has guided our development of a focused AI taxonomy.”
Glacier continually seeks ways for its clients to leverage their data to enhance operational efficiency. “As we enrich our database and bolster our AI capabilities, many clients are increasingly utilizing this data as a separate asset, reminiscent of a conventional manufacturing environment,” Hu-Thrams mentioned.
“We’ve facilitated several clients in identifying lost revenue amounting to up to one million dollars annually due to overlooked recyclables, helping them adjust their systems to see real-time impacts on their recovery rates,” she elaborated.
Looking ahead, Hu-Thrams envisions Glacier not simply as a robotics or AI firm, but as a valuable partner aiding recycling facilities in resolving broader operational challenges. For instance, if a customer struggles with paper contamination, Glacier could strategically deploy vision systems and robots to enhance sorting purity.
“For perhaps the first time, we can empower these facilities with the capability to analyze real-time data at an item level, understanding exactly what is entering and exiting their production bales, as well as what is being diverted to landfills,” Hu-Thrams concluded. “This tool provides valuable insights into their recovery processes and optimizes sorting methods.”
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