The waste crisis is devastating our planet. A lot of the stuff we throw gets dumped in landfills. These massive waste disposal sites are often created by destroying forests or inhabitable lands. For example, the United States has lost land roughly the size of the state of Maryland to landfills.
Unfortunately, materials that could be recycled or composted continually end up in landfills. With waste generation projected to rise by 73% by 2050, the problem could get worse. We need more efficient and innovative solutions to manage waste.
Artificial Intelligence (AI) is emerging as a powerful tool to help sort waste and divert recyclable materials away from landfills. Let’s look at some innovative solutions that improve recycling, prolong the life of existing landfills, and help us achieve our sustainability goals.
The growing waste problem
The United States leads the world in waste generation per capita, with each citizen producing an average of 1800 pounds of waste per year. Despite efforts to manage this waste, only 24% is currently recycled. This starkly contrasts with countries like South Korea and Germany, which recycle nearly 60% and 50% of their waste, respectively. This inefficiency not only strains landfill capacities but also worsens environmental and health issues.
Landfills destroy natural habitats, with over 1.8 million acres of land being used as active landfills, in addition to 6 million acres lost to landfills closed in the past. Emissions from landfills pose significant health risks to nearby communities, increasing the likelihood of congenital malformations in children by 12% and decreasing property values.
To tackle such issues, several states have taken significant steps to address the waste management crisis. Vermont has banned all recyclables from landfills. Meanwhile, Maine has introduced stringent producer responsibility laws requiring companies to manage their products and packaging long after consumers use them.
Sortation, the key to efficient waste management
Waste collected from cities is transported to a materials recovery facility (MRF), where it is usually sorted. This sorting process is not effective, as incorrectly classified materials contaminate the recyclables. Further, the sortation is largely manual and labor-intensive, with humans racing against time to scan and sort recyclables on a fast-moving conveyer belt.
Technological innovations offer new hope for tackling these challenges. “AI is transforming waste management through automated sorting systems that utilize robotics and machine learning to accurately categorize waste and enhance efficiency,” Marc Acampora, vice-president and market leader of WasteExpo at Informa Markets’ Infrastructure and Construction Group, told me.
His firm organizes WasteExpo, North America’s largest tradeshow for solid waste, recycling, organics, food waste recovery, and sustainability, serving both the private and public sectors. At the recently held event in Las Vegas, key industry players showcased the latest innovations in solid waste sortation.
Enhanced material identification with computer vision
A huge variety of solid waste materials are collected from homes and businesses. AI algorithms need extensive training to learn about the different types of trash we throw. EverestLabs, a company with a self-contained industrial 3D vision system, has built a proprietary dataset of over 5 billion recyclable objects to train its algorithms.
Their data and robotics platform, RecycleOS, can sort objects with over 95% accuracy. “Our AI provides accurate data on the shape, size, weight, material, packaging type, commodity value, and even brand information of every recyclable flowing through the plant,” JD Ambati, founder and CEO of EverestLabs, told me.
These systems improve over time and adapt to new types of waste, ensuring the adaptability of sorting processes even as the composition of waste changes. For example, Alameda County Industries (ACI) reduced its labor costs by 59% in three years thanks to EverestLabs’ robots, which have picked up approximately 30 million objects.
Preventing contamination with high-precision algorithms
AI solutions employ advanced algorithms to differentiate between many variants of similar-looking materials. “Glacier Robotics’ AI model can detect over 30 types of items, from beverage bottles to toothpaste tubes,” Rebecca Hu, founder and CEO of Glacier Robotics, told me.
Accurately identifying recyclable materials, such as fiber, PET, HDPE, or black plastic, helps reduce contamination rates and increase the purity of recyclables. For example, Glacier’s robots can be trained to identify and remove plastic bags that accidentally end up in the paper stream. This makes the quality of the end paper product higher and more valuable.
Talking about the impact of their robots, Rebecca Hu shares that they helped a recycling customer quantify a $900,000 annual revenue opportunity by identifying the value of recyclables that one site was incorrectly sending to landfills.
Efficient real-time sorting with robotics
As trash moves on conveyor belts, an average human can pick 20 to 40 items per minute based on the material. On the other hand, AI-powered robotic arms can sort materials at remarkable speeds.
AMP, one of the earliest innovators in AI-driven sortation, introduced high-powered jet systems. “It’s capable of making thousands of picks per minute on conveyer belts that move at speeds of 600 feet-per-minute,” Chase Brumfield – site reliability engineering manager of AMP, told me. In addition to consuming just a fraction of the manual effort, these systems need minimal downtime, vastly improving the throughput of waste facilities.
Additionally, intelligent sorting systems can unlock novel value-creation opportunities. For example, if a buyer is looking for a specific type of recycled plastic material, say a white-colored, post-consumer polypropylene – this is possible thanks to AI-driven sortation systems that can see, remember, and act by separating the desired type of waste in real-time.
How AI can help build a sustainable future
Meeting sustainability goals calls for an ability to benchmark and track performance. “Without the ability to measure our country’s baseline recycling performance, it’s near impossible to make progress on it,” says Rebecca Hu.
The ability to track recyclables at a never-before-seen level of granularity can act as a source of truth. This can help measure progress across recyclers, brands, policymakers, and other players in the circular economy as we work towards our sustainability goals. This remarkable ability of AI-driven sorting systems could catalyze progress towards the US National Recycling Goal of 50% recycling rate by 2030.
We must remember that solving the waste management crisis requires more than just technology. Individuals must play their part by disposing of waste responsibly and adhering to the principles of reduce, reuse, and recycle. Simple actions by each of us, such as separating recyclables from general waste, composting organic materials, and reducing single-use plastics, can make a substantial difference.
By combining AI-driven solutions with responsible human behavior, we can significantly reduce the amount of waste ending up in landfills and move towards a more sustainable future.
