In this shifting landscape, real-time data becomes more than just a safety tool鈥攊t becomes a strategic asset. Facilities that adopt AI-enabled X-ray technology position themselves at the forefront of the recycling industry.
Raghav Mecheri
The growing complexity of waste streams poses a mounting risk for industrial waste processors. As the volume of consumer electronics entering the waste system rises, so too does the frequency of hazardous incidents, including fires, explosions, and mechanical failures. In particular, lithium-ion batteries have emerged as a consistent and highly volatile threat.
听Challenges with Current Detection Practices
In recent years, fires attributed to hidden hazardous materials have become a regular occurrence across the waste industry, including at MRFs. According to the 2023 RRS MRF Fire Report, an estimated 5,000 fires now occur annually at MRFs across North America. As insurance rates surge and operational downtime increases, facility operators face growing pressure to identify hazards before they cause damage.
However, many facilities still rely on outdated and insufficient methods of hazard detection. Traditional RGB camera systems struggle to identify dangerous objects that are buried beneath layers of material on a conveyor belt. Their inability to penetrate the waste stream means they often miss batteries entirely, especially those embedded in consumer electronics. Infrared cameras, while useful for detecting heat signatures, typically only register a threat after a fire has already ignited. By that point, the opportunity for prevention has passed, leading to risks for the facility and increased insurance premiums.
Forward-Looking Solutions
The industry is beginning to recognize that these reactive, surface-level tools are no longer sufficient for the scale and pace of today鈥檚 waste streams. As a result, forward-looking processors are turning to AI-enabled X-ray systems designed to close the visibility gap. Trained on vast datasets, machine learning models can detect and classify unwanted objects such as lithium-ion batteries, fuel tanks, and pressurized canisters, even when they are partially obscured or mixed in with other waste. By automatically flagging these anomalies, facilities gain the ability to intervene early and remove threats before they reach shredders, balers, or other critical equipment.
Beyond immediate incident prevention, these systems generate a detailed and searchable dataset on the composition of inbound material. This real-time, longitudinal data unlocks powerful insights for operational improvement, while also serving a growing external demand for traceability. Regulators, insurers, and supply chain partners are increasingly expecting facilities to demonstrate that they are proactively managing hazards, rather than merely responding to incidents after they occur.
This becomes especially important considering expanding EPR policies. As producers are held accountable for the end-of-life management of their products, they are demanding better reporting and more precise tracking from downstream processors. Facilities that can produce automated, real-time data on material inputs, including hazardous items, are better equipped to meet these compliance expectations. Rather than relying on manual sampling or generalized estimates, they can provide granular documentation on exactly what was received, how it was handled, and where it went.
Essential Features for Advanced Hazard Identification
Effective platforms should include X-ray imaging capable of seeing through occluded material paired with AI models trained on real-world waste and recycling data, with demonstrated accuracy in operational environments. Systems should provide access to a detailed data log, supporting internal analysis and external reporting requirements. Customizable alerting mechanisms are also essential, enabling facilities to tailor system responses to align with existing operational protocols.
听A Strategic Asset
In this shifting landscape, real-time data becomes more than just a safety tool鈥攊t becomes a strategic asset. Facilities that adopt AI-enabled X-ray technology position themselves at the forefront of a recycling industry that is becoming more intelligent, more traceable, and more aligned with producer and regulatory demands. They not only improve day-to-day safety and operational continuity, but they also gain a competitive edge in a market where accountability, precision, and automation are rapidly becoming the standard.
Raghav听Mecheriis the CEO and co-founder of Visia, an AI-powered material visibility platform transforming industrial waste processing. Under his leadership, Visia has developed advanced technologies that enable recyclers and waste processors to gain real-time insights into inbound materials, enhancing operational efficiency and safety. Previously,听Raghav studied Computer Science at Columbia University. He can be reached at 332-207-7094, e-mail [email protected] or visit .
Case Study: Visia and Rumpke Waste & Recycling鈥擠eploying AI for Fire Prevention
Rumpke Waste & Recycling, a fourth-generation, family-owned company serving the Midwest, has experienced firsthand the devastating consequences of battery-induced fires. More than a decade ago, a fire destroyed one of its core facilities, prompting a long-term investment in advanced fire prevention and hazard mitigation strategies. In 2023, Rumpke partnered with Visia to further enhance its safety posture through the deployment of the Visia AI platform.
Visia installed a proprietary AI-enabled X-ray scanner at Rumpke鈥檚 Cincinnati MRF, strategically placed on the inbound line prior to manual pre-sorting. The system leverages machine learning models trained on real facility data to identify hazardous items, particularly lithium-ion batteries, in real time. Once detected, a laser pointer enables sorters to quickly and precisely remove the item from the stream.
Since implementation, the system has consistently detected ~100 batteries per day with an accuracy rate exceeding 95%. Unlike traditional approaches that rely solely on manual labor, the Visia platform provides a continuous, non-invasive means of detecting a broad spectrum of battery chemistries, including those embedded in devices.
Beyond hazard detection, the system delivers real-time data on inbound material composition through a centralized dashboard, allowing facility operators to monitor trends, audit performance, and adjust workflows accordingly. For Rumpke, the partnership has resulted in improved safety, greater operational resilience, and a scalable model for addressing one of the most urgent challenges in modern recycling infrastructure.