Combining artificial intelligence and recycling It has ceased to be a futuristic idea and has become a tangible reality in Spain and the rest of Europe. Treatment plants, city councils, and startups are incorporating computer vision algorithms, sensors, and robotics with the aim of improving waste sorting, reducing costs, and moving towards a more circular economy.
This change comes at a key moment, marked by the European recycling targets and due to social and regulatory pressure to better manage waste. Although significant challenges remain, such as low demand for recycled materials and the necessary initial investment, AI is establishing itself as a central element for modernizing a sector that had been operating for decades with fairly traditional processes.
A recycling sector undergoing transformation thanks to AI
For years, recycling has relied primarily on manual sorting processes or in mechanical crushing systems that, in many cases, degraded the quality of the recovered material. Added to this were the contamination of waste streams, the reliance on intensive labor, and tight margins that made scaling the model difficult.
The emergence of artificial intelligence is changing this landscape. Through systems of computer vision, advanced sensors, and machine learning algorithmsTreatment plants are able to identify materials in real time on conveyor belts, separate them more accurately, and reduce the amount of waste that ends up in landfills or incinerators.
In Europe, specialized technology companies have developed solutions such as Recycleye Vision or RUBSEEThese systems use cameras and AI models to analyze waste flows, recognize packaging, plastics, or metals, and provide detailed data on what enters and leaves the facilities. This type of platform makes it easier to detect anomalies, adjust operating parameters, and make more informed decisions.
One of the major changes compared to the classic model is that AI allows us to stop treating waste as an undifferentiated mass and start managing it as a set of valuable materialsBy improving the quality of the separation process, recycled materials become more competitive, which is key to boosting the circular economy In the continent.
In parallel, the public sector is beginning to incorporate these technologies into its urban management strategies. Several European cities, including capitals and large metropolitan areas, are investing in digital waste management platforms AI-based systems that integrate data from containers, collection fleets, and treatment plants to optimize the entire cycle.
Key applications: from machine vision to container sensing
Within the range of AI solutions applied to recycling, intelligent waste sorting is probably the most visible. Through RGB cameras, NIR sensors and 3D lasersThe systems can distinguish types of plastic, paper, metals or glass with a degree of accuracy that seemed difficult to imagine a few years ago.
European research centers, such as the case of TECNALIA with its SortingRobotThey have developed equipment capable of recognizing construction and demolition wastefreeing operators from repetitive and dangerous tasks. These collaborative robots coordinate with conveyor belts and automatic actuators, increasing material recovery and improving workplace safety.
In the field of plastic recyclingOther companies have managed to get their AI systems to recognize dozens of different types of materials with coverage of nearly 90% of waste streams. Their robots can recover millions of objects per year, which translates into thousands of tons of COâ‚‚ avoided by reducing the extraction of virgin raw materials.
AI has also opened the door to the automation of complex processes such as electronic recyclingRobots guided by artificial vision can disassemble devices, locate high-value components (logic boards, precious metals) and separate those that require specific treatments, multiplying productivity compared to traditional manual lines.
Beyond the plant, another application with great potential is the container sensorization and collection points. Through connected devices, equipped with sensors and AI algorithms, it is possible to know in real time the fill level, the type of waste being deposited and the frequency of use, which allows adjusting routes and improving the public service.
Spain and Europe: leading AI and recycling projects
In the European context, the European Union has set ambitious goals: to achieve at least 55% recycling of municipal waste by 2025 and 65% by 2035. Many Spanish municipalities acknowledge that they are still far from these percentages, so they are beginning to see digitalization and AI as a necessary tool to accelerate change.
Spain is positioning itself as one of the most dynamic countries in digital circular economy solutions. A prime example is the ecosystem cleantech in CataloniaThis area is home to several startups focused on advanced recycling, energy efficiency, and sustainable mobility. In this environment, the application of AI to waste management has become a particularly active field.
In parallel, various Spanish regions and cities have announced the implementation of digital predictive management platforms of industrial and urban waste. These solutions integrate artificial intelligence, automatic license plate reading, OCR and advanced analytics to anticipate waste generation patterns, better plan collection and coordinate the work of the plants.
The trend is towards more integrated management, where information generated by smart containers, truck fleets, and sorting systems is combined in real time. This allows administrations and operators to... act on the system in a much more refined way, adjusting collection frequencies, redesigning routes or detecting anomalous behavior in neighborhoods or industrial parks.
All of this is part of a broader commitment to the circular economy in Europe, where the combination of AI, IoT and no-code digital solutions is enabling SMEs and small local entities to adopt these technologies without having to undertake overly complex custom developments.
Candam Technologies and the digitization of recycling from Spain
A particularly representative case of how AI is being applied to recycling in Spain is that of Candam Technologies, a company founded in Barcelona in 2017 and specializing in intelligent waste management systems. Its proposal is structured around the platform RecySmart, which combines proprietary hardware, sensor fusion and artificial intelligence to identify recyclable materials in real time.
Candam's technology is integrated directly into the selective collection containersThese containers become connected devices capable of recognizing the deposited packaging. Often, through municipal programs, these bins reward people who recycle correctly, creating a direct incentive to properly separate waste.
Thanks to AI, the system automatically verifies that the separation has been carried out correctly, recording information about the quality of the waste and the usage patterns of each collection point. In this way, it is possible to... design more tailored awareness campaigns and improve transparency about what is actually recycled.
The sensorization of the containers also provides a significant operational benefit: by having data on filling and usage, operators can Optimize collection routes and reduce kilometers traveledThis results in fuel savings and reduced emissions. AI helps predict when each container will be full and prioritize the most efficient routes.
With this approach, Candam has extended its technology to 11 countries and has deployed thousands of devices in Europepositioning itself as one of the Spanish circular economy solutions with the greatest international reach. Among its most notable projects is the implementation of One of the largest deployments of sensorized containers in Europe in the Community of Madridwhich serves as a reference for other cities interested in digitizing their recycling system.
Economic, social and regulatory impact of AI on recycling
The implementation of artificial intelligence in recycling has not only technological implications, but also economic and socialMany studies estimate that, with intensive use of these tools, sorting accuracy can reach levels close to 95%, significantly reducing the amount of waste that ends up in landfills and improving the profitability of the plants.
By increasing the quality and purity of the recovered materials, operators can to obtain better prices in the secondary raw materials marketFurthermore, the automation of certain tasks makes it possible to redirect labor towards higher value-added tasks, such as equipment maintenance, data management, or logistics coordination.
From a regulatory perspective, AI facilitates compliance with European traceability and reporting requirements. Real-time monitoring platforms generate detailed reports on quantities, types of waste and final destinationsThis simplifies the relationship with government agencies and strengthens transparency for citizens.
Companies like Candam also emphasize the social impact of these projects, by aligning themselves with the Sustainable Development Goals These initiatives are linked to decent work, industrial innovation, sustainable cities, and responsible production and consumption. The deployment of smart containers and AI systems can translate into fewer emissions from transportation, more recovered materials, and a more efficient use of public resources.
However, a fundamental challenge remains: Limited demand for recycled materialsAlthough technology allows for highly accurate sorting, if industry does not increase its use of secondary materials, some of the recovered waste will still not find a commercial outlet, which affects the economic viability of many projects.
New business models and the role of European startups
Artificial intelligence is pushing the recycling sector to reconsider its traditional economic model. Instead of relying solely on municipal fees or the value of scrap metal and other materials, new technologies are beginning to emerge. more diversified income schemessupported by data, digital services and advanced valuation.
Some companies are exploring the production of biochar and other value-added byproducts, taking advantage of organic waste and associating these processes with carbon creditsOther models are based on SaaS platforms that offer municipalities and companies detailed information on their waste flows, recycling levels and environmental footprint, thus monetizing the knowledge generated by AI.
Examples of this can also be seen in Europe. swarm robots, mobile sensors, and cloud-based analytics tools Focused on specific sectors, such as PET plastic recycling or construction waste, these solutions, presented at trade fairs and specialized hubs, serve as a benchmark for other European countries and regions seeking to replicate the model.
Spanish and European startups are in an interesting position to combine hardware, software, and data, offering modular solutions that can adapt to both large operators and medium-sized municipalities. The key lies in balancing technological innovation and scalabilityso that the cost per ton handled is competitive compared to traditional methods.
In parallel, a field opens up for the development of tools for design focused on recyclabilitywhere AI can help industrial companies design products that are easier to dismantle and recycle at the end of their useful life. In this way, the technology acts not only at the end of the chain, but also in the design and production phases.
Outstanding challenges and the role of waste prevention
Despite the progress made in intelligent sorting, robotics, and sensor technology, the deployment of AI in recycling still faces challenges. structural obstaclesThe initial investment in equipment, the cost of integration with existing systems, and the need for trained personnel in data analysis can deter some operators, especially smaller ones.
Another sensitive issue is the very structure of the materials market. Sometimes, products made with virgin raw materials remain cheaper than their recycled equivalents, reducing the incentive to use the latter. Without a regulatory and fiscal framework that favor the use of secondary materialsTechnology alone will not solve the problem.
Sustainability experts also point out that the best waste is the waste that is never generated. From this perspective, AI can play a significant role not only in managing existing waste, but also in... prevention and reduction of waste from the sourceFor example, by optimizing supply chains, adjusting inventories, or detecting waste patterns in industrial processes.
The combination of AI and recycling should therefore be understood as part of a broader strategy that prioritizes eco-design, reuse, repair, and responsible consumption policies. When these pieces fit together, technology becomes a powerful accelerator for achieving environmental and economic goals.
In this context, projects like those of Candam and other European startups show that it is possible to move towards a more transparent, traceable and efficient waste management, aligned with climate goals and the creation of skilled jobs in green sectors.
The advancement of artificial intelligence applied to recycling is redefining how waste is collected, sorted, and valued in Spain and Europe: from automated plants with artificial vision and robotics to smart sensorized containers, and data platforms that help comply with regulations and design better public policies, an ecosystem is being configured in which technology, regulation, and new business models must go hand in hand so that the leap towards a circular economy does not fall short.
