Enfinium is deploying advanced artificial intelligence technology across all of its UK energy from waste facilities, marking a significant step forward in how plants manage incoming waste streams and maintain operational efficiency.
The system, developed by German software specialist Wasteer, uses visual AI and machine learning to analyse waste as it enters a facility, automatically identifying non conforming materials before they can disrupt processing. Following successful trials, the technology is now being rolled out across enfinium’s full operational portfolio during 2026.
The move reflects a broader shift within the energy from waste sector, where operators are increasingly turning to automation and data driven systems to improve plant reliability. EfW facilities are highly sensitive to the composition of incoming waste, with contaminants such as large metal items, batteries, or inappropriate materials capable of causing unplanned shutdowns, damaging handling equipment or reducing combustion efficiency.
Wasteer’s system addresses this challenge at the front end of the process. Using high resolution camera systems positioned along waste reception and feed lines, the technology continuously scans incoming loads. Machine learning models then classify materials in real time, flagging items that fall outside predefined parameters. Alerts are sent directly to operators, allowing intervention before the waste reaches critical plant areas such as shredders, bunkers or combustion chambers.
enfinium has already validated the technology through pilot deployments at its Ferrybridge 2 facility in West Yorkshire and Parc Adfer in North Wales. According to the company, the system demonstrated a strong ability to identify contaminants early, helping operators maintain smoother throughput and reduce disruption to plant operations.
Following these trials, the rollout will extend to additional sites including Ferrybridge 1, Kemsley, Kelvin and Skelton Grange, creating a standardised AI driven monitoring layer across the company’s fleet.
From a materials handling perspective, the introduction of visual AI into waste reception areas represents a notable evolution. Traditionally, contamination control has relied heavily on manual inspection, operator experience and upstream waste sorting. While these remain important, increasing waste volumes and variability have made consistent detection more challenging.
By automating the inspection process, systems like Wasteer’s effectively act as a digital quality control layer within the materials handling chain. This is particularly relevant in EfW environments, where continuous feed systems depend on consistent material characteristics to maintain stable combustion and energy output.
Operational gains are expected to be measurable. enfinium reports that the technology has the potential to reduce plant downtime by up to 30 percent, largely by preventing blockages, equipment damage and unplanned maintenance events linked to unsuitable waste. In parallel, improvements in feedstock consistency are projected to increase plant efficiency by around 2 percent, a significant margin in facilities where throughput and energy generation are closely optimised.
Beyond immediate operational benefits, the system also generates detailed data on waste composition. This creates opportunities for closer collaboration with waste suppliers, enabling better identification of contamination sources and supporting improvements earlier in the supply chain. Over time, this feedback loop could help reduce the volume of non compliant material arriving on site altogether.
The deployment also highlights how AI is becoming embedded within heavy industrial and materials handling environments, moving beyond warehouse automation into process critical infrastructure. Unlike traditional automation systems that rely on fixed rules, machine learning models can adapt to changing waste profiles, improving detection accuracy as they process more data.
For EfW operators, this is particularly important given the evolving nature of municipal and commercial waste streams, influenced by changes in packaging, recycling behaviour and regulation.
Dr Jane Atkinson, Chief Operating Officer at enfinium, said the rollout forms part of a wider strategy to improve performance and reliability across the company’s operations. The success of the initial pilots made expansion a straightforward decision, with the technology expected to support both operational efficiency and consistent energy generation for the national grid.
Wasteer’s CEO Benedict von Spankeren added that the system requires minimal on site infrastructure beyond connectivity and power, with the AI platform handling analysis and system management. This ease of deployment has helped accelerate adoption across multiple facilities.
As EfW operators face increasing pressure to maximise uptime, reduce emissions and handle more complex waste streams, technologies that improve visibility and control at the point of handling are likely to become standard. For materials handling operations within these plants, the focus now shifts towards integrating intelligent detection systems alongside conveyors, grabs and feed mechanisms, ensuring that unsuitable materials are identified before they become a mechanical or process issue.
With rollout underway across enfinium’s network, the effectiveness of AI driven waste inspection will ultimately be measured in reduced stoppages, improved throughput and the ability to maintain stable plant performance under increasingly variable input conditions.