An independent judging panel at the Global Recognition Awards has named Modcon Systems as a 2026 award recipient, citing its contribution to how artificial intelligence is applied within hydrogen production infrastructure. The decision places the company among a small group of recipients selected from thousands of international applicants, following an assessment process designed to allow comparison across different industries and disciplines.
According to the award organisers, entries are evaluated using the Rasch measurement model, a statistical approach intended to provide objective scoring regardless of field or category. Only a small proportion of applicants receive recognition each year, with scores reflecting performance across areas such as leadership, originality of research, relevance to global challenges and the practical impact of the work.
The judges’ focus, in this case, was on a technical issue that has attracted growing attention as hydrogen production scales up: the reliance of AI-driven management systems on indirect indicators or delayed laboratory data. In many industrial hydrogen facilities, artificial intelligence platforms are used to optimise efficiency, manage operating conditions and support safety-related decisions. However, these systems often depend on inferred process states rather than direct, real-time measurements at points of highest risk.
Modcon’s work addresses this gap by integrating oxygen and hydrogen analysers directly into electrolysers, purification systems and high-pressure process lines. These analysers provide continuous, in-situ readings that feed directly into control and optimisation software. The result, according to evaluators, is a framework in which AI systems respond to verified physical conditions rather than estimates derived from models or historical trends.
The company’s approach treats measurement as a core part of digital infrastructure rather than as a supporting instrument. By embedding sensors at critical locations, the system can detect oxygen ingress or loss of isolation earlier, allowing corrective actions to be taken before operational or safety limits are reached. Field deployments referenced by the judges indicated improvements in equipment availability and shorter recovery times following process upsets.
From a broader perspective, the award reflects ongoing debates within industrial automation about the balance between modelling and measurement. As hydrogen facilities become larger and more complex, the margin for error narrows, and the consequences of incorrect assumptions increase. The judging panel noted that direct, high-integrity data can constrain AI optimization systems in a way that reduces the risk of optimisation strategies drifting into unsafe operating regions.
The recognition also highlights the intersection between hydrogen expansion and digitalisation. Governments and industry are investing heavily in hydrogen as part of decarbonisation strategies, while simultaneously adopting AI-based control and optimisation tools. Ensuring that these tools are grounded in reliable, real-time data has become a central concern for operators and regulators alike.
In its statement, the awards committee described the work as a contribution to establishing clearer boundaries for Modcon.AI decision-making in protection-critical environments. Rather than relying solely on increasingly complex algorithms, the emphasis is placed on improving the quality and immediacy of the data on which those algorithms act.
While awards do not, by themselves, determine the success or adoption of a technology, they often signal areas where industry practice may be shifting. In this case, the recognition suggests growing acknowledgement that real-time, direct measurement may be an essential prerequisite for the safe and effective use of artificial intelligence in hydrogen production.
As hydrogen infrastructure continues to expand globally, questions around verification, safety and operational integrity are likely to become more prominent. The work recognised by the Global Recognition Awards points to one possible direction: AI systems that remain closely tied to physical reality through continuous, high-quality measurement rather than relying primarily on inference or delayed confirmation.