Modcon Systems has announced the rollout of a next-generation analyzer and data-optimization platform designed to support the energy industry’s transition toward digital, low-carbon operations. The platform integrates real-time process analyzers with advanced data-analytics and machine-learning methods, enabling operators in hydrocarbon and emerging hydrogen value chains to monitor critical parameters more accurately and optimize their processes under dynamic conditions.
The development comes at a time when industrial facilities are seeking ways to align safety, efficiency, and sustainability goals while preparing for increasing variability in feedstocks, operating modes, and regulatory expectations. Modcon’s new approach combines established process-measurement technologies with continuously updated computational models, aiming to provide more dependable insight into real-world process behaviour.
A core element of the platform is the integration of physical chemical-composition measurements with artificial-intelligence models. Process analyzers — including gas composition analyzers, optical measurement systems, and crude-oil property analyzers—provide continuous, high-integrity data for validation of these models. According to the company, this capability is increasingly important as facilities adopt predictive control architectures based on neural networks or reinforcement-learning algorithms. Such models can be sensitive to drift when operating conditions move outside the range of their training data, especially in multivariable environments common in refineries and hydrogen plants.
The platform’s design focuses on maintaining alignment between digital models and actual plant conditions through continuous verification, automated anomaly detection, and model updating. In hydrogen production and blending systems, in-situ analyzers help determine oxygen and hydrogen concentrations at critical locations, providing an additional layer of protection against unsafe crossover or off-spec product streams. In hydrocarbon processing, analyzers monitor crude-oil properties, distillation behaviour, and product quality metrics, supporting consistent operation even with frequent feedstock changes.
Alongside measurement systems, Modcon has incorporated dynamic AI-enabled optimization tools capable of evaluating process trends, predicting quality outcomes, and proposing setpoints. The approach is built on techniques such as deep reinforcement learning, which can explore a wide range of operating conditions within a digital-twin environment. Real-time analyzer feedback constrains this exploration to safety-verified regions, improving the reliability of the resulting optimisation strategies.
The company notes that the platform is applicable to a wide range of process-industry contexts, including refining, petrochemical production, gas processing, and hydrogen systems. Its modular design supports integration with existing analyzer houses, fibre-optic remote measurement infrastructures, and modern control-system architectures.
Modcon Systems reports that the rollout reflects its broader strategy to support digital transformation initiatives in process industries by linking high-resolution field measurements with adaptive computational tools. The company’s ongoing research includes further development of data-driven models for advanced process monitoring, as well as new measurement technologies for hydrogen and renewable-fuel applications.
