This technical paper presents a joint case study by INEOS, Clariant, and Navigance, showcasing how AI-based tools, hybrid models, and digital collaboration platforms are transforming process optimization in syngas plants.

The study focuses on an incident at INEOS’s ammonia plant in Germany, where a mechanical failure in the feed desulfurization section threatened catalyst performance. Using CLARITY™, Clariant's digital service portal on the Navigance cloud-based platform, the plant team and Clariant experts collaborated in real-time to mitigate damage and monitor catalyst health. Through digital dashboards and automated data exchange, the platform enabled rapid decision-making and continuous expert support during a critical operational phase.

To facilitate long-term decision making and assess catalyst lifetime risks, Navigance has developed in collaboration with Clariant hybrid AI models combining first-principles modelling with machine learning. These models support strategic evaluation of catalyst operations and turnaround planning and are offered as two advanced tools in an easy-to-use web-based interface:

• Interactive Reformer Optimization Tool: A plant-specific, web-based model enabling operators to simulate “what-if” process changes and predict their impact on key reformer KPIs.
• Catalyst Performance Projection Tool: Integrated into CLARITY™ Prime, it lets engineers forecast catalyst behavior under different operating conditions, using high-resolution, real-time data.

The paper highlights both the power and limitations of AI-based models in real-world applications. While data quality and training periods remain key factors, the approach significantly reduces reliance on manual simulations and improves operational agility.

These tools are offered as software-as-a-service and represent a leap forward in accessible, scalable optimization for ammonia, methanol, and hydrogen producers.

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