Field Technology Becomes an Active Value Driver
The roles of sensors, actuators, and valves in particular are changing significantly. They form the physical interface between the process and the digital world and are increasingly becoming active components of process optimization.
Sensors now perform plausibility checks, pattern recognition, and condition analysis. Actuators adapt dynamically to changing process conditions and support self-optimizing control strategies. Valves continuously provide data for condition monitoring and predictive maintenance.
Practical examples from brownfield facilities show that existing systems can be significantly optimized through retrofitting and AI-based analytics—often without interfering with safety-critical core functions. Applications such as fouling detection in heat exchangers or intelligent control-loop monitoring are already considered realistic standard scenarios.
However, a stable data foundation remains essential. Technologies such as Ethernet-APL, NAMUR Open Architecture, and Module Type Package provide the infrastructure needed to integrate field-level information securely and consistently into analytical platforms.
AI as a Lever for Efficiency and Resilience
Companies in the chemical industry increasingly view digitalization as a strategic resilience factor. AI not only helps unlock efficiency gains but also supports sustainability goals and operational stability.
Data-based forecasts accelerate decision-making, predictive maintenance approaches reduce downtime, and optimized processes lower energy consumption and emissions. At the same time, transparency increases across the entire value chain—an important advantage in economically challenging times.
Agentic AI: The Next Step Toward Autonomy
One of the most dynamic areas of development is the concept of so-called agentic AI. Here, AI systems independently perform clearly defined tasks, such as the continuous analysis of complex control loops or the automatic generation of condition reports.
For the process industry, this means new levels of autonomy: plant sections can be monitored independently, anomalies automatically identified, and prioritized recommendations generated for operations personnel. Functional safety remains unaffected, while operational intelligence increasingly shifts toward software-based assistance systems.
Technology Exists — Implementation Is Key
The necessary technological building blocks are largely available today: standardized interfaces, digital twins, modular plant concepts, and powerful AI algorithms. The central challenge is therefore less about development and more about consistent implementation.
Autonomy emerges step by step—through connected field devices, interoperable data models, intelligent analysis layers, and organizations willing to actively shape transformation.
Conclusion: Intelligence Moves into the Field
In the process industry, artificial intelligence does not represent a break with existing automation concepts. Rather, it extends proven structures with data-driven decision support and new forms of operational intelligence.
Sensors, actuators, and valves are evolving from passive execution elements into active carriers of information. Intelligence is moving closer to the process itself, thereby laying the foundation for scalable autonomy.
Autonomous plants will not become reality overnight. But the direction is clear: those who invest today in connectivity, structured data, and targeted AI applications will secure the decisive competitive advantages of tomorrow.
