Industrial Automation and AI in Chemical Manufacturing
Automa Chem 2026 in Berlin showcased digital twins and AI-driven predictive maintenance to reduce carbon footprints and optimize chemical production yields.
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Automa Chem 2026, held on October 26 and 27, 2026, in Berlin and hosted by Evonik, convened industry leaders to examine automated digital infrastructure and smart manufacturing systems designed for the chemical sector. The event focused on practical applications of artificial intelligence and digital twins to address regulatory constraints like EU CBAM and Scope 3 reporting requirements.
Core Technology and System Architecture
Chemical production facilities integrated real-time energy optimisation platforms and digital twins to model thermodynamic and kinetic processes continuously. These architectures interface with distributed control systems to process live sensor telemetry, adjusting process variables to minimize energy consumption. AI-driven predictive maintenance algorithms monitor equipment vibration, thermal signatures, and motor current data to detect mechanical anomalies before component failure occurs. Automated data exchange frameworks facilitate the scalable transfer of digital product passports across supply chain tiers, ensuring compliance with international transparency standards.
Deployment or Implementation
Implementation strategies focused on retrofitting existing industrial infrastructure with edge-computing modules and IoT sensor nodes without interrupting continuous chemical processing. Engineering teams from companies including BASF, WACKER, Covestro, and LyondellBasell deployed these systems across continuous reaction units and distillation columns. Technical integration involved bridging legacy programmable logic controllers with modern cloud-based analytics layers via secure industrial ethernet protocols, supported by engineering partners such as Worley and Linde.
Industry Applications
The deployed automation frameworks address critical operational challenges across petrochemical refining, specialty chemical synthesis, and polymer production facilities. Concrete use cases involved dynamic yield optimization during exothermic reactions and automated cleaning cycles managed by predictive software. These applications maintained process stability, improved thermal efficiency, and ensured strict adherence to environmental emission limits across large-scale manufacturing plants.
Operational Impact and Performance Metrics
Quantitative evaluations from plant deployments demonstrated a twenty to thirty percent reduction in facility carbon footprints achieved through real-time thermal energy management and load shifting. Unplanned mechanical downtime was systematically eliminated through continuous predictive health monitoring of rotating equipment. Furthermore, dynamic optimization algorithms stabilized product purity parameters, securing production margins while meeting rigorous global sustainability mandates.
Edited by Sucithra Mani, Induportals editor – adapted by AI
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