- 02/09/2026
- Article
- Analysing & automating
Autonomous plants in the fast lane
What if a process plant could not only act automatically, but also independently? What if it could learn from data, optimise itself and react faster than any human in the event of a malfunction? Current examples show that this vision is already becoming reality. The development of autonomous process plants is a response to economic pressure, the shortage of skilled workers and the demands for efficiency and sustainability. However, this poses new challenges not only for operators but also for equipment suppliers.
Written by Armin Scheuermann
A thin stream of polyester resin flows through a glass tube, drips into a measuring vessel and is automatically analysed. No hand regulates the flow anymore, no one controls the pressure. Since mid-2024, an AI has been controlling operations on a production line at Covestro in Dormagen, North Rhine-Westphalia. It controls, plans, analyses and reacts faster than a human could. It is one of the first fully autonomous batch plants in the global chemical industry. A milestone, say some. The start of a new future, say others.
Because the vision goes further: in the future, chemical plants will not only execute individual processes automatically, but will also act in concert, learn from experience and optimise themselves. Autonomous, networked, intelligent. This is referred to as a ‘dark factory,’ in reference to dark car factories where robots work without light. But for the chemical industry, it is more like ‘bright autonomy’: it is not about turning people off, but about turning on new possibilities.
What is an autonomous plant?
What does ‘autonomous plant’ actually mean? The answer is both technical and philosophical. It refers to process plants that make decisions based on sensor data, models and AI technologies without the need for human guidance at every step. Experts distinguish between different levels of autonomy – based on autonomous driving: from Level 0 (no autonomy) to Level 2 (semi-autonomous start and stop sequences) to Level 4 (self-optimising plants that receive targets instead of commands). Level 5 would be total autonomy – a target that remains more of a vision in practice.
Where does the chemical industry stand in terms of autonomy?
In Germany – but not only here – the chemical industry is still in its infancy when it comes to autonomy. At the Namur user meeting of process automation specialists in November 2025, most companies placed themselves somewhere between levels 1 and 2. Why not higher? ‘Basic automation is often insufficient,’ explains Olaf Abel from chemical giant BASF. ‘And the investments are high, while the potential savings are not always visible at first glance.’ Nevertheless, the first flagship projects are visible: BASF is already transforming several dozen plants towards autonomy. Evonik uses learning systems in its technical centre. Lanxess uses AI to retrieve process data from previously blind areas and even analyses fouling in heat exchangers. And Covestro shows that even batch processes can run fully autonomously.
Why autonomous plants?
But why all this effort? The answer is simple: pressure. The European chemical industry is in crisis. High energy prices, crowded markets, a shortage of skilled workers. ‘If we don't make our plants smarter, others will soon be producing for us,’ says Abel. Autonomous plants promise not only efficiency but also resilience. They help to produce more stably with fewer staff, use energy more efficiently and react more quickly to fluctuations in the market or in raw materials.
This is made possible by technological milestones: networked sensor technology (Ethernet APL), standardised data models (NOA, administration shells), digital twins and increasingly intelligent AI systems. The latter can learn from process data, detect anomalies and even control variables themselves.
In a field test conducted by Yokogawa and JSR in Japan, an AI controlled a distillation plant for 35 days – without human intervention. The system maintained stable quality and energy consumption while continuously learning.
What does this change mean for plant equipment suppliers?
With the advance of autonomous process plants, the role of equipment suppliers is fundamentally shifting. Those who previously supplied pipes, control technology or skids must now reposition themselves as providers of networked, modular and AI-enabled system solutions. Machine and plant manufacturers (OEMs, EPCs) must not only consider standardised interfaces (MTP), data models and OT/IT integration, but also offer scalable autonomy concepts that can be put into operation step by step. The focus is shifting from project business to the life cycle model: software, analytics, digital twins and remote services are replacing classic differentiation features.
Field device manufacturers and component suppliers are faced with the challenge of making their products ‘autonomy-ready’. Only those who offer networked, modelled and semantically integrable sensor technology will remain part of the new architectures. Competition is shifting from pure measurement accuracy to data quality, plug-and-produce capability and AI integration.
The business model is also changing: operators expect ongoing upgrades, performance guarantees and long-term partnerships instead of one-off capex investments. Those who fail to keep up risk being disconnected from the value chain. At the same time, the trend opens up enormous opportunities: equipment suppliers who enrich their solutions with autonomous functions, predefined control strategies and lifecycle services can position themselves as key players in the ‘Plant of the Future’ programmes.
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