By Dennis Rohe, Business Consulting Team Lead at Imubit
I recently talked with Mark Venables about closed-loop AI in process control for an article in Connected Technology Solutions. The piece asks whether the technology can deliver what it promises, and it does not avoid the hard questions. I am glad this debate is happening in public. It is no longer confined to conference rooms and pilot projects, and the wider engineering community can weigh the potential against the problems.
In the interview I explained how Imubit's approach differs from traditional process control, and we covered results in production as well as concerns about scalability and trust. When an industry publication asks whether closed-loop AI delivers, the technology has entered mainstream discussion. Our field needs that kind of open scrutiny to move past the hype.
Why the debate matters now
Closed-loop AI is no longer a niche topic. It now comes up in discussions that span operations, process engineering, control and plant leadership. Closed-loop applications are running on production units. ARC Advisory Group calls the operator's new role "human on the loop", in which the AI calculates and executes and the operator moves from manual controller to high-level supervisor. At the same time, engineers are raising fair concerns about trust and scale.
The technology is maturing, and so is the conversation about it. That matters because it pushes the industry to think through plant operations, optimization and decision-making with AI in practice, with input from every part of the industry.
Where closed-loop AI sits in the control hierarchy
Engineers who come from traditional process control usually start by asking where this sits relative to the DCS, APC and MPC. Closed-loop AI builds on those layers and does not take them out. The DCS keeps running the regulatory loops, and APC keeps holding the unit against its constraints. The AI layer learns from the plant's own behavior and adjusts the targets those layers work toward as conditions change.
In Imubit's case the technology is Deep Learning Process Control® (DLPC®). A Deep Learning Predictor learns a nonlinear dynamic model of the unit from its own history, guided by first principles. A Deep Learning Controller, trained by reinforcement learning across millions of simulations, learns the control policy. The controller runs on-premise and writes to the APC or DCS minute by minute, inside limits set by plant engineers.
If your reaction is that you have heard big promises before, that is fair. What is different now is the combination of results in production, open discussion and a growing body of shared experience across the industry.
The hard questions
The article raises scalability, transparency and long-term reliability. These are fair questions, and engineers are right to ask them. In the interview I stressed that AI has to be demystified and built together with the plant's process engineers, so that the models reflect how the unit actually runs and the people responsible for the unit trust them. Data quality and model drift are real engineering concerns. Working through them with the plant's engineers is how better systems get built.
Open discussion helps here. When plants share what is working and raise what is still unclear, the whole field learns faster than any one site could on its own.
Join the conversation
I would like to hear from others in industrial AI and process control. Whether you have seen results, run into problems or are still forming a view, your experience is useful to the rest of us.
There is hype in this field. There is also real progress and a growing track record to learn from. If we engage openly now, we can shape that progress with care. Let's keep asking the hard questions and keep sharing what we learn.
Imubit brings Closed-Loop Physical AI to refining, chemicals and polymers, cement, and mining and minerals, with over 100 closed-loop controllers deployed worldwide.
