Imubit CEO Gil Cohen wrote for Control Global magazine on where AI fits in industrial plant modeling, covering controllable AI, reinforcement learning and generative AI.
He argues that the process companies that get the most from their technology spending will be the ones that put AI to work where it moves margin, reliability and control. That calls for models that are transparent, tied to the plant's economics and able to handle the complexity of a specific site. He set out five considerations for process teams as AI becomes part of daily operations.
Five considerations for process teams
1. Engineers need to see inside the model
Engineers are slow to trust a black-box model. They want to test it, understand why it made a decision and adjust it with confidence. Controllable AI gives them that visibility and that ability to adjust.
2. First principles still matter
A good model builds on years of engineering experience at the plant. Combining plant data, reinforcement learning and the domain knowledge of the people who run the unit produces a system that is grounded in how the process behaves.
3. Plant-wide optimization is becoming practical
Newer AI architectures make it possible to link individual unit models into a plant-wide framework. That supports better coordination across teams, with fewer trade-offs between throughput, energy and margin.
4. Generative AI helps when it is grounded in plant context
Large language models are not plug-and-play for process operations. When they are tailored to plant data and built into decision workflows, they can shorten onboarding, improve troubleshooting and raise the capability of the whole team.
5. The goal is to support the experts
The next stage of industrial AI keeps people at the center of the loop and gives them better tools for making faster, better-informed decisions.
How these principles show up in practice
At Imubit, these principles shape Deep Learning Process Control® (DLPC®), which runs in refining, chemicals and polymers, cement, and mining and minerals. A Deep Learning Predictor learns a nonlinear dynamic model of the unit from its own history, guided by first principles. Process engineers build those models in DLPC® Builder with no Python and no machine-learning background, and evaluate them in DLPC® Explorer. A Deep Learning Controller, trained by reinforcement learning across millions of simulations, then runs on-premise and writes targets to the existing APC or DCS minute by minute, inside limits set by plant engineers. ARC Advisory Group describes this as human on the loop: the AI calculates and executes, and the operator moves from manual controller to high-level supervisor.
Read Gil Cohen's article in Control Global: the next decade of industrial process modeling
