For an energy-intensive plant, decarbonization is now a direct lever on competitiveness. Rising carbon prices, tighter regulation and customer scrutiny are arriving at the same time as volatile feedstock costs, and together they squeeze margins. Production efficiency and emissions intensity move together: a unit that runs closer to its efficient operating point burns less fuel per tonne of product.
The control room, meanwhile, is full of sensor data that sits idle in the historian. The opportunity is to turn that data into real-time action that trims carbon and improves production economics at the same time.
Industrial AI has moved past dashboards and monthly reports. Current models learn plant-specific behavior, write setpoints to the distributed control system (DCS), and run in closed loop, inside limits set by plant engineers without constant manual tuning. The four approaches below use refinery and cement examples, and the same logic applies in chemicals, polymers and mineral processing.
1. Energy efficiency without manual tuning
Heaters, chillers and compressors run around the clock, so small inefficiencies multiply into large energy bills and carbon footprints. Conventional controls catch the obvious waste but miss the interactions between feed rate, ambient conditions and equipment wear. The historian already holds the patterns that plant teams often miss.
A model learned from the unit's history captures how process variables relate to energy use. A controller trained on that model with reinforcement learning (RL), across millions of simulations, acts on those relationships continuously. If heat exchanger fouling raises fuel demand, the controller rebalances the moves available to it, maintaining throughput while reducing excess firing.
In compression services, the same approach balances suction pressure, valve position and motor speed without compromising stability. Because the controller respects quality constraints, it guards against off-spec events while it saves electricity and lowers emissions.
A cement kiln is a good example. Fuel quality there can change hour to hour. With a model learned from the kiln's operating data, producers can reduce coal use while blending alternative fuels safely. The payoff is lower fuel spend and more resilient operation, without the team chasing every loop by hand.
2. Feed and crude selection
When feed composition shifts, so does its carbon footprint. A heavier crude with higher sulfur or metals demands more hydrogen, generates more coke and raises CO2 per barrel. The impact shows up quickly in fuel expense, flaring and emissions reporting. Feedstock decisions, often made days before the material reaches the unit, are one of the faster levers for decarbonization.
Planning tools usually rest on linear models. A process model learned from years of operating data and lab results represents the nonlinear links between a crude slate, the downstream constraints it creates and the emissions that follow. Used as a simulator, it can evaluate many blend scenarios and flag the combinations that hold margin while keeping carbon intensity low. In the units, closed-loop control then adjusts cut points and operating targets as the new feed arrives, writing to the APC or DCS minute by minute.
The same evaluation can carry crude discounts, hydrogen cost and carbon price exposure in a single objective, so the economic and carbon cases are weighed together. Steadier combustion with fewer upsets also reduces flaring and Scope 1 emissions. A refiner can defend profitability while stepping down its emissions trajectory, without waiting for the next turnaround or capital project.
The data and security groundwork
A blend decision depends on the data behind it, and that is where integration hurdles appear. Many refineries still run legacy historians and proprietary protocols that were never built for high-frequency AI workloads, so bridging them means secure interfaces over existing assets. Data gaps, such as missing density readings, delayed sulfur results and inconsistent unit tags, erode model confidence. Cleaning and contextualizing those streams is critical.
Cybersecurity is the other constraint. Opening historian ports to cloud analytics widens the attack surface, so projects that succeed build security in from the start, with role-based access and on-premise inference. With that foundation in place, the same architecture can extend across crude tanks, condensate splitters and bio-feed co-processing lines.
3. Hard-to-model units
Units such as the fluid catalytic cracker (FCC), the reformer and the hydrocracker turn small disturbances into excess fuel use and emissions. Conventional advanced process control (APC) relies on linear models identified around one operating region. When feed quality shifts or ambient conditions swing, those models lose accuracy and operators end up chasing setpoints instead of optimizing them. First-principles simulators are hard to keep calibrated to the unit as it changes.
These units are nonlinear and multivariable by design. Temperatures, pressures, catalyst activity and feed composition interact in ways that defy simple cause and effect. On an FCC, a change in riser outlet temperature moves conversion and coke make, shifts the regenerator heat balance, changes wet gas compressor load, and alters flue gas CO2 and NOx. The same coupling exists in a cement kiln's burning zone and in a polymer reactor's heat removal. Operating on intuition or a simulator alone forces a wide safety margin that gives up efficiency, and emissions performance with it.
Deep learning closes much of that gap. A model learned from years of the unit's own history has seen catalyst deactivation, fouling cycles and seasonal swings, which is where a fixed linear model drifts. The controller is trained with reinforcement learning across millions of simulations before it writes a single setpoint, so the plant does not have to learn by trial and error on the live unit.
The model also gives earlier warning. By recognizing the patterns that precede high flue gas opacity or rising hydrogen make, it can recommend corrective moves before an operator would normally see the problem on the trends. Holding the unit inside a tighter operating envelope means steadier combustion, fewer off-spec products and lower direct emissions.
Data security stays intact. The model runs on-premise, reads live tags and writes setpoints to the APC or DCS minute by minute, without exporting raw plant data to the cloud, and production targets hold without added manual tuning.
Once the hard-to-model units run efficiently around the clock, carbon-aware optimization across the wider site becomes much easier.
4. Carbon-aware operation
Plants already tune furnaces, compressors and boilers for energy efficiency, yet the carbon footprint of those assets still swings hour by hour. Grid mix, production rate, feed quality and weather all change the emissions intensity of each unit of energy consumed.
One approach the industry is developing brings carbon signals into the optimization. A model that combines historian, sensor and grid data can estimate the carbon cost of each control move. When the grid leans on fossil generation, flexible electric loads are shifted. When renewable supply rises, those loads can be raised to protect throughput. Operators see carbon and production KPIs side by side.
Tracking energy flows at the equipment level supports a continuous emissions inventory without manual spreadsheets. It also supports carbon-aware scheduling, which moves energy-intensive tasks into the cleaner upcoming windows. The economics often line up, because lower-carbon hours tend to coincide with off-peak tariffs.
The model also quantifies trade-offs before they are made. If reducing a heater's firing rate saves CO2 but risks a yield loss, the model shows the break-even point at which the move pays. It starts from the data streams the plant already has, and it gives operators a way to manage carbon alongside throughput and compliance.
How Imubit applies this
Imubit is Closed-Loop Physical AI for refining, chemicals and polymers, cement, and mining and minerals. Map traces excess fuel, power and emissions to the recurring decisions behind them. Model learns each unit's nonlinear behavior from its own history with Deep Learning Process Control® (DLPC®), guided by first principles. Control takes those decisions to closed loop on top of the existing APC and DCS, inside limits set by plant engineers. Where energy is the optimization target, deployments have delivered a 15 to 30% reduction in energy consumption. Read more about the Imubit Platform.
