Heavy-industry plants carry two linked constraints: the cost of lost production and energy, and the pressure to cut emissions. Cement production alone accounts for about 7% of global CO₂ emissions, according to a low-carbon transition roadmap for the cement industry published by the OECD.
Industrial AI addresses both by adding learned process models and reinforcement learning controllers on top of the existing distributed control system (DCS) and advanced process control (APC), without equipment overhauls. The sections below look at refining and gas processing, polymers, cement, chemicals, and mining and minerals: the operating constraints in each, and where AI makes plant-specific improvements, inside limits set by plant engineers.
Refining and gas processing
Rising energy prices and unpredictable feedstock quality put steady pressure on refineries and gas plants. Learned models of compressors, turbines, distillation columns, crackers and reformers capture each unit's nonlinear behavior. A closed-loop controller writes setpoints back to the DCS and the existing APC, coordinating units across the plant instead of in silos. When feed sulfur drifts, a model that sees the change can retune hydrogen management and furnace duty before a flare event.
Steadier heater, steam and compressor loads reduce energy intensity. Around the control layer, vibration models on rotating equipment can flag bearing wear early enough to plan a service window instead of taking an unplanned shutdown.
In refining, Imubit's published result is a $0.25/bbl margin improvement. Read more about industrial AI in oil and gas production and Imubit's work in refining.
Polymers
Temperature swings in high-pressure reactors, rising catalyst costs and the waste that creeps in during every grade transition all erode margin. When variability spikes, even a short run of non-prime pellets forces giveaway, rework and lost production.
Models trained on years of plant data capture the reactor's nonlinear behavior and adjust conditions before deviations appear. Working with the existing APC, they support real-time optimization of reactor temperatures, feed ratios and finishing speeds. They forecast quality properties minutes ahead and write setpoints to the DCS to keep each tonne on grade. During grade changes, transition logic acts ahead of the change to limit variability and cut non-prime production.
The models also find lower-heat operating points that hold conversion, which reduces energy use. They learn from existing data, without new sensors or equipment upgrades, and they adapt when feedstock quality or ambient conditions shift. Read more about industrial AI in polymer manufacturing.
Cement and building materials
Cement plants are under close scrutiny for emissions, and fuel is one of their largest operating costs. When burning conditions drift, emissions and costs both rise quickly. A learned model keeps the rotary kiln in its thermal window by capturing the nonlinear links between fuel flow, draft, feed chemistry and free-lime targets, and a closed-loop controller writes corrected setpoints to the control system.
The model predicts coating stability and burning-zone temperature continuously and adjusts primary air, secondary air and fuel rate before deviations escalate. It accounts for raw meal moisture, trimming excess air without risking CO spikes. The same logic extends downstream to finish mill grinding, where steadier clinker hardness lowers electricity demand. Some plants add vision-based state recognition alongside the control layer, flagging hot or dusty kiln conditions so operators can step in early.
On one cement kiln's pyroprocess running in closed loop, the result was a 20 to 30% reduction in process variability.
Adoption usually begins in advisory mode to build operator trust, then moves to closed loop, inside limits set by plant engineers. The model works on top of the existing PLC or DCS and shows every move on operator displays, and a simulator built from it lets crews rehearse responses before closed loop starts. Read more about cement manufacturing and Imubit in cement.
Chemicals
In chemical manufacturing, even brief process swings can produce costly non-prime batches. To keep batch and continuous lines stable, models learn from the historian and DCS the relationships among feed quality, reaction kinetics and utility constraints. Once trained, they act as a dynamic model of plant behavior, anticipate equipment problems and update advanced process control setpoints in real time, which improves yield and trims energy use.
Around the control layer, plants also use computer-vision stations to detect surface defects before packaging, and language-model assistants that assemble step-by-step work orders so maintenance crews arrive with the right parts and a clear plan.
At one chemical manufacturer, modeling a critical unit with a dozen operating regimes led to a 0.5 to 1% increase in yield, reducing feed costs, across multiple sites. Displays that explain each move let engineers audit what the model does, and the models keep learning as catalysts age and feedstocks change. Read more about industrial AI in chemical manufacturing.
Mining and mineral processing
Grinding is typically the largest single power consumer in a mineral processing plant, so every efficiency gain in the circuit counts. Models turn sensor streams, camera feeds and historian data into control moves that keep the plant on its best grade and recovery curve.
Predictive models forecast particle size and mill power draw minutes ahead and adjust mill speed, water addition and cyclone pressure before energy use spikes. Upstream, vision-based sorting on conveyor belts can identify and divert waste rock, which raises the head grade reaching the mill and lowers the load on downstream circuits.
Reinforcement learning controllers extend the same logic to flotation, balancing air rate, froth depth and reagent dosage for metal recovery. Time-series anomaly detection flags developing equipment failures before they cause unplanned downtime. Simulation lets engineers test the balance among throughput, recovery and energy before changing the actual circuit.
In mining, minerals and metals, Imubit's published results are a 1 to 3% metal-recovery improvement, 5 to 10% grinding-energy savings and a 2 to 5% throughput increase. Read more about energy in mining and Imubit in mining and minerals.
What to look for in an industrial AI provider
- Closed-loop capability. The system writes control moves to the APC or DCS instead of stopping at recommendations.
- Explainability and operator training. Operators understand each move and trust it enough to use it.
- Transparent measurement. Results are measured against an agreed baseline.
- Integration and support. The system works with the existing control infrastructure, with support from the starting application through ongoing optimization.
How Imubit applies this
Imubit applies Map, Model and Control across refining, chemicals and polymers, cement, and mining and minerals. Map traces plan-to-plant gaps to the recurring decisions behind them. Model uses Deep Learning Process Control® (DLPC®) to learn a nonlinear dynamic model of each unit from its own history, guided by first principles. Control takes the critical decisions to closed loop, writing setpoints to the existing APC and DCS minute by minute while operators keep authority. With over 100 closed-loop controllers deployed worldwide, Imubit has the largest install base in the industry. See the platform overview.
