Cement is everywhere and almost invisible. It holds up the apartment block you live in, the bridge you drove over this morning, and the runway your last flight landed on. It is also one of the dirtiest things humans make: the cement sector accounts for roughly 6–7% of global carbon dioxide emissions, according to figures published by the International Energy Agency and IRENA. That is exactly why AI in cement plants has quietly become one of the most consequential industrial uses of machine learning anywhere — not because it is glamorous, but because a few percent of fuel saved at a cement kiln is an enormous number.
Over the past three years the world’s largest cement producers — Holcim, Cemex, Heidelberg Materials, Adani Cement — have handed parts of their plants over to software. Not the whole plant, and not without a human watching. But specifically the parts that human operators find hardest to hold steady. Here is what those systems actually do, and what they genuinely cannot do.
How do cement plants use AI?
Cement plants use AI in two main ways. Process-control models read hundreds of live sensor readings from the kiln, mills and clinker cooler, then continuously adjust fuel, air and feed rates to keep the process stable at the lowest possible energy cost. Separate predictive-maintenance models watch vibration and temperature data to flag equipment weeks before it fails.
Both are unglamorous. Neither involves a robot. What they have in common is that they replace human judgement operating on a handful of dials with statistical judgement operating on the whole plant at once — every minute, on every shift, without getting tired at 4am.
Why the kiln is the hardest thing in the plant to control
A cement kiln is a rotating steel tube, sometimes 60 metres long, that heats crushed limestone and clay to about 1,450°C to produce clinker — the grey nodules that get ground into cement. It is the most energy-hungry step in the entire process, and it behaves like a very slow, very hot animal.
Change the fuel rate now and you will see the effect twenty minutes from now. Feed chemistry drifts as the quarry face changes. Raw material moisture shifts with the weather. Fuel calorific value varies load by load. An experienced operator handles this by leaving a safety margin — running the kiln slightly hotter than strictly necessary, because an unstable kiln that produces off-spec clinker is far more expensive than a little wasted coal.
That safety margin is precisely what AI eats. A model trained on years of the plant’s own operating history can predict where the temperature is heading before it gets there, which means the margin can shrink without the risk growing.
Can AI actually cut cement emissions?
Partly — and it is important to be honest about the limit. AI reduces the fuel a plant burns, which cuts combustion emissions and costs. But roughly 60% of cement’s CO₂ comes from calcination, the chemical reaction that releases carbon dioxide from limestone itself. No control algorithm can change that chemistry.
So the realistic framing is this: AI is very good at the 40% of cement emissions that come from burning things, and completely powerless against the 60% that come from the rock. Solving the rest requires carbon capture, alternative binders, or clinker substitution — all of them capital projects measured in hundreds of millions, not software licences.
That said, the fuel side is not trivial. Cement plants are among the largest single energy consumers in most countries that have them, and a mid-single-digit efficiency gain across a global fleet is measured in hundreds of thousands of tonnes of CO₂.
AI that predicts breakdowns before they happen
The second use of AI in cement plants is predictive maintenance, and it is the one with the clearest published track record.
Holcim began piloting C3 AI’s Reliability application in May 2023 and has since scaled it to 45 plants, monitoring around 3,000 sensors on critical equipment such as vertical roller mills, according to C3 AI. In June 2024 the company announced it would expand AI in manufacturing to more than 100 plants worldwide over the following four years, as part of its “Plants of Tomorrow” programme.
The logic is the same one that now runs under lifts, wind farms and telecom towers: a bearing that is about to fail sounds different long before it sounds broken. We covered the same pattern in detail in our piece on how AI predicts elevator breakdowns before they happen.
The economics are brutal in cement’s favour. An unplanned kiln stoppage can cost a large plant a six-figure sum per day in lost production, plus the fuel burned reheating a cold kiln. Catching one failure a year pays for the software several times over.
What is “autosteer” in a cement plant?
Autosteer is supervised autonomous operation: the AI proposes and executes control moves on the kiln, cooler and mills in real time, while a human operator monitors and can take back control at any moment. It is closer to aviation autopilot than to a driverless car — the person stays in the room and stays responsible.
Cemex signed a global agreement with Petuum to deploy its Industrial AI Autopilot with autosteer across its cement plants, covering the rotary kiln, preheater, clinker cooler and both ball and vertical mills. Cemex said at the time it expected yield and energy improvements of up to seven per cent from the connected autopilots.
More recently, AI systems from the startup Gigaton have been deployed at plants run by Adani Cement, Heidelberg Materials and Holcim, with the companies reporting early results in the region of US$1 million in annual savings per plant.
The alternative fuel problem AI is unusually good at
Cement plants increasingly burn alternative fuels — shredded municipal waste, used tyres, biomass, solvent residues — instead of coal or petcoke. It is cheaper and lowers net emissions, but it creates a control nightmare: unlike coal, a truckload of refuse-derived fuel has an unpredictable energy content and burns unevenly.
This is where predictive control earns its keep. A model that has learned how the kiln responds to fuel variability can compensate in advance rather than reacting after the temperature has already sagged, which lets plants push their alternative fuel share higher without destabilising clinker quality. The quotable version: AI does not make waste fuel better, it makes the kiln more tolerant of how bad it is.
What AI in cement plants cannot fix
- Process emissions. The 60% from calcination is chemistry, not control.
- Bad sensors. These models live entirely on plant instrumentation. Drifting thermocouples and uncalibrated flow meters produce confident nonsense.
- Old equipment. AI can optimise how a kiln is run; it cannot upgrade a preheater tower that was built in 1978.
- Operator trust. The most common failure mode of these projects is not technical — it is control-room staff switching autosteer off during difficult conditions, which are exactly the conditions where it helps most.
Why this matters beyond cement
Cement is a preview. Steel mills, glass furnaces, paper machines and refineries all share the same profile: enormous continuous processes, decades of sensor history, thin margins, and heavy emissions. Vision-based systems have already reshaped discrete manufacturing — see our article on how AI detects defects on manufacturing production lines — but continuous heavy industry is where the energy numbers get genuinely large.
The lesson from cement is a modest and useful one. The winning applications were not the ambitious ones. They were narrow models pointed at the two things a plant manager loses sleep over: fuel bills and unplanned downtime.
Frequently Asked Questions
How much fuel can AI save at a cement plant?
Published industry claims generally sit in the low single digits to around 7% of thermal energy, and Cemex publicly expected up to seven per cent yield and energy improvement from its Petuum deployment. Actual results vary widely with the age of the plant and the quality of its instrumentation.
Does AI replace cement plant operators?
No. Every major deployment to date is supervised — the AI recommends or executes control moves while a human operator monitors and retains override authority. The job shifts from adjusting setpoints to supervising a system that adjusts them.
Which cement companies use AI?
Holcim (with C3 AI), Cemex (with Petuum), and Heidelberg Materials and Adani Cement (with Gigaton) all have publicly announced deployments. Holcim’s Plants of Tomorrow programme is the largest disclosed rollout, targeting more than 100 plants.
Can AI make cement carbon neutral?
No. AI addresses the roughly 40% of cement emissions that come from burning fuel. The remaining ~60% from limestone calcination requires carbon capture, alternative binders or clinker substitution — hardware solutions, not software ones.
What data do these AI systems need?
They run on existing plant historian data: kiln temperatures, gas analysis, motor loads, feed rates, mill vibration and pressure readings, typically spanning several years. Poor sensor calibration is the single most common reason a deployment underperforms.
