Closed-Loop Physical AI for cement
How the Imubit Platform maps, models and controls the cement line, from raw mix and kiln to clinker cooler, alternative fuels and finish mill.
The plan sets clinker targets, fuel mix, alternative fuel rates and cement grades. In many plants it lives in multiple places, from the budget to the experience of a few people, and Map pieces them together. The kiln line then runs against quarry chemistry that shifts by face, fuels that change by the truckload, and lab results that arrive after the kiln has already moved. In between, the plan depends on recurring decisions, made every shift by different people with different information.
The Imubit Platform applies Closed-Loop Physical AI to that gap. It maps the gap to the recurring decisions behind it, models the causality of the kiln line from the plant's own data, and takes the critical decisions to closed-loop control on top of the existing APC and DCS, inside the envelope your engineers set.
Where margin is lost between the plan and the plant
Every team on a cement line makes sound decisions against its own constraint. Quality holds a margin on free lime, kiln operators burn a little hotter to stay clear of high free lime, mill operators grind finer than target, and the fuel team backs off alternative fuel when the kiln gets unstable. Each call is reasonable on its own. Together they push the line toward conservative operation, and the losses concentrate in a few places.
- Over-burning against lab delay. By the time the lab reports free lime, the sample can reflect kiln conditions from 30 minutes to several hours earlier. Operators carry extra heat margin in between, which costs fuel and refractory life without making better clinker.
- Unstable combustion on alternative fuels. Refuse-derived fuel, tire-derived fuel and biomass change in moisture, calorific value and ash chemistry from load to load. The burning zone drifts, and the thermal substitution rate stalls below what the kiln could take.
- A cooler that caps the kiln. Weak heat recovery adds fuel to every tonne of clinker, and a feed rate cut made as a temporary response to a red river upset often becomes the default.
- Raw mix drift. Quarry chemistry moves the lime saturation factor, silica modulus and alumina modulus. When they drift, the kiln runs hotter and longer to make clinker on spec.
- Overgrinding in the finish mill. Blaine and 45-micron residue are sampled once or twice a shift and reported 30 to 60 minutes later. Operators grind finer than target to cover the delay, at a cost in specific energy and tonnes per hour.
The same gap shows up across safety, reliability, margin, compliance and competency. Red river events, ring formation and unplanned kiln stops are reliability and safety exposures. Fuel mix and clinker factor set CO2 per tonne, which is where compliance pressure lands. The judgment that keeps a kiln steady sits with experienced board operators, and the share of US manufacturing workers over 55 more than doubled in twenty years, while retirement rates have surged (McKinsey, 2025).
How Map, Model and Control apply to a cement line
Map
Map traces each plan-to-plant gap to the recurring decision behind it: who makes it, how often, against which KPI and with what information. On a cement line these include the burn target against the free lime target, the alternative fuel rate for this shift's fuel, cooler grate and fan settings after an upset, separator speed for a Blaine target, and raw mix corrections when the analyzer moves. The result shows which gaps carry the most impact and which decisions are ready to model.
Model
The models are built with Deep Learning Process Control® (DLPC®). Process engineers build them in DLPC® Builder from the plant's own historian, with no Python and no machine-learning background. A Deep Learning Predictor learns a nonlinear dynamic model of each unit from its history, guided by first principles.
On the kiln, that gives a free lime estimate every few minutes from signals already in the DCS, such as burning zone temperature, kiln drive current, feed rate, fuel flow, kiln speed, preheater exit temperatures and gas composition. In the finish mill it gives a Blaine prediction from mill process signals ahead of the lab. The models recalibrate as lab results come in, so they stay tied to current raw materials and fuels.
Operations, quality, maintenance and process engineering work from the same model, so a fuel change or a new burn target can be judged on predicted free lime before anyone commits to it. DLPC® Sim App runs the controller offline, so a move can be tested without touching the plant. See the result of a change before you make it.
Control
A Deep Learning Controller, trained by reinforcement learning across millions of simulations, learns the control policy for the unit. It runs on-premise and writes setpoints to the APC or DCS minute by minute, within the limits operators set. The DCS and APC keep handling regulatory control and stabilization. The controller recalculates the targets above them as fuel, feed and clinker conditions change, toward objectives such as the lowest specific heat consumption at the free lime target.
Operators stay in charge. ARC Advisory Group describes this as human on the loop, where the AI calculates and executes and the operator moves from manual controller to high-level supervisor. Plants typically start in advisory mode, comparing recommendations with their own calls, and move to closed loop as the model earns trust.
Where it runs on the cement line
Kiln and pyroprocess
The preheater, calciner and kiln form one thermal system with long dead times, where burner momentum, secondary air, preheater draft, kiln speed and feed rate interact with feed chemistry and fuel quality. DLPC® holds free lime and the burning zone in range while trimming the heat margin that lab delay forces operators to carry, within constraints such as preheater exit conditions, stack oxygen, NOx and kiln drive load.
A steadier burning zone also protects coating and refractory, because thermal cycling and uneven coating shorten lining campaigns more than brick selection does. More consistent clinker makes it easier to raise supplementary cementitious materials and lower the clinker factor. See AI-optimized pyroprocessing, free lime prediction for real-time fCaO control and kiln refractory management.
Alternative fuels
At higher thermal substitution rates, fuel variability becomes the normal operating condition. Most fuel handling doses by volume, so heat input moves with calorific value even at a constant feed rate. A wet winter load of refuse-derived fuel cools the flame and raises free lime, and alkali in fuel ash affects clinker mineralogy. APC models calibrated at commissioning lose accuracy each time the fuel mix changes. DLPC® recalculates setpoints with each change in the blend and gives operators a free lime estimate ahead of the lab, which helps plants hold clinker quality and kiln stability as substitution rises. See alternative fuels in cement kilns.
Clinker cooler
Clinker leaves the kiln at around 1,400°C, and the grate cooler returns that heat as secondary air to the kiln and tertiary air to the calciner. Recuperation depends on bed depth, air distribution across the fan zones and grate speed. Particle segregation channels air through the coarse side of the bed, and a single loop on undergrate pressure can correct one zone while it worsens another. DLPC® manages these variables together with secondary air temperature and kiln draft, which matters most during feed rate changes. The aim is steadier heat recovery, fewer red river conditions and fewer feed cuts that outlast the upset. Secondary air temperature, clinker discharge temperature and fan power per tonne of clinker are the indicators to watch. See clinker cooler heat recovery and air distribution.
Raw mill and raw mix
Raw meal chemistry sets the kiln's starting point. Online analyzers and blending silos hold the lime saturation factor, silica modulus and alumina modulus against quarry face variability. When a limestone quality change calls for a correction at the raw mill feeders, the team can see how it moves predicted free lime before committing to it. The raw mix and the burn can then be set together, so less of the chemistry drift reaches the kiln.
Finish mill
The finish mill is the largest single consumer of electrical energy in most cement plants, whether it is a ball mill, a vertical roller mill or a roller press ahead of a ball mill. Feed rate, separator speed, mill ventilation, water injection and grinding aid dosage interact. Raising separator speed tightens fineness, but circulating load climbs and throughput drops. Static models also lose accuracy as clinker grindability and additive moisture change.
DLPC® treats the circuit as one coupled system. It predicts Blaine from process signals and moves the setpoints together toward the economic optimum, which is fineness on spec at the highest throughput and lowest specific energy the circuit allows. It can also coordinate grade changes between ordinary Portland and blended cements to shorten the off-spec window. Steadier clinker from the kiln reduces the load on the mill as well. See finish mill throughput, fineness and the energy trade-off.
Proof
Imubit has over 100 closed-loop controllers deployed worldwide. That is the largest install base in the industry, backed by 7+ years of closed-loop engagement in production. Each result below carries the scope it applies to.
- 20 to 30% reduction in process variability on one customer's kiln pyroprocess, running in closed loop.
- 1 to 3% average yield improvement across deployments.
- 15 to 30% reduction in energy consumption where energy is the optimization target.
Oxbow: closed-loop control in petroleum coke calcining
Oxbow calcines petroleum coke in rotary kilns. Like a cement kiln, a calcining kiln has to balance yield against energy while feed and fuel vary and product specifications stay strict. Oxbow's SmartKiln® petroleum coke calcination runs on Imubit closed-loop control. The controller anticipates volatility in the process and adapts the kiln in real time, raising yield and energy efficiency while consistently meeting product specifications. Higher yield from the same kiln is also how SmartKiln® supports decarbonization.
We have these younger, newer engineers that are coming in place of 30 year employees, who no longer have to wait 30 years to acquire that experience. With Imubit, they have an opportunity to come in and make their mark on the organization.
Read the Oxbow case study.
Related reading
All articles
Oxbow runs petroleum coke calcining in closed loop
Oxbow runs its rotary kiln operations for petroleum coke calcining in closed loop with Imubit, raising yield and cutting natural gas use within specification.
Free lime prediction for real-time fCaO control in cement clinker
Why lab delay pushes cement kilns into over-burning, and how a soft sensor built on DCS signals gives operators a continuous free lime estimate between samples.
Alternative fuels in cement kilns and combustion control
Why alternative fuels destabilize the kiln as substitution rises, and how learned models hold clinker quality ahead of the free lime lab result.
Find out what your gap to plan is costing you.
We start by mapping where each of your plant's operating decisions sits today on the path to autonomous operation, and which kiln, cooler and mill decisions are worth taking to closed loop first.
One to two hours for your team to prepare and upload the data package
Results within a few working days of approval
No workshops
Send your data as it is. Gaps, outages and noisy tags are normal, and our engineers screen and clean them in the platform. If anything limits a use case, we tell you.