Free lime control rarely breaks down because operators do not understand the kiln. The bigger constraint is time. Lab-based clinker testing cannot keep pace with moving process conditions. Between samples the burning zone drifts, fuel consumption changes, and operators work from the last lab result rather than the current state of the process.
That delay pushes plants toward conservative margins that raise cement manufacturing costs, and it can leave off-specification clinker undetected until the next sample. Much of what is at stake is fuel. The IEA reports that the thermal energy intensity of the global cement sector has stayed roughly flat at about 3.6 GJ per tonne of clinker.
Why free lime carries a cost on both sides of the target
When free lime (fCaO) runs too high, clinker leaves the kiln with more unreacted calcium oxide. High free lime can cause volume expansion during hydration, which compromises soundness and strength development in the finished cement. The quality risk moves forward into the product and can mean rework, blending adjustments or lost batches downstream.
Plants often respond by running hotter, and hotter operation has its own price. The acceptable range typically sits between about 0.6% and 2.0% free lime, with 1.0 to 1.5% often preferred as the window that balances clinker quality with fuel economy. Energy is a substantial share of cement production costs, according to the World Economic Forum's Net-Zero Industry Tracker, so small temperature shifts matter. Over-burning by 20 to 30°C across a full campaign wastes fuel without producing meaningfully better clinker.
The operating window also keeps moving. Raw mix chemistry, thermal shifts in the burning zone, variation through the pyroprocessing circuit and fuel blend changes can all push free lime higher or lower during normal operation. As plants raise their share of alternative fuels, variability often grows, because calorific value, moisture content and ash chemistry fluctuate more than they do with a steady coal or petcoke supply.
Free lime is a live indicator of how chemistry, heat and combustion are lining up inside the rotary kiln at any given moment.
Why lab delay pushes the kiln off target
The core control problem is feedback delay. By the time a clinker sample is collected, transported, prepared and tested, it may describe kiln conditions from 30 minutes to several hours earlier. Operators then adjust fuel rate, feed rate or burning zone conditions based on where the process was rather than where it is.
That delay changes behavior in the control room. Teams hold extra margin because they have no fast way to confirm whether free lime is trending high or low between samples. The margin protects quality, but it makes hotter-than-needed operation more likely, and it can mask the early stage of a drift that becomes harder to correct later.
Over a campaign, the cost of conservative over-burning is rarely tracked explicitly. It shows up in fuel bills, in refractory wear, and sometimes in hard-burnt clinker that takes more energy to grind in the finish mill.
What conventional measurement misses
Conventional measurement adds further limits:
- Wet chemistry results can vary with analyst judgment at the titration endpoint.
- XRF reports total calcium without phase information, so it cannot distinguish reacted from unreacted CaO.
- XRD identifies mineral phases more directly, but sample preparation and peak overlap limit practical turnaround.
Even when plants test more often, each sample still has to be collected, cooled, ground and interpreted before anyone can act on it. The control loop runs on intermittent snapshots rather than continuous feedback. In steady conditions that is manageable. When raw mix, fuel blend or thermal conditions shift, the gap between what the operator sees and what the kiln is doing widens, and decisions made in that gap default to conservative kiln operating strategies that protect quality at the expense of energy and throughput.
How a soft sensor gives operators a usable signal
A soft sensor estimates fCaO from process variables already flowing through the distributed control system (DCS). Burning zone temperature, kiln drive current, feed rate, fuel flow, kiln speed, preheater exit temperatures and gas analyzer readings carry much of the same information operators already use to judge kiln condition. Most plants already have these signals.
A model trained on the plant's historical data, aligned against past lab results, uses those signals to estimate free lime every few minutes instead of every few hours. Operators see an estimate that tracks the current state of the kiln rather than reacting to a result that is already behind it, and they can work closer to target without carrying as much protection against a long feedback delay.
Because the model relates free lime to many inputs at once, operators can also isolate faster whether a shift traces to raw meal chemistry, a fuel blend change or a preheater issue, instead of interpreting each trend in sequence.
Why a multi-signal model holds up better than a single proxy
Some plants track the burning zone through a single kiln gas relationship, such as a NOx correlation. A proxy like that can follow burning zone conditions while chemistry and fuel are stable, but it often needs recalibration after a material or fuel change. A soft sensor that draws on many DCS variables covers a wider operating range. Trained across fuel transitions, it can relate changing conditions to clinker quality, which matters most for plants increasing their use of alternative fuels, where conditions are less stable.
Keeping the estimate calibrated
A soft sensor needs ongoing calibration to stay accurate. As lab results continue to arrive, the model recalibrates so its estimate stays tied to current plant conditions instead of an older operating period. Raw material shifts, new fuel blends and post-maintenance changes all alter kiln behavior, and the model adjusts with them. Plants with consistent sampling practices tend to calibrate faster.
Building trust before closed loop
Trust usually starts with the model in an advisory role. Predicted free lime appears alongside existing measurements and operators still make the decisions. Experienced operators compare the estimate with lab results and with what they see in the kiln, and learn where the model tracks well and where it lags. Newer operators gain a reference for how process signals connect to clinker quality, something that used to take years of shifts to develop.
No model captures every instinct behind a veteran board operator's call during unstable conditions. A model can keep the patterns that connect process conditions to the moves that held the kiln in range, drawn from the plant's own operating data rather than idealized assumptions. Once the estimate has proven itself, it can feed closed-loop control of the kiln, within limits operators set.
How a shared prediction improves decisions across teams
A continuous free lime estimate is useful well beyond the control room. When operations, maintenance, quality and process engineering work from the same prediction, they stop arguing from different versions of kiln reality. A maintenance deferral, a fuel change or a burn target adjustment becomes easier to evaluate because each group sees the same process response on the same timeline.
Maintenance teams can correlate a creeping free lime trend with equipment degradation they already track, rather than finding the connection after a quality event. Quality teams and board operators see the same real-time estimate instead of separate snapshots. When a process engineer proposes adjusting the raw mix for a change in limestone quality, the team can see the predicted effect on free lime before committing. Kiln decisions rest on a shared, data-grounded picture of plant performance rather than individual judgment alone.
Cross-shift consistency improves as well. Crews develop different operating styles, and free lime results reflect them. When every shift works from the same estimate, variation between crews narrows. For plants with energy intensity targets, where shift-to-shift differences in fuel use and kiln stability add up over a year, that consistency compounds.
How Imubit applies this
In cement, Map traces fuel lost to over-burning to the recurring decisions behind it, such as burn targets set conservatively against a delayed lab result. Model learns the kiln's behavior from the plant's own history with Deep Learning Process Control® (DLPC®), including a continuous estimate of free lime. Control takes those decisions to closed loop on top of the existing APC and DCS, inside limits set by plant engineers. On a cement kiln pyroprocess running in closed loop, the result was a 20 to 30% reduction in process variability.
