Cement plants face high energy costs, variable raw materials and tightening environmental rules. Cement production accounts for roughly 8% of global CO₂ emissions, according to the World Economic Forum, and the fuel and electricity used in the kiln and mills are among a plant's largest production costs.
Conventional control struggles to hold kilns and mills at their best operating point as raw materials and equipment change. AI models can track many process variables at once and adjust setpoints in real time to keep up with those changes. Three applications address the main operating constraints: lowering kiln and grinding energy, stabilizing clinker quality, and reducing unplanned downtime.
1. Cut kiln and grinding energy use
Energy is one of the largest costs in a cement plant, and most of it goes to the kiln and the grinding circuits. As energy prices rise and emissions rules tighten, fuel per tonne of clinker and kilowatt-hours per tonne of cement both come under pressure.
Many kiln and mill control systems rely on rule-based expert systems or on linear model predictive control. Both rest on relationships fixed at commissioning or at the last retune. When raw material quality shifts or equipment degrades, those relationships stop matching the process, and operators widen their margins to stay safe. The result is wasted energy, higher cost and avoidable emissions.
A neural network model trained on the plant's historical and real-time data captures the nonlinear relationships among kiln and grinding variables, including temperature profiles, oxygen levels, fan speeds, material flow rates and energy per tonne. It identifies the operating states that use the least energy for the required quality. When raw materials change or equipment drifts, a closed-loop controller adjusts several control points at once to hold efficiency and stability. It writes setpoints to the existing controls, and operators can take over at any time.
Where to start
- Collect the data. Make sure the process historian is capturing high-frequency data from the key control points, such as kiln temperatures, oxygen, fan speeds and energy use per tonne.
- Start on one kiln. Build and test the model on a single line, compare its predictions with actual operation, and refine it before going further.
- Extend the scope. Once the model shows accuracy and stability, move from advisory recommendations to closed-loop control, then extend it to other circuits.
Raw material swings cause many of the energy spikes on a kiln line. A controller that absorbs them lowers both energy use and emissions.
2. Stabilize clinker quality and reduce waste
Raw material changes, shifting process conditions and slow lab results all lead to off-spec product, waste and customer complaints.
AI models can act as soft sensors that estimate key quality parameters in real time, such as free lime, lime saturation factor (LSF) and Blaine fineness. They infer quality from process variables like kiln temperatures, mill power, gas analysis and feed rates. Lab samples remain the reference. Each result recalibrates the soft sensor, and the soft sensor covers the hours between results. See free lime prediction for real-time control.
With a real-time estimate, the controller can make small, continuous corrections. If predicted free lime moves toward its limit, it adjusts kiln temperature, residence time or the raw mix to bring it back into range. Steadier quality reduces waste and cuts the quality giveaway that comes from burning harder or grinding finer than the specification requires.
Implementation steps
- Connect the data systems. Integrate the laboratory information management system (LIMS) with the process historian to build one data foundation.
- Train the models. Use at least two years of historical data so the models cover seasonal patterns and the plant's range of operating scenarios.
- Test against the lab. Compare the model's predictions with actual lab results before the controller writes setpoints in closed loop.
- Move to closed loop and track results. Once prediction accuracy is acceptable, shift step by step to closed-loop control, inside limits set by plant engineers, and track waste and energy per tonne of good cement.
3. Reduce unplanned downtime with equipment monitoring
Unplanned downtime is a major cost driver in cement. A breakdown brings lost production, emergency repairs and supply disruption. Traditional maintenance often misses the early signs of failure, so repairs are reactive.
AI-based condition monitoring analyzes real-time data from sensors on vibration, temperature, sound, motor current and pressure across critical assets such as kilns, mills, crushers and conveyors. By learning the normal behavior of each machine, it detects subtle changes, such as rising vibration or unusual temperature patterns, and alerts the maintenance team before a failure develops. Repairs can then be scheduled into planned stops, which avoids emergency costs and extends equipment life.
Stable process control supports this. A kiln and mill circuit that runs with less variability puts less thermal and mechanical stress on refractory, drives and bearings.
How the three fit together
The applications reinforce each other. Lower energy use and tighter quality come from the same process models, and stable operation supports equipment health. Most plants start with one kiln or mill circuit, prove the model in advisory mode, and extend closed-loop control from there.
How the Imubit Platform applies
In cement, Map traces energy and quality losses to the recurring kiln and mill decisions behind them, such as fuel rate, burning zone temperature and separator settings. Model learns each circuit's nonlinear dynamics from its own history with Deep Learning Process Control® (DLPC®), including quality estimates ahead of the lab. Control takes those decisions to closed loop on top of the existing APC and DCS, with operators able to take over at any time. On a kiln pyroprocess running in closed loop, this approach has delivered a 20 to 30% reduction in process variability.
