Cimento Itambé

Cimento Itambé, a Brazilian cement producer, faced persistent inefficiencies in two of its most energy-intensive and production-critical processes: rotary kiln operation and cement mill grinding. In cement manufacturing, the kiln is the core thermal process where limestone is converted to clinker, and even marginal underperformance translates directly into higher fuel consumption and reduced output capacity. Mill operations similarly suffer from variability in feed material hardness and moisture, making consistent throughput difficult to maintain manually. Operators relying on experience-based adjustments struggled to respond quickly enough to process disturbances, leaving measurable throughput and efficiency gains on the table.

Cimento Itambé partnered with Rockwell Automation to implement advanced process optimization across its kiln and cement mill operations. Rockwell Automation deployed model-based process control technology — drawing on its FactoryTalk Analytics PavilionX platform, which uses multivariable predictive control and machine learning models to continuously analyze process conditions and issue real-time setpoint recommendations or direct control adjustments. The system integrates with existing plant instrumentation and control infrastructure, allowing the optimization layer to operate on top of the existing DCS environment without requiring full system replacement. The solution was configured to the specific thermal and mechanical characteristics of Itambé's kiln and mill circuits, with models trained on historical plant data before going live in closed-loop control mode.

The implementation delivered measurable improvements across both target processes:

• Kiln throughput: improved by 3%, reflecting more consistent thermal control and reduced process variability
• Mill performance: improved by 5–10%, driven by tighter control of grinding parameters and faster response to feed variability

Beyond the headline numbers, operators gained a more stable process environment with fewer manual interventions required during steady-state production. The performance gains translate directly into lower energy cost per tonne of clinker and cement produced — a meaningful impact given energy's share of cement manufacturing operating costs.

• Model-based predictive control delivers compounding value in cement plants because kilns and mills operate continuously — even a 3% throughput gain compounds significantly over annual production volumes.
• Integration with existing DCS infrastructure lowers deployment risk; full system replacement is not a prerequisite for advanced process optimization.
• Mill performance improvements (5–10%) tend to exceed kiln gains because grinding circuits have more tunable variables and respond well to faster closed-loop control.
• Success depends on quality historical process data for model training — plants should assess data availability and sensor coverage before project scoping.
• Expect an initial period of model refinement after go-live; closed-loop control performance improves as models adapt to real operating conditions.