This white paper details the application of model predictive control (MPC) in a Texas cement plant to optimize two finish mills and a dry kiln line. The MPC system used neural network models and advanced controllers to improve kiln stability, reduce NOx emissions, and enhance clinker quality. Key results included a 2.5% feed increase, 8% reduction in NOx, 41% less oxygen variability, and higher tertiary air temperatures. Free lime control and advanced filtering ensured consistent clinker quality. The system achieved 89% uptime, minimized operator intervention, and paid back in under six months by boosting efficiency, throughput, and environmental performance.

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