AI Cement Plant Energy Optimization

Machine learning for reducing energy consumption in cement kilns, grinding mills, and clinker production

The Problem

“Cut cement energy waste with real-time optimization”

Organizations face these key challenges:

High and volatile energy costs (fuel and electricity) with limited ability to optimize against real-time prices and demand charges

Process variability and long time delays (kiln and mills) make it difficult to detect root causes and tune setpoints without risking quality or stability

Siloed OT/IT data (DCS/SCADA, lab LIMS, maintenance, alternative fuel tickets) prevents holistic optimization and consistent operator decision-making

Impact When Solved

2–5% lower thermal energy per ton clinker while maintaining clinker quality targets (free lime, LSF/SM/AM)3–7% lower grinding electricity (kWh/ton) via optimized mill load, separator settings, and fan control with fewer quality deviations (Blaine/residue)Reduced unplanned stops and upsets (e.g., fewer kiln rings/coating issues, mill vibrations), improving availability by ~0.5–1.5% and stabilizing production

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI for Optimizing Power Plant Operations

AI helps power plants run better and save money.

optimizationgrowing

7.0