Fig. 2: Performance comparison of machine learning architectures for clinker phase prediction.
From: Industrial-scale prediction of cement clinker phases using machine learning
Mean Absolute Percentage Error (MAPE) across nine ML models for predicting (a) alite, (b) belite, and (c) ferrite compositions using complete feature sets (KF, PP, HM, CO), respectively. Values in parentheses indicate test-set MAPE. The best-performing models are shown in bold. Quantitative performance metrics (R2 and MAPE) for the best-performing model against traditional Bogue calculations represented as parity plot and temporal evolution, respectively, for (d, e), alite, (f, g), belite, and (h, i), ferrite with inset histograms showing error distributions (ϵ = predicted - actual) for ML models (top) and Bogue calculations (bottom). Red-shaded regions in histograms represent 95% confidence intervals ( ± 2σ), with x-axis limits set at 99.9% confidence ( ± 4σ). The temporal evolution of predictions is over a two-month test period showing plant data (red), ML predictions (black dashed), and Bogue calculations (green dotted). Grey bands represent model uncertainty ( ± 3σ), while red bars (right axis) indicate absolute prediction errors. All error metrics are reported in weight percentage (wt.%).
