DIGITAL TWIN OF A SAG MILL: MULTIHORIZON FORECASTING (5-30 MINUTES) WITH QUANTIFIED UNCERTAINTY (10TH-90TH PERCENTILE BANDS) *Renato Javier Martinez Ordonez, J. Campos, Alfonso Munoz Veron Minera Chinalco Peru S.A., Peru (*Presenting author: rmartinez@chinalco.com.pe) ABSTRACT This paper presents a probabilistic digital twin based on deep learning for a semiautogenous grinding (SAG) mill. The twin provides multihorizon forecasts (5–30 minutes) and what-if scenario analyses with quantified uncertainty via 10th–90th percentile bands (P10–P90). The approach combines a convolutional variational autoencoder (CVAE) to learn nonlinear latent process states from multivariate plant observations, and a mixture-density recurrent neural network (MDN-RNN) to model latent dynamics conditioned on recent history and operator setpoints (e.g., mill speed, percent solids, and water inflow). In industrial practice, short-horizon consequences of setpoint changes are rarely quantified before acting and deterministic tools predominate; our model delivers probabilistic forecasts suitable for anticipative decision support and for integration with existing expert logic. Key novelties are: (i) a representation-to-dynamics split (CVAE → MDNRNN) that improves robustness to noise and collinearity; (ii) operational use of P10–P90 bands to compare alternatives before acting; and (iii) a deployment-oriented validation protocol with chronological training/validation/test splits, per-type normalization, and affine calibration. On unseen test data (1-minute sampling; 30-minute effective input history), at a 30-minute forecast horizon we achieve MAPE ≤ 5% for throughput (TPH), feed fines/intermediates, percent solids, fill level, SAG power, feed pressure, and discharge pressure; MAPE 5–10% for the coarse fraction; and MAPE > 10% for impact intensity, load width, toe position, and pebble tonnage. Variables with MAPE < 10% are deemed suitable for short-term decision support within 5–30 minutes, while higher-error variables remain useful for trend monitoring. A what-if case reflecting a common manual operating adjustment (−2% solids, +2% water inflow, −5% SAG speed, and +3% throughput setpoint) yields an estimated ΔTPH ≈ +46 t/h with P10–P90 [+19, +73] t/h and a discharge pressure change of −83 kPa with P10–P90 [−142, −28] kPa relative to a no-change baseline, communicating both expected impact and uncertainty. The current scope corresponds to pre-deployment forecasting and what-if evaluation; the same probabilistic twin can be coupled to the plant’s expert logic to provide model-based predictive capabilities for closed-loop operation in follow-up work. KEYWORDS Keywords: SAG mill; digital twin; multihorizon forecasting; uncertainty quantification; P10-P90; what-if scenario analysis; convolutional variational autoencoder; mixture density network; probabilistic forecasting.
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