horizon. The experiment is formulated as a paired comparison between two simulations initialized at the same time t: • Baseline scenario (no changes): the model forecasts the future evolution under nominal continuation of operation, producing the expected trajectories together with uncertainty bands (P10–P90). • What-if scenario (with changes): the forecast is repeated while injecting, from tonward, a deliberate intervention consistent with a common plant adjustment: −2%in percent solids, +2%in water inflow, −5%in SAG mill speed, and +3%in the throughput/tonnage setpoint. Because the focus of this paper is to demonstrate forecasting and scenario-simulation capability, the analysis follows a deployment-consistent validation logic. The 30-minute horizon is generated via a rollout from the 1-minute dynamics model; and, to isolate the effect of the scenario intervention, teaching forcing is applied to actions and exogenous variables (i.e., the model is conditioned on their known trajectories over the horizon), while the observations evolve according to the learned process dynamics. The results indicate that the twin responds with operationally coherent behavior under the imposed changes. For throughput (TPH), the what-if scenario yields a sustained increase relative to the baseline, with a positive mean ΔTPHand P10–P90 bands that quantify the uncertainty of the expected impact (Figure 10a). In parallel, discharge pressure shows a systematic reduction under the what-if changes, consistent with expected responses to solids/water and speed adjustments (Figure 10b). In both cases, the baseline vs what-if comparison provides not only an expected difference, but also a plausible range of outcomes, supporting pre-action evaluation within short horizons (5–30 min), particularly under rapidly varying operating conditions. Figure 10 – 30-min what-if vs baseline: (a) TPH forecast with ΔTPH and P10–P90 bands; (b) discharge pressure forecast with ΔP and P10–P90 bands. 7. DISCUSSION The results show that the probabilistic CVAE + MDN-RNN twin consistently captures the primary grinding dynamics over 5–30 minute horizons, with performance suitable for operational decision support on several critical variables. Although nine signals achieve MAPE < 10%, this paper discusses only two representative cases due to space constraints; the remaining metrics are reported in the Results section.
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