Model R2 (%) MAE (%) RMSE (microns) R2 (%) MAE (%) RMSE (microns) Baseline 0.62 6.25 9.61 0.60 6.47 9.79 Featureengineering 0.71 5.10 8.35 0.69 5.43 8.68 Autoregressive 0.94 2.08 3.53 0.90 2.88 4.82 Hybrid autoregressive + featureengineering 0.95 2.13 3.56 0.90 2.91 4.82 Although the Hybrid model (Model 4) achieved a slightly higher coefficient of determination (R2 = 0.95) compared to the Autoregressive model (Model 3, R2 = 0.94), the performance difference is marginal and may not be statistically significant. For this reason, Model 3 will be used for RL agent. RL agent is represented by a Differentiable Actor-Critic architecture where the LSTM serves as a "world model", allowing for high-precision control. The model successfully navigated from an initial P80 deviation (approx. 183.45 µm) to the target zone of 160 µm. By iteration 300, it achieved a positive Mean Return of +1.9363, proving it can maintain the process within the desired ±10 µm as shown in Table 4. Table 4 – Results and metrics for Differentiable Actor-Critic architecture. Metric Initial Value (iter=1) Final Value (iter=2) Mean return –32.83 +1.94 Across Loss 32.83 –1.94 P80 ~23.4 µm < ±10 µm Verification N/A Gradient Flow Verified 5. CONCLUSIONS The four LSTM architectures evaluated in this study confirm that autoregressive feedback is the dominant factor governing short-horizon P80 prediction accuracy in closed grinding circuits, autoregressive LSTM successfully captures P80 dynamics and supports differentiable gradient flow, a key requirement for model-based RL. The introduction of lagged P80 (at the 5-minute horizon) as an additional input produced a step-change: the autoregressive (model 3) achieved R² = 0.94 and MAE = 2.08 µm, reducing absolute prediction error by 67 % relative to the baseline. The hybrid (model 4) which combines autoregressive feedback with metallurgically derived composite features, reached R² = 0.95 and MAE = 2.13 µm which represents a statistically marginal improvement of 0.01 in R² at the cost of eight additional input features. These results establish that temporal self-reference captures the dominant share of achievable predictive improvement, and that domain-engineered features provide diminishing returns once
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