observed across the complete large-scale dataset, confirming the robustness and generalizability of the optimization methodology. Increased Copper Recovery - Across all examined intervals, the optimized setpoints produced higher predicted copper recovery relative to the corresponding plant operating conditions. The improvement was particularly significant during periods of variable ore grade or declining organic phase strength, where the optimizer dynamically compensated for reduced extraction capacity through more responsive setpoint selection. Reduced Fe Co-Extraction - The recommendation engine demonstrated superior control of extraction strength and pH, resulting in a reduction of iron transfer to the rich electrolyte. This reduction was achieved without compromising copper loading, indicating that the optimization strategy improved selectivity and strengthened upstream control of electrolyte quality. Enhanced SX Circuit Stability - The ranking-based selection of feasible operating points minimized variability in the O/A ratio and restricted abrupt shifts in phase flow distribution. These adjustments produced more stable mixer–settler hydrodynamics, reducing transient disturbances and supporting improved phase disengagement and mass-transfer consistency. Improved Process Responsiveness and KPI Driven Optimization- By systematically evaluating feasible setpoints against multiple KPIs, the optimizer converged on operating conditions that delivered superior metallurgical outcomes. This approach enabled rapid identification of high-performance operating windows, ensuring consistent adherence to equipment, chemical, and hydraulic constraints while optimizing multi-objective performance. A small reduction of 0.01 g/L Fe³⁺ in the rich electrolyte produces a measurable improvement in electrowinning performance. Literature shows that ferric ions cause a roughly 2– 3% loss in current efficiency per 1 g/L Fe³⁺ in typical copper electrowinning systems [10] and can contribute to up to 12% efficiency loss at elevated concentrations [9]. Based on this trend, lowering Fe³⁺ by 0.01 g/L yields an estimated 0.02–0.03% increase in current efficiency. This modest CE improvement reduces parasitic Fe driven redox cycling and lowers electrowinning energy consumption, translating to roughly ~0.07 GWh/year in savings at typical industrial EW power intensities. The link between elevated Fe³⁺ and increased energy loss is well supported in literature, with [10] showing CE declines proportional to ferric concentration and [9] reporting significant efficiency losses at higher Fe³⁺ due to intensified Fe³⁺/Fe²⁺ cycling and anode degradation. Together with improved Cu extraction in SX, this small Fe reduction strengthens overall circuit stability, energy efficiency, and copper production. The results presented in this paper and the full historical dataset, the optimization framework consistently identified higher-performing, constraint-compliant operating points. These optimized conditions increased predicted copper recovery, lowered Fe co-extraction through improved pH and extraction strength management, and enhanced SX circuit stability via smoother O/A ratio trajectories. Additional improvements were observed in electrolyte quality and organic phase utilization, demonstrating the system’s ability to optimize interconnected processes holistically. The consistency of these trends across the complete dataset confirms the scalability and reliability of the recommendation engine for continuous industrial application. 7. CONCLUSIONS This study demonstrates that a hybrid digital‑twin architecture—combining first‑principles solvent‑extraction chemistry with machine‑learned residual corrections—can significantly
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