410 During each operating window, the optimizer generates candidate setpoints, evaluates them using the hybrid digital twin, and filters infeasible options through the constraint validator. The remaining setpoints are ranked by KPI performance. The recommended pH setpoints demonstrated close agreement with plant-applied values, generally remaining within ±0.05–0.10 units. Larger deviations were observed primarily during intervals affected by delayed laboratory assays or elevated Fe loading, conditions under which operator heuristics tended to diverge from model-inferred optima. The recommended O/A ratios showed strong alignment with operator adjustments while providing smoother operational trajectories and improved anticipation of Fe co-extraction peaks. Across all evaluation periods, the optimized setpoints either matched or outperformed historical operator selections and remained fully compliant with equipment, chemical, and hydraulic constraints. The final evaluation quantified the operational impact of the recommendation engine by comparing plant-measured KPIs with optimizer-predicted KPIs across each experimental interval. The analysis incorporated actual process data covering copper recovery, solvent extraction (SX) efficiency, iron concentration in the rich electrolyte, raffinate pH stability, organic phase utilization, and, when available, predicted electrowinning (EW) current efficiency. Although representative subsets of the data are shown in this paper, identical performance trends were 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
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