as copper concentrations in pregnant leach solution (PLS), raffinate, and electrolyte samples, which are critical for calibration and reconciliation of predictive models. Field Sensors deliver continuous real-time measurements, including flow rates, temperatures, pH, oxidation-reduction potential (ORP), and phase ratios, enabling dynamic monitoring of process states. Complementing these streams, Historical Data offers long-term operational records that support trend analysis, parameter estimation, and anomaly detection. All these inputs converge within the Operator Assistant, which functions as the central intelligence layer. This component is subdivided into two key modules: the Digital Twin and the Optimizer. The Digital Twin acts as a virtual representation of the solvent extraction circuit, integrating first principle -based models with data-driven corrections to simulate process behavior under varying conditions. It assimilates real-time sensor data for current state estimation, laboratory assays for calibration, and historical trends for parameter tuning and uncertainty quantification. Predictions generated by the Digital Twin—such as copper extraction efficiency, phase continuity, and reagent consumption—are then passed to the Optimizer. The Optimizer applies mathematical optimization techniques to identify operating strategies that satisfy multiple objectives, such as maximizing copper recovery, minimizing acid consumption, and maintaining phase stability, while adhering to equipment and safety constraints. The data flow within the architecture is bidirectional, enabling continuous learning and adaptation. After recommendations are implemented, updated sensor readings and laboratory results are fed back into the Digital Twin for model refinement and performance evaluation. Connectivity across all layers is achieved through open industrial communication standards, including Modbus TCP IP, Ethernet, OPC UA, MQTT, and REST APIs etc., ensuring interoperability with existing plant infrastructure and facilitating integration with control systems and operator dashboards. The final output of this architecture is a set of actionable insights and ranked recommendations presented to operators, accompanied by confidence scores and expected KPI improvements. This human-in-the-loop approach ensures that decision-making remains transparent, safe, and aligned with operational objectives. Figure 38 – Architecture of Operator Assistant for Improving SX Operational Performance 3. DIGITAL TWIN (FIRST PRINCIPLE -BASED AND HYBRID MODELING)
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