2020 BP-2020-03 Centrifugal Pump 283.2 2.87 0.4268 283.2 2020 MI-2020-01 Scrubber Drum 203.1 1.8 0.4001 203.1 2020 PN-2020-03 Schenck Screen 95.68 0.91 0.4826 95.68 2030 TR-2030-01 Conveyor Belt 299.7 0.96 0.4956 299.7 The model's validity was established through a calibration phase using the 2024 plant configuration and operational data. The system was stress-tested under high-grade conditions (19.44 % P2O5), with a success criterion defined as an absolute deviation in annual throughput of less than ±1% relative to historical production. This ensures that all expansion projections are derived from a robust technical baseline that accurately mirrors the plant's physical and operational constraints. 3.4 Simulation Execution and Performance Metrics To ensure statistical stability, each simulation scenario was executed using a warm-up period of 120 simulated hours, allowing internal buffers and recycle loops to reach operational equilibrium—a practice recommended for infrastructure expansion planning by (J. T. McGinty et al., 2010). The model executed 50 Monte Carlo replicates per scenario, with each replicate representing a full calendar year (8,760 hours). The primary output metrics included Effective Utilization (Eᵤ) and dynamic capacity constraints. By tracking these KPIs, the simulation captured the volumetric constraints triggered by the 76% increase in mass pull, consistent with the flowsheet simulation approaches for specialized technologies documented by (C. Stewart, 2005). 3.5 Model Calibration and Validation The calibration of the stochastic discrete event simulation (DES) model was a fundamental phase in establishing the study's technical authority, transforming the simulation into a verified analytical tool. Utilizing high-fidelity historical data from the 2024 operational cycle, the model’s internal logic and throughput parameters were iteratively refined to replicate the plant's physical response. This rigorous calibration achieved a global throughput variance of less than 1% relative to historical production records, confirming the accuracy of the modeled interactions between asset reliability and process constraints. As a result, the validated model provides a robust baseline for stress-testing expansion scenarios, ensuring that all capacity projections are grounded in a proven operational reality rather than theoretical design parameters. 4 SCENARIO ANALYSIS AND SYSTEM CONSTRAINTS 4.1 Experimental Design and Scenarios The simulation campaign evaluated 17 distinct configurations to determine the optimal path for maintaining production targets through 2030. These scenarios were categorized into three primary evolutionary phases:
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