internal storage systems. The underlying control philosophy and material transport loops are detailed in the functional logic flowchart. Figure 2 – Functional logic flowchart for the Miski Mayo DES model, illustrating integrated process control and material transport loops. 3.2 Stochastic Inputs and Reliability Analysis A critical differentiator of this study is the high-fidelity modeling of equipment reliability, transitioning from static availability factors to dynamic failure distributions. This approach follows the principles of dynamic process simulation for design and optimization highlighted by (J. Tapia et al., 2009). Variability in feed characteristics, including particle size distribution (PSD) and clay content, was modeled using Probability Density Functions (PDFs) derived from metallurgical sampling campaigns. Asset reliability was characterized by analyzing four years of historical maintenance data from SAP records (2020–2024). This data enabled statistical fitting for over 50 critical asset groups using Two-Parameter Weibull distributions. This method accurately represents variable failure rates, capturing both early-life failures (β < 1) and the wear-out phases of rotating equipment, a technique successfully applied in brownfield expansion studies by (A. Noiseux & J. P. Côté, 2010). 3.3 Baseline Validation Protocol The reliability analysis focused on identified constraint areas, particularly the classification, pumping, and dewatering-related constraint areas. The resulting MTBF, mean time to repair (MTTR), and Weibull parameters used for the simulation baseline are summarized in Table 1. The model executed 50 Monte Carlo replicates per scenario, ensuring a confidence interval greater than 95% for the final throughput results. Table 1 – Reliability Parameters for Representative Critical Assets Included in the Baseline Area Asset Description MTBF (h) MTTR (h) Parameter β Parameter η 2030 FI-2030-01 Horizontal Belt Filter 149.9 1.38 0.6093 149.9
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