561 2.3 Technical Basis and Assumptions To ensure comparability across investment scenarios, the following bases were established: • Battery Limits: The model integrates areas from the coarse stockpile (Area 1090) to concentrate dispatch (Area 2030), excluding external services. • Fluid Properties: A constant water density of 1.025 g/ml was applied, representative of local process conditions. • Baseline Integrity: For model calibration, the hardware configuration prior to December 2024 modifications was used, ensuring the Stochastic Dynamic Simulation captures the system's actual historical response. . 3. DYNAMIC SIMULATION METHODOLOGY 3.1 Discrete Event Simulation (DES) Logic The Stochastic Dynamic Simulation was developed to replicate the inherent variability of the Miski Mayo physical beneficiation circuit. Unlike traditional steady-state mass balances, the DES framework explicitly captures time-dependent interactions between process units by modeling the system as a sequence of discrete events. As established by (D. G. Hulbert, 2003) and (K. Nikkhah et al., 2019), this approach is essential for capturing complex interactions that static models often overlook. The model integrates operational interlocks, equipment ramp-up/down sequences, and surge bin level controls that regulate volumetric flows. This granular approach, aligned with the methodologies for metallurgical projects described by (W. D. Stepto et al., 1990), enables the observation of how variability propagates through the circuit or is dampened by 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.
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