790 2. KEY CHALLENGES IN OBTAINING “RELIABLE” RELIABILITY DATA IN MINING AND METALS Obtaining reliable reliability data for mining equipment remains difficult due to persistent challenges in data quality, consistency, and accessibility. These include: Data Quality Issues: Historical maintenance data is often incomplete, inaccurate, or stored in unstructured formats that require extensive cleansing and interpretation. Unique Operating Conditions: Mining equipment operates under highly variable and frequently harsh conditions—such as extreme temperatures, dust, and moisture—that accelerate wear and differ across sites. As a result, generic datasets rarely reflect the realities of a specific operation, increasing the need for integration with dynamic process simulation. Lack of Standardization and Sharing: Industry-specific reliability libraries do not exist for mining and metals, leading companies to rely on internal records or data borrowed from other sectors (e.g., OREDA in oil and gas companies) Legacy System Limitations: Modern data-collection tools are often difficult to integrate with older legacy systems, limiting visibility into overall equipment health. Proprietary Restrictions: OEMs and mining companies frequently treat equipment performance and failure data as commercially sensitive, restricting access. Some vendors—such as Metso and Emerson—offer specialized condition-monitoring services to address this gap. Data Exchange and Model Building Data exchange between Dynamic Process Simulation (DPS) and Reliability, Availability and Maintenance (RAM) analysis focuses on transferring the critical information needed to align system behaviour with reliability expectations. Failure Data Integration: The RAM study defines equipment-specific failure rates, mean time to repair (MTTR), mean time between failures (MTBF), and maintenance strategies. These parameters are incorporated into the DPS model to simulate how equipment failures influence process performance. Process Constraints: The DPS model provides key operating constraints—such as buffer capacity, interdependencies, and critical operating limits (e.g., maximum temperature or pressure). —which the RAM analysis uses to assess the true production impacts of equipment failures. Operational Logic: Control logic and operating procedures modelled in the DPS—such as system responses to failures or shutdowns—are directly reflected in the RAM study to ensure availability predictions accurately represent operational reality. 3. SUMMARY The mining industry continues to generate valuable reliability data internally, even where off-the-shelf datasets do not exist. This information is increasingly used to enhance operations and maintenance strategies and to optimise capital (Capex) and operational (Opex) costs before a facility is built. Integrated DPS–RAM studies convert this data into actionable insights that support informed design and operational decisions. • Combining Dynamic Process Simulation (DPS) and Reliability, Availability and Maintainability (RAM) analysis provides a holistic view of system performance—moving beyond theoretical availability to reflect actual production throughput and product quality under realistic operating constraints. This integration enables data-driven Capex and Opex decisions by quantifying the true financial benefits of reliability improvements early in the project lifecycle. • The combined approach strengthens risk and resilience planning by identifying critical failure paths and supporting the design of more robust, mitigated systems and contingency strategies. It also enhances design optimisation, providing clarity on equipment redundancy, buffer capacity, and maintenance scheduling.
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