https://www.icarus-orm.com/ram-study Figure 3. Typical RAM model output Step C: Feedback from RAM to DPS: Critical failure points and operational constraints identified in the RAM study are incorporated into the DPS model. This enables simulation of realistic downtime scenarios and quantifies impacts on surge capacity, storage levels, product quality, and overall system stability. Step D: Feedback from DPS to RAM: Insights from DPS—such as actual response times, buffer depletion rates, or safety limit exceedance—are fed back into the RAM model. These updates refine availability predictions and improve the accuracy of maintenance planning and operational logic. Step E: Optimization and Validation: The process iterates until both models converge on a realistic and operationally achievable representation of system performance. The integrated results guide optimisation of equipment design, maintenance strategies, and spare parts planning to ensure reliable and feasible plant availability outcomes. 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.
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