Optimizing Capital and Operational costs by integrating Dynamic Process Simulation Studies and RAM modelling Dr. W van Butzelaar1, E.Wingate1 A. Harrison1 1 Bechtel Mining and Metals, Australia, (*Presenting author: wvanbut@Bechtel.com) Optimizing plant design and equipment selection for mining projects involves a holistic, iterative approach that considers all inter-related aspects from initial design to operation, aiming to minimize both capital expenditure (Capex) and operating expenditure (Opex). Key strategies include selecting purpose-fit off the shelf equipment, leveraging technology and automation for efficiency and safety, integrating operational and economic data to make informed decisions, and designing the entire plant to be adaptable and sustainable. This requires a detailed analysis of site parameters, operational performance, and long-term costs to ensure a robust and cost-effective final design. To reduce costs, challenging plant designs and equipment selection can be achieved by using RAM (Reliability, Availability, and Maintainability) and Dynamic Process modelling to assess the impact of different operating scenarios on capital and operating expenses during the Feasibility Study of a project. This involves simulating various equipment combinations and plant layouts to identify the most costeffective options, optimize performance, and minimize downtime by evaluating their impact on production and budget. Dynamic process modelling and operations simulation is a probabilistic engineering approach used to predict how systems (like plants or equipment) will perform over time, identifying bottlenecks, quantifying downtime, and optimizing design, maintenance, and spare parts to boost uptime, profit, and efficiency, far beyond basic flow calculations. It involves breaking down systems, simulating failures and repairs, and using data to find cost-effective ways to improve performance, crucial for decision-making from design to operation. This paper will demonstrate how the integration of RAM analysis into dynamic process simulation provides a comprehensive view that considers both the physical behaviour of a system and its operational reliability allowing engineers to make informed decisions during the design phase to enhance the overall efficiency and profitability of an operation, and to assure that the design will deliver the expected throughput. The integration process Engineers can integrate Dynamic Process Simulation (DPS) and Reliability, Availability, and Maintainability (RAM) analysis through an iterative, data-sharing process rather than a single combined tool. The two approaches rely on shared operational and reliability data—including equipment performance, failure rates, and maintenance strategies—to develop a comprehensive view of system behaviour. By linking RAM-derived failure scenarios with DPS time-dependent process responses, engineers can better understand how equipment reliability affects throughput, stability, and overall plant performance. This integrated approach delivers more realistic insights into asset and process performance, supporting stronger, data-driven design and operational decisions. The integration of DPS and RAM studies enables a comprehensive assessment of system performance under realistic, time-dependent conditions. This approach strengthens design and operational decision-making through: Dynamic Failure Modeling: Using RAM-derived failure data, DPS can simulate how failures evolve over time, revealing transient effects such as buffer-stock endurance during upstream outages or the rate at which operating parameters drift beyond safe limits. Consequence Assessment: The combined methodology provides a detailed assessment of failure consequences, including potential production losses, safety risks, and potential environmental impacts. Sensitivity Analysis: Scenario testing identifies the components with the greatest impact on overall availability—for example, whether improving pump reliability delivers more value than adding surge capacity.
RkJQdWJsaXNoZXIy MTM0Mzk2