Nov 12, 2022
This case exemplifies the application of our products for optimizing CAPEX in engineering projects or operating processes. We collaborated with an international engineering office, leading the design of a large greenfield mining project in Chile. Our objective was to assess the ability of the Crushing and Milling areas to meet the target throughput, while considering detailed life-cycle asset performance data.
Working alongside the engineering team, we utilized our advanced Throughput + RAM (T-RAM) simulation software to evaluate the project’s performance. Our bottom-up approach, based on digital models, enables engineers to understand how granular equipment-level variables affect process-level utilization and CAPEX.
Our methodology consisted of five key steps, including collaborating with customers to understand the design criteria, building a digital model of the process, simulating baseline performance, analyzing flexibility for future expansions, and evaluating alternative scenarios to optimize CAPEX and production requirements.
The baseline assessment demonstrated high confidence in meeting the expected throughput, using systemic availability and utilization projections to account for equipment stoppages, maintenance plans, process redundancies, and stockpiles, among other factors.
We then evaluated alternative scenarios to optimize CAPEX and different production requirements, such as different throughput targets, crushing line redundancy levels, conveyor discharge configurations, stockpile levels, and stand-by configurations. Our results showed that stockpiles could be optimized at different points in the process without sacrificing throughput targets, resulting in potential savings that amounted to US$200 million in CAPEX:
Overall, this case study highlights the importance of advanced simulations in evaluating industrial projects and emphasizes the value of granular bottom-up performance analysis.
Production loss analysis in a copper concentrator plant
A reliability team was able to increase the plant systemic runtime by 3% using the RMES Suite to focus on the actual performance bottlenecks.