Rocking the Boat: Capturing Existing Value through Pit-to-Port Optimization


Danielle Zyngier, Hatch; Nooshin Nekoiemehr, Hatch; Hao Li, Hatch

Rocking the Boat: Capturing Existing Value through Pit-to-Port Optimization Danielle Zyngier, Nooshin Nekoiemehr, Hao Li Coordinating mining value chains is challenging at best. There are several stakeholders involved (mine, processing plant, transportation, port/terminal, shipping), typically with conflicting performance metrics. The competing priorities that ensue significantly reduce overall system performance. Mining companies often resort to stakeholder-specific decision-support systems to improve system performance. However, these solutions are typically limited to a single element of the value chain (e.g., mine, rail, etc.), and are mostly based either on Excel spreadsheets or at best on a combination of simulation and/or rules and heuristics that illustrate the impact of user-defined decisions on the system. These strategies require significant modeling and maintenance investments and cannot suggest the best course of action without the constant need of input from a domain expert. Operations research and optimization techniques (also known as Prescriptive Analytic tools), on the other hand, proactively guide users towards the best operating strategies for the integrated value chain. Examples of such decisions are when and how much ore to reclaim from specific mine stockpiles, which processing facilities to send ore to, how, when and where ore should be blended, when to dispatch trains, and where and how to stack and reclaim ore at the port. By implementing prescriptive decision-support tool, companies can maximize the benefits of existing infrastructure on an ongoing basis. Typical outcomes are smoother overall operations that allow higher production targets to be met more consistently, better grade control, and being able to investigate additional spot sales opportunities or contracts. By combining recent advances in computing power with powerful, industry-proven value chain modeling frameworks, optimization models can be created for any portion of (or the entire) value chain, according to what is required for a specific application. This paper provides a “behind the scenes”
Mots Clés: mining value chain ; decision-support systems ; prescriptive analytics; prescriptive decision-support; integrated value chain; optimization models ; value chain modeling frameworks