Paradyn.
How BlendOpt optimises

Collaborative Mathematical Optimisation for Mining Value Chains

Combining machine intelligence with human expert knowledge across the mining value chain, so optimised plans are both mathematically strong and operationally acceptable.

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Finding a needle in a haystack

Mathematical optimisation is a powerful technique which can search many thousands of potential solutions to tough mining, metals and commodity value-chain optimisation problems that start from run-of-mine (ROM) material and incorporate ROM reserving, logistics, inventory management, processing decisions and feed-grade control, while satisfying many hundreds or thousands of constraints. In the mining space, mathematically generated solutions to these problems must also satisfy different time-horizon objectives, from day of operations to budget forecasts to life-of-mine considerations. The complexity of finding feasible and optimised solutions to these problems is analogous to finding a needle in a haystack.

There are a variety of techniques that can be employed to reduce the size of the space of possible solutions that needs to be searched, allowing mathematical optimisation to find solutions to such problems. But even with such techniques, generated solutions often appear unacceptable or strange to the planners and schedulers who might make use of them, despite being “optimised”. This is perfectly understandable, because mathematical optimisation techniques suffer from the limitation of not capturing the nuanced insight of human decision makers, so while appearing optimal these solutions are limited in their practical application. This is not just an issue of the mathematical model not capturing sufficient detail, but relates to the significant experience and knowledge human decision makers bring to a scheduling or planning solution, which cannot be captured by any model.

Diagram showing human expertise, shared operating context, and machine intelligence combining into an accepted optimised plan
Figure 1. Collaborative optimisation combines planner expertise, shared operating context and machine intelligence, so generated plans are both mathematically strong and operationally acceptable.
Three pillars

High Quality Decisions with Collaborative Mathematical Optimisation (CMO)

Paradyn’s BlendOpt application is engineered with technical solutions to address these challenges, built on three pillars.

  1. 01
    Expert knowledgeLeveraging the expertise of decision makers who understand their problem space.
  2. 02
    Synchronisation across silosEnabling synchronisation and communication between decision makers across silos.
  3. 03
    Collaborative machine intelligenceMachine intelligence which collaborates with users to find optimised solutions that satisfy constraints, optimality and acceptability for decision makers.

Local knowledge of your experts

The BlendOpt application incorporates the local knowledge of decision makers, which enhances solution quality. It also communicates the rationale for suggested improvements to the schedule or plan, so users can execute them rapidly with confidence. We call this collaborative optimisation.

Integrated decision making

Using end-to-end material tracking and traceability, BlendOpt communicates the rationale for upstream and downstream decisions, so alignment can be achieved between silos and across multiple time horizons. This addresses the challenge of synchronisation between silos, so the rationale for high-quality decisions is rapidly understood across your value chain, from tactical to strategic planning.

Machine intelligence

The BlendOpt application leverages the power of mathematical optimisation to find optimal and feasible solutions, but it goes beyond pure mathematical optimisation: it learns how to search for solutions that are acceptable to your end users by leveraging the unique aspects of your particular value-chain problem and the needs of your human decision makers. Solutions are generated in the form of prescriptive analytics and dashboards, which allow a planner to further refine their plans and assumptions within the application by trialling “what-if” scenarios that focus on the aspects of the problem that matter to them, while satisfying feasibility, maximising profit and reducing costs for the organisation.

Loop diagram showing objectives, scenario search, expert review, trade-off explanation, execution, and learning in BlendOpt
Figure 2. The collaborative loop gives planners room to guide objectives, inspect trade-offs, challenge assumptions and feed operating outcomes back into future schedules.
Customer results

BlendOpt in practice

The collaborative optimisation technology behind BlendOpt has delivered real value to our clients. A snapshot of some of the outcomes, each written up as a case study:

Lower mill feed variability For a copper producer, BlendOpt helped the operation discover small adjustments in ROM stockpile definitions and stack/reclaim decisions, with dramatic reductions in mill feed variability and higher concentrate recovery. Copper mine-to-mill optimisation → 30% fewer violations A 30% reduction in quality target violations, and elimination of forecast rejection limits across 12-month and 5-year plans. Coal quality consistency optimisation → +$24M / yr Revenue improvement forecasts of over $24M per year due to changes in ROM stockyard capacity. Stockpile optimisation → +13% revenue Revenue uplift over the existing life-of-mine plan through changes in production strategy. Multi-plant life-of-mine optimisation → +3 Mt / yr Production increase, improved margins, and consolidation of the product portfolio resulting in lower operational complexity. Multi-mine optimisation → More results Read the full write-ups of BlendOpt deployments and optimisation studies. All customer case studies →
Keep exploring

Connected solutions, customer case studies and insights from Paradyn Systems.

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Tell us about the decision you are trying to make. We will show you how BlendOpt models it, whether you license the platform, commission a study, or have our specialists run it for you.

  • Do you optimise product placement into the market?
  • Can you spot when demurrage is value-justified?
  • Do you know when your stockpile quality models increase contract rejection risk?
  • Does your planning software assume stable operations and predictable markets?
  • Can you run scenarios quickly to understand your options?
  • Does your tactical schedule stay in sync with your longer-term plans?
  • Do your schedules reflect conditions on the ground?
  • Are your schedules thrown out on day one?