Dispatch Stockpile Composition Optimization Through Rail Ore Allocation
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Solution Overview
Problem
Mining operations face challenges in consistently meeting chemical component composition tolerance bands at dispatch stockpiles while minimizing variance, as precise ore content is unknown due to limited sampling and blending ore to meet specifications is complex.
Innovation Solution
A method involving an optimization engine to determine decision variables for transporting material from mine to dispatch stockpiles, ensuring compliance with tolerance bands and minimizing variance by using quadratic programs to allocate material proportions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ore from multiple mine stockpiles is blended to meet chemical component specifications, then the product compliance with tolerance bands is improved, but the complexity of blending operations and variance minimization increases
Solution Approach 1:
The patent transforms the complex blending problem into a mathematical optimization problem by changing parameters to decision variables (weights w_i,j) that represent the proportion of material from each mine stockpile to each dispatch stockpile. The optimization engine solves quadratic programs to determine these weights, converting operational complexity into computational parameter adjustment.
Solution Approach 2:
The patent replaces the mechanical blending process with a computational optimization system. Instead of physically managing complex blending operations, the system uses mathematical models (quadratic programs) and optimization engines to calculate optimal material allocations, substituting mechanical complexity with computational algorithms.
2Stability of the object's composition
If selective transportation of material from mine stockpiles is implemented to build dispatch stockpiles with minimized variance, then the consistency of chemical component composition is improved, but the complexity of transport coordination and optimization increases
Solution Approach 1:
The patent performs preliminary calculations using the optimization engine to determine the optimal transportation plan before physical material movement occurs. The system pre-calculates decision variables (weights w_i,j) based on current stockpile compositions and future requirements, allowing coordinated transportation to achieve consistent composition without real-time complex decision-making during transport.
Solution Approach 2:
The optimization engine acts as an intermediary between mine stockpiles and dispatch stockpiles. It receives input data (chemical component compositions, tolerance bands, required tonnages) and outputs optimal transportation decisions, mediating the complexity of coordinating multiple transportation operations to achieve compositional consistency.
3Manufacturing precision
If optimization engines are used to determine material transportation decisions, then the precision of meeting chemical component specifications is improved, but the computational resources and system complexity required increases
Solution Approach 1:
The patent formulates the precision problem as a quadratic programming problem with specific mathematical parameters (objective function minimizing variance, constraints for tolerance bands and material balance). This transformation allows the use of well-established optimization algorithms that can achieve high precision efficiently, avoiding the need for more complex computational approaches.
Solution Approach 2:
The patent applies different levels of optimization to different parts of the system. The optimization engine focuses computational resources on the critical blending decisions at dispatch stockpiles, while mine stockpile operations and transportation execution can use simpler control mechanisms. This localized optimization approach achieves high precision where needed without requiring complex computational systems throughout the entire supply chain.
Data Source
AI summary
A network product planner (29) ascertains material levels required at dispatch stockpiles (15), for example by checking upcoming orders for material to be shipped by transport ships (19) to end customers. Tolerance bands for chemical components required at the dispatch stockpiles are then ascertained. The tolerance bands are defined by upper and lower expected value control limits for each chemical component. The network product planner (29) then passes the collected information to the optimization engine (29a), which finds the values of decision variables in the form of weights, that minimize variance of the chemical components in the dispatch stockpiles, whilst being constrained to comply with the specified tolerance bands of the chemical components. The network product planner (29) then passes the weight values to the rail dispatch controller (23) in messages (14). The rail dispatch controller (23) refers to the weight values when generating schedules for the trains (13) so that as they travel over rail network (11), trains (13) pick up material from mine stockpiles (9) and deposit material to dispatch stockpiles (15) in accordance with the weights to thereby create the dispatch stockpiles with chemical components complying with the tolerance bands and with variance of chemical components minimized.