Related Experiment Video
Updated: May 5, 2026
06:24
Generic Protocol for Optimization of Heterologous Protein Production Using Automated Microbioreactor Technology
Published on: December 15, 2017
13.5K
Joint stochastic optimisation of stope layout, production scheduling and access network
Cristina Penadillo1,2, Roussos Dimitrakopoulos1,2, Mustafa Kumral2
1COSMO - Stochastic Mine Planning Laboratory, McGill University, Montreal, Canada.
Mining Technology (Abingdon, Eng.)
|May 4, 2026
Summary
This study introduces a new joint optimization model for sublevel stoping in underground mining, integrating stope layout, production schedule, and access networks. The model enhances net present value (NPV) by accounting for grade uncertainty, outperforming traditional methods.
Area of Science:
- Mining Engineering
- Operations Research
- Geostatistics
Background:
- Sublevel stoping methods involve optimizing stope layout, production schedule, and access networks.
- Previous research solved these optimization components sequentially due to computational challenges.
- Joint optimization offers potential value but remains underexplored in literature.
Purpose of the Study:
- To develop a novel joint optimization model for sublevel stoping.
- To integrate stope layout, production schedule, and access network optimization.
- To incorporate stochastic simulations for grade uncertainty and variability.
Main Methods:
- A two-stage stochastic integer programming (SIP) formulation was developed.
- The model maximizes net present value (NPV) and minimizes planned dilution.
- Stochastic simulations were used to model grade uncertainty.
Main Results:
- The proposed SIP model was applied to a copper deposit.
- SIP demonstrated superior performance compared to mixed integer programming.
- The SIP model achieved approximately 20% higher NPV over a seven-year mine life.
Conclusions:
- Joint optimization of sublevel stoping components is crucial for maximizing project value.
- Accounting for grade uncertainty and variability significantly improves outcomes.
- The developed SIP model provides a robust framework for integrated mine planning.
Related Concept Videos
01:21Distributed Loads: Problem Solving
1.3K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.3K
01:23Stresses under Combined Loadings
604
When analyzing a bent tube with a circular cross-section subjected to multiple forces, it is crucial to determine the stress distribution in order to maintain structural integrity under varied load conditions.
The process begins by slicing the tube at critical points and analyzing the internal forces and stress components at these sections, focusing on the centroid. Normal stresses, generated by axial forces and bending moments, are either compressive or tensile and vary across the section from...
The process begins by slicing the tube at critical points and analyzing the internal forces and stress components at these sections, focusing on the centroid. Normal stresses, generated by axial forces and bending moments, are either compressive or tensile and vary across the section from...
604
01:25Multimachine Stability
698
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
698
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
438
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
438
01:26Optimization Problems
220
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
220
01:28Methods of Medium Optimization
70
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
70