Kwame Awuah-Offei

Large-scale open pit production scheduling using Mixed Integer Linear Programming

One of the main obstacles in using Mixed Integer Linear Programming (MILP) formulations for open pit production scheduling is the size of the problem. The main objective of this paper is to present and implement a practical MILP formulation for open pit production scheduling problem. To reduce the number of binary integer variables in the formulation, we aggregate blocks into larger units referred to as mining-cuts. We also present the numerical modelling approach to setup the optimisation problem. Finally, we verify and validate the MILP production scheduler by a comparative case study against Whittle strategic mine planning software.

Predicting equipment requirements using SIMAN simulation - a case study

Abstract A mining contractor is expected to provide equipment flexibility in spite of the fact that haulage unit production varies as mining progresses. Consequently, predicting the equipment needs well in advance is important for the mining contractor. African Mining Services (Ghana) Limited (AMS) is a mining contractor, responsible for the loading and hauling operations of Abosso Goldfields Limited (AGL) in addition to other tasks. This paper seeks to forecast the truck and shovel requirements in the Juno II Pit of AGL for the years 2001–2004. The load and haul practices of AMS have been studied using time and motion studies as well as other data collected from mine records. These data were used to construct a simulation model of the system using the SIMAN computer simulation package. This study shows that AMS will need 6, 7 and 8 Caterpillar 777D dump trucks to achieve the required production rate in December 2001, 2002 and the period 2003–2004, respectively.

Open pit optimisation using discounted economic block values

Abstract Strategic mine planning and the management of the future cash flows are a vital core of surface mining operations. The time dimension, which is an integral part of the scheduling problem, is not embedded in traditional ultimate pit outline optimisation algorithms. This study explores the validity of the theorem that a pit outline determined by an optimal long term schedule algorithm is constrained by the conventional Lerchs and Grossmanns (LG) optimised pit outline. This hypothesis was investigated through a case study using the intelligent open pit simulator (IOPS) founded on agent based learning theories. The optimal pushback schedule was determined using IOPS before determination of the optimised final pit outline. The economic block values were discounted with respect to the allocated extraction time, followed by final pit limits optimisation using LG algorithm.

Asphalt Mix Design Optimization for Efficient Plant Management

The role of aggregate gradation in hot-mix asphalt performance is well documented in the literature. Yet the Bailey method is the only tool available for guidance on aggregate gradation selection for optimal performance. Also, there is a lack of tools for design engineers and plant managers of quarry sites to manage stockpile inventory levels and control cost of aggregate used in asphalt mixes. This work presents a linear programming model of the asphalt mix design problem and a numerical algorithm to solve the model. The algorithm is implemented in MATLAB as an asphalt mix design optimization (AMIDO) program. The program is successfully verified with an example. The results show that using the Bailey method alone results in suboptimal results and that cheaper mixes with similar aggregate ratios can be designed with the same aggregate stockpiles. For the specific stockpiles used in the verification, the AMIDO mix design resulted in a 53-cent/ton reduction in aggregate cost. This work improves the state of the art in asphalt mix design for dense-graded mixes and could be modified for other mixes.

Numerical Simulation of Cable Shovel Resistive Forces in Oil Sands Excavation

The cable shovel is an important primary excavator in surface mining operations. The production function of this shovel becomes more critical in the Athabasca oil sands formation with little or no pre-production blasting. The random occurrence of shales, dolomites and limestones in this formation causes a high digging resistance, mechanical wear, tear and failure, resulting in high maintenance costs. Research is currently underway to develop an intelligent navigation technology to provide smart shovel excavation in this formation. This paper contributes to this research by developing a numerical simulation model for determining the resistive force on a shovel dipper during excavation. The spatial dynamics of the dipper geometry and the loaded material weight are modelled using ordinary differential equations and solved using the Runge – Kutta algorithm. Numerical simulation results show that the depth of cut increases with an increase in crowd arm extension speed and a decrease in hoist rope retraction speed. The results also show that the dipper digging and loading rate is proportional to the speed of crowd arm extension and hoist rope retraction. For a constant hoist rope retraction speed, the optimum dipper trajectory is defined for crowd arm extension speeds and vice versa. Also, the digging time for crowd arm extension and hoist rope retraction speeds sampled from a uniform distribution between 0.15 and 0.35 m/s follows a triangular distribution with minimum 6.12 s, mode 7.26 s and maximum 13.7 s. Using these results, production engineers can parameterize shovel excavation schemes for optimum production performance.

Framework for mining community consultation based on discrete choice theory

The significance of local community acceptance to the success of a mine cannot be over-stated. Project sustainability has implications for local community acceptance as the development and operation of a mine can be viewed as a development intervention with concrete social, economic and environmental impacts. Although, some work has been done to understand these relationships, very little has been done to quantitatively model the relationship between project sustainability and community preferences. This paper presents a framework for quantitative community consultation for mining projects based on discrete choice theory. The paper establishes the most appropriate choice model and uses a review of the literature to select key determinants of mining community acceptance. Recommendations are made for future research to implement the proposed framework. This framework will allow engineers and regulators to better evaluate community input.

Formation excavation resistance modelling for shovel dippers

Modelling and simulation of formation resistance to excavation has become important in evaluating performance and designing efficient earthmoving equipment. Previous attempts at extending the passive earth theory to excavator dippers have only covered snap-shot estimates for a right-angled framed assembly. This work applies cable shovel kinematics and dynamics to estimate the dynamic cutting depth and angle, making it possible to apply the passive earth theory to model dipper dynamic formation resistance. Geometric simulation is applied to model the resistance due to the material payload. The models are then applied to simulate the formation resistance for a 17-yd³ dipper. The results show that the payload resistance (141-kN) is far more significant than the cutting resistance (between 16-kN and 31-kN).

Cable shovel health and longevity and operator efficiency in oil sands excavation

Cable shovel excavation in the Athabasca oil sands formation is carried out with little or no pre-fragmentation. The stress loading of a shovels front-end assembly must continually be monitored and managed to prevent fatigue failure. In this paper, the authors advance the reliability of the cable shovel boom through dynamic and stress modelling using rigid and flexible multi-body dynamics theory in CASES I, II and III formations. A numerical example is used to examine the forces and torques on strategic points (A, E, G, H and O) on the boom. The results show that the respective maximum force and torque on the boom are 2.25 × 10 6 N at point A for CASE I and 3.0 × 10 9 Nm at point A for CASE III. Stress fields simulation also showed that six FE nodes (188, 228, 226, 211, 224 and 222) are highly stressed in the boom structure. The maximum stress values are:

Effect of ignoring input correlation on truck–shovel simulation

Abstract This paper presents an approach for handling correlated input variables in discrete event simulation (DES) modelling of truck–shovel systems using commercial DES software and uses a case study to investigate the effect of ignoring correlation between input variables. Multivariate random vectors, instead of independent probability distributions, are used for variables found to be correlated. The authors prove that correlations do exist in truck–shovel haulage systems. The model with multivariate random vectors performs better than the original model. The significance of modelling correlation in input variables depends on the strength of the correlation and the output’s sensitivity to the input variables.

Aggregate Cost Minimization in Hot-Mix Asphalt Design

Hot-mix asphalt is a mixture of aggregates and asphalt binder in appropriate ratios to produce a high-performing material for asphalt pavements. The aggregate structure, which depends on the gradation, is an important factor in determining the volumetric properties of HMA. The design process to determine the optimal aggregate blend is currently iterative and engineers rely almost exclusively on experience. This approach is time consuming and often results in suboptimal HMA mixtures. This study presents linear programming optimization models and attendant solution procedures that minimize HMA aggregate cost and produce high-quality HMA. The models are validated with real-life examples, and results indicate that the models are useful to replicate HMA mixes during field modifications, reduce the aggregate cost in a mixture, and manage stockpile inventory. The application of optimization models will increase the application of the Bailey method in the United States.