Genetic algorithms for the optimisation of the Schwartz–Smith two-factor model: a case study on a copper deposit

Mining companies typically seek ways to hedge risks affecting their production. One useful instrument to mitigate the financial risk is the futures contracts on commodity prices. Information from the transactions in futures markets is publicly available and can be analysed with the Schwartz–Smith two-factor model (SSTF). However, finding the parameters governing this model can be very challenging. This step is done using a deterministic optimisation approach called the Expectation–Maximisation algorithm (EM). The starting values of the model will have a significant effect on the convergence of the EM. To ensure the solution does not get stuck in a local maximum, the EM algorithm is performed multiple times with different starting values. This paper assesses the value of genetic algorithms (GA) to optimise the parameters of the SWTF model. Although they are slower than EM algorithms because they use random number generators to search for the optimal solution, GA optimise a population of solutions instead of working on only one solution at the time. Moreover, a constraint on the range parameter can be applied to ensure the parameter has a sound economic meaning. Once the SWTF parameters have been calibrated on the observation of futures contracts, the model can be used for the simulation of spot and futures prices. To demonstrate the performance of the proposed approach, a case study was conducted on a copper deposit. The simulations based on the SWTF model whose parameters are determined by GA are used. An active management strategy of the stockpile, dependent on discrepancies in commodity futures prices is tested. Results show that the active management strategy produces positive returns over the passive investment approach.     

Forecasting time-to-failure of machine using hybrid Neuro-genetic algorithm – a case study in mining machinery

Forecasting of time-to-failure is an important aspect of a mining machine for the performance assessment, fault detection and schedule maintenance. The knowledge of failure time allows more defined arrangement of preventive maintenance. Traditional methods, including lifetime distribution models, fault tree analysis and Markov models, have a limitation of assuming a specific statistical distribution function to fit the failure time data. In this study, a hybrid data-driven method using neural network and genetic algorithm is proposed to forecast failure time. The forecasting model was developed using neural network algorithm and all the neural network parameters, i.e. input nodes, hidden nodes and the learning algorithms, are selected automatically using the genetic algorithm. The developed model was validated using the failure data of a mining machine. A case study was conducted investigating a load-haul-dump machine (LHD) in the mining industry. Failure historical data for the LHD machine were collected, and cumulative failure time was calculated for time-to-failure forecasting. Study results demonstrate that the developed model performs satisfactory in the prediction of next failure time. A comparative study reveals that the proposed method performs better than existing methods.     

Dependence of fluid flow on cleat aperture distribution and aperture–length scaling: a case study from Gondwana coal seams of Raniganj Formation,Eastern India

Dimensions and mutual relationships amongst fracture(cleat)parameters such as maximum aperture width,average aperture width,length,spacing etc.control the connectivity within a fracture network and the fluid flow in a coal seam as the matrix permeability,here,is negligible.In this paper,we document cleat size distributions and investigate length–aperture relationships from coals of Raniganj coalfield in Eastern India.This coalfield has a proven extractable reserve of six billion tons of coal and holds immense potential to be one of the largest coal bed methane fields serving India’s growing energy needs.Here,cleat length(L)correlates with corresponding maximum aperture width(Dmax)in a power-law function with an exponent of 0.84(DmaxαL0.84)instead of the commonly observed exponents of 1 or 0.5 applicable for other natural‘opening-mode’fractures.The conventional wisdom pertains that laminar fluid flow(Q)through an isolated,smooth-walled,parallel-plate fracture,embedded in an impermeable matrix,is directly proportional to the cube of its aperture width(b,equivalent to Davg;cubic law:Qαb3).This assumes a linear relationship between length and fracture aperture.However,the modified relationship between cleat length and average aperture width changes the cubic law applicable for Raniganj coal seam and now fluid flow correlates with aperture width in a power-law function with an exponent of 4.25(Qαb4.25)instead of 3(cube).Such simplifications will come handy for the modeling and estimation of fluid flow as it will reduce the effort of cleat length measurement which is anyway difficult and can be misleading due to the risk of undersampling.     

Comparative study of the explosion pressure characteristics of micro- and nano-sized coal dust and methane–coal dust mixtures in a pipe

Coal dust explosion accidents often cause substantial property damage and casualties and frequently involve nano-sized coal dust.In order to study the impact of nano-sized coal on coal dust and methane–coal dust explosions,a pipe test apparatus was used to analyze the explosion pressure characteristics of five types of micro-nano particle dusts(800 nm,1200 nm,45μm,60μm,and 75μm)at five concentrations(100 g/m3,250 g/m3,500 g/m3,750 g/m3,and 1000 g/m3).The explosion pressure characteristics were closely related to the coal dust particle size and concentration.The maximum explosion pressure,maximum rate of pressure rise,and deflagration index for nano-sized coal dust were larger than for its micro-sized counterpart,indicating that a nano-sized coal dust explosion is more dangerous.The highest deflagration index Kst for coal dust was 13.97 MPa/(m·s),indicating weak explosibility.When 7%methane was added to the air,the maximum deflagration index Kst for methane–coal dust was 42.62 MPa/(m·s),indicating very strong explosibility.This indicates that adding methane to the coal dust mixture substantially increased the hazard grade.     

Application of CFD to modelling of the flow in hydrocyclones. Is this a realizable option or still a research challenge?

The search continues for accurate and efficient 3D computational fluid dynamics algorithms for hydrocyclone flows. A review of recent work and new developments are presented. This contribution is based on the authors' past experience both in creating their own specialized numerical approaches and using commercial packages for simulating swirling flow. In particular, the paper presents the application of the developed 3D finite element code to hydrocyclone modelling. Some important factors in the numerical solution of the model equations, namely proper representation of geometry, imposition of boundary conditions and the choice of turbulence model, are discussed. Numerical results indicate the versatility and the effectiveness of the new approaches. A scheme that utilizes numerical results in the multi-objective design is also suggested. Remaining technical difficulties are highlighted in order to suggest key directions for future work.     

A decade of lysimeter experience to study mercury behaviour in Almadén (Spain) soil and the transferable fraction to plants

The aim of this work is the application of ex situ lysimeter experiments containing undisturbed soil monoliths to determine the behaviour of mercury within the soil plant system. These experiments under close-to-real conditions were instrumented with a set of electrodes and sensors to monitor physical and chemical parameters of soil and were sown with typical crop cultivations of the study area where total mercury concentration was measured along different growth stages. Low total mercury concentration levels in the cultivations coincide with the maturity stage and with the maximum biomass yield. Mercury accumulation in vegetative organs represents a higher potential risk for animal consumption of all the crops tested, while seeds and fruits can be used, both for human and animal consumption, according to international legislation. These experiments are an important step for the implementation of future recuperation strategies within a contaminated site.     

Influence Analysis of Mining’s Effect on Failure Characteristics of a Coal Seam Floor with Faults: A Numerical Simulation Case Study in the Zhaolou Coal Mine

A fluid–solid coupled numerical simulation was carried out for the Zhaolou coal mine using Flac3D software. The results showed that: (1) when the failure depth of the floor exceeded the extension depth of the floor rock mass and the hydrostatic seepage pressure of the fractured structure surface exceeded the stress state of the fractured element, floor water inrush will occur. (2) With other factors being equal, if the footwall is mined first, the pore pressure concentration near the fault is more obvious, and the floor-confined water is lifted 10–25 m higher than when the hanging-wall is mined first. Mining the floor adversely affects its plastic zone, and the failure depth is ≈?10 m greater than when the hanging wall is mined first. (3) When advancing against the fault dip, the pore pressure concentration near the fault is more obvious, and the floor confined water is lifted 5–20 m higher than when the mine advances along the fault dip. Again, mining the floor first disrupts the floor plastic zone, and the failure depth is approximately 5 m greater than when the mine is advanced along the fault dip. (4) The risk of floor-water inrush is minimized when the hanging-wall is mined first and the mine advances along the fault dip; the risk is greatest when the footwall is mined first and is advanced against the fault dip. These results provide a theoretical basis for preventing mine water inrush.