Carrying coal and loading potatoes; Juedische Winterhilfe der Juedische Gemeinde; Berlin Jewish Institutions

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Incorporating the interrelationship between primary and support processes in monitoring and optimising longwall coal mining efficiency

This thesis increased the researchers understanding of the relationship between operations and maintenance in underground longwall coal mines, using data from a Queensland underground coal mine. The thesis explores various relationships between recorded variables. Issues with human recorded data was uncovered, and results emphasised the significance of variables associated with conveyor operation to explain production.

Coal desulfurization in a fluidized bed reactor

Fluidized bed reactor technology was investigated as a means of developing a new simple and low cost process for coal desulfurization. Preliminary experimental results obtained in a 2.54 cm batch fluidized bed reactor have shown that over 80% total sulfur reductions can be achieved by sequential chlorination and dechlorination/ hydrodesulfurization of high sulfur pulverized coals. Proximate and ultimate analyses of desulfurized coals have revealed enhanced carbon and fixed carbon levels and substantially reduced volatile, oxygen and hydrogen contents. While there was a minor increase in the ash content and heating value, nitrogen and chlorine contents were essentially unchanged. Compared to an earlier slurry phase process, the fluidized bed reactors process has specific advantages such as shorter reaction times, fewer processing steps and reduced reactant requirements. A fluidized bed reactor process may thus have a potential of being developed into a simple and economic means of converting high sulfur coals to environmentally acceptable fuels.

Ion exchange of sodium chloride and sodium bicarbonate solutions using strong acid cation resins in relation to coal seam water treatment

Coal seam gas production has resulted in the production of large volumes of associated water which contains dissolved salts dominated by sodium chloride and sodium bicarbonate. Ion exchange using synthetic resins has been proposed as a method for desalination of coal seam water to make it suitable for various beneficial reuse options. This study investigated the behaviour of solutions of sodium chloride and sodium bicarbonate with respect to exchange with Lanxess S108H strong acid cation (SAC) resin. Equilibrium isotherms were created for solutions of NaCl and NaHCO3 and an actual sample of coal seam water from the Surat Basin in southern Queensland. The exchange of sodium ions arising from sodium bicarbonate was found to be considerably more favourable than exchange of sodium ions from sodium chloride solutions. This latter behaviour was attributed to the secondary decomposition of bicarbonate species under acidic conditions which resulted in the evolution of carbon dioxide and formation of water. The isotherm profiles could not be satisfactorily fitted by a single isotherm model such as the Langmuir expression. Instead, two Langmuir equations had to be simultaneously applied in order to fit the sections of the isotherm attributable to sodium ion exchange from sodium bicarbonate and sodium chloride. The shape of the isotherm profile was dependent upon the ratio of sodium chloride to sodium bicarbonate in solution and there was a high degree of correlation between simulated and actual coal seam water solutions.

Behaviour of natural zeolites used for the treatment of simulated and actual coal seam gas water

Coal seam gas operations produce significant quantities of associated water which often require demineralization. Ion exchange with natural zeolites has been proposed as a possible approach. The interaction of natural zeolites with solutions of sodium chloride and sodium bicarbonate in addition to coal seam gas water is not clear. Hence, we investigated ion exchange kinetics, equilibrium, and column behaviour of an Australian natural zeolite. Kinetic tests suggested that the pseudo first order equation best simulated the data. Intraparticle diffusion was part of the rate limiting step and more than one diffusion process controlled the overall rate of sodium ion uptake. Using a constant mass of zeolite and variable concentration of either sodium chloride or sodium bicarbonate resulted in a convex isotherm which was fitted by a Langmuir model. However, using a variable mass of zeolite and constant concentration of sodium ions revealed that the exchange of sodium ions with the zeolite surface sites was in fact unfavourable. Sodium ion exchange from bicarbonate solutions (10.3 g Na/kg zeolite) was preferred relative to exchange from sodium chloride solutions (6.4 g Na/kg zeolite). The formation of calcium carbonate species was proposed to explain the observed behaviour. Column studies of coal seam gas water showed that natural zeolite had limited ability to reduce the concentration of sodium ions (loading 2.1 g Na/kg zeolite) with rapid breakthrough observed. It was concluded that natural zeolites may not be suitable for the removal of cations from coal seam gas water without improvement of their physical properties.

Post-liquefaction undrained monotonic behaviour of sands: experiments and DEM simulations

This paper presents the results of a series of servo-controlled cyclic triaxial tests and numerical simulations using the three- dimensional discrete element method (DEM) on post-liquefaction undrained monotonic strength of granular materials. In a first test series,undrained monotonic tests were carried out after dissipating the excess pore water pressure developed during liquefaction. The influence of different parameters such as amplitude of axial strain,relative density and confining pressure prior to liquefaction on the post-liquefaction undrained response have been investigated.The results obtained highlight an insignificant influence of amplitude of axial strain, confining pressure and a significant influence of relative density on the post-liquefaction undrained monotonic stress-strain response.In the second series, undrained monotonic tests were carried out on similar triaxial samples without dissipating the excess pore water pressure developed during liquefaction. The results highlight that the amplitude of axial strain prior to liquefaction has a significant influence on the post-liquefaction undrained monotonic response.In addition,DEM simulations have been carried out on an assembly of spheres to simulate post-liquefaction behaviour.The simulations were very similar to the experiments with an objective to understand the behaviour of monotonic strength of liquefied samples from the grain scale. The numerical simulations using DEM have captured qualitatively all the features of the post-liquefaction undrained monotonic response in a manner similar to that of the experiments.In addition,a detailed study on the evolution of micromechanical parameters such as the average coordination number and induced anisotropic coefficients has been reported during the post-liquefaction undrained monotonic loading.

BDST modelling of sodium ion exchange column behaviour with strong acid cation resin in relation to coal seam water treatment

Reverse osmosis is the dominant technology utilized for desalination of saline water produced during the extraction of coal seam gas. Alternatively, ion exchange is of interest due to potential cost advantages. However, there is limited information regarding the column performance of strong acid cation resin for removal of sodium ions from both model and actual coal seam water samples. In particular, the impact of bed depth, flow rate, and regeneration was not clear. Consequently, this study applied Bed Depth Service Time (BDST) models to reveal that increasing sodium ion concentration and flow rates diminished the time required for breakthrough to occur. The loading of sodium ions on fresh resin was calculated to be ca. 71.1 g Na/kg resin. Difficulties in regeneration of the resin using hydrochloric acid solutions were discovered, with 86% recovery of exchange sites observed. The maximum concentration of sodium ions in the regenerant brine was found to be 47,400 mg/L under the conditions employed. The volume of regenerant waste formed was 6.2% of the total volume of water treated. A coal seam water sample was found to load the resin with only 53.5 g Na/kg resin, which was consistent with not only the co-presence of more favoured ions such as calcium, magnesium, barium and strontium, but also inefficient regeneration of the resin prior to the coal seam water test.

Optimising the operational energy efficiency of an open-pit coal mine system

This thesis investigates factors that impact the energy efficiency of a mining operation. An innovative mathematical framework and solution approach are developed to model, solve and analyse an open-pit coal mine. A case study in South East Queensland is investigated to validate the approach and explore the opportunities for using it to aid long, medium and short term decision makers.

A new constraint programming approach for optimising a coal rail system

Because of the bottlenecking operations in a complex coal rail system, millions of dollars are costed by mining companies. To handle this issue, this paper investigates a real-world coal rail system and aims to optimise the coal railing operations under constraints of limited resources (e.g., limited number of locomotives and wagons). In the literature, most studies considered the train scheduling problem on a single-track railway network to be strongly NP-hard and thus developed metaheuristics as the main solution methods. In this paper, a new mathematical programming model is formulated and coded by optimization programming language based on a constraint programming (CP) approach. A new depth-first-search technique is developed and embedded inside the CP model to obtain the optimised coal railing timetable efficiently. Computational experiments demonstrate that high-quality solutions are obtainable in industry-scale applications. To provide insightful decisions, sensitivity analysis is conducted in terms of different scenarios and specific criteria. Keywords Train scheduling · Rail transportation · Coal mining · Constraint programming

Probabilistic evaluation of seismic soil liquefaction potential based on SPT data

The performance-based liquefaction potential analysis was carried out in the present study to estimate the liquefaction return period for Bangalore, India, through a probabilistic approach. In this approach, the entire range of peak ground acceleration (PGA) and earthquake magnitudes was used in the evaluation of liquefaction return period. The seismic hazard analysis for the study area was done using probabilistic approach to evaluate the peak horizontal acceleration at bed rock level. Based on the results of the multichannel analysis of surface wave, it was found that the study area belonged to site class D. The PGA values for the study area were evaluated for site class D by considering the local site effects. The soil resistance for the study area was characterized using the standard penetration test (SPT) values obtained from 450 boreholes. These SPT data along with the PGA values obtained from the probabilistic seismic hazard analysis were used to evaluate the liquefaction return period for the study area. The contour plot showing the spatial variation of factor of safety against liquefaction and the corrected SPT values required for preventing liquefaction for a return period of 475 years at depths of 3 and 6 m are presented in this paper. The entire process of liquefaction potential evaluation, starting from collection of earthquake data, identifying the seismic sources, evaluation of seismic hazard and the assessment of liquefaction return period were carried out, and the entire analysis was done based on the probabilistic approach.

Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity

The use of the shear wave velocity data as a field index for evaluating the liquefaction potential of sands is receiving increased attention because both shear wave velocity and liquefaction resistance are similarly influenced by many of the same factors such as void ratio, state of stress, stress history and geologic age. In this paper, the potential of support vector machine (SVM) based classification approach has been used to assess the liquefaction potential from actual shear wave velocity data. In this approach, an approximate implementation of a structural risk minimization (SRM) induction principle is done, which aims at minimizing a bound on the generalization error of a model rather than minimizing only the mean square error over the data set. Here SVM has been used as a classification tool to predict liquefaction potential of a soil based on shear wave velocity. The dataset consists the information of soil characteristics such as effective vertical stress (sigma'(v0)), soil type, shear wave velocity (V-s) and earthquake parameters such as peak horizontal acceleration (a(max)) and earthquake magnitude (M). Out of the available 186 datasets, 130 are considered for training and remaining 56 are used for testing the model. The study indicated that SVM can successfully model the complex relationship between seismic parameters, soil parameters and the liquefaction potential. In the model based on soil characteristics, the input parameters used are sigma'(v0), soil type. V-s, a(max) and M. In the other model based on shear wave velocity alone uses V-s, a(max) and M as input parameters. In this paper, it has been demonstrated that Vs alone can be used to predict the liquefaction potential of a soil using a support vector machine model. (C) 2010 Elsevier B.V. All rights reserved.

Machine learning modelling for predicting soil liquefaction susceptibility

This study describes two machine learning techniques applied to predict liquefaction susceptibility of soil based on the standard penetration test (SPT) data from the 1999 Chi-Chi, Taiwan earthquake. The first machine learning technique which uses Artificial Neural Network (ANN) based on multi-layer perceptions (MLP) that are trained with Levenberg-Marquardt backpropagation algorithm. The second machine learning technique uses the Support Vector machine (SVM) that is firmly based on the theory of statistical learning theory, uses classification technique. ANN and SVM have been developed to predict liquefaction susceptibility using corrected SPT (N-1)(60)] and cyclic stress ratio (CSR). Further, an attempt has been made to simplify the models, requiring only the two parameters (N-1)(60) and peck ground acceleration (a(max)/g)], for the prediction of liquefaction susceptibility. The developed ANN and SVM models have also been applied to different case histories available globally. The paper also highlights the capability of the SVM over the ANN models.