Effect of some operating variables on the efficiency index of a coal flotation operation

Coal cleaning performance can be evaluated by a simple index called the efficiency index. The effect of operating parameters on the efficiency index has been qualitatively discussed. Maximization of the efficiency index implies process optimization and a method is indicated to optimize the process variables for maximization of the efficiency index.     

A modified laboratory cell for the flotation of coal

The need to obtain accurate, operator-independent data from small-scale laboratory flotation tests has become particularly apparent in the treatment of fine coal. The work described suggests a method for reducing operator dependency by removing the flotation froth uniformly. This is achieved using a perspex insert to change the geometry of the cell, by maintaining a constant pulp level height and by removing froth from the full width of the cell and at a fixed depth.A comparison is made between the modified cell and a conventional Denver cell by obtaining equilibrium and kinetic data for each cell from two independent operators.The comparison shows a reduction in the variability of the results obtained from the modified cell.     

Interpretation of interaction effects and optimization of reagent dosages for fine coal flotation

The effect of three most important reagents for coal flotation namely sodium meta silicate, collector (kerosene) and frother (MIBC) was studied using 2³ full factorial design. The regression models were developed using factorial experiment data to quantify the effect of sodium meta silicate, collector and frother and to predict grade and recovery of combustible material for different reagent conditions. The addition of sodium meta silicate increased the recovery without affecting the grade significantly. The MIBC addition reduce the surface tension at the liquid–vapor interface, which results in the production of finer bubble size distribution and thus improves flotation rates and recovery values. However, a finer bubble size distribution also increases water recovery, which results in a greater recovery of entrainable ash bearing particles and thus degradation of the product grade. The interaction between OH group of MIBC and hydrated mineral matter improves flotability of high ash coal particles and degrades the product grade further. The negative effect of kerosene and MIBC interaction on both grade and recovery could be due to the recovery of high ash coal particles in preference to low ash coal particles. The highest possible grade of product is 94.19% combustibles with 25.33% recovery. A product with 91.11% combustibles grade at 95.58% recovery was obtained at 0.1 g/kg sodium silicate, 0.4 g/kg collector and 0.075 g/kg frother from the coal fines tested.     

Characterisation of brown coal humic acids and modified humic acids using pyrolysis gcms and other techniques

It has been proposed that Victorian brown coal can be considered as a two-component structure — a lignocellulosic “host”, containing various amounts of weakly bound or entrapped “guest” material together with very small amounts of inorganic and/or mineral matter. The latter predominantly consists of wax esters and/or terpenoid material. In this paper we describe attempts to gain structural information regarding the more complex, “host” component of the coal. Our initial model compound has been humic acid that can be readily obtained from the coal by alkaline extraction. It has been found that “pure” humic acid, free from material associated with the “guest” components of the coal, can be obtained by a highly selective, low-yielding alkaline extraction. This humic acid has been studied by nmr spectroscopy and pyrolysis gas chromatography-mass spectroscopy (py-gc/ms). The products arising from py-gc/ms have been compared with those obtained from similar pyrolysis of whole coals. Alkylation of humic acids using alkyl halides in the presence of base has been successfully carried out and reactivity of the resulting materials compared with those of the parent coal and humic acid.     

基于统计理论方法的水文模型参数敏感性分析

参数敏感性分析是模型不确定性量化的重要环节,有助于有效识别关键参数,减少参数的不确定性影响,进而提高参数优化效率。利用Morris筛选方法定性识别相对重要参数,耦合方差分解的Sobol方法和统计理论的响应曲面模型构建一种新的定量敏感性分析方法——RSMSobol方法。以长江支流沿渡河流域的日降雨径流过程模拟为例,系统分析4种不同目标函数响应条件下新安江模型的参数敏感性。结果表明Morris方法和RSMSobol方法的集成应用极大地提高了全局敏感性分析的效率,Morris定性筛选结果为定量评估减少了模型参数维数,采用代理模型技术的RSMSobol方法减少了模型的计算消耗。     

Dissolved air flotation (DAF) of fine quartz particles using an amine as collector

Experimental studies concerning the dissolved air flotation (DAF) of fine (d_p < 100 μm) quartz particles, using two different flotation cells (setups), are presented. Pure and well characterised quartz samples were treated with a commercial amine as collector prior to flotation and bubbles were characterised by the LTM-BSizer technique. Bubble size distribution showed 71% (by volume) and 94% (by number) of the bubbles having sizes (d_b) lower than 100 μm (i.e. microbubbles). The Sauter and arithmetic mean diameters were 79 μm and 56 μm, respectively, for the bubbles generated at 300 kPa (gauge) saturation pressure (after 30 minute saturation time). Quartz particle size distribution (obtained by laser diffraction) showed a volume-moment diameter of 13 μm. The Rosin–Rammler–Bennett, Gates–Gaudin–Schumann and log-normal distribution functions were well fitted (R² > 0.96) to the bubble size distribution and quartz particle size distribution data. Values of total quartz recovery ranging from 6% to 53% (by mass) were obtained for the DAF experiments under different collector concentrations (up to 2 mg g^− 1), with an optimal collector concentration found at 1 mg g^− 1. These results are significant considering that 27% (by volume) of the quartz particles are ultrafine (d_p < 5 μm), demonstrating the widely-known efficiency of DAF to remove small particles when applied in the field of water and wastewater treatment. The true flotation behaviour, as a function of particle diameter (d_p), exhibits a local minimum when particles are approximately 3–5 μm in size. The results contribute to the discussion in the literature about the existence of such a minimum, which is generally interpreted as a change in the mechanism of particle collection from convection (collision) to diffusion at lower particle sizes.     

Identification of constitutive parameters of soil using an optimization strategy and statistical analysis

The paper at hand investigates a strategy to calibrate different constitutive models for soils via back analysis. The efficiency and reliability of the parameter identification for soil models is worked out. In order to demonstrate of how to utilise identification procedures, results from optimisation against conventional oedometer and drained triaxial compression tests on natural Pappadai clay are presented and discussed. The aim of geotechnical optimisation problems is to obtain a set of model parameter values that provide the best match between soil model simulations and appropriate measurements. For the parameter identification process, a constrained population-based algorithm is chosen, namely the Particle Swarm Optimiser. The identification is carried out in an initial step separately on each test and then simultaneously on oedometer and triaxial tests. The evaluation is performed employing three different constitutive models of varying complexity and number of constitutive parameters.A subsequent residual analysis and the computation of confidence intervals for the parameters provide valuable results to assess the quality of the identified parameters in correlation with the evaluated data. Therefore, criteria of the utility and reliability of the mathematical models for further prognosis computations can be estimated.     

Study of celestite flotation efficiency using sodium dodecyl sulfonate collector: factorial experiment and statistical analysis of data

Flotation tests of synthetic mixtures of celestite (SrSO₄) and calcite (CaCO₃) minerals using sodium dodecyl sulfonate as collector were carried out using a factorial experimental design 2³. The independent experimental variables included celestite grade in the feed (50% and 90% SrSO₄), conditioning pH (6.5 and 9) and sodium silicate depressant concentration (10⁻⁵ and 10⁻³ M). All experiments were performed at a constant collector concentration (10⁻⁴ M) and conditioning time of 15 min. The response variables were recovery and grade of SrSO₄ in the concentrate.The factors that had the greatest effect on the grade and recovery of celestite were the celestite grade in the feed (L) and the depressant concentration (C); also the combination of these factors significantly affected the response variables. The highest celestite yield (96% recovery, 98% grade) was obtained when a 90% of celestite grade in the feed was used.     

Estimation of soil compaction parameters by using statistical analyses and artificial neural networks

This study presents the application of different methods (simple–multiple analysis and artificial neural networks) for the estimation of the compaction parameters (maximum dry unit weight and optimum moisture content) from classification properties of the soils. Compaction parameters can only be defined experimentally by Proctor tests. The data collected from the dams in some areas of Nigde (Turkey) were used for the estimation of soil compaction parameters. Regression analysis and artificial neural network estimation indicated strong correlations (r ² = 0.70–0.95) between the compaction parameters and soil classification properties. It has been shown that the correlation equations obtained as a result of regression analyses are in satisfactory agreement with the test results. It is recommended that the proposed correlations will be useful for a preliminary design of a project where there is a financial limitation and limited time.     

Estimating the depth of underground coal fires using data integration techniques

Underground coal fires in China cause serious environmental problems, in addition to the loss of valuable coal resources. The present study aims at developing a quick and practical method to estimate the depth of coal fires using data integration techniques.
  In coal fields which have underground coal fires, the subsurface fires are associated with surface thermal anomalies. Airborne thermal infrared scanner data and colour infrared photographs were used in this study to depict the coal fire front and the outcrop of coal seams, respectively. A Digital Elevation Model (DEM) of the study area was produced from topographic maps at 1:25 000 scale. Finally, taking into account the spreading direction of the coal fires and the relationships between thermal anomalies, relief factors and the occurrence of coal seams, the depths of the coal fires were obtained automatically by means of integration of remote sensing data and GIS techniques. This helped to target the fire fighting operations and made them more cost effective.     

Evaluation of spatial and seasonal variations in surface water quality using multivariate statistical techniques

In this study, spatial and seasonal variations of water quality in Haraz River Basin were evaluated using multivariate statistical techniques, such as cluster analysis, principal component analysis and factor analysis. Water quality data collected from 8 sampling stations in river during 4 seasons (Summer and Autumn of 2007, Winter and Spring of 2008) were analyzed for 10 parameters (dissolved oxygen, Fecal Coliform, pH, water temperature, biochemical oxygen demand, nitrate, total phosphate, turbidity, total solid and discharge). Cluster analysis grouped eight sampling stations into three clusters of similar water quality features and thereupon the whole river basin may be categorized into three zones, i.e. low, moderate and high pollution. The principle component analysis/factor analysis assisted to extract and recognize the factors or origins responsible for water quality variations in four seasons of the year. The natural parameters (temperature and discharge), the inorganic parameter (total solid) and the organic nutrients (nitrate) were the most significant parameters contributing to water quality variations for all seasons. Result of principal component analysis and factor analysis evinced that, a parameter that can be significant in contribution to water quality variations in river for one season, may less or not be significant for another one.     

Assessment of groundwater quality using multivariate statistical techniques in Hashtgerd Plain, Iran

Multivariate statistical techniques, such as cluster analysis (CA), factor analysis (FA), principal component analysis (PCA), and discriminant analysis (DA), were applied for the evaluation of variations and the interpretation of a large complex groundwater quality data set of the Hashtgerd Plain. In view of this, 13 parameters were measured in groundwater of 26 different wells for two periods. Hierarchical CA grouped the 26 sampling sites into two clusters based on the similarity of groundwater quality characteristics. FA based on PCA, was applied to the data sets of the two different groups obtained from CA, and resulted in three and five effective factors explaining 79.56 and 81.57% of the total variance in groundwater quality data sets of the two clusters, respectively. The main factors obtained from FA indicate that the parameters influencing groundwater quality are mainly related to natural (dissolution of soil and rock), point source (domestic wastewater) and non-point source pollution (agriculture and orchard practices) in the sampling sites of Hashtgerd Plain. DA provided an important data reduction as it uses only three parameters, i.e., electrical conductivity (EC), magnesium (Mg²⁺) and pH, affording more than 98% correct assignations, to discriminate between the two clusters of groundwater wells in the plain. Overall, the results of this study present the effectiveness of the combined use of multivariate statistical techniques for interpretation and reduction of a large data set and for identification of sources for effective groundwater quality management.     

Comparing spatial grain-size trends inferred from textural parameters using percentile statistical parameters and those based on the log-hyperbolic method

The Folk&Ward (F&W) and the log-hyperbolic methods are applied to a small - and easy to overlook - number of typical sand sized grain-size distributions from the Danish Wadden Sea. The sand originates from the same source, and the pattern of change in the grain-size distributions is, therefore, exclusively linked to dynamic sorting. In general, the F&W parameters reflect the observed grain-size trends far better than the corresponding log-hyperbolic parameters. The log-hyperbolic “typical log grain size”, ν, is sensitive to changes in skewness and cannot replace mean grain size, Mz, in grain-size trend analysis. The four log-hyperbolic parameters describing dispersion δ, τ^− 1, κ^− 1 and ζ are not able to give an unambiguous picture of spreading trends. The F&W sorting parameter, Sd, can therefore not be replaced by any of the log-hyperbolic parameters in grain-size trend analysis. In skewed grain-size distributions, there seems to be a general defect in the log-hyperbolic approximation of the best represented tail. This makes χ less sensitive to indicate the correct sign of slightly skewed grain-size distributions than Sk. However, when examining trends, the relative change between χ and Sk by and large seems to be the same. The log-hyperbolic peakedness parameter, ξ, is preferable to the F&W peakedness (kurtosis) parameter, Kg, because the latter is hypersensitive to small, insignificant variations in a grain-size distribution. By contrast, the concept of peakedness as defined by ξ, seems to relate more directly to sedimentary environmental conditions. In conclusion, the log-hyperbolic method has both advantages and disadvantages when compared with the F&W method. It is thus suggested that a sensible combination of the two methods could be advantageous to sediment trend analysis.     

An appreciation of galena concentration using a steady-state flotation model

A general-purpose computer program has been developed for the steady-state modelling of flotation plants. It permits simulation of flotation banks, made up of any number of cells, interconnected if necessary through a regrind unit. Specification of the fresh feed material in terms of the relative amounts and flotation-rate behaviour of its flotable components is necessary so that the program can then calculate the concentration of these components in all the plant streams.The use of the program for plant investigation is demonstrated using data for a lead concentration plant. The model was fitted to steady-state plant data and then tested by comparing the predictions of the model with actual plant measurements at a second feed condition. Full circuit simulation results are also presented to show how steady-state modelling can be used to give information on the likely response of the plant to other changes in feed material and the benefits which might result from rearrangements of the circuit.     

Water quality assessment of the Jinshui River (China) using multivariate statistical techniques

Multivariate statistical techniques have been widely utilized to assess water quality and evaluate aquatic ecosystem health. In this study, cluster analysis, discriminant analysis, and factor analysis techniques are applied to analyze the physical and chemical variables in order to evaluate water quality of the Jinshui River, a water source area for an interbasin water transfer project of China. Cluster analysis classifies 12 sampling sites with 22 variables into three clusters reflecting the geo-setting and different pollution levels. Discriminant analysis confirms the three clusters with nine discriminant variables including water temperature, total dissolved solids, dissolved oxygen, pH, ammoniacal nitrogen, nitrate nitrogen, turbidity, bicarbonate, and potassium. Factor analysis extracts five varifactors explaining 90.01% of the total variance and representing chemical component, oxide-related process, natural weathering and decomposition processes, nutrient process, and physical processes, respectively. The study demonstrates the capacity of multivariate statistical techniques for water quality assessment and pollution factors/sources identification for sustainable watershed management.     

Effects of process variables on the residence time distribution of a solid in a continuously operated flotation cell

Effects of aeration rate, slurry flow rate, stirring rate, pulp density, and particle size on the residence time distribution of a solid in a continuously operated flotation cell under non-flotation conditions (no frother, no concentrate removal) have been examined. The mixing behaviour of the solid in the cell has been quantitatively analysed using a multiple parameter approach. The results indicate that only a part of the cell volume behaves as a perfect mixer, the remaining being dead. The exit residence time distribution E(t) and the mean residence time ( ) of solid are described by: and where Q is the volumetric slurry flow rate. For the variables ranges examined, it is observed that the aeration does not have any significant effect on the mixing behaviour, V_eff increases with increasing slurry flow rate and stirring rate but decreases with increasing pulp density. The mean residence time, on the other hand, increases with increasing stirring rate, decreases with increasing slurry flow rate and pulp density, and exhibits a maximum with respect to particle size. Following empirical equation correlating the effective volume of the cell with the operating variables for a unisize feed is obtained: where B is the weight fraction of solid in the pulp.     

Water and sediment quality assessment in the mid-Black Sea coast of Turkey using multivariate statistical techniques

The aim of this study was to investigate the water and sediment quality in the mid-Black Sea coast of Turkey. The samples were collected from six stations during 2007. Investigated parameters were total carbon (TC), total inorganic carbon (TIC), total organic carbon (TOC), ammonium-nitrogen (NH₄-N), nitrate-nitrogen (NO₃-N), nitrite-nitrogen (NO₂-N), total phosphorus (TP), sulphate, total hardness, methylene blue active substances (MBAS), phenol, adsorbable organic halogens (AOX), dissolved oxygen (DO), pH and electrical conductivity (EC) in water samples and TC, TIC, TOC, TP, pH, electrical conductivity (EC), redox potential (Eh) and water content (WC) in sediment samples. Different multivariate statistical techniques were used to evaluate variations in surface water and sediment quality. Principal component analysis helped in identifying the factors or sources responsible for water and sediment quality variations. Five factors were found responsible for 87.63% of the total variance in the surface waters. In sediments, three factors explained 84.73% of the observed total variance. Cluster analysis classified the monitoring sites into two groups based on similarities of water and sediment quality characteristics.     

Assessment of surface water quality using multivariate statistical analysis techniques: a case study from Ghrib dam,Algeria

This study explores the water quality status and pollution sources in Ghrib Dam, Algeria. It allows us to obtain more accurate information on water quality by applying a series of multivariate statistical techniques, including principal component analysis (PCA)/factor analysis (FA), hierarchical cluster analysis (CA), and multiple regression analysis (MRA). On 19 physicochemical parameters dataset over 5 years and from 6 different sites located in and around the lake. One-way analysis of variance (ANOVA) was used to investigate the statistically considerable spatial and seasonal differences. The results of ANOVA suggest that there exist a statistically significant temporal variation in the water quality of the dam for all parameters. On the other hand, only organic matter has a statistically significant spatial variation. In the multiple linear models, an association between organic and inorganic parameters was found; their origin comes from the mechanical erosion process of agricultural lands in the watershed. The PCA/FA identifies five dominant factors as responsible of the data structure, explaining more than 94.96% of the total variance in the water quality dataset. This suggests that the variations in water compounds’ concentration are mainly related to the multiple anthropogenic activities, as well as natural processes. The results of cluster analysis demonstrate that the sampling stations were divided in two similar groups, which indicates spatial homogeneity. While seasonal grouping has showed that the source of pollution was related to the level of runoff in the seasons.     

CFD simulation of fine coal segregation and stratification in jigs

The literature review on discrete element (DEM) model analysis of jigging reveals that an idealized fluid behavior is assumed and the damping of the fluid motion across the mineral bed is generally ignored. A microscopic model based on the principles of Computational Fluid Dynamics (CFD) is used to simulate the liquid flow and stratification of coal particles with a wide size range and density distribution in jigging. Fluid motion is calculated by directly solving the Navier–Stokes equations. Coal particles are moved in a Lagrangian frame through the action of forces imposed by the fluid and gravity. Particle effects on fluid motion are fed back at each time step through calculating the velocity disturbance caused by the particle. Particle–particle and particle–wall collisions are also considered. The snapshots of particle configurations for the simulation of stratification in oscillating flow show that the model predicts the macroscopic behavior, such as segregation and stratification, of particles reasonably well.