Burden prediction in blasting operation using rock geomechanical properties

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).     

Study on infiltration rate based on primary particle size distribution data in arid and semiarid region soils

Soil particle size distribution (PSD) is used to estimate some soil processes, soil moisture characteristics, and infiltration rate (IR). Prediction of infiltration rate from soil texture data requires an accurate characterization of PSD. The objective of this study was to determine more important primary particle diameters that control IR. The experiments were conducted using double-ring method with constant head of 5 cm in 15 different soils and three replications. The range of measured IR for studied soils varied from 1.6 to 30.66 cm h^?1. The results indicated that the primary PSD had a significant influence on IR. In other words, most D _n fractions had significant positive effect on the final IR. Among different fractions, D ₃₀, D ₄₀, and D ₆₀ showed higher relationships with IR than the others. These diameters are attributed to particles with diameter of 0.05, 0.08, and 0.16 mm, respectively. The results also showed that increasing the percent of sand have intensified influence on increasing the final IR. Reversely, clay and silt contents showed negative effects on final IR. Furthermore, the CaCO₃ had a meaningful effect on the IR that showed the importance of lime in arid and semiarid regions. Finally, it is revealed that the role of texture was important, especially in behavior of infiltration, runoff, and production capability.     

Composition, porosity, and reservoir potential of the Middle Jurassic Kashafrud Formation, northeast Iran

In the Kopet-Dagh Basin of Iran, deep-sea sandstones and shales of the Middle Jurassic Kashafrud Formation are disconformably overlain by hydrocarbon-bearing carbonates of Upper Jurassic and Cretaceous age. To explore the reservoir potential of the sandstones, we studied their burial history using more than 500 thin sections, supplemented by heavy mineral analysis, microprobe analysis, porosity and permeability determination, and vitrinite reflectance.The sandstones are arkosic and lithic arenites, rich in sedimentary and volcanic rock fragments. Quartz overgrowths and pore-filling carbonate cements (calcite, dolomite, siderite and ankerite) occluded most of the porosity during early to deep burial, assisted by early compaction that improved packing and fractured quartz grains. Iron oxides are prominent as alteration products of framework grains, probably reflecting source-area weathering prior to deposition, and locally as pore fills. Minor cements include pore-filling clays, pyrite, authigenic albite and K-feldspar, and barite. Existing porosity is secondary, resulting largely from dissolution of feldspars, micas, and rock fragments, with some fracture porosity. Porosity and permeability of six samples averages 3.2% and 0.0023 mD, respectively, and 150 thin-section point counts averaged 2.7% porosity. Reflectance of vitrinite in eight sandstone samples yielded values of 0.64-0.83%, in the early mature to mature stage of hydrocarbon generation, within the oil window.Kashafrud Formation petrographic trends were compared with trends from first-cycle basins elsewhere in the world. Inferred burial conditions accord with the maturation data, suggesting only a moderate thermal regime during burial. Some fractures, iron oxide cements, and dissolution may reflect Cenozoic tectonism and uplift that created the Kopet-Dagh Mountains. The low porosity and permeability levels of Kashafrud Formation sandstones suggest only a modest reservoir potential. For such tight sandstones, fractures may enhance the reservoir potential.     

Lee yang theory for cosmological many body problem

We study the phase transition in a gravitating system by analyzing grand canonical partition function as a function of complex fugacity. We extend the Yang-Lee theory to study phase transitions in the gravitational galaxy clustering of galaxies having a variety of masses. This generalizes our previous work based on the same theory for the single-component system to a multicomponent system. We find that galaxy clustering is sensitive to masses and number densities of individual galaxies at early stages while at later stages collective behavior of the particles is more pronounced. This validates our earlier work obtained from different considerations.     

Factor and cluster analysis of water quality data of the groundwater wells of Kushtia, Bangladesh: Implication for arsenic enrichment and mobilization

The study area is located in the southwestern part of Bangladesh. Twenty-six groundwater samples were collected from both shallow and deep tube wells ranging in depth from 20 to 60 m. Multivariate statistical analyses including factor analysis, cluster analysis and multidimensional scaling were applied to the hydrogeochemical data. The results show that a few factors adequately represent the traits that define water chemistry. The first factor of Fe and HCO₃ is strongly influenced by bacterial Fe (III) reduction which would raise both Fe and HCO₃ concentrations in water. Na, Cl, Ca, Mg and PO₄ are grouped under the second factor representing the salinity sources of waters. The third factor, represented by As, Mn, SO₄ and K is related to As mobilization processes. Cluster analysis has been applied for the interpretation of the groundwater quality data. Initially Piper methods have been employed to obtain a first idea on the water types in the study area. Hierarchical cluster analysis was carried out for further classification of water types in the study area. Twelve components, namely, pH, Fe, Mn, As, Ca, Mg, Na, K, HCO₃, Cl, SO₄ and NO₃ have been used for this purpose. With hierarchical clustering analysis the water samples have been classified into 3 clusters. They are very high, high and moderately As-enriched groundwater as well as groundwater with elevated SO₄.     

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most influential parameters from seven biophysical parameters including surface temperature (T _s), surface albedo (α), leaf area index (LAI), soil heat flux (G _o), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (R _n). T _s and G _o were found as the most effective parameters by this method. However, T _s, LAI, α, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be efficiently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study confirmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter.     

Landslide susceptibility assessment along the southern coast of the Caspian Sea,Iran

China is a disaster-prone country, and these disasters have diverse characteristics, a wide scope of distribution, high frequency, and large losses. China has advanced community-based disaster management (CBDM) capacity. Community is the bottom unit of the society, and CBDM is the foundation of the entire society’s disaster management system. A series of domestic major emergency incidents and disasters and international disaster reduction activities have promoted the formation of the CBDM concept, the implementation of capacity building activities, and the improvement of policy and laws. Thus far, the CBDM system has been preliminarily formed in China, and relevant rules and regulations have been promulgated and implemented. Furthermore, disaster reduction activities, such as the construction of the national comprehensive disaster reduction community and national safe community, have been promoted nationwide. As a result, China’s disaster-resistance capacity has largely improved. However, it is only in the initial phase of CBDM implementation, which remains plagued by several challenges and problems, such as the deficiency of community resident participation, management organizations, disaster risk assessment methods, NGO development, and safety culture cultivation.     

Stochastic simulation of patterns using Bayesian pattern modeling

In this paper, a Bayesian framework is introduced for pattern modeling and multiple point statistics simulation. The method presented here is a generalized clustering-based method where the patterns can live on a hyper-plane of very low dimensionality in each cluster. The provided generalizationallows a remarkable increase in variability of the model and a significant reduction in the number of necessary clusters for pattern modeling which leads to more computational efficiency compared with clustering-based methods. The Bayesian model employed here is a nonlinear model which is composed of a mixture of linear models. Therefore, the model is stronger than linear models for data modeling and computationally more effective than nonlinear models. Furthermore, the model allows us to extract features from incomplete patterns and to compare patterns in feature space instead of spatial domain. Due to the lower dimensionality of feature space, comparison in feature space results in more computational efficiency as well. Despite most of the previously employed methods, the feature extraction filters employed here are customized for each training image (TI). This causes the features to be more informative and useful. Using a fully Bayesian model, the method does not require extensive parameter setting and tunes its parameters itself in a principled manner. Extensive experiments on different TIs (either continuous or categorical) show that the proposed method is capable of better reproduction of complex geostatistical patterns compared with other clustering-based methods using a very limited number of clusters.     

Provisional Elemental Values for IAEA‐Sewage Sludge by Instrumental Neutron Activation Analysis

The International Atomic Energy Agency (IAEA) organised a proficiency test (PT), IAEA‐CU‐2010‐02, for the determination of elements in sewage sludge. The PT sample was analysed by semi‐absolute standardless k₀‐instrumental neutron activation analysis (k₀‐INAA). Results for seven elements (As, Co, Cr, Fe, Hg, Se, Zn) were submitted to the IAEA by our laboratory. All of our results were scored ‘acceptable’ by the ‘result evaluation criteria’ adopted by the IAEA. The same analytical methodology produced quantitative results for twenty‐six additional elements. In total, thirty‐six elements were determined with uncertainty varying from 4 to 11%. This paper presents the provisional mass fractions of twenty‐six additional elements (Ag, Al, Br, Ca, Ce, Cl, Dy, Eu, Ga, Hf, I, K, La, Mg, Mn, Na, Rb, Sb, Sc, Sm, Ta, Tb, Th, V, U, W) not reported by the IAEA. The analytical methodology was discussed with important sources of spectral, nuclear and fission‐product interferences. It was shown that the important components of uncertainties were the k₀ factor, Q₀ factor, detector efficiency, mass and counting statistics. The methodology was validated by analysing the IAEA‐S7 reference material.