Potential of natural bed soil in adsorption of heavy metals in industrial waste landfill

Development of higher welfare could not be realized unless by energy consumption and other natural resources. Growth of industrial complexes has shown an unprecedented trend during recent years. Many of these towns have no treatment systems for the industrial wastes leachates. Besides, the chemical composition of wastes in such complexes varies considerably due to the different kinds of industries. It is endeavored in the present work to study the natural potential of soil to treat leachate of such industrial wastes. For this purpose, the Aliabad industrial complex in Tehran — Garmsar road was selected as the study area. The potential of adsorption of elements such as nickel, copper, cadmium, zinc, chromium, lead and manganese was investigated. The results indicated that the soil potential to adsorb heavy metals (except for manganese) was very high (95 %) in the adsorption of heavy metals (except for manganese). Further, chemical partitioning studies revealed that heavy metals are associated with various soil phases such as loosely bonded ions, sulfide and organics to various extents. Among the mentioned soil phases, one can deduce that major portion of metal contaminants is absorbed as loosely bonded ions. Organic bond and sulfide bond are in the 2^nd and 3^rd positions of metal contaminants adsorption, respectively. The results of the present study apparently showed that soil column had ample capacity to adsorb metal contaminants. Thus, determination of soil potential in adsorption of heavy metals during site selection is as important criteria.     

Numerical modeling of strongly coupled microscale multiphase flow and solid deformation

When fluid flows in porous media under subsurface conditions, significant deformation can occur. Such deformation is dependent on structural and phase characteristics. In this paper, we investigate the effect of multiphase flow on the deformation of porous media at the pore scale by implementing a strongly coupled partitioned solver discretized with finite volume (FV) technique. Specifically, the role of capillary forces on grain deformation in porous media is investigated. The fluid and solid subdomains are meshed using unstructured independent grids. The model is applied for solving multiphase coupled equations and is capable of capturing pore scale physics during primary drainage by solving the Navier-Stokes equation and advecting fluid indicator function using volume of fluid (VOF) while the fluid is interacting with a nonlinear elastic solid matrix. The convergence of the coupled solver is accelerated by Aitken underrelaxation. We also reproduce geomechanical stress conditions, at the pore scale, by applying uniaxial stress on the solid while simultaneously solving the multiphase fluid-solid interaction problem to investigate the effect of external stress on fluid occupancy, velocity-field distribution, and relative permeability. We observe that the solid matrix exhibits elasto-capillary behavior during the drainage sequence. Relative permeability endpoints are shifted on the basis of the external stress exerted.     

Multiple-point geostatistical simulation using enriched pattern databases

Stochastic Environmental Research and Risk Assessment - This study presents a new approach of generating a set of alternative training images (ATI) to use in patch-based multiple-point simulation....     

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.     

3D multi-fractal analysis of porous media using 3D digital images: considerations for heterogeneity evaluation

Pore structure heterogeneity is a critical parameter controlling mechanical, electrical and flow transport behaviour of rock. Multi-fractal analysis method was used for a heterogeneity comparison of three-dimensional rock samples with different lithology. Six real digital samples, containing three sandstones and three carbonates, were used. Based on the mercury injection capillary pressure test on these samples, we found that the carbonate samples are more heterogeneous than sandstones, but primary results of multi-fractal behaviours for all samples were similar. We show that if multi-fractal is used to evaluate and compare heterogeneity of different samples, one needs to follow some considerations such as (1) all samples must have the same size in pixel, (2) samples volume must be bigger than representative volume element, (3) multi-fractal dimensions should be firstly normalized to a determined porosity value and (4) multi-fractal results should be interpreted based on resolution of the imaging tool (effects of fine scale sub-resolution pores are missed). Results revealed that using normalized fractal dimensions, the real samples were divided to less and high heterogeneous groups. Moreover, the study of scale effect also showed that porous structures of these samples are scale invariant in a wide range of scales (from one to eight times bigger).     

Application of a Modular Feedforward Neural Network for Grade Estimation

This article presents new neural network (NN) architecture to improve its ability for grade estimation. The main aim of this study is to use a specific NN which has a simpler architecture and consequently achieve a better solution. Most of the commonly used NNs have a fully established connection among their nodes, which necessitates a multivariable objective function to be optimized. Therefore, the more the number of variables in the objective function, the more the complexity of the NN. This leads the NN to trap in local minima. In this study, a new NN, in which the connections based on the final performance are eliminated, is used. Toward this aim, several network architectures were tested, and finally a network which yielded the minimum error was selected. This selected network has low complexity and connection among nodes which help the learning algorithm to converge rapidly and more accurately. Furthermore, this network has this ability to deal with the small number of data sets. For testing and evaluating this new method, a case study of an iron deposit was performed. Also, to compare the obtained results, some common techniques for grade estimation, e.g., geostatistics and multilayer perceptron (MLP) were used. According to the obtained results, this new NN architecture shows a better performance for grade estimation.     

Coal Cleat/Fracture Segmentation Using Convolutional Neural Networks

Natural Resources Research - Cleats/fractures play a vital role in gas extraction from coal bed methane reservoirs. As such, fractures are identified in microcomputed tomography images by...     

Multiple-point geostatistical modeling based on the cross-correlation functions

An important issue in reservoir modeling is accurate generation of complex structures. The problem is difficult because the connectivity of the flow paths must be preserved. Multiple-point geostatistics is one of the most effective methods that can model the spatial patterns of geological structures, which is based on an informative geological training image that contains the variability, connectivity, and structural properties of a reservoir. Several pixel- and pattern-based methods have been developed in the past. In particular, pattern-based algorithms have become popular due to their ability for honoring the connectivity and geological features of a reservoir. But a shortcoming of such methods is that they require a massive data base, which make them highly memory- and CPU-intensive. In this paper, we propose a novel methodology for which there is no need to construct pattern data base and small data event. A new function for the similarity of the generated pattern and the training image, based on a cross-correlation (CC) function, is proposed that can be used with both categorical and continuous training images. We combine the CC function with an overlap strategy and a new approach, adaptive recursive template splitting along a raster path, in order to develop an algorithm, which we call cross-correlation simulation (CCSIM), for generation of the realizations of a reservoir with accurate conditioning and continuity. The performance of CCSIM is tested for a variety of training images. The results, when compared with those of the previous methods, indicate significant improvement in the CPU and memory requirements.