Electrokinetic remediation of metal contaminated glacial tills

This paper presents the results of an experimental investigation which studied the feasibility of using the electrokinetic process to remediate contaminated clays of glacial origin, otherwise known as glacial tills. An overview of the electrokinetic phenomena, as well as previously performed laboratory and field investigations, is first presented. The methodology of the electrokinetic experiments which were conducted to investigate the removal of metals from a glacial till is then described. A total of 16 experiments were conducted using glacial till samples obtained from a project site near Chicago. Sodium and calcium were used as the surrogate cationic metallic contaminants. These experiments demonstrated that ion transport during the electrokinetic process occurs due to both electro-osmosis and electromigration, but that due to electromigration is significantly higher than that due to electro-osmosis. Unlike other clays such as kaolinite, the glacial till used for this investigation possessed high buffering capacity because of its high carbonate content which prevented the acid front migration from the anode to the cathode during the electrokinetic process. The ion removal efficiency of the electrokinetic process was found to increase when: (1) the voltage gradient applied to the soil was increased, (2) the initial concentration of the contaminants was increased, and (3) the duration of the treatment process was increased. The ion removal efficiency was also greater for smaller ions which possess less ionic charge and when the ions existed independently in the soil as compared to when they coexisted. This investigation suggests that the electrokinetic process has significant potential for remediating glacial tills contaminated with metals. However, the properties of Na and Ca are not representative of contaminants, such as heavy metals, so further investigations are needed.     

Geochemical characteristics of mineral air particles emitted inside a clay atomisation plant which introduces waste recycling processes

The objective of this work is to assess the concentrations of three factions of air particles (settable particles, TSP and PM10) and the levels of several toxic elements in a clay atomisation industry through aerosol sampling at several points inside an industrial plant. Mechanical activities, which produce diffuse emissions, are the main process of discharge of particles in both indoor and outdoor workplace environments in the atomisation plant. The levels of As, Cd, Pb, Zn, Ba and Ni increase in the zones with higher concentrations of particles and lower ventilation. The concentrations of As and F are not influenced by the recycling processes. The levels of Cd and Pb do not show great enrichment in air particles collected inside the atomisation plant although the content of both elements is associated with ceramic muck recycling. Finally, the content of B in waste water is mainly transferred in gaseous phase to the atmosphere during the process of drying by atomisation.     

Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction

Blasting is a widely used technique for rock fragmentation in opencast mines and tunneling projects. Ground vibration is one of the most environmental effects produced by blasting operation. Therefore, the proper prediction of blast-induced ground vibrations is essential to identify safety area of blasting. This paper presents a predictive model based on gene expression programming (GEP) for estimating ground vibration produced by blasting operations conducted in a granite quarry, Malaysia. To achieve this aim, a total number of 102 blasting operations were investigated and relevant blasting parameters were measured. Furthermore, the most influential parameters on ground vibration, i.e., burden-to-spacing ratio, hole depth, stemming, powder factor, maximum charge per delay, and the distance from the blast face were considered and utilized to construct the GEP model. In order to show the capability of GEP model in estimating ground vibration, nonlinear multiple regression (NLMR) technique was also performed using the same datasets. The results demonstrated that the proposed model is able to predict blast-induced ground vibration more accurately than other developed technique. Coefficient of determination values of 0.914 and 0.874 for training and testing datasets of GEP model, respectively show superiority of this model in predicting ground vibration, while these values were obtained as 0.829 and 0.790 for NLMR model.     

Curie Point Depth Estimations for Northwest Iran Through Spectral Analysis of Aeromagnetic Data for Geothermal Resources Exploration

Natural Resources Research - The northwest of Iran is considered as a promising geothermal zone owing to its geographical properties, tectonic features, and thermal activities, particularly in...     

COMPARISON OF MEASUREMENT METHODS BASED ON A MODEL FOR THE ERROR STRUCTURE

Comparison of methods is a technique often used for investigation of systematic errors of measurementmethods.As concerns the design and analysis of such comparisons,much variety of opinion and practiceexists.In one approach a few specimens are measured several times by different operators in differentlaboratories(reproducibility conditions)and in another approach several specimens are measured on oneor a few occasions by one operator in the same laboratory(repeatability conditions).In this paper amodel for the error structure of measurements is formulated and it is emphasized that one has todistinguish between two types of systematic errors:the first type depends only on the level of themeasured quantity and the second type is specific for the separate specimens.On the basis of this modelthe information which can be obtained from the different designs of method comparisons is discussed.A new approach for the analysis of method comparisons with many specimens is also proposed.     

Appraisal of soft computing techniques in prediction of total bed material load in tropical rivers

This paper evaluates the performance of three soft computing techniques, namely Gene-Expression Programming (GEP) (Zakaria et al 2010), Feed Forward Neural Networks (FFNN) (Ab Ghani et al 2011), and Adaptive Neuro-Fuzzy Inference System (ANFIS) in the prediction of total bed material load for three Malaysian rivers namely Kurau, Langat and Muda. The results of present study are very promising: FFNN (R ² = 0.958, RMSE = 0.0698), ANFIS (R ² = 0.648, RMSE = 6.654), and GEP (R ² = 0.97, RMSE = 0.057), which support the use of these intelligent techniques in the prediction of sediment loads in tropical rivers.     

First Research on Marine and Nonmarine Sedimentary Sequences and Micropaleontologic Significance across Permian／Triassic Boundary in Iran （Isfahan and Abadeh）

Abundant ichthyoid remains, conodonts and holothurians sclerites were recovered near the Permian/Triassic boundary from a section south of Isfahan. Recovered ichthyoid remains include shark micro teeth and scales. The ichthyolith material is similar to a Fasanian ichthyolith from the Zakazane area in the Slovak karst of the Western Carpathians, which represents a subspecies of Acodina triassia. Conodont species are mostly neogondolellids. This fauna indicates that the sedimentary environment was marine, while to the north of localities near Isfahan and Zagross, terrestrial deposition was dominant at that time. Aluminasilicate and kaolin are present in a continental unit in Dopolan refractory main (Shahid Nilchian mine) and a section south of Chahriseh Village, north of Isfahan. Pisolitie, ironstone facies and bauxite clay are common near the Permian/Triassic boundary in the Chahriseh region.     

Bedload transport of small rivers in Malaysia

Numerous time-consuming equations, based on the relationship between the reliability and representativeness of the data utilized in defining variables and constants, require complex parameters to estimate bedload transport. In this study the easily accessible data including flow discharge, water depth, water surface slope, and surface grain diameter （ds0） from small rivers in Malaysia were used to estimate bedload transport. Genetic programming （GP） and artificial neural network （ANN） models are applied as complementary tools to estimate bed load transport based on a balance between simplicity and accuracy in small rivers. The developed models demonstrate higher performance with an overall accuracy of 97% and 93% for ANN and GP, respectively compared with other traditional methods and empirical equations.     

An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals

Coal, as an initial source of energy, requires a detailed investigation in terms of ultimate analysis, proximate analysis, and its biological constituents (macerals). The rank and calorific value of each type of coal are managed by the mentioned properties. In contrast to ultimate and proximate analyses, determining the macerals in coal requires sophisticated microscopic instrumentation and expertise. This study emphasizes the estimation of the concentration of macerals of Indian coals based on a hybrid imperialism competitive algorithm (ICA)–artificial neural network (ANN). Here, ICA is utilized to adjust the weight and bias of ANNs for enhancing their performance capacity. For comparison purposes, a pre-developed ANN model is also proposed. Checking the performance prediction of the developed models is performed through several performance indices, i.e., coefficient of determination (R ²), root mean square error and variance account for. The obtained results revealed higher accuracy of the proposed hybrid ICA-ANN model in estimating macerals contents of Indian coals compared to the pre-developed ANN technique. Results of the developed ANN model based on R ² values of training datasets were obtained as 0.961, 0.955, and 0.961 for predicting vitrinite, liptinite, and inertinite, respectively, whereas these values were achieved as 0.948, 0.947, and 0.957, respectively, for testing datasets. Similarly, R ² values of 0.988, 0.983, and 0.991 for training datasets and 0.989, 0.982, and 0.985 for testing datasets were obtained from developed ICA-ANN model.