Azadeh Agah; Faramarz Doulati Ardejani; Mohamad Javad Azinfar
Abstract
This work investigates the reactive transport of volatile hydrocarbons in the unconfined aquifer system of Tehran oil refinery and the industrial area of Ray, Tehran. A 2D finite volume model is presented to predict the soil gas contamination caused by LNAPL traveling on the phreatic surface through ...
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This work investigates the reactive transport of volatile hydrocarbons in the unconfined aquifer system of Tehran oil refinery and the industrial area of Ray, Tehran. A 2D finite volume model is presented to predict the soil gas contamination caused by LNAPL traveling on the phreatic surface through the vadose zone of the aquifer incorporating physical, chemical, and biological processes. A multi-purpose commercial software called PHOENICS is modified by incorporating extra codes to solve the model equations numerically. The model predictions closely agree with the field measurements, showing that the LNAPL migration is typically affected by the volatilization process. LNAPLs represent a potential long-term source of soil and groundwater contamination in the studied site. A comparison of the simulation results in a time step of 36 years with the results of field studies shows that the presented numerical model can simulate the reaction transfer of evaporated hydrocarbons in the unsaturated region. The concentrations have decreased in the time step of 36 years compared to the values shown in the time step of 50 years. This decrease in the hydrocarbon gas-phase concentrations in the unsaturated zone is due to excavations at the site for field studies. Through these excavations, a significant volume of the gaseous phase trapped below the earth's surface is released into the atmosphere, which reduces the accumulation of volatile gases beneath the earth's surface.
F. Doulati Ardejani; S. Maghsoudy; M. Shahhosseini; B. Jodeiri Shokri; Sh. Doulati Ardejani; F. Shafaei; F. Amirkhani Shiraz; A. Rajaee
Abstract
Considering that mining has many environmental impacts from the exploration phase to production and finally closure, it is necessary to plan the activities so that the concept of green mining is realized in its true meaning. This means that mining is carried out in order to obtain the minerals that are ...
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Considering that mining has many environmental impacts from the exploration phase to production and finally closure, it is necessary to plan the activities so that the concept of green mining is realized in its true meaning. This means that mining is carried out in order to obtain the minerals that are used in various industries; however, by taking appropriate measures, the impacts of mining on the environment are reduced to a minimum level. Since there is little information about the environmental, ecological, hydrological, and hydrogeological status in most mining areas, a comprehensive study of the area's water, soil, plants, and animal species should be conducted. The existence of permanent and seasonal rivers in the vicinity of some mines, in some cases being located in protected areas of the Iranian Department of Environment, and the presence of vegetation near some mines are among the matters that cause many environmental challenges in the mining areas. For this purpose, a series of comprehensive studies are critical in the pre-mining, during mining, and closure phases of the mine life. In addition, detailed studies should be done on factories such as smelters located in the mining areas. Life cycle assessment (LCA) is widely used in order to determine the environmental status of these factories. Furthermore, the issue of process water and water recycling, as well as waste management, should be considered. Nowadays, the environmental monitoring technology is one of the widely used tools in many mines in the world. Moreover, these mining companies' green space management system should be given special attention according to the obligatory standards of the Iranian Department of Environment. In this paper, a conceptual framework for the green mining method will be introduced for the coal mines to consider the economic and social aspects, and we pay a special attention to the health, safety, and environmental requirements.
F. Hadadi; B. Jodeiri Shokri; M. Zare Naghadehi; F. Doulati Ardejani
Abstract
In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during ...
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In this paper, we investigate a probabilistic approach in order to predict how acid mine drainage is generated within coal waste particles in NE Iran. For this, a database is built based on the previous studies that have investigated the pyrite oxidation process within the oldest abandoned pile during the last decade. According to the available data, the remaining pyrite fraction is considered as the output data, while the depth of the waste, concentration of bicarbonate, and oxygen fraction are the input parameters. Then the best probability distribution functions are determined on each one of the input parameters based on a Monte Carlo simulation. Also the best relationships between the input data and the output data are presented regarding the statistical regression analyses. Afterward, the best probability distribution functions of the input parameters are inserted into the linear statistical relationships to find the probability distribution function of the output data. The results obtained reveal that the values of the remaining pyrite fraction are between 0.764% and 1.811% at a probability level of 90%. Moreover, the sensitivity analysis carried out by applying the tornado diagram shows that the pile depth has, by far, the most critical factors affecting the pyrite remaining
B. Jodeiri Shokri; H. Dehghani; R. Shamsi; F. Doulati Ardejani
Abstract
This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate ...
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This work presents a quantitative predicting likely acid mine drainage (AMD) generation process throughout tailing particles resulting from the Sarcheshmeh copper mine in the south of Iran. Indeed, four predictive relationships for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH have been suggested by applying the gene expression programming (GEP) algorithms. For this, after gathering an appropriate database, some of the most significant parameters such as the tailing particle depths, initial remaining pyrite and chalcopyrite fractions, and concentrations of bicarbonate, nitrite, nitrate, and chloride are considered as the input data. Then 30% of the data is chosen as the training data randomly, while the validation data is included in 70% of the dataset. Subsequently, the relationships are proposed using GEP. The high values of correlation coefficients (0.92, 0.91, 0.86, and 0.89) as well as the low values of RMS errors (0.140, 0.014, 150.301, and 0.543) for the remaining pyrite fraction, remaining chalcopyrite fraction, sulfate concentration, and pH prove that these relationships can be successfully validated. The results obtained also reveal that GEP can be applied as a new-fangled method in order to predict the AMD generation process.
R. Siyar; F. Doulati Ardejani; M. Farahbakhsh; M. Yavarzadeh; S. Maghsoudy
Abstract
Copper smelting and refinery factories are the final stages of a pyrometallurgical processing chain, and they cause many environmental challenges around the world. One of the most common environmental problems of these factories is toxic emissions. These toxic gases have harmful effects on the vegetation, ...
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Copper smelting and refinery factories are the final stages of a pyrometallurgical processing chain, and they cause many environmental challenges around the world. One of the most common environmental problems of these factories is toxic emissions. These toxic gases have harmful effects on the vegetation, animal species, soils, and water resources around the factories. Phytoremediation can play an important role in the reduction of the adverse effects of environmental pollutions arising from copper smelting and refinery factories. In this paper, we first discuss different types of pollutions caused by copper metallurgical factories, and present the main research approaches and studies conducted on these factories. In the second part, we provide a summary and comparison of different remediation technologies used to reduce the environmental pollutions of these factories. Besides, the advantages and disadvantages of each method is also investigated. In the third part, we review the different aspects of the phytoremediation including the effective mechanisms, different types of plants, application environments, and the effective factors. The next part includes the selection of suitable plants for the phytoremediation process applied for copper metallurgical factories and investigation of the native and cultivated hyperaccumulator plants. In addition, different efficiency indices are introduced for evaluating the phytoremediation efficiency and selecting an appropriate hyperaccumulator plant. At the final stage, some appropriate plant species for various types of phytoremediation are introduced. The effects of different environmental stresses and the possibilities of integrating phytoremediation with other remediation technologies as well as the advantages and disadvantages of phytoremediation are eventually investigated.
A. Aryafar; R. Mikaeil; F. Doulati Ardejani; S. Shaffiee Haghshenas; A. Jafarpour
Abstract
The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant ...
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The process of pollutant adsorption from industrial wastewaters is a multivariate problem. This process is affected by many factors including the contact time (T), pH, adsorbent weight (m), and solution concentration (ppm). The main target of this work is to model and evaluate the process of pollutant adsorption from industrial wastewaters using the non-linear multivariate regression and intelligent computation techniques. In order to achieve this goal, 54 industrial wastewater samples gathered by Institute of Color Science & Technology of Iran (ICSTI) were studied. Based on the laboratory conditions, the data was divided into 4 groups (A-1, A-2, A-3, and A-4). For each group, a non-linear regression model was made. The statistical results obtained showed that two developed equations from the A-3 and A-4 groups were the best models with R2 being 0.84 and 0.74. In these models, the contact time and solution concentration were the main effective factors influencing the adsorption process. The extracted models were validated using the t-test and F-value test. The hybrid PSO-based ANN model (particle swarm optimization and artificial neural network algorithms) was constructed for modelling the pollutant adsorption process under different laboratory conditions. Based on this hybrid modeling, the performance indices were estimated. The hybrid model results showed that the best value belonged to the data group A-4 with R2 of 0.91. Both the non-linear regression and hybrid PSO-ANN models were found to be helpful tools for modeling the process of pollutant adsorption from industrial wastewaters.
H. Sabeti; A. Moradzadeh; F. Doulati Ardejani; A. Soares
Abstract
Stochastic seismic inversion is a family of inversion algorithms in which the inverse solution was carried out using geostatistical simulation. In this work, a new 3D stochastic seismic inversion was developed in the MATLAB programming software. The proposed inversion algorithm is an iterative procedure ...
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Stochastic seismic inversion is a family of inversion algorithms in which the inverse solution was carried out using geostatistical simulation. In this work, a new 3D stochastic seismic inversion was developed in the MATLAB programming software. The proposed inversion algorithm is an iterative procedure that uses the principle of cross-over genetic algorithms as the global optimization technique. The model perturbation towards the objective function is performed recurring to direct sequential simulation and co-simulation. This new algorithm was applied to a synthetic dataset with and without noise. The results obtained for the inverted impedance were satisfactory in both cases. In addition, a real dataset was chosen to be applied by the algorithm. Good results were achieved regarding the real dataset. For the purpose of validation, blind well tests were done for both the synthetic and real datasets. The results obtained showed that the algorithm was able to produce inverted impedance that fairly matched the well logs. Furthermore, an uncertainty analysis was performed for both the synthetic and real datasets. The results obtained indicate that the variance of acoustic impedance is increased in areas far from the well location.
A. Khojamli; F. Doulati Ardejani; A. Moradzadeh; A. Nejati Kalateh; A. Roshandel Kahoo; S. Porkhial
Abstract
The Ardabil geothermal area is located in the northwest of Iran, which hosts several hot springs. It is situated mostly around the Sabalan Mountain. The Sabalan geothermal area is now under investigation for the geothermal electric power generation. It is characterized by its high thermal gradient and ...
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The Ardabil geothermal area is located in the northwest of Iran, which hosts several hot springs. It is situated mostly around the Sabalan Mountain. The Sabalan geothermal area is now under investigation for the geothermal electric power generation. It is characterized by its high thermal gradient and high heat flow. In this study, our aim is to determine the fractal parameter and top and bottom depths of the magnetic sources. A modified spectral analysis technique named “de-fractal spectral depth method” is developed and used to estimate the top and bottom depths of the magnetized layer. A mathematical relationship is used between the observed power spectrum (due to fractal magnetization) and an equivalent random magnetization power spectrum. The de-fractal approach removes the effect of fractal magnetization from the observed power spectrum, and estimates the parameters of the depth to top and depth to bottom of the magnetized layer using the iterative forward modelling of the power spectrum. This approach is applied to the aeromagnetic data of the Ardebil province. The results obtained indicated variable magnetic bottom depths ranging from 10.4 km in the northwest of Sabalan to about 21.1 km in the north of the studied area. In addition, the fractal parameter was found to vary from 3.7 to 4.5 within the studied area.
S. Bahrami; F. Doulati Ardejani
Abstract
In this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. Novel hybrid method coupling artificial neural network (ANN) with genetic algorithm (GA) called ANN-GA, was utilised. Ratios of pit depth to aquifer thickness, pit bottom radius ...
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In this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. Novel hybrid method coupling artificial neural network (ANN) with genetic algorithm (GA) called ANN-GA, was utilised. Ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (HH) in the observation wells to the distance of observation wells from the centre of pit were used as inputs to the network. An ANN-GA with 4-5-3-1 arrangement was found capable to predict the groundwater inflow to mine pit. The accuracy and reliability of model was verified by field data. Predicted results were very close to the field data. The correlation coefficient (R) value was 0.998 for training set, and in testing stage it was 0.99.
