Effect of confining stress and loading frequency on dynamic behavior of municipal solid waste in Kahrizak landfill

In order to analyze the stability of a landfill site, it should analyze some properties of waste introducing the main structural elements. Up to now, it has not been done much research on the properties of municipal solid waste. In addition, due to the differences in waste properties from one country to another and even from one to another landfill site, it is impossible to generalize the results. These conditions caused local research on the evaluation of static and dynamic parameters of municipal solid waste to be done. Because Iran is a seismic country, the short-term behavior of waste controls landfill sites stability during seismic loads; so it is necessary to know the dynamic behavior of these materials. In this paper, about 18 cyclic tests were performed, and in addition to determining the dynamic parameters of municipal solid waste of Tehran Kahrizak Disposal Site using cyclic triaxial test, the effect of confining stress and loading frequency on dynamic properties of these materials was evaluated. The results have shown that with increasing the confining stress and loading frequency, shear modulus and damping ratio was increased and decreased, respectively, and it is related to the composition of the municipal solid waste materials.     

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.     

The removal of color and COD from wastewater containing water base color by coagulation process

In this study acrylic water base color was removed from synthetic wastewater using coagulation process. Experiments were conducted on the sample containing 100 mg/L and 400 mg/L of acrylic water base color. Destruction of color by means of coagulation/flocculation techniques using ferrous sulfate, alum, lime and polyelectrolyte (cationic, anionic and non ionic). The study was performed in a systematic approach searching optimum values of alum and FeSO₄ concentration, pH and temperature. All the experiments were run in a laboratory scale.The obtained results show that treatment with alum and ferrous sulfate alone proved to be very effective in removing the color (> 99 %) and part of COD (60–70 %) from aqueous solution. Lime alone did not significant change on COD and color removal.     

Static and dynamic analysis on slope stability using a DFN-DEM approach on the right abutment of the Karun 4 dam

Uncertainty in input fracture geometric parameters during analysis of the stability of jointed rock slopes is inevitable and therefore the stochastic discrete fracture network (DFN) — distinct element method (DEM) is an efficient modeling tool. In this research, potentially unstable conditions are detected in the right abutment of the Karun 4 dam and downstream of the dam body as a case study. Two extreme states with small and relatively large block sizes are selected and a series of numerical DEM models are generated using a number of validated DFN models. Stability of the rock slope is assessed in both static and dynamic loading states. Based on the design basis earthquake (DBE) and maximum credible earthquake (MCE) expected in the dam site, histories of seismic waves are applied to analyze the stability of the slope in dynamic earthquake conditions. The results indicate that a MCE is likely to trigger sliding of rock blocks on the rock slope major joint. Furthermore, the dynamic analysis also shows a local block failure by the DBE, which can consequently lead to slope instability over the long term. According to the seismic behavior of the two models, larger blocks are prone to greater instability and are less safe against earthquakes.

     

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).     

Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran

Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.

     

Landfill site selection using combination of fuzzy logic and multi-attribute decision-making approach

During the last decades, growth of urbanization and industrialization led to an increase in solid waste generation. Landfilling is the most prevalent ultimate disposal method for the municipal solid wastes in developing countries. The rapid municipal solid waste generation in Markazi province (central part of Iran) causes the need for precision in finding a suitable landfill site selection. In the present study, 12 factors (environmental and socioeconomic factors) have been applied to select the landfill site in Markazi province, Iran. The different methods including the analytic network process (ANP) combined with fuzzy linguistic quantifier, ordered weighted average (OWA), and weighted linear combination (WLC) approach in geographic information system was applied to find an appropriate landfill site. The OWA operator function permits the evaluation of the wide spectrum of consequences (with different scenario) obtained from different management strategies. Results revealed that integration of fuzzy logic, ANP, and OWA provides flexible and better ideas compared to the Boolean logic and WLC to select a suitable landfill site.     

Biosorption of Heavy Metals in Leachate Derived from Gold Mine Tailings Using Aspergillus fumigatus

Leachate derived from bioleaching process contains high amount of metals that must be removed before discharging the water. Aspergillus fumigatus was isolated from a gold mine tailings and its ability to remove of As, Fe, Mn, Pb, and Zn from aqueous solutions and leachate of bioleaching processes was assessed. Batch sorption experiments were carried out to characterize the capability of fungal biomass (FB) and iron coated fungal biomass (ICFB) to remove metal ions in single and multi‐solute systems. The maximum sorption capacity of FB for As(III), As(V), Fe, Mn, Pb, and Zn were 11.2, 8.57, 94.33, 53.47, 43.66, and 70.4 mg/g, respectively, at pH 6. For ICFB, these values were 88.5, 81.3, 98.03, 66.2, 50.25, and 74.07 mg/g. Results showed that only ICFB was found to be more effective in removing metal ions from the leachate. The amount of adsorbed metals from the leachate was 2.88, 21.20, 1.91, 0.1, and 0.08 mg/g for As, Fe, Mn, Zn, and Pb, respectively. The FT‐IR analysis showed involvement of the functional groups of the FB in the metal ions sorption. Scanning electron microscopy revealed that surface morphological changed following metal ions adsorption. The study showed that the indigenous fungus A. fumigatus was able to remove As, Fe, Mn, Pb, and Zn from the leachate of gold mine tailings and therefore the potential for removing metal ions from metal‐bearing leachate.     

Tachyon reconstruction of ghost dark energy

Recently it has been argued that a possible source for the dark energy may arise due to the contribution to the vacuum energy of the QCD ghost in a time-dependent background. In this paper we establish a connection between interacting ghost dark energy and tachyon field. It is demonstrated that the evolution of the ghost dark energy dominated universe can be described completely by a single tachyon scalar field. The potential and the dynamics of the tachyon field are reconstructed according to the evolutionary behavior of ghost energy density.