Removal of airborne hexavalent chromium mist using chitosan gel beads as a new control approach

Airborne hexavalent chromium is a known human respiratory carcinogen and allergen. Many workers are exposed to hexavalent chromium in various processes which chromium electroplating plants are the most common. In this study, the feasibility of a new control approach to remove this pollutant using chitosan beads as a biosorbent was investigated. Hexavalent chromium sorption was studied relative to pH, pollution concentration, sorbent concentration, temperature, and air velocity using one factor at a time approach and Taguchi experimental design. Polluted air with different chromium mist concentrations (10–5000 μg/m³) was contacted to chitosan beads (3.3–20 g/L), floating in distilled water with adjusted pH (3–7), using an impinger at different temperatures (20 and 35 °C), and various velocities (1.12 and 2.24 m/s). The ANOVA test result showed that, there were statistical significant differences between factor levels except optimized pH levels. The higher ions removal efficiencies were achieved at lower levels of air velocities, pollution concentrations, and higher levels of solution pH values, temperatures, and sorbent concentrations.     

Modeling the cyclic swelling pressure of mudrock using artificial neural networks

The stochastic nature of the cyclic swelling behavior of mudrock and its dependence on a large number of interdependent parameters was modeled using Time Delay Neural Networks (TDNNs). This method has facilitated predicting cyclic swelling pressure with an acceptable level of accuracy where developing a general mathematical model is almost impossible. A number of total pressure cells between shotcrete and concrete walls of the powerhouse cavern at Masjed–Soleiman Hydroelectric Powerhouse Project, South of Iran, where mudrock outcrops, confirmed a cyclic swelling pressure on the lining since 1999. In several locations, small cracks are generated which has raised doubts about long term stability of the powerhouse structure. This necessitated a study for predicting future swelling pressure. Considering the complexity of the interdependent parameters in this problem, TDNNs proved to be a powerful tool. The results of this modeling are presented in this paper.     

Water availability for the greening of Kuwait

Water resources for agricultural development and landscape enhancement in Kuwait are very scarce; the quality is saline (brackish), and the soil texture is sandy, incapable of holding nutrients and moisture. Most of the rainfall in Kuwait either evaporates due to the high temperatures or percolates through the soil. The rainfall is not sufficient to recharge the underground water supply. A variety of alternative water sources have been studied, including seawater desalination and wastewater treatment. Wastewater treatment proved to be convenient due to the relatively low salinity detected in the treated water. Furthermore, the amount of wastewater is expected to increase as more residential areas are connected to the sewage system as the population increases.

The development of a greenery plan for Kuwait requires intensive water management. This goal can be achieved through manpower training in landscape development, selection of plants tolerant to arid environments, usage of appropriate irrigation and drainage systems, promotion of greenery programs within public and the governmental agencies for their direct participation in urban area beautification, and extension of wastewater treatment techniques.     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

Ranking of productivity improvement strategies in Iran mineral sector based on integrated SWOT-FAHP-FTOPSIS analysis

The Iran’s mineral sector, as a major supplier of mineral industries, plays a central role on Iran economy. Promoting the productivity in this area leads to the enhancement of the business environment. Also, it can influence the production chain and minerals value added. In this study, a comprehensive study was conducted on the major barriers/drivers factors affecting the efficiency in the mining sector. Furthermore, 16 key factors were identified using the expert views. These parameters are divided into four main categories: strengths, weaknesses, opportunities, and threats. In addition, a hierarchy model was designed in which the mentioned four efficient strategic parameters were implemented and the SWOT analysis was applied, as well as the nine macro strategies were determined. In order to prioritize these strategies, multiple steps were carried out. First, the subfactors were weighted by implementing the fuzzy multi attribute decision-making technique by combining the experts’ views and triangular fuzzy numbers. Subsequently, the strategies were prioritized by employing the group Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The sensitive analysis of the results presents some minor changes in score of the alternatives, but no alteration was affected in the prioritized strategies. The results revealed the importance of resource allocation strategies related to the existing investments as well as supporting and facilitating the new technology in the mining sector.     

In situ LA-ICPMS Isotopic and Geochronological Studies on Carbonatites and Phoscorites from the Guli Massif,Maymecha-Kotuy,Polar Siberia

In this study we present a fresh isotopic data, as well as U–Pb ages from different REE-minerals in carbonatites and phoscorites of Guli massif using in situ LA-ICPMS technique. The analyses were conducted on apatites and perovskites from calcio-carbonatite and phoscorite units, as well as on pyrochlores and baddeleyites from the carbonatites. The ⁸⁷Sr/⁸⁶Sr ratios obtained from apatites and perovskites from the phoscorites are 0.70308–0.70314 and 0.70306–0.70313, respectively; and 0.70310–0.70325 and 0.70314–0.70327, for the pyrochlores and apatites from the carbonatites, respectively.Furthermore, the in situ laser ablation analyses of apatites and perovskites from the phoscorite yield εNd from 3.6 (±1) to 5.1 (±0.5) and from 3.8 (±0.5) to 4.9 (±0.5), respectively; εNd of apatites, perovskites and pyrochlores from carbonatite ranges from 3.2 (±0.7) to 4.9 (±0.9), 3.9 (±0.6) to 4.5 (±0.8) and 3.2 (±0.4) to 4.4 (±0.8), respectively. Laser ablation analyses of baddeleyites yielded an eHf(t)d of +8.5 (± 0.18); prior to this study Hf isotopic characteristic of Guli massif was not known. Our new in situ εNd, ⁸⁷Sr/⁸⁶Sr and eHf data on minerals in the Guli carbonatites imply a depleted source with a long time integrated high Lu/Hf, Sm/Nd, Sr/Rb ratios.In situ U–Pb age determination was performed on perovskites from the carbonatites and phoscorites and also on pyrochlores and baddeleyites from carbonatites. The co-existing pyrochlores, perovskites and baddeleyites in carbonatites yielded ages of 252.3 ± 1.9, 252.5 ± 1.5 and 250.8 ± 1.4 Ma, respectively. The perovskites from the phoscorites yielded an age of 253.8 ± 1.9 Ma. The obtained age for Guli carbonatites and phoscorites lies within the range of ages previously reported for the Siberian Flood Basalts and suggest essentially synchronous emplacement with the Permian-Triassic boundary.     

Prediction of Enhanced Soil–Anchored Geogrid Interactions in Direct Shear Mode Using Gene Expression Programming

Design of reinforced soil structures is greatly influenced by soil–geosynthetic interactions at interface which is normally assessed by costly and time consuming laboratory tests. In present research, using the results of large-scale direct shear tests conducted on soil–anchored geogrid samples a model for predicting Enhanced Interaction Coefficient (EIC) is proposed enabling researchers/engineers easily, quickly and at no cost to estimate soil–geosynthetic interactions. In this regard well and poorly graded sands, anchors of three different size and anchorage lengths from the shear surface together with normal pressures of 12.5, 25 and 50 kPa were used. Artificial Intelligence (AI) called the Gene Expression Programming (GEP) was adopted to develop the model. Input variables included coefficients of curvature and uniformity, normal pressure, effective grain size, anchor base and surface area, anchorage length and the output variable was EIC. Contributions of input variables were evaluated using sensitivity analysis. Excellent correlation between the GEP-based model and the experimental results were achieved showing that the proposed model is well capable of effectively estimating soil–anchored geogrid enhanced interaction coefficient. Sensitivity analysis for parameter importance shows that the most influential variables are normal pressure (σ_n) and anchorage length (L) and the least effective parameters are average particle size (D₅₀) and anchor base area (A_b).