M. Mahjoore; A. Aryafar; M. Honarmand
Abstract
In the present work, the cadmium oxide (CdO) nanoparticles (NPs) are synthesized using the Ferula extract. Ferula acts as a naturally-sourced reducing agent and stabilizer for the construction of the CdO NPs. The biosynthesized CdO NPs are characterized by different techniques such as X-ray powder diffraction ...
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In the present work, the cadmium oxide (CdO) nanoparticles (NPs) are synthesized using the Ferula extract. Ferula acts as a naturally-sourced reducing agent and stabilizer for the construction of the CdO NPs. The biosynthesized CdO NPs are characterized by different techniques such as X-ray powder diffraction (XRD), Fourier transform-infrared (FT-IR), spectroscopy and field emission-scanning electron microscopy (FE-SEM). After ensuring a successful synthesis of the CdO NPs, their photocatalytic activity is studied for the degradation of ciprofloxacin antibiotic in aqueous media under the sunlight. Approximately 95% degradation of ciprofloxacin using the CdO NPs is achieved after 60 minutes. The recycling experiments confirm the high stability and durability of the CdO NPs. Therefore, this work illustrates an efficient strategy for the photo-degradation of ciprofloxacin, and provides a new insight into the removal of pharmaceutical contaminants in aquatic environments.
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. Moeini; A. Aryafar
Abstract
Anomaly recognition has always been a prominent subject in preliminary geochemical explorations. Among the regional geochemical data processing, there are a range of statistical and data mining techniques as well as different mapping methods, which serve as presentations of the outputs. The outlier’s ...
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Anomaly recognition has always been a prominent subject in preliminary geochemical explorations. Among the regional geochemical data processing, there are a range of statistical and data mining techniques as well as different mapping methods, which serve as presentations of the outputs. The outlier’s values are of interest in the investigations where data are gathered under controlled conditions. These values in exploration geochemistry indicate the mineralization occurrences, and therefore, their identification is vital. Both the robust parametric (based on Mahalanobis distance) and non-parametric (based on depth functions) techniques have been developed for a multivariate outlier identification in geochemistry data. In this research work, we applied the local multivariate outlier identification approach to delineate the geochemical anomaly halos in the Hamich region, which is located in the SE of Birjand, South Khorasn province, East of Iran. For this purpose, 396 litho-geochemical samples that had been analyzed for 44 elements were used. The obtained results show a good agreement with the geological and mineral indices of Pb, Zn, and Cu in the southern part of the area. Such studies can be used by a project director to optimize the core drilling places in detailed exploration steps.
