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RISS 인기검색어
- 검색키워드
- 저자 : Ebrahim Allahkarami (검색결과 3 건)
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Aghil Igder,Ali Fazlavi,Ebrahim Allahkarami,Ali Dehghanipour 한국자원공학회 2019 Geosystem engineering Vol.22 No.2
In this study, mono-dispersed carboxymethyl chitosan (CCS)-bounded Fe3O4 (OCMCS/Fe3O4) nanoparticles were used as a novel magnetic nano-adsorbent for the removal of Ni(II) and Co(II) ions from wastewater. Chitosan (CS) was first carboxymethylated and then covalently bounded on the surface of Fe3O4 nanoparticles. The micrographs of the scanning electron microscopy analysis showed that the nanoparticles were mono-dispersed and had spherical morphology with mean diameter of 33 nm. X-ray diffraction patterns indicated that the magnetic nanoparticles were pure Fe3O4 with a spinel structure, and the binding of O-CCS did not change the phase of Fe3O4. In this study, the most significant factors affecting adsorption process, i.e., pH, adsorbent dosage, contact time and concentration of Ni(II) and Co(II) ions were studied. Box–Behnken design and analysis of variance were used to determine the main effects and their interactions. The optimization study revealed that pH, adsorbent dosage and metal concentration had a significant effect on metal removal. In addition, results indicated that contact time parameter had no significant effect on the removal of Ni(II) and Co(II) ions (p-value > 0.01).
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Omid Salmani Nuri,Ebrahim Allahkarami,Mehdi Irannajad,Aliakbar Abdollahzadeh 한국자원공학회 2017 Geosystem engineering Vol.20 No.1
Artificial neural network was used to predict the copper ore flotation indices of Separation Efficiency (SE) and Selectivity Index (SI) within different operational conditions. The aim was to predict SECu and SIFe and SIMo as a function of chemical reagent dosages (collector, frother, modifier), feed rate, solid percentage, and the feed grade of Cu, Fe, and Mo. A three-layered back propagation neural network with the structure of 9-10-10-3 is selected and standard Bayesian regularization was used as a training function in which, it is unnecessary the validation data-set being apart from the training data-set. The advantage of this algorithm is the minimization of weights and linear combinations of squared errors of producing the appropriate network. In the training and testing stages, the quite satisfactory correlation coefficient of 1 for three training outputs and .93, .9, and .88 for testing outputs was achieved. The results show that the proposed approach models can be used to determine the most advantageous industrial conditions for the expected SE and SI in the froth flotation process.
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Analysis of kinetic models for chalcopyrite flotation: effect of operating parameters
Mehrshad Asghari,Omid Salmani Nuri,Ebrahim Allahkarami 한국자원공학회 2019 Geosystem engineering Vol.22 No.5
In this research, the influence of pH, particle size, the type of collector and frother and their concentration was studied on the flotation kinetic parameters, rate constant and ultimate recovery. In this regard, three different kinetic flotation models such as Classical model, Klimpel model, and fully mixed reactor model were applied to evaluate the batch flotation results. The results showed that the fully mixed reactor model gave the best fit to the experimental data. In the second stage, variation of flotation rate constants and ultimate recoveries are investigated with respect to particle size and chemical conditions. It was found that the rate constant (k) and ultimate recovery (R ∞ R∞ ) were strongly dependent on pH and types of collector and their frother and dosages. It was also observed that best values of R ∞ R∞ , K, separation efficiency, and selectivity index are obtained in the presence of a mixture of isopropyl-n-ethyl thionocarbamate (C4132) and Flomin C7240 (a mixture of 10–20 wt% sodium alkyl dithiophosphate and 20–30 wt% sodium mercaptobenzothiozole) as the collector and a mixture of polypropylene glycol (A65) and methyl isobutyl carbinol as the frother and dosages of 40 g/ton for both reagents, respectively.
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