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This study was designed to compare one-step, two-step and spent medium bioleaching of spent catalyst by adapted Aspergillus niger in batch cultures. Aspergillus niger, which was adapted to heavy metal ions, Ni, Mo, Fe, and W, was grown in medium containing up to 5% (w/v) of spent catalyst. The main lixiviant in bioleaching was gluconic acid, which was produced at all pulp densities in the one-step bioleaching process. Gluconic acid was also produced in the two-step bioleaching process when the spent catalyst was present at pulp densities greater than 1% (w/v). In the spent medium leaching, however, the primary agent was citric acid. The pulp density of the spent catalyst was varied, and this... 

This study was designed to compare one-step, two-step and spent medium bioleaching of spent catalyst by adapted Penecillum simplicissimum in batch cultures. Penecillum simplicissimum which was adapted to heavy metal ions Ni, Mo, Fe, and W grew in the presence of up to 5 %w/v of spent catalyst in the medium. The main lixiviant in bioleaching was gluconic acid which was produced mainly in present and absence of spent catalyst. A total of 3 %w/v spent catalyst generally gave maximum extraction yields in two-step bioleaching process, which the amounts of leached metals were 100 % of W, 100 % of Fe, 92.7 % of Mo, 66.43 % of Ni, and 25 % of Al. The red pigment produced by fungus could also... 

The kinetics of bioleaching of Mo, Ni, and Al from spent hydrocracking catalyst, using Aspergillus niger was studied. The four most effective bioleaching variables were selected in accordance with the Plackett-Burman design and were further optimized via central composite design (CCD). The optimal values of the variables for maximum multi-metal bioleaching were as follows: particle size 150-212. μm, sucrose 93.8. g/L, pulp density 3%. w/v, and pH 7. The maximum metal recoveries corresponding to these conditions were 99.5 ± 0.4% Mo, 45.8 ± 1.2% Ni, and 13.9 ± 0.1% Al. The relatively low Ni extraction was attributed to the precipitation of Ni in the presence of oxalic acid. Under the optimal... 

In this investigation, bioleaching of vanadium from uranium ore residue of the leaching stage was studied by Aspergillus niger in a two-step process at 30 °C and 150 rpm. The first step was initiated by growth of fungi in the absence of mine waste. Using response surface methodology, three factors were surveyed for fungal growth: initial pH, sucrose concentration and spore population. Also concentrations of oxalic, citric, and gluconic acids were measured as response in this step. During 30 days, maximum productions of these acids were 3265, 11578, and 7988 mg/l, respectively. Initial pH and sucrose concentration were significant factors for oxalic and citric acid production; however, for... 

Adaptation of Penicillium simplicissimum with different heavy metals present in a spent hydrocracking catalyst, as well as one-step, two-step, and spent medium bioleaching of the spent catalyst by the adapted fungus, was examined in batch cultures. Adaptation experiments with the single metal ions Ni, Mo, Fe, and W showed that the fungus could tolerate up to 1500. mg/L Ni, 8000. mg/L Mo, 3000. mg/L Fe, and 8000. mg/L W. In the presence of multi-metals, the fungus was able to tolerate up to 300. mg/L Ni, 200. mg/L Mo, 150. mg/L Fe and 2500. mg/L W. A total of 3% (w/v) spent catalyst generally gave the maximum extraction yields in the two-step bioleaching process (100% of W, 100% of Fe, 92.7%... 

A quantitative structure-mobility relationship was developed to accurately predict the electrophoretic mobility of organic acids. The absolute electrophoretic mobilities (μ0) of a diverse dataset consisting of 115 carboxylic and sulfonic acids were investigated. A set of 1195 zero- to three-dimensional descriptors representing various structural characteristics was calculated for each molecule in the dataset. Classification and regression trees were successfully used as a descriptor selection method. Four descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system. The root mean square errors for the calibration and prediction sets are 1.61 and 2.27, respectively,...