Precipitate Flotation

Pinfold, T. A.

doi:10.1016/b978-0-12-443350-2.50010-1

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…As the respective concentrations are raised, this may lead to precipitation of the ion-surfactant floatable product before air is passed. This means that it is no longer a solution, but rather a dispersion (i.e., precipitate flotation) [18]. The method of ion flotation was employed to remove metal ions from solutions containing low concentrations of heavy metal, which may be produced from any industrial process: the metal working, semiconductor, metal industries, and mine water [19,20].…”

Section: Flotation Of Metal Ionsmentioning

confidence: 99%

Flotation in Water and Wastewater Treatment

2018

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Flotation constitutes a separation process that originated from mineral processing. Nowadays, wider applications have been found and compared to flotation for water and wastewater treatment. Stress in the present review paper was mainly applied to heavy metal ions recovery by flotation and the respective mechanism followed, being either ion, precipitate, or sorptive flotation. In the latter case, the use of adsorbents is included (such as powdered activated carbon, zeolites, and goethite), as well as various biosorbents. The flotation of the following metals was reviewed: copper, zinc, nickel, lead, iron, chromium, arsenic, gold, and others. The bubble generation method could be applied for typical dispersed-air flotation column, electroflotation, or dissolved-air flotation; the latter being the most appropriate established technique in water treatment. The role of particle size (for example, studying flotation of salt-type mineral fines) was also examined.

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Section: Flotation Of Metal Ionsmentioning

confidence: 99%

Flotation in Water and Wastewater Treatment

2018

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“…Therefore, at RL/Cr ratios higher than 0.05, the carrying capacity of bubbles significantly decreases with regard to the lack of sufficient number of active, free RL molecules. Another negative consequence of excessive concentration of biosurfactant can be attributed to the potential competition between free RL anions and coligand-RL complex for a free space on the surface of air bubbles [ 63 ].…”

Section: Resultsmentioning

confidence: 99%

Advanced Simulation of Removing Chromium from a Synthetic Wastewater by Rhamnolipidic Bioflotation Using Hybrid Neural Networks with Metaheuristic Algorithms

Khoshdast¹,

Gholami

Hassanzadeh

et al. 2021

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This work aims at presenting an advanced simulation approach for a novel rhamnolipidic-based bioflotation process to remove chromium from wastewater. For this purpose, the significance of key influential operating variables including initial solution pH (2, 4, 6, 8, 10 and 12), rhamnolipid to chromium ratio (RL:Cr = 0.010, 0.025, 0.050, 0.075 and 0.100), reductant (Fe) to chromium ratio (Fe:Cr of 0.5, 1.0, 1.5, 2.0, 2.5, 3.0), and air flowrate (50, 100, 150, 200 and 250 mL/min) were investigated and evaluated using Analysis of Variance (ANOVA) method. The RL as both collector and frother was produced using a pure strain of Pseudomonas aeruginosa MA01 under specific conditions. The bioflotation tests were carried out within a bubbly regimed column cell with the dimensions of 60×5.70×0.1 cm. Four optimization techniques based on Artificial Neural Network (ANN) including Cuckoo, genetic, firefly and biogeography-based optimization algorithms were applied to 113 experiments to identify the optimum values of studied factors. The ANOVA results revealed that all four variables influence the bioflotation performance through a non-linear trend. Their influences, except for aeration rate, were found statistically significant (p-value < 0.05), and all parameters followed the normal distribution according to Anderson-Darlin (AD) criterion. Maximum chromium removal of about 98% was achieved at pH of 6, rhamnolipid to chromium ratio of 0.05, air flowrate of 150 mL/min, and Fe to Cr ratio of 1.0. Flotation kinetics study indicated that chromium bioflotation follows the first-order kinetic model with a rate of 0.023 sec−1. According to the statistical assessment of the model accuracy, the firefly algorithm (FFA) with a structure of 4-9-1 yielded the highest level of reliability with the mean squared, root mean squared, percentage errors and correlation coefficient values of test-data of 0.0038, 0.0617, 3.08% and 96.92%, respectively. These values were evidences of the consistency of the well-structured ANN method to simulate the process.

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“…The removal of metal ions by precipitate flotation (such as hydroxides, insoluble salts or complexes with surfactants), adsorbing colloids and adsorbing particulate flotation has been proposed by several authors (Pinfold, 1972;Rubio et al, 2002a,b;Rubio, 2003). The latter method is a variant of the adsorbing colloid flotation process which, instead of colloids, employs particles as carriers and adsorbent material for the metal ions.…”

Section: Introductionmentioning

confidence: 98%