Comparative evaluation of microbial and chemical leaching processes for heavy metal removal from dewatered metal plating sludge

Bayat, Belgin; Sarı, Bülent

doi:10.1016/j.jhazmat.2009.09.117

Cited by 106 publications

(34 citation statements)

References 33 publications

(41 reference statements)

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“…The methodology followed in this study has been previously Sari 16197 described in details (Bayat and Sari, 2010a). Therefore, the materials and methods were concisely described herein.…”

Section: Methodsmentioning

confidence: 99%

“…The physicochemical characteristics of the dewatered sludge samples are presented in Table 1. All analyses were carried out in duplicate (Bayat and Sari, 2010a, b). The biological leaching experiments were carried out in a completely mixed batch (CMB) reactor with a volume of 3 L and dimensions of 15 cm inner diameter (ID) and 17 cm height equipped with pH and temperature controllers, a stirrer, and an aerator system (Figure 1) (Bayat and Sari, 2010a).…”

Section: Methodsmentioning

confidence: 99%

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Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

Sarı¹

2012

Afr. J. Biotechnol.

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Artifical neural networks practices were used to predict the recovery of heavy metals (Zn, Cu, Ni, Pb, Cd and Cr) from dewatered metal plating sludge (with no sulfide or sulfate compounds) using bioleaching process involving Acidithiobacillus ferrooxidans. The bioleaching process was operated as a completely mixed batch (CMB) reactor. The leaching performance data of the CMB reactor in terms of heavy metals was applied to a multi-layer perceptron (MLP) neural network technique for simulation. The performance of the reactor was evaluated with this robust model using the experimental data obtained under varying heavy metal concentrations in the sludge. Agitation time, pulp density of the sludge, and pH were used as inputs for the model, whereas the heavy metals (Cd, Cr, Cu, Ni, Pb, and Zn) concentrations were the output variables. The results of the models were compared using statistical criteria such as mean square error (MSE), mean absolute error (MAE), mean absolute relative error (MARE), and determination coefficient (R 2 ). The results show that the MLP neural network produced highly accurate estimation of the aforementioned metals with R 2 over 97.9%.

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“…The methodology followed in this study has been previously Sari 16197 described in details (Bayat and Sari, 2010a). Therefore, the materials and methods were concisely described herein.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

Sarı¹

2012

Afr. J. Biotechnol.

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“…Figure 1 presents the leaching efficiencies achieved in a comparative study. Bayat and Sari [22] proved that bioleaching was more effective than chemical leaching by Fe(III) or sulfuric acid. Escobar et al [23] also reported higher removal efficiencies by biological means than that of chemical leaching with inorganic acids (i.e., sulfuric).…”

Section: Overview Of Organisms and Mechanismsmentioning

confidence: 99%

“…Metal removal efficiencies obtained by chemical (i.e., sulfuric or ferric chloride leaching) and bioleaching using A. ferrooxidans. The results were obtained at optimum operating conditions: pH, 2; pulp density, 2% (w/v); agitation speed, 120 rpm; and, temperature, 25 ± 2 • C. Data obtained from Bayat and Sari 2010 [22]. …”

Section: Classificationmentioning

confidence: 99%

Experiences and Future Challenges of Bioleaching Research in South Korea

et al. 2016

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This article addresses the state of the art of bioleaching research published in South Korean Journals. Our research team reviewed the available articles registered in the Korean Citation Index (KCI, Korean Journal Database) addressing the relevant aspects of bioleaching. We systematically categorized the target metal sources as follows: mine tailings, electronic waste, mineral ores and metal concentrates, spent catalysts, contaminated soil, and other materials. Molecular studies were also addressed in this review. The classification provided in the present manuscript details information about microbial species, parameters of operation (e.g., temperature, particle size, pH, and process length), and target metals to compare recoveries among the bioleaching processes. The findings show an increasing interest in the technology from research institutes and mineral processing-related companies over the last decade. The current research trends demonstrate that investigations are mainly focused on determining the optimum parameters of operations for different techniques and minor applications at the industrial scale, which opens the opportunity for greater technological developments. An overview of bioleaching of each metal substrate and opportunities for future research development are also included.

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“…Bioleaching technology applies the direct effect of natural microorganisms or the indirect effect of metabolites for oxidation, reduction, complexation, adsorption, or dissolution to separate and leach insoluble components in solid phases [2,3]. Thiobacillus ferrooxidans and T. thiooxidans are the most important autotrophic microorganisms in bioleaching technology, whereas heterotrophic microorganisms are widely applied in leaching technology.…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Sewage Sludge Leached by Aspergillus Niger in Land Utilization

Xu¹,

Feng²

2016

Pol. J. Environ. Stud.

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Aspergillus niger is adopted in bioleaching to dispose of sludge from an urban wastewater treatment plant to determine the bioleaching influence of sludge on heavy metal fraction and nutritive material loss. The enrichment effect on wheat seedlings of Cu, Zn, Pb, and Cd in sludge is researched by a soil-cultivated experiment. Results indicate that the removal rates of Cu, Zn, Pb, and Cd are 74.1%, 87.6%, 82.1%, and 97.8% in the sludge after bioleaching, respectively. The remaining heavy metal in the sludge is mainly residual fraction followed by oxidizable fraction. The bioefficiency of the heavy metal decreases significantly. The loss rates of N, P, and K in the sludge are 29.8%, 13.3%, and 28.8%, respectively, with good fertility after bioleaching. The enrichment capacity order of heavy metals is Zn>Pb>Cu>Cd in wheat root and stem leaf, which is consistent with the order of unstable fraction content in the sludge after bioleaching.

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Comparative evaluation of microbial and chemical leaching processes for heavy metal removal from dewatered metal plating sludge

Cited by 106 publications

References 33 publications

Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

Modeling effluent heavy metal concentrations in a bioleaching process using an artificial neural network technique

Experiences and Future Challenges of Bioleaching Research in South Korea

Feasibility of Sewage Sludge Leached by Aspergillus Niger in Land Utilization

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