Application of response surface methodology in optimizaing the sulfation-roasting-leaching process of nickel laterite

Guo, Xueyi; Dong, Li; Wu, Zhan; Tian, Qinghua

doi:10.1007/s12613-012-0538-x

Cited by 15 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Amini and Younesi investigated the biosorption of Cd(II), Ni(II), and Pb(II) from aqueous solutions by dried biomass of Aspergillus niger using RSM. Guo et al optimized the sulfation‐roasting‐leaching process of nickel laterite by RSM. Hadavifar et al , Galehdar et al , and Bezerra et al also used RSM in their investigations as an optimizing method.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Employing Response Surface Methodology for Optimization of Mercury Bioremediation by Vibrio parahaemolyticus PG02 in Coastal Sediments of Bushehr, Iran

Jafari

Cheraghi

Mirbakhsh³

et al. 2014

CLEAN Soil Air Water

View full text Add to dashboard Cite

Three bacterial species of PG01, PG02, and PG03 were isolated from coastal sediments of Bushehr (Iran) as mercury‐resistant bacteria. According to 16S ribosomal DNA gene sequence, the strains were identified as Vibrio natriegens, Vibrio parahaemolyticus, and Oceanimonas baumannii, respectively. Minimum inhibitory concentration test in the presence of HgCl2 revealed that the strain PG02 was the most resistant one, which tolerated up to 45 mg L−1 Hg2+. Tracing the growth curves for the PG02 showed that when it is initially exposed to 5 mg L−1 Hg2+ in the medium, only 10% of its natural growth is reduced, while it simultaneously succeeds to remove 90% of mercury. However, initially 10 mg L−1 Hg2+ in the medium led to 30% growth inhibition and 80% mercury removal. According to a linear relationship between the growth inhibition percentage and the initial mercury concentrations, the median effective concentration was calculated as 21.84 mg L−1 for PG02. Central composite design under response surface methodology well proposed a model with high values of R2 = 0.999 and adjusted R2 = 0.998 which predicted the maximum mercury removal of 89.47% by PG02 under optimum pH of 7.2, temperature of 37.5°C and the initial mercury concentration of 5 mg L−1.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Amini and Younesi [24] investigated the biosorption of Cd(II), Ni(II), and Pb(II) from aqueous solutions by dried biomass of Aspergillus niger using RSM. Guo et al [25] optimized the sulfation-roasting-leaching process of nickel laterite by RSM.…”

Section: Optimization Of Mercury Bioremediation By the Selected Straimentioning

confidence: 99%

Employing Response Surface Methodology for Optimization of Mercury Bioremediation by Vibrio parahaemolyticus PG02 in Coastal Sediments of Bushehr, Iran

Jafari

Cheraghi

Mirbakhsh³

et al. 2014

CLEAN Soil Air Water

View full text Add to dashboard Cite

show abstract

“…[19,20]. A full experimental design allows all factorial effects to be estimated independently, however, the essential factors (less than 4), which have a great effect on the result, are commonly used in practice to reduce the number of experiments and simplify analysis [24]. Accordingly, in the single factor experiments conducted in our study, it is found that the material particle size less than 0.048 mm, oxygen partial pressure more than 600 kPa, and leaching time greater than 80 min will have little impact on the leaching rate of valuable metals, thus fixed particle size, oxygen partial pressure, and leaching time for the corresponding value were performed in the response surface methodology optimization experiments [14].…”

Section: Model Establishment For Selective Leachingmentioning

confidence: 99%

Recovery of the valuable metals from complex converter slag at elevated temperature with sulfuric acid solution

Liao

Shi

Huang

et al. 2019

J min metall B Metall

View full text Add to dashboard Cite

The aim of this paper was exploring the effective utilization of nickel converter slag by means of the methodology of pressure oxidative leaching. The central composite design of response surface methodology was employed to optimize controlling conditions for the leaching of more valuable metals such as nickel, cobalt, and copper, while dissolution of iron was curbed. XRD, SEM-EDS were performed for characterizing the structure of leach residues for analyzing the mechanism of selective leaching. Experimental results demonstrate that the effects of temperature and sulfuric acid concentration on the metals extraction and filtration rate are significant, followed by liquid/solid (L/S) ratio. The optimized conditions for the leaching of converter slag are: temperature 208 °C, sulfuric acid concentration 0.35 mol/L, and L/S ratio 5.4 mL/g. Under these conditions, 99.60% Co, 99.20% Ni, and 96.80% Cu were extracted into solution together with only 0.21% Fe, and the filtration rate of leach slurry reached 576.86 L•m-2 •h-1. The mechanism for achieving selective leaching of nickel, cobalt, and copper against iron dissolution and good filtration performance of the leach slurry was enabled by iron that dissolved in the solution, decomposed, and hydrolyzed mainly to form hematite (α-Fe 2 O 3 and γ-Fe 2 O 3), and letting silicic acid form precipitated SiO 2 in the leach residue.

show abstract

“…Approximate model is an efficient research method to reduce the time consumption. Many kinds of approximate model are able to achieve the approximation of bending springback process, such as artificial neural network [8][9][10][11], radial basis function [12,13], and response surface method [14,15]. The error backpropagation neural network (BPNN) is one of the widely used and successful networks at present.…”

Section: Introductionmentioning

confidence: 99%

Bending Angle Prediction Model Based on BPNN‐Spline in Air Bending Springback Process

Guo

Tang

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

In order to rapidly and accurately predict the springback bending angle in V-die air bending process, a springback bending angle prediction model on the combination of error back propagation neural network and spline function (BPNN-Spline) is presented in this study. An orthogonal experimental sample set for training BPNN-Spline is obtained by finite element simulation. Through the analysis of network structure, the BPNN-Spline black box function of bending angle prediction is established, and the advantage of BPNN-Spline is discussed in comparison with traditional BPNN. The results show a close agreement with simulated and experimental results by application examples, which means that the BPNN-Spline model in this study has higher prediction accuracy and better applicable ability. Therefore, it could be adopted in a numerical control bending machine system.

show abstract

Application of response surface methodology in optimizaing the sulfation-roasting-leaching process of nickel laterite

Cited by 15 publications

References 8 publications

Employing Response Surface Methodology for Optimization of Mercury Bioremediation by Vibrio parahaemolyticus PG02 in Coastal Sediments of Bushehr, Iran

Employing Response Surface Methodology for Optimization of Mercury Bioremediation by Vibrio parahaemolyticus PG02 in Coastal Sediments of Bushehr, Iran

Recovery of the valuable metals from complex converter slag at elevated temperature with sulfuric acid solution

Bending Angle Prediction Model Based on BPNN‐Spline in Air Bending Springback Process

Contact Info

Product

Resources

About