Bioinorganic Chemistry and Applications

Bioinorganic Chemistry and Applications / 2018 / Article

Research Article | Open Access

Volume 2018 |Article ID 1067512 | https://doi.org/10.1155/2018/1067512

P. Sivakumar, D. Prabhakaran, M. Thirumarimurugan, "Optimization Studies on Recovery of Metals from Printed Circuit Board Waste", Bioinorganic Chemistry and Applications, vol. 2018, Article ID 1067512, 10 pages, 2018. https://doi.org/10.1155/2018/1067512

Optimization Studies on Recovery of Metals from Printed Circuit Board Waste

Academic Editor: Albrecht Messerschmidt
Received20 Jun 2018
Accepted18 Sep 2018
Published01 Nov 2018

Abstract

The aim of the study was to recover copper and lead metal from waste printed circuit boards (PCBs). The electrowinning method is found to be an effective recycling process to recover copper and lead metal from printed circuit board wastes. In order to simplify the process with affordable equipment, a simple ammonical leaching operation method was adopted. The selected PCBs were incinerated into fine ash powder at 500°C for 1 hour in the pyrolysis reactor. Then, the fine ash powder was subjected to acid-leaching process to recover the metals with varying conditions like acid-base concentration, electrode combination, and leaching time. The relative electrolysis solution of 0.1 M lead nitrate for lead and 0.1 M copper sulphate for copper was used to extract metals from PCBs at room temperature. The amount of lead and copper extracted from the process was determined by an atomic absorption spectrophotometer, and results found were 73.29% and 82.17%, respectively. Further, the optimum conditions for the recovery of metals were determined by using RSM software. The results showed that the percentage of lead and copper recovery were 78.25% and 89.1% should be 4 hrs 10 A/dm2.

1. Introduction

Recycling of e-waste is an important subject not only from the point of waste treatment but also from the recovery aspect of valuable materials [14]. Among the resources in e-waste, metals contribute more than 95% of the materials market value. Hence, the recovery of valuable metals is the inherent motive in e-waste disposal. In the past decades, many techniques for recovering valuable metals from e-waste have been developed such as gravity separation, magnetic separation, and electrostatic separation [5] synthesis of CuCl with e-waste, separation of PCBs with organic solvent method [6, 7], cyanide and noncyanide lixiviants leaching methods, ammonium persulfate leaching bioleaching methods [810], or a combination of these approaches. Among those methods, hydrometallurgical methods are more accurate, predictable, and controllable [11]. Therefore, hydrometallurgical techniques are most active in the research of valuable metal recovery from electronic scraps in the past two decades. However, traditional hydrometallurgical methods are acid dependent, time-consuming, and inefficient for simultaneous recovery of precious metals. Remarkably, a large amount of corrosive or toxic reagents, such as aqua regia, nitric acid, cyanide and halide, are consumed, producing large quantities of toxic and corrosive fumes or solution [12, 13]. Therefore, it is necessary to seek a more environmental friendly method for the recovery of valuable metals from e-wastes. Hydrometallurgical methods are used in the upgrading and refining stages of the recycling chain [1416]. In this research article, the recovery of lead and copper metals from e-waste is widely investigated. The PCBs were converted into fine ash powder and subjected to electrowinning process for the recovery of metals. The experimental results were determined by EDS and AAS, respectively. Furthermore, the experimental results are validated through RSM software at different parameters like acid-base concentration, electrode combination, and leaching time [1722].

2. Materials and Methods

2.1. Materials

The computer PCBs were collected from various sources for the recovery of metals. The collected PCBs were crushed using roll crusher and powdered by a hammer mill. The crushed PCBs were incarnated through pyrolysis to avoid side reaction in the leaching process with the electrolyte solution. The optimum condition of the pyrolysis reactor was 500°C in atmospheric pressure for 1 h where the epoxy resins and polymers were volatized at the temperature less than 500°C. The volatized contents were condensed and collected separately. The ferrous materials present in the obtained ash were separated by a magnetic separator.

2.2. Electrowinning Process

The fine ash powder was treated with aqua regia solution (3 : 1 ratio of HCl and HNO3) in the incineration chamber in order to avoid the liberation of toxic fumes. Then the precipitated salts obtained from the leaching was analyzed by EDS to determine the composition of metal present in the salts (Figure 1). The electrowinning setup consists of bath arrangement and amplifier. The bath having two slots for the anode and cathode fixing and the electrode is connected with amplifier, and the current density was varied through the amplifier (Figure 2).

2.3. Extraction Process of Lead

About 25 g of incinerated fine ash was added into the acid bath followed by the addition of ammonical electrolyte solution. The current density was set to 1 to 10 (A/dm2). The solution was agitated at regular interval to get an effective electrodeposition:

After the stipulated time of operation, pure lead was deposited on lead cathode. The deposited elements were scrapped and stored in an air tight container. The recovered lead quantitated from the EDS method. The spent acid left with mud filtered at pH 6–10 was stored in a glass container for further treatment.

2.4. Extraction Process of Copper

About 25 g of incinerated fine ash was added into the acid bath followed by the addition of ammonical electrolyte solution. The current density was set to 1 to 10 (A/dm2). The solution was agitated at regular intervals to get an effective electrodeposition. After the stipulated time of operation, pure copper (cupric) was deposited on the cathode and impure copper (cuprous ion) were deposited on the anode. The deposited elements were scrapped and stored in an air tight container. The recovered copper quantitated from the EDS method. The spent acid left with mud (nonleached elements) was filtered (pH–8.4) and were stored in a glass container for further treatment (Figure 3):

The spent solution collected from the electrodeposition was neutralized to 6.9 for the safe disposal as per the standard. Moreover, the presence of any metal in the spent solution was analyzed by Fourier-transform infrared spectroscopy. The results (Figure 4) show that the metallic traces were found to be absent which confirms that all the metals recovered from the ashes deposited on the electrode.

3. Results and Discussion

3.1. RSM for Lead

The response surface methodology (RSM) is a statistical modeling technique employed for multiple regression analysis using quantitative data obtained from designed experiments to solve multivariable equations (Table 1). The response surfaces can be visualized as three-dimensional plots that exhibit the response as a function of two factors while keeping the other factors constant. In this above plot, the red zone corresponds to the extract percentage above 85%, yellow zone shows 60 to 70%, and the blue zone confirms below 40% extraction of lead (Figures 5 and 5(a)). The regression equation for the RSM data plots for the lead is


StdRunFactor 1Factor 2Factor 3
A: CDB: solventC: time
A/dm2mlHrs

1814002.5
29194002.5
31717002.5
412197002.5
5115501
614195501
7615504
85195504
94104001
1010107001
117104004
1211107004
132105502.5
1416105502.5
1513105502.5
1615105502.5
173105502.5

The model as a function of coded factor could be utilized to predict the response of each parameter within the given limit. Here, the maximum limit of process parameters (factors) is termed (coded) as +1 and minimum limit is terms (coded) as −1. The modifed equation or coded equation is very much useful in order to find the comparative effect of the process parameters by relating the coefficient of factors. The final equation in terms of actual factors is

Equation (4) in terms of process parameters could be utilized to predict the response for the provided levels of each parameter (Table 2). In this equation, the original units of each parameters should be considered for each levels. In order to evaluate the comparative effect of each factor, the above equation should not be considered since the coefficients are balanced to embrace the units of each parameters. Also, the intercept does not fall at design space center.


StdRunFactor 1Factor 2Factor 3
A: CDB: solventC: time
A/dm2mlHrs

1814002.5
29194002.5
31717002.5
412197002.5
5115501
614195501
7615504
85195504
94104001
1010107001
117104004
1211107004
132105502.5
1416105502.5
1513105502.5
1615105502.5
173105502.5

3.2. Analysis of Variance (ANOVA)

Analysis of variance is used to determine the significant effects of process variables on current efficiency (Table 3) along with the factor coding. The sum of squares is found to be Type III—partial derived from the ANOVA quadratic model. The model value of 4.43 implies the model is significant. A minimum value of 3.12% is possible for the value due to noise. values less than 0.0500 indicate model terms are significant. In this case A, A2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve the model. The lack of fit value of 63.27 implies the lack of fit is significant. There is only a 0.08% chance that a lack of fit value could be large that could occur due to noise. The coefficient represents the expected change in response per unit change in the factor value, when all remaining factors were constant. The intercept in an orthogonal design is the overall average response of all the runs. The coefficients are adjustments around the average factor settings. When the factors are orthogonal, the variance inflation factors (VIFs) are 1; VIFs greater than 1 indicate multicolinearity; the higher the VIF, the more severe the correlation of factors. As a rough rule, VIFs less than 10 are tolerable. Hence, from the data obtained (Table 4), the VIF values of lead are found to be tolerable.


SourceSum of squaresDOFMean square value value

Model3152.459350.274.430.0312Significant
A-CD650.521650.528.230.0240
B-solvent434.981434.985.510.0514
C-time330.121330.124.180.0802
AB6.1016.100.07720.7891
AC0.115610.11560.00150.9706
BC204.631204.632.590.1516
A21459.6111459.6118.470.0036
B211.76111.760.14890.7111
C213.89113.890.17580.6876
Residual553.05779.01
Lack of fit541.643180.5563.270.0008Significant
Pure error11.4142.85
Total3705.5016


FactorCoefficient estimateDOFStandard error95% CI low95% CI highVIF

Intercept66.3613.9856.9675.76
A-CD9.0213.141.5916.451.0000
B-solvent7.3713.14−0.057314.801.0000
C-time6.4213.14−1.0113.851.0000
AB1.2414.44−9.2711.741.0000
AC0.170014.44−10.3410.681.0000
BC−7.1514.44−17.663.361.0000
A2−18.6214.33−28.86−8.381.01
B2−1.6714.33−11.918.571.01
C2−1.8214.33−12.068.431.01

3.3. Model Terms

For a standard deviation of 1, the power calculations are performed using response type “continuous,” and parameters are Δ = 2 and σ = 1. The power is evaluated over −1 to +1 coded factor space. From (Table 5), the standard errors should be similar to each other in a balanced design. The ideal VIF value should be 1, VIFs above 10 are cause for concern, and VIFs above 100 are cause for alarm, indicating coefficients are poorly estimated due to multicolinearity, where ideal Ri2 is 0.0. High Ri2 means terms are correlated with each other, possibly leading to poor models. If the design has multilinear constraints, then multicolinearity will exist to a greater degree. This inflates the VIFs and the Ri2, rendering these statistics would not perform well. Hence, FDS could be used. Power is an inappropriate tool to evaluate response surface designs. Use prediction-based metrics provided in this program via fraction of design space (FDS) statistics.


TermStandard errorVIFRi2Power (%)

A0.353610.000068.1
B0.353610.000068.1
C0.353610.000068.1
AB0.500010.000040.8
AC0.500010.000040.8
BC0.500010.000040.8
A20.48731.005880.005893.8
B20.48731.005880.005893.8
C20.48731.005880.005893.8

3.4. Fit Statistics

A negative predicted R2 implies that the overall mean may be a better predictor of the response than the current model. In some cases, a higher order model may also predict better. Adeq. precision measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 5.915 indicates an adequate signal. This model can be used to navigate the design space. The optimization of current efficiency is shown in Figure 6. From the results, it is observed that 69% of lead extract is obtained at current density = 10 A dm−2, solvent ratio = 5 : 2, and the electrolysis time = 4 hours (Figures 7 and 8). The significance of regression coefficients were analyzed using the p-test and t-test. The values are used to check the effect of interaction among the variables. A larger magnitude of t-value and a smaller magnitude of value are significant in the corresponding coefficient term. The coefficient of current efficiency and the corresponding t and values are shown in Table 6. Finally, the coefficients in the interaction terms for current density-electrolysis time is significant compared to current density-solvent ratio, and current density-electrolysis time.


Std. dev.8.89
Mean55.96
CV (%)15.88
R20.8507
Adjusted R20.6589
Predicted R2−1.3436
Adeq. precision5.9146

3.5. RSM for Copper

The regression equation for the RSM data plots for the copper is in terms of coded factors form as follows:

The model (Equation 5) as a function of coded factor could be utilized to predict the response of each parameter within the given limit. Here, the maximum limit of process parameters (factors) is termed(coded) as +1 and minimum limit is termed (coded) as −1. The modified equation or coded equation is very much useful in order to find the comparative effect of the process parameters by relating the coefficient of factors (Table 7).


StdRunFactor 1Factor 2Factor 3
A: CDB: solventC: time
A/dm2mlHrs

11214002
214194002
3216002
44196002
51815001
613195001
71615003
811195003
98104001
101106001
116104003
127106003
135105002
149105002
1517105002
163105002
1715105002

The final equation in terms of actual factors is

Equation (5) in terms of process parameters could be utilized to predict the response for the provided levels of each parameter. In this equation, the original units of each parameters should be considered for each levels. In order to evaluate the comparative effect of each factor, the above equation should not be considered since the coefficients are balanced to embrace the units of each parameters. Also, the intercept does not falls at design space center (Table 8). In this contour plot, the red zone indicates extract percentages above 85%. And yellow and blue zones indicate 60 to 70% and below 40% extraction of copper (Figures 9 and 9(a)).


StdRunFactor 1Factor 2Factor 3
A: CDB: solventC: time
A/dm2mlHrs

11214002
214194002
3216002
44196002
51815001
613195001
71615003
811195003
98104001
101106001
116104003
127106003
135105002
149105002
1517105002
163105002
1715105002
1810105002

3.6. Analysis of Variance (ANOVA)

Analysis of variance is used to determine the significant effects of process variables on current efficiency along with the factor coding. The sum of squares is found to be Type III—partial derived from the ANOVA quadratic model. The model value of 155.08 in the Table 9 implies the model is significant. A minimum value of 0.01% is possible for the F value due to noise. P values less than 0.0500 indicate model terms are significant. In this case, A, B, C, AC, A2, and B2 are significant model terms. Values greater than 0.1000 indicate the model terms are not significant. If there are many insignificant model terms (not counting those required to support hierarchy), model reduction may improve the model. The lack of fit F value is nil that implies the lack of fit is significant. The coefficient represents the expected change in response per unit change in factor value, when all remaining factors were constant. The intercept in an orthogonal design is the overall average response of all the runs. The coefficients are adjustments around the average factor settings. When the factors are orthogonal, the VIFs are 1; VIFs greater than 1 indicate multicolinearity; the higher the VIF, the more severe the correlation of factors. As a rough rule, VIFs less than 10 are tolerable. Hence, from the data obtained (Table 10), the VIF Values of lead are found to be tolerable.


SourceSum of squaresDOFMean square value value

Model7763.509862.61155.08<0.0001Significant
A-CD4608.0014608.00828.40<0.0001
B-solvent544.501544.5097.89<0.0001
C-time1922.0011922.00345.53<0.0001
AB1.000011.00000.17980.6827
AC225.001225.0040.450.0002
BC25.00125.004.490.0668
A2184.361184.3633.140.0004
B2279.271279.2750.210.0001
C29.8219.821.770.2206
Residual44.5085.56
Lack of fit44.50314.83
Pure error0.000050.0000
Total7808.0017


FactorCoefficient estimateDOFStandard error95% CI low95% CI highVIF

Intercept48.0010.962945.7850.22
A-CD24.0010.833922.0825.921.0000
B-solvent8.2510.83396.3310.171.0000
C-time15.5010.833913.5817.421.0000
AB0.500011.18−2.223.221.0000
AC7.5011.184.7810.221.0000
BC2.5011.18−0.21935.221.0000
A2−6.5011.13−9.10−3.901.02
B28.0011.135.4010.601.02
C21.5011.13−1.104.101.02

3.7. Model Terms

For a standard deviation of 1 the power calculations are performed using response type “continuous,” and the parameters are Δ = 2 and σ = 1. The power is evaluated over −1 to +1 coded factor space (Table 11). The standard errors should be similar to each other in a balanced design. The ideal VIF value should be 1, VIFs above 10 are cause for concern and VIFs above 100 are cause for alarm, indicating coefficients are poorly estimated due to multicolinearity, where ideal Ri2 is 0.0. High Ri2 means terms are correlated with each other, possibly leading to poor models. If the design has multilinear constraints, then multicolinearity will exist to a greater degree. This inflates the VIFs and the Ri2, rendering these statistics would not perform well. Hence, FDS could be used. Power is an inappropriate tool to evaluate response surface designs. Use prediction-based metrics provided in this program via fraction of design space (FDS) statistics.


TermStandard errorVIFRi2Power (%)

A0.353610.000069.8
B0.353610.000069.8
C0.353610.000069.8
AB0.500010.000042.1
AC0.500010.000042.1
BC0.500010.000042.1
A20.47871.018520.018295.4
B20.47871.018520.018295.4
C20.47871.018520.018295.4

3.8. Fit Statistics

A predicted R2 implies that the overall mean may be a better predictor of the response than the current model. In some cases, a higher order model may also predict better. Adeq. precision measures the signal to noise ratio. A ratio greater than 4 is desirable. A ratio of 44.9 indicates an adequate signal. This model can be used to navigate the design space. The optimization of current efficiency is shown in Figure 10. The optimum extraction of 69% Cu is obtained at current density = 19 A dm−2, solvent ratio = 5 : 2, and electrolysis time = 4 hour (Figures 11 and 12). The significance of regression coefficients was analyzed using the p-test and t-test. The values are used to check the effect of interaction among the variables. A larger magnitude of t-value and a smaller magnitude of value are significant in the corresponding coefficient term. The coefficient of current efficiency and the corresponding t and p values are shown in (Table 12). Finally, the coefficients in the interaction terms for current density-electrolysis time is significant compared to current density-solvent ratio and current density-electrolysis time.


Std. dev.2.36
Mean49.33
CV (%)4.78
R20.9943
Adjusted R20.9879
Predicted R20.9088
Adeq. Precision44.9395

4. Conclusion

The ammonia-lead nitrate and ammonia-copper sulphate system have been employed as a leaching agent for recovery of lead and copper from scraped printed circuit board wastes. A two-stage leaching was employed, wherein the first stage consisted of leaching the scrap board with 0.1 M Pb(NO3)2 and 0.1 M CuSO4 which results in the selective dissolution of lead and copper leaching rate, and other metals was found in lower amounts, respectively. The undissolved residue portion from the leaching stage containing nickel, tin, and silica were leached out in respective treatments. The current efficiency was found to increase with current density and concentration ratio with the contact time in acid bath. Hence, 73.29% lead and 82.17% copper have been successfully recovered from the electrolysis process. And, also by RSM Software prediction, the recovery of lead and copper are as 78.25% and 89.1%, respectively. In addition to the quadratic model equation, ANOVA, model terms, and fit statistics were also tested for the experimental conditions.

Data Availability

All the data used to support the findings of this study are included within the article.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Acknowledgments

The authors convey sincere thanks to the Management and Principal of Coimbatore Institute of Technology, Coimbatore-641014, for sponsoring the project through the TEQIP-II fund.

References

  1. A. Mecucci and K. Scott, “Leaching and electrochemical recovery of copper, lead and tin from scrap printed circuit boards,” Journal of Chemical Technology and Biotechnology, vol. 77, no. 4, pp. 449–457, 2002. View at: Publisher Site | Google Scholar
  2. B.-S. Kim, J.-C. Lee, S.-P. Seo, Y.-K. Park, and H. Y. Sohn, “A process for extracting precious metals from spent printed circuit boards and automobile catalysts,” Journal of Minerals, Metals and Materials, vol. 56, no. 12, pp. 55–58, 2004. View at: Publisher Site | Google Scholar
  3. J. Cui and L. Zhang, “Metallurgical recovery of metals from electronic waste: a review,” Journal of Hazardous materials, vol. 158, no. 2-3, pp. 228–256, 2008. View at: Publisher Site | Google Scholar
  4. F.-R. Xiu and F.-S. Zhang, “Recovery of copper and lead from waste printed circuit boards by supercritical water oxidation combined with electrokinetic process,” Journal of Hazardous materials, vol. 165, no. 1–3, pp. 1002–1007, 2009. View at: Publisher Site | Google Scholar
  5. J. Guo and Z. Xu, “Recycling of non-metallic fractions from waste printed circuit boards: a review,” Journal of Hazardous materials, vol. 168, no. 2-3, pp. 567–590, 2009. View at: Publisher Site | Google Scholar
  6. Y. J. Park and D. J. Fray, “Recovery of high purity precious metals from printed circuit boards,” Journal of Hazardous materials, vol. 164, no. 2-3, pp. 1152–1158, 2009. View at: Publisher Site | Google Scholar
  7. Z.-F. Cao, H. Zhong, G.-Y. Liu, and S.-J. Zhao, “Techniques of Copper recovery from Mexican copper oxide ore,” Mining Science and Technology, vol. 19, no. 1, pp. 45–48, 2009. View at: Publisher Site | Google Scholar
  8. B. Bayat and B. Sari, “Comparative evaluation of microbial and chemical leaching processes for heavy metal removal from dewatered metal plating sludge,” Journal of Hazardous materials, vol. 174, no. 1–3, pp. 763–769, 2010. View at: Publisher Site | Google Scholar
  9. H. Z. Mousavi, A. Hosseynifar, V. Jahed, and S. A. M. Dehghani, “Removal of lead from aqueous solution using waste tire rubber ash as an adsorbent,” Brazilian Journal of Chemical Engineering, vol. 27, no. 1, pp. 79–87, 2010. View at: Publisher Site | Google Scholar
  10. M. Yilmaz, T. Tay, M. Kivanc, and H. Turk, “Removal of corper(II) ions from aqueous solution by a lactic acid bacterium,” Brazilian Journal of Chemical Engineering, vol. 27, no. 2, pp. 309–314, 2010. View at: Publisher Site | Google Scholar
  11. J. L. P. Sheeja and P. Selvapathy, “Comparative study on the removal efficiency of cadmium and lead using hydroxide and sulfide precipitation with the complexing agents,” International Journal of Current Research in Chemistry and Pharmaceutical Sciences, vol. 1, no. 6, pp. 38–42, 2014. View at: Google Scholar
  12. L. H. Yamane, V. T. de Moraes, D. C. R. Espinosa, and J. A. S. Tenório, “Recycling of WEEE: characterization of spent printed circuit boards from mobile phones and computers,” Waste Management, vol. 31, no. 12, pp. 2553–2558, 2011. View at: Publisher Site | Google Scholar
  13. M. Delfini, M. Ferrini, A. Manni, P. Massacci, L. Piga, and A. Scoppettuolo, “Optimization of precious metal recovery from waste electrical and electronic equipment boards,” Journal of Environmental Protection, vol. 2, no. 6, pp. 675–682, 2011. View at: Publisher Site | Google Scholar
  14. D. Pant, D. Joshi, M. K. Upreti, and R. K. Kotnala, “Chemical and biological extraction of metals present in E-waste: a hybrid technology,” Waste Management, vol. 32, no. 5, pp. 979–990, 2012. View at: Publisher Site | Google Scholar
  15. E. Nishikawa, A. F. A. Neto, and M. G. A. Vieira, “Equilibrium and thermodynamic studies of zinc adsorption on expanded vermiculite,” Adsorption Science and Technology, vol. 30, no. 8-9, pp. 759–772, 2012. View at: Publisher Site | Google Scholar
  16. J. Sohaili, S. K. Muniyandi, and S. S. Mohamad, “A review on printed circuit boards waste recycling technologies and reuse of recovered nonmetallic materials,” International Journal of Scientific and Engineering Research, vol. 3, no. 2, pp. 138–144, 2012. View at: Google Scholar
  17. B. Ali, M. M. Salarirad, and F. Veglio, “Process development for recovery of copper and precious metals from waste printed circuits board with emphasize on palladium and gold leaching and precipitation,” Waste Management, vol. 33, no. 11, pp. 2354–2363, 2013. View at: Publisher Site | Google Scholar
  18. B. Oleksiak, G. Siwiec, and A. B. Grzechnik, “Recovery of precious metals from waste materials by the method of flotation process,” Metalurgija, vol. 52, no. 1, pp. 107–110, 2013. View at: Google Scholar
  19. A. Vidyadhar and A. Das, “Enrichment implication of froth flotation kinetics in the separation and recovery of metal values from printed circuit boards,” Separation and Purification Technology, vol. 118, pp. 305–312, 2013. View at: Publisher Site | Google Scholar
  20. S. Fogarasi, F. Imre-Lucaci, A. Imre-Lucaci, and I. Petru, “Copper recovery and gold enrichment from waste printed circuit boards by mediated electrochemical oxidation,” Waste Management, vol. 273, pp. 215–221, 2014. View at: Publisher Site | Google Scholar
  21. U. Jadhav and H. Hocheng, “Hydrometallurgical recovery of metals from large printed circuit board pieces,” Scientific Reports, vol. 5, no. 1, Article ID 14574, 2015. View at: Publisher Site | Google Scholar
  22. J. Sheeja, “Studies on the removal of chromium with the complexing agents,” Oriental Journal of Chemistry, vol. 32, no. 4, pp. 2209–2213, 2016. View at: Publisher Site | Google Scholar

Copyright © 2018 P. Sivakumar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


More related articles

 PDF Download Citation Citation
 Download other formatsMore
 Order printed copiesOrder
Views2963
Downloads911
Citations

Related articles

We are committed to sharing findings related to COVID-19 as quickly as possible. We will be providing unlimited waivers of publication charges for accepted research articles as well as case reports and case series related to COVID-19. Review articles are excluded from this waiver policy. Sign up here as a reviewer to help fast-track new submissions.