optimizationstudiesonrecoveryofmetalsfromprinted...

Research ArticleOptimization Studies on Recovery of Metals from PrintedCircuit Board Waste

P Sivakumar D Prabhakaran and M Thirumarimurugan

Department of Chemical Engineering Coimbatore Institute of Technology Coimbatore 641 014 Tamil Nadu India

Correspondence should be addressed to P Sivakumar chemsiva13gmailcom

Received 20 June 2018 Accepted 18 September 2018 Published 1 November 2018

Academic Editor Albrecht Messerschmidt

Copyright copy 2018 P Sivakumar et al is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

e aim of the study was to recover copper and leadmetal fromwaste printed circuit boards (PCBs)e electrowinningmethod isfound to be an effective recycling process to recover copper and lead metal from printed circuit board wastes In order to simplifythe process with affordable equipment a simple ammonical leaching operation method was adopted e selected PCBs wereincinerated into fine ash powder at 500degC for 1 hour in the pyrolysis reactor en the fine ash powder was subjected to acid-leaching process to recover the metals with varying conditions like acid-base concentration electrode combination and leachingtimee relative electrolysis solution of 01M lead nitrate for lead and 01M copper sulphate for copper was used to extract metalsfrom PCBs at room temperature e amount of lead and copper extracted from the process was determined by an atomicabsorption spectrophotometer and results found were 7329 and 8217 respectively Further the optimum conditions for therecovery of metals were determined by using RSM software e results showed that the percentage of lead and copper recoverywere 7825 and 891 should be 4 hrs 10Adm2

1 Introduction

Recycling of e-waste is an important subject not only from thepoint of waste treatment but also from the recovery aspect ofvaluable materials [1ndash4] Among the resources in e-wastemetals contribute more than 95 of the materials marketvalue Hence the recovery of valuable metals is the inherentmotive in e-waste disposal In the past decades many tech-niques for recovering valuable metals from e-waste have beendeveloped such as gravity separation magnetic separationand electrostatic separation [5] synthesis of CuCl withe-waste separation of PCBs with organic solvent method[6 7] cyanide and noncyanide lixiviants leaching methodsammonium persulfate leaching bioleaching methods [8ndash10]or a combination of these approaches Among thosemethodshydrometallurgical methods are more accurate predictableand controllable [11] erefore hydrometallurgical tech-niques are most active in the research of valuable metal re-covery from electronic scraps in the past two decadesHowever traditional hydrometallurgical methods are aciddependent time-consuming and inefficient for simultaneousrecovery of precious metals Remarkably a large amount of

corrosive or toxic reagents such as aqua regia nitric acidcyanide and halide are consumed producing large quantitiesof toxic and corrosive fumes or solution [12 13] erefore itis necessary to seek amore environmental friendlymethod forthe recovery of valuable metals from e-wastes Hydrometal-lurgical methods are used in the upgrading and refining stagesof the recycling chain [14ndash16] In this research article therecovery of lead and copper metals from e-waste is widelyinvestigated e PCBs were converted into fine ash powderand subjected to electrowinning process for the recovery ofmetalse experimental results were determined by EDS andAAS respectively Furthermore the experimental results arevalidated through RSM software at different parameters likeacid-base concentration electrode combination and leachingtime [17ndash22]

2 Materials and Methods

21 Materials e computer PCBs were collected fromvarious sources for the recovery of metals e collectedPCBs were crushed using roll crusher and powdered bya hammer mill e crushed PCBs were incarnated through

HindawiBioinorganic Chemistry and ApplicationsVolume 2018 Article ID 1067512 10 pageshttpsdoiorg10115520181067512

pyrolysis to avoid side reaction in the leaching process withthe electrolyte solution e optimum condition of thepyrolysis reactor was 500degC in atmospheric pressure for 1 hwhere the epoxy resins and polymers were volatized at thetemperature less than 500degC e volatized contents werecondensed and collected separately e ferrous materialspresent in the obtained ash were separated by a magneticseparator

22 Electrowinning Process e fine ash powder was treatedwith aqua regia solution (3 1 ratio of HCl and HNO3) in theincineration chamber in order to avoid the liberation of toxicfumes en the precipitated salts obtained from theleaching was analyzed by EDS to determine the compositionof metal present in the salts (Figure 1) e electrowinningsetup consists of bath arrangement and amplifier e bathhaving two slots for the anode and cathode fixing and theelectrode is connected with amplifier and the currentdensity was varied through the amplifier (Figure 2)

23ExtractionProcess ofLead About 25 g of incinerated fineash was added into the acid bath followed by the addition ofammonical electrolyte solution e current density was setto 1 to 10 (Adm2) e solution was agitated at regularinterval to get an effective electrodeposition

Pb + 4HCl⟶ PbCl4 + 2H2 (1)

After the stipulated time of operation pure lead wasdeposited on lead cathode e deposited elements werescrapped and stored in an air tight container e recoveredlead quantitated from the EDS method e spent acid leftwith mud filtered at pH 6ndash10 was stored in a glass containerfor further treatment

24 Extraction Process of Copper About 25 g of incineratedfine ash was added into the acid bath followed by the ad-dition of ammonical electrolyte solutione current densitywas set to 1 to 10 (Adm2) e solution was agitated atregular intervals to get an effective electrodeposition Afterthe stipulated time of operation pure copper (cupric) wasdeposited on the cathode and impure copper (cuprous ion)were deposited on the anode e deposited elements werescrapped and stored in an air tight container e recoveredcopper quantitated from the EDSmethode spent acid leftwith mud (nonleached elements) was filtered (pHndash84) andwere stored in a glass container for further treatment(Figure 3)

2Cu2+(aq) + 2H2O(l)⟶ 2Cu(s) + 4H+

(aq) + O2(g)

(2)

e spent solution collected from the electrodepositionwas neutralized to 69 for the safe disposal as per thestandard Moreover the presence of any metal in the spentsolution was analyzed by Fourier-transform infrared spec-troscopy e results (Figure 4) show that the metallic traceswere found to be absent which confirms that all the metalsrecovered from the ashes deposited on the electrode

3 Results and Discussion

31RSMforLead e response surface methodology (RSM)is a statistical modeling technique employed for multipleregression analysis using quantitative data obtained fromdesigned experiments to solve multivariable equations(Table 1) e response surfaces can be visualized as three-dimensional plots that exhibit the response as a function oftwo factors while keeping the other factors constant In thisabove plot the red zone corresponds to the extract per-centage above 85 yellow zone shows 60 to 70 and theblue zone confirms below 40 extraction of lead (Figures 5and 5(a))e regression equation for the RSM data plots forthe lead is

Extract 6636 + 90175lowastA + 737375lowastB + 642375lowastC

+ 1235lowastAB + 017lowastAC +(minus71525lowastBC)

+ minus186187lowastA21113872 1113873 + minus167125lowastB2

1113872 1113873

+ minus181625lowastC21113872 1113873

(3)

emodel as a function of coded factor could be utilizedto predict the response of each parameter within the givenlimit Here the maximum limit of process parameters(factors) is termed (coded) as +1 and minimum limit isterms (coded) as minus1 e modifed equation or codedequation is very much useful in order to find the compar-ative effect of the process parameters by relating the co-efficient of factors e final equation in terms of actualfactors isExtract minus702665 + 506454lowastCD + 0201188lowast solvent

+ 256766lowast time + 0000914815lowastCDlowast solvent

+ 00125926lowastCDlowast time

+(minus00317889lowast solventlowast time)

+ minus0229861lowastCD21113872 1113873 + minus742778eminus 05lowast solvent21113872 1113873

+ minus0807222lowast time21113872 1113873

(4)

Equation (4) in terms of process parameters could beutilized to predict the response for the provided levels ofeach parameter (Table 2) In this equation the original unitsof each parameters should be considered for each levels Inorder to evaluate the comparative effect of each factor theabove equation should not be considered since the co-efficients are balanced to embrace the units of each pa-rameters Also the intercept does not fall at design spacecenter

32 Analysis of Variance (ANOVA) Analysis of variance isused to determine the significant effects of process variableson current efficiency (Table 3) along with the factor codinge sum of squares is found to be Type IIImdashpartial derivedfrom the ANOVA quadratic model e model F value of443 implies the model is significant A minimum value of312 is possible for the F value due to noise p values less

2 Bioinorganic Chemistry and Applications

than 00500 indicate model terms are signicant In this caseA A2 are signicant model terms Values greater than 01000indicate the model terms are not signicant If there are

many insignicant model terms (not counting those re-quired to support hierarchy) model reduction may improvethe model e lack of t F value of 6327 implies the lack of

002 4 6 8 10

(keV)

cps

eVMg

TaCu

aClSn

Pb

BrSi

ClPb Sn

CaCuTa

Pb Br

12 14 16 18 20

05

10

15

20

25

30

35

40

45

Ca

Figure 1 Initial analysis of raw materials

Figure 2 Experimental setup of electrowinning process

Figure 3 Bath solutions of copper and lead

Bioinorganic Chemistry and Applications 3

t is signicantere is only a 008 chance that a lack of tF value could be large that could occur due to noise ecoecient represents the expected change in response perunit change in the factor value when all remaining factorswere constant e intercept in an orthogonal design is theoverall average response of all the runs e coecients areadjustments around the average factor settings When thefactors are orthogonal the variance ination factors (VIFs)are 1 VIFs greater than 1 indicate multicolinearity thehigher the VIF the more severe the correlation of factors Asa rough rule VIFs less than 10 are tolerable Hence from thedata obtained (Table 4) the VIF values of lead are found tobe tolerable

33 Model Terms For a standard deviation of 1 the powercalculations are performed using response type ldquocontinu-ousrdquo and parameters are Δ 2 and σ 1 e power isevaluated over minus1 to +1 coded factor space From (Table 5)the standard errors should be similar to each other in

a balanced design e ideal VIF value should be 1 VIFsabove 10 are cause for concern and VIFs above 100 arecause for alarm indicating coecients are poorly estimateddue to multicolinearity where ideal Ri2 is 00 High Ri2means terms are correlated with each other possiblyleading to poor models If the design has multilinearconstraints then multicolinearity will exist to a greaterdegree is inates the VIFs and the Ri2 rendering thesestatistics would not perform well Hence FDS couldbe used Power is an inappropriate tool to evaluateresponse surface designs Use prediction-based metricsprovided in this program via fraction of design space (FDS)statistics

34 Fit Statistics A negative predicted R2 implies that theoverall mean may be a better predictor of the response thanthe current model In some cases a higher order model mayalso predict better Adeq precision measures the signal tonoise ratio A ratio greater than 4 is desirable e ratio of5915 indicates an adequate signalis model can be used tonavigate the design space e optimization of current ef-ciency is shown in Figure 6 From the results it is observedthat 69 of lead extract is obtained at current density 10Admminus2 solvent ratio 5 2 and the electrolysis time 4 hours(Figures 7 and 8) e signicance of regression coecientswere analyzed using the p-test and t-test e p values areused to check the eect of interaction among the variables Alarger magnitude of t-value and a smaller magnitude of pvalue are signicant in the corresponding coecient terme coecient of current eciency and the corresponding tand p values are shown in Table 6 Finally the coecients inthe interaction terms for current density-electrolysis time issignicant compared to current density-solvent ratio andcurrent density-electrolysis time

35 RSM for Copper e regression equation for the RSMdata plots for the copper is in terms of coded factors form asfollows

350030

40

50

60

70

80

90

100

3000 2500 2000 1500 1000 500Wavenumber (cmndash1)

3328

67

2124

92

1636

48

1417

06

615

4052

347

3500

30

40

50

60

70

80

90

100


3331

62

2124

03

1636

13

550

0252

374

Figure 4 FTIR analysis of bath solution

Table 1 RSM parameters for lead extraction

Std RunFactor 1 Factor 2 Factor 3A CD B solvent C timeAdm2 ml Hrs

1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25


Extract(E) 48 + 24lowastA + 825lowastB + 155lowastC + 05lowastAB

+ 75lowastAC + 25lowastBC + minus65lowastA2( ) + 8lowastB2

+ 15lowastC2

(5)

e model (Equation 5) as a function of coded factorcould be utilized to predict the response of each parameterwithin the given limit Here the maximum limit of processparameters (factors) is termed(coded) as +1 and minimumlimit is termed (coded) as minus1 e modied equation orcoded equation is very much useful in order to nd thecomparative eect of the process parameters by relating thecoecient of factors (Table 7)

e nal equation in terms of actual factors is

10

Extra

ct (

)

A CD (Adm2)B

solve

nt (m

l)

1 4 7 10 13 16 19 400460

520580

640700

20304050607080

Extract ()2885X1 = A CDX2 = B solventActual factorC time = 31

7825

A CD (Adm2)

Extract ()

20

30

50

40

60

70

40

B so

lven

t (m

l)

4001 4 7 10 13 16 19

460

520

580

640

700

Extract ()

X1 = A CDX2 = B solventActual factorC time = 1

2885 7825Design points

Figure 5 Contour plot for recovery of Lead

Table 2 BoxndashBehnken experimental design table for recovery oflead


1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25

Table 3 ANOVA quadratic model for lead

Source Sum ofsquares DOF Mean

square F value p value

Model 315245 9 35027 443 00312 SignicantA-CD 65052 1 65052 823 00240B-solvent 43498 1 43498 551 00514C-time 33012 1 33012 418 00802AB 610 1 610 00772 07891AC 01156 1 01156 00015 09706BC 20463 1 20463 259 01516A2 145961 1 145961 1847 00036B2 1176 1 1176 01489 07111C2 1389 1 1389 01758 06876Residual 55305 7 7901Lack of t 54164 3 18055 6327 00008 SignicantPureerror 1141 4 285

Total 370550 16


Extract 191503 + 232716lowastCD +(minus0773056lowast solvent)+(minus113333lowast time) + 0000555556lowastCDlowast solvent

+ 0833333lowastCDlowast time + 0025lowast solventlowast time

+ minus00802469lowastCD2( ) + 00008lowast solvent2

+ 15lowast time2(6)

Equation (5) in terms of process parameters could beutilized to predict the response for the provided levels ofeach parameter In this equation the original units of eachparameters should be considered for each levels In orderto evaluate the comparative eect of each factor the aboveequation should not be considered since the coecientsare balanced to embrace the units of each parametersAlso the intercept does not falls at design space center(Table 8) In this contour plot the red zone indicatesextract percentages above 85 And yellow and blue zonesindicate 60 to 70 and below 40 extraction of copper(Figures 9 and 9(a))

36 Analysis of Variance (ANOVA) Analysis of variance isused to determine the signicant eects of process variableson current eciency along with the factor coding e sumof squares is found to be Type IIImdashpartial derived from theANOVA quadratic model e model F value of 15508 inthe Table 9 implies the model is signicant A minimumvalue of 001 is possible for the F value due to noise Pvalues less than 00500 indicate model terms are signicant

Table 4 Coecients in terms of coded factors for lead

Factor Coecientestimate DOF Standard

error

95CIlow

95CIhigh

VIF

Intercept 6636 1 398 5696 7576A-CD 902 1 314 159 1645 10000B-solvent 737 1 314 minus00573 1480 10000C-time 642 1 314 minus101 1385 10000AB 124 1 444 minus927 1174 10000AC 01700 1 444 minus1034 1068 10000BC minus715 1 444 minus1766 336 10000A2 minus1862 1 433 minus2886 minus838 101B2 minus167 1 433 minus1191 857 101C2 minus182 1 433 minus1206 843 101

Table 5 Model terms in RSM for lead

Term Standard error VIF Ri2 Power ()A 03536 1 00000 681B 03536 1 00000 681C 03536 1 00000 681AB 05000 1 00000 408AC 05000 1 00000 408BC 05000 1 00000 408A2 04873 100588 00058 938B2 04873 100588 00058 938C2 04873 100588 00058 938

100

80

60

40

Extr

act (

)

One factor

Warning factor involved in multiple interactions

20

0

ndash20

1 4 7 1310A CD (Adm2)

16 19

X1 = A CDActual factorsB solvent = 700C time = 4

Extract ()Design points95 CI bands

Figure 6 Current density vs extract for lead

100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580 640 700

X1 = B solvent

Actual factorsA CD = 10C time = 1


Figure 7 Solvent vs extract for lead


In this case A B C AC A2 and B2 are signicant modelterms Values greater than 01000 indicate the model termsare not signicant If there are many insignicant modelterms (not counting those required to support hierarchy)model reduction may improve the model e lack of t Fvalue is nil that implies the lack of t is signicant ecoecient represents the expected change in response perunit change in factor value when all remaining factors wereconstant e intercept in an orthogonal design is theoverall average response of all the runs e coecients areadjustments around the average factor settings When thefactors are orthogonal the VIFs are 1 VIFs greater than 1indicate multicolinearity the higher the VIF the moresevere the correlation of factors As a rough rule VIFsless than 10 are tolerable Hence from the dataobtained (Table 10) the VIF Values of lead are found to betolerable

37 Model Terms For a standard deviation of 1 the powercalculations are performed using response type ldquocontinu-ousrdquo and the parameters are Δ 2 and σ 1 e power isevaluated over minus1 to +1 coded factor space (Table 11) estandard errors should be similar to each other in a balanceddesign e ideal VIF value should be 1 VIFs above 10 arecause for concern and VIFs above 100 are cause for alarmindicating coecients are poorly estimated due to multi-colinearity where ideal Ri2 is 00 High Ri2 means terms arecorrelated with each other possibly leading to poor modelsIf the design has multilinear constraints then multi-colinearity will exist to a greater degree is inates theVIFs and the Ri2 rendering these statistics would notperform well Hence FDS could be used Power is an in-appropriate tool to evaluate response surface designs Useprediction-based metrics provided in this program viafraction of design space (FDS) statistics

100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4

X1 = C timeActual factorsA CD = 19B solvent = 700


Figure 8 Time vs extract for lead

Table 6 Fit statisticsStd dev 889Mean 5596CV () 1588R2 08507Adjusted R2 06589Predicted R2 minus13436Adeq precision 59146

Table 7 RSM parameters for copper extraction


1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2

Table 8 BoxndashBehnken experimental design table for recovery ofcopper


1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2


38 Fit Statistics A predicted R2 implies that the overallmean may be a better predictor of the response than thecurrent model In some cases a higher order model may alsopredict better Adeq precision measures the signal to noiseratio A ratio greater than 4 is desirable A ratio of 449indicates an adequate signal is model can be used tonavigate the design space e optimization of current ef-ciency is shown in Figure 10 e optimum extraction of69 Cu is obtained at current density 19A dmminus2 solventratio 5 2 and electrolysis time 4 hour (Figures 11 and12) e signicance of regression coecients was analyzedusing the p-test and t-test e p values are used to check the

eect of interaction among the variables A larger magnitudeof t-value and a smaller magnitude of p value are signicantin the corresponding coecient term e coecient of

4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)

A CD (Adm2 )B solvent (ml)

Extract ()


12 89

Design points above predicted valueDesign points below predicted value

Figure 9 Contour plot for recovery of copper

Table 9 ANOVA quadratic model for copper


squareF

value p value

Model 776350 9 86261 15508 lt00001 SignicantA-CD 460800 1 460800 82840 lt00001B-solvent 54450 1 54450 9789 lt00001C-time 192200 1 192200 34553 lt00001AB 10000 1 10000 01798 06827AC 22500 1 22500 4045 00002BC 2500 1 2500 449 00668A2 18436 1 18436 3314 00004B2 27927 1 27927 5021 00001C2 982 1 982 177 02206Residual 4450 8 556Lack oft 4450 3 1483

Pureerror 00000 5 00000

Total 780800 17

Table 10 Coecients in terms of coded factors for copper


error95 CIlow

95CIhigh

VIF

Intercept 4800 1 09629 4578 5022A-CD 2400 1 08339 2208 2592 10000B-solvent 825 1 08339 633 1017 10000

C-time 1550 1 08339 1358 1742 10000AB 05000 1 118 minus222 322 10000AC 750 1 118 478 1022 10000BC 250 1 118 minus02193 522 10000A2 minus650 1 113 minus910 minus390 102B2 800 1 113 540 1060 102C2 150 1 113 minus110 410 102

Table 11 Model terms in RSM for copper



current eciency and the corresponding t and p values areshown in (Table 12) Finally the coecients in the in-teraction terms for current density-electrolysis time is sig-nicant compared to current density-solvent ratio andcurrent density-electrolysis time

4 Conclusion

e ammonia-lead nitrate and ammonia-copper sulphatesystem have been employed as a leaching agent for recoveryof lead and copper from scraped printed circuit boardwastes A two-stage leaching was employed wherein the rststage consisted of leaching the scrap board with 01M Pb(NO3)2 and 01M CuSO4 which results in the selective

120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19



Figure 10 Current density vs extract for copper

100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700

X1 = B solventActual factorsA CD = 10C time = 1



120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3

Std 3 run 2X1 = C time = 2

Y = extract () = 30CI = (179211 305789)

Actual factorsA CD = 1B solvent = 600



Table 12 Fit statisticsStd dev 236Mean 4933CV () 478R2 09943Adjusted R2 09879Predicted R2 09088Adeq Precision 449395


dissolution of lead and copper leaching rate and othermetals was found in lower amounts respectively e un-dissolved residue portion from the leaching stage containingnickel tin and silica were leached out in respective treat-ments e current efficiency was found to increase withcurrent density and concentration ratio with the contacttime in acid bath Hence 7329 lead and 8217 copperhave been successfully recovered from the electrolysisprocess And also by RSM Software prediction the recoveryof lead and copper are as 7825 and 891 respectively Inaddition to the quadratic model equation ANOVA modelterms and fit statistics were also tested for the experimentalconditions

Data Availability

All the data used to support the findings of this study areincluded within the article

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

e authors convey sincere thanks to the Management andPrincipal of Coimbatore Institute of TechnologyCoimbatore-641014 for sponsoring the project through theTEQIP-II fund

References

[1] A Mecucci and K Scott ldquoLeaching and electrochemical re-covery of copper lead and tin from scrap printed circuitboardsrdquo Journal of Chemical Technology and Biotechnologyvol 77 no 4 pp 449ndash457 2002

[2] B-S Kim J-C Lee S-P Seo Y-K Park and H Y Sohn ldquoAprocess for extracting precious metals from spent printedcircuit boards and automobile catalystsrdquo Journal of MineralsMetals and Materials vol 56 no 12 pp 55ndash58 2004

[3] J Cui and L Zhang ldquoMetallurgical recovery of metals fromelectronic waste a reviewrdquo Journal of Hazardous materialsvol 158 no 2-3 pp 228ndash256 2008

[4] F-R Xiu and F-S Zhang ldquoRecovery of copper and lead fromwaste printed circuit boards by supercritical water oxidationcombined with electrokinetic processrdquo Journal of Hazardousmaterials vol 165 no 1ndash3 pp 1002ndash1007 2009

[5] J Guo and Z Xu ldquoRecycling of non-metallic fractions fromwaste printed circuit boards a reviewrdquo Journal of Hazardousmaterials vol 168 no 2-3 pp 567ndash590 2009

[6] Y J Park and D J Fray ldquoRecovery of high purity preciousmetals from printed circuit boardsrdquo Journal of Hazardousmaterials vol 164 no 2-3 pp 1152ndash1158 2009

[7] Z-F Cao H Zhong G-Y Liu and S-J Zhao ldquoTechniques ofCopper recovery from Mexican copper oxide orerdquo MiningScience and Technology vol 19 no 1 pp 45ndash48 2009

[8] B Bayat and B Sari ldquoComparative evaluation of microbialand chemical leaching processes for heavy metal removalfrom dewatered metal plating sludgerdquo Journal of Hazardousmaterials vol 174 no 1ndash3 pp 763ndash769 2010

[9] H Z Mousavi A Hosseynifar V Jahed andS A M Dehghani ldquoRemoval of lead from aqueous solution

using waste tire rubber ash as an adsorbentrdquo Brazilian Journalof Chemical Engineering vol 27 no 1 pp 79ndash87 2010

[10] M Yilmaz T Tay M Kivanc and H Turk ldquoRemoval ofcorper(II) ions from aqueous solution by a lactic acid bac-teriumrdquo Brazilian Journal of Chemical Engineering vol 27no 2 pp 309ndash314 2010

[11] J L P Sheeja and P Selvapathy ldquoComparative study on theremoval efficiency of cadmium and lead using hydroxide andsulfide precipitation with the complexing agentsrdquo In-ternational Journal of Current Research in Chemistry andPharmaceutical Sciences vol 1 no 6 pp 38ndash42 2014

[12] L H Yamane V T de Moraes D C R Espinosa andJ A S Tenorio ldquoRecycling ofWEEE characterization of spentprinted circuit boards from mobile phones and computersrdquoWaste Management vol 31 no 12 pp 2553ndash2558 2011

[13] M Delfini M Ferrini A Manni P Massacci L Piga andA Scoppettuolo ldquoOptimization of precious metal recoveryfrom waste electrical and electronic equipment boardsrdquoJournal of Environmental Protection vol 2 no 6 pp 675ndash6822011

[14] D Pant D Joshi M K Upreti and R K Kotnala ldquoChemicaland biological extraction of metals present in E-waste a hy-brid technologyrdquo Waste Management vol 32 no 5pp 979ndash990 2012

[15] E Nishikawa A F A Neto andM G A Vieira ldquoEquilibriumand thermodynamic studies of zinc adsorption on expandedvermiculiterdquo Adsorption Science and Technology vol 30no 8-9 pp 759ndash772 2012

[16] J Sohaili S K Muniyandi and S S Mohamad ldquoA review onprinted circuit boards waste recycling technologies and reuseof recovered nonmetallic materialsrdquo International Journal ofScientific and Engineering Research vol 3 no 2 pp 138ndash1442012

[17] B Ali M M Salarirad and F Veglio ldquoProcess developmentfor recovery of copper and precious metals fromwaste printedcircuits board with emphasize on palladium and gold leachingand precipitationrdquo Waste Management vol 33 no 11pp 2354ndash2363 2013

[18] B Oleksiak G Siwiec and A B Grzechnik ldquoRecovery ofprecious metals from waste materials by the method of flo-tation processrdquo Metalurgija vol 52 no 1 pp 107ndash110 2013

[19] A Vidyadhar and A Das ldquoEnrichment implication of frothflotation kinetics in the separation and recovery of metalvalues from printed circuit boardsrdquo Separation and Purifi-cation Technology vol 118 pp 305ndash312 2013

[20] S Fogarasi F Imre-Lucaci A Imre-Lucaci and I PetruldquoCopper recovery and gold enrichment from waste printedcircuit boards by mediated electrochemical oxidationrdquoWasteManagement vol 273 pp 215ndash221 2014

[21] U Jadhav and H Hocheng ldquoHydrometallurgical recovery ofmetals from large printed circuit board piecesrdquo ScientificReports vol 5 no 1 article 14574 2015

[22] J Sheeja ldquoStudies on the removal of chromium with thecomplexing agentsrdquo Oriental Journal of Chemistry vol 32no 4 pp 2209ndash2213 2016


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pyrolysis to avoid side reaction in the leaching process withthe electrolyte solution e optimum condition of thepyrolysis reactor was 500degC in atmospheric pressure for 1 hwhere the epoxy resins and polymers were volatized at thetemperature less than 500degC e volatized contents werecondensed and collected separately e ferrous materialspresent in the obtained ash were separated by a magneticseparator

22 Electrowinning Process e fine ash powder was treatedwith aqua regia solution (3 1 ratio of HCl and HNO3) in theincineration chamber in order to avoid the liberation of toxicfumes en the precipitated salts obtained from theleaching was analyzed by EDS to determine the compositionof metal present in the salts (Figure 1) e electrowinningsetup consists of bath arrangement and amplifier e bathhaving two slots for the anode and cathode fixing and theelectrode is connected with amplifier and the currentdensity was varied through the amplifier (Figure 2)

23ExtractionProcess ofLead About 25 g of incinerated fineash was added into the acid bath followed by the addition ofammonical electrolyte solution e current density was setto 1 to 10 (Adm2) e solution was agitated at regularinterval to get an effective electrodeposition

Pb + 4HCl⟶ PbCl4 + 2H2 (1)

After the stipulated time of operation pure lead wasdeposited on lead cathode e deposited elements werescrapped and stored in an air tight container e recoveredlead quantitated from the EDS method e spent acid leftwith mud filtered at pH 6ndash10 was stored in a glass containerfor further treatment

24 Extraction Process of Copper About 25 g of incineratedfine ash was added into the acid bath followed by the ad-dition of ammonical electrolyte solutione current densitywas set to 1 to 10 (Adm2) e solution was agitated atregular intervals to get an effective electrodeposition Afterthe stipulated time of operation pure copper (cupric) wasdeposited on the cathode and impure copper (cuprous ion)were deposited on the anode e deposited elements werescrapped and stored in an air tight container e recoveredcopper quantitated from the EDSmethode spent acid leftwith mud (nonleached elements) was filtered (pHndash84) andwere stored in a glass container for further treatment(Figure 3)

2Cu2+(aq) + 2H2O(l)⟶ 2Cu(s) + 4H+

(aq) + O2(g)

(2)

e spent solution collected from the electrodepositionwas neutralized to 69 for the safe disposal as per thestandard Moreover the presence of any metal in the spentsolution was analyzed by Fourier-transform infrared spec-troscopy e results (Figure 4) show that the metallic traceswere found to be absent which confirms that all the metalsrecovered from the ashes deposited on the electrode

3 Results and Discussion

31RSMforLead e response surface methodology (RSM)is a statistical modeling technique employed for multipleregression analysis using quantitative data obtained fromdesigned experiments to solve multivariable equations(Table 1) e response surfaces can be visualized as three-dimensional plots that exhibit the response as a function oftwo factors while keeping the other factors constant In thisabove plot the red zone corresponds to the extract per-centage above 85 yellow zone shows 60 to 70 and theblue zone confirms below 40 extraction of lead (Figures 5and 5(a))e regression equation for the RSM data plots forthe lead is

Extract 6636 + 90175lowastA + 737375lowastB + 642375lowastC

+ 1235lowastAB + 017lowastAC +(minus71525lowastBC)

+ minus186187lowastA21113872 1113873 + minus167125lowastB2

1113872 1113873

+ minus181625lowastC21113872 1113873

(3)

emodel as a function of coded factor could be utilizedto predict the response of each parameter within the givenlimit Here the maximum limit of process parameters(factors) is termed (coded) as +1 and minimum limit isterms (coded) as minus1 e modifed equation or codedequation is very much useful in order to find the compar-ative effect of the process parameters by relating the co-efficient of factors e final equation in terms of actualfactors isExtract minus702665 + 506454lowastCD + 0201188lowast solvent

+ 256766lowast time + 0000914815lowastCDlowast solvent

+ 00125926lowastCDlowast time

+(minus00317889lowast solventlowast time)

+ minus0229861lowastCD21113872 1113873 + minus742778eminus 05lowast solvent21113872 1113873

+ minus0807222lowast time21113872 1113873

(4)

Equation (4) in terms of process parameters could beutilized to predict the response for the provided levels ofeach parameter (Table 2) In this equation the original unitsof each parameters should be considered for each levels Inorder to evaluate the comparative effect of each factor theabove equation should not be considered since the co-efficients are balanced to embrace the units of each pa-rameters Also the intercept does not fall at design spacecenter

32 Analysis of Variance (ANOVA) Analysis of variance isused to determine the significant effects of process variableson current efficiency (Table 3) along with the factor codinge sum of squares is found to be Type IIImdashpartial derivedfrom the ANOVA quadratic model e model F value of443 implies the model is significant A minimum value of312 is possible for the F value due to noise p values less




002 4 6 8 10

(keV)

cps

eVMg

TaCu

aClSn

Pb

BrSi

ClPb Sn

CaCuTa

Pb Br

12 14 16 18 20

05

10

15

20

25

30

35

40

45

Ca










350030

40

50

60

70

80

90

100


3328

67

2124

92

1636

48

1417

06

615

4052

347

3500

30

40

50

60

70

80

90

100


3331

62

2124

03

1636

13

550

0252

374




1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25




+ 15lowastC2

(5)



10

Extra

ct (

)

A CD (Adm2)B

solve

nt (m

l)

1 4 7 10 13 16 19 400460

520580

640700

20304050607080


7825

A CD (Adm2)

Extract ()

20

30

50

40

60

70

40

B so

lven

t (m

l)

4001 4 7 10 13 16 19

460

520

580

640

700

Extract ()






1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25





Total 370550 16





+ 15lowast time2(6)





error

95CIlow

95CIhigh

VIF




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 4 7 1310A CD (Adm2)

16 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580 640 700

X1 = B solvent







100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4







1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2



1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2




4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls






002 4 6 8 10

(keV)

cps

eVMg

TaCu

aClSn

Pb

BrSi

ClPb Sn

CaCuTa

Pb Br

12 14 16 18 20

05

10

15

20

25

30

35

40

45

Ca










350030

40

50

60

70

80

90

100


3328

67

2124

92

1636

48

1417

06

615

4052

347

3500

30

40

50

60

70

80

90

100


3331

62

2124

03

1636

13

550

0252

374




1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25




+ 15lowastC2

(5)



10

Extra

ct (

)

A CD (Adm2)B

solve

nt (m

l)

1 4 7 10 13 16 19 400460

520580

640700

20304050607080


7825

A CD (Adm2)

Extract ()

20

30

50

40

60

70

40

B so

lven

t (m

l)

4001 4 7 10 13 16 19

460

520

580

640

700

Extract ()






1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25





Total 370550 16





+ 15lowast time2(6)





error

95CIlow

95CIhigh

VIF




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 4 7 1310A CD (Adm2)

16 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580 640 700

X1 = B solvent







100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4







1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2



1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2




4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls









350030

40

50

60

70

80

90

100


3328

67

2124

92

1636

48

1417

06

615

4052

347

3500

30

40

50

60

70

80

90

100


3331

62

2124

03

1636

13

550

0252

374




1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25




+ 15lowastC2

(5)



10

Extra

ct (

)

A CD (Adm2)B

solve

nt (m

l)

1 4 7 10 13 16 19 400460

520580

640700

20304050607080


7825

A CD (Adm2)

Extract ()

20

30

50

40

60

70

40

B so

lven

t (m

l)

4001 4 7 10 13 16 19

460

520

580

640

700

Extract ()






1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25





Total 370550 16





+ 15lowast time2(6)





error

95CIlow

95CIhigh

VIF




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 4 7 1310A CD (Adm2)

16 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580 640 700

X1 = B solvent







100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4







1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2



1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2




4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls






+ 15lowastC2

(5)



10

Extra

ct (

)

A CD (Adm2)B

solve

nt (m

l)

1 4 7 10 13 16 19 400460

520580

640700

20304050607080


7825

A CD (Adm2)

Extract ()

20

30

50

40

60

70

40

B so

lven

t (m

l)

4001 4 7 10 13 16 19

460

520

580

640

700

Extract ()






1 8 1 400 252 9 19 400 253 17 1 700 254 12 19 700 255 1 1 550 16 14 19 550 17 6 1 550 48 5 19 550 49 4 10 400 110 10 10 700 111 7 10 400 412 11 10 700 413 2 10 550 2514 16 10 550 2515 13 10 550 2516 15 10 550 2517 3 10 550 25





Total 370550 16





+ 15lowast time2(6)





error

95CIlow

95CIhigh

VIF




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 4 7 1310A CD (Adm2)

16 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580 640 700

X1 = B solvent







100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4







1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2



1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2




4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls







+ 15lowast time2(6)





error

95CIlow

95CIhigh

VIF




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 4 7 1310A CD (Adm2)

16 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580 640 700

X1 = B solvent







100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4







1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2



1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2




4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls






100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

1 16 22 28C time (hrs)

34 4







1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 2



1 12 1 400 22 14 19 400 23 2 1 600 24 4 19 600 25 18 1 500 16 13 19 500 17 16 1 500 38 11 19 500 39 8 10 400 110 1 10 600 111 6 10 400 312 7 10 600 313 5 10 500 214 9 10 500 215 17 10 500 216 3 10 500 217 15 10 500 218 10 10 500 2




4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls






4001 4

40

60 80

100

7 10A CD (Adm2)

Extract ()

B so

lven

t (m

l)

13 16 19

450

500

550

600

Extract ()


12 89Design points

600

020406080

100120

550500

450400 1

47

1013

1619

Extr

act (

)


Extract ()


12 89





squareF

value p value


Pureerror 00000 5 00000

Total 780800 17



error95 CIlow

95CIhigh

VIF







4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





4 Conclusion


120

100

80

60

Extr

act (

)

One factor


40

20

0

1 74 10A CD (Adm2)

1613 19




100

80

60

40

Extr

act (

)

One factor


20

0

ndash20

400 460 520 580B solvent (ml)

640 700




120

100

80

60

Extr

act (

)

One factor


40

20

0

1 15 2C time (hrs)

25 3


Y = extract () = 30CI = (179211 305789)







Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls





Data Availability




Acknowledgments


References






























Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



MaterialsJournal of






Na

nom

ate

ria

ls









Journal of

Chemistry





Journal of

Volume 2018






Volume 2018




Journal of






Enzyme Research


Journal of



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