Statistical Optimization of Heavy Metal (Cu2+ and Co2+) Extraction

Article

Statistical Optimization of Heavy Metals (Cu2+/Co2+) Extraction from Printed Circuit Board and Mobile Batteries Using Chelation Technology Nitin Sharma, Garima Chauhan, Arinjay Kumar, and S.K. Sharma Ind. Eng. Chem. Res., Just Accepted Manuscript • Publication Date (Web): 25 May 2017 Downloaded from http://pubs.acs.org on May 28, 2017

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Industrial & Engineering Chemistry Research

Statistical Optimization of Heavy Metals (Cu2+/Co2+) Extraction from Printed Circuit Board and Mobile Batteries Using Chelation Technology Nitin Sharma, Garima Chauhan*, Arinjay Kumar, S. K. Sharma University School of Chemical Technology, Guru Gobind Singh Indraprastha University, New Delhi-110078 (INDIA) *[email protected]

Abstract: Electronic waste have emerged as one of the fastest growing segments of solid waste stream in last two decades due to higher consumption and obsolescence rates of electronic products. Significant content of metals and polymers present in e-waste may affect the ecosystem and human health if dealt inappropriately using primitive recycling methods. Several studies have been reported in literature for the recovery of metals from e-waste, however limitations associated with on-going methods incited this study to move towards modern approaches based on green chemistry principles. Present study is a pioneer effort to employ a novel green technology based on a unique combination of chelation-dechelation concept to extract metals from printed circuit board and mobile batteries. Applicability of response surface methodology (RSM) was explored to investigate mutual interaction effect of process parameters and to provide elaborated quality of information. Statistical optimization of extraction process was performed by coupling of Box-Behnken design (BBD) and Central Composite design (CCD) matrices with RSM. Nearly 85.3% Cu2+ and 86.2% Co2+ recovery was predicted at center level of design matrices using quadratic regression models for respective metals. Maximum ±4% deviation was observed between experimental and predicted extraction efficiency. High values of regression coefficients (R2=0.994 for Cu2+ recovery and R2=0.998 for Co2+ recovery) depicted that >99% of response variability could be explained by regression models. Relatively less p-values (0.95. Characterization of raw material and residues corroborated the significant extraction of metals. Also, recovered chelating agent was successfully employed in subsequent extraction cycles which fortify the concept of a zero waste technology. The proposed design correlations may prove to be a useful tool in designing the pilot plant/commercial plant for extraction of heavy metals using environmental friendly chelation technology.

Keywords: Electronic waste, Heavy metals, Response surface methodology, Design of experiments, Desirability function

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1. Introduction Rapid innovations in the field of information & communication sector are superseding millions of electronic gadgets at a much faster pace than ever. Market demand of new and advanced electronic items is increasing at an exponential rate. Cadena and coworkers1 reported 10 times (500 million to 5000 million) increase in the number of mobile phone users across the globe from year 2000 to year 2011. Advancement in technology, attractive consumer designs and irresistible ways of marketing are the driving forces to replace old EEE (electronic and electrical equipment) frequently. It, consequently, reduces the service life of EEE and thereby generating large amount of electronic waste. The average lifespan of mobile phone and computer have been foreshortened to 2 years in last decade2,3, though these electronic goods may still have a value in terms of performance and strength. Generation of WEEE (waste electronic and electrical equipment) is predicted to grow up to 34% higher in year 2017 than that of in year 20124 . It is quite predictable that highly saturated markets for EEE promote the faster pace of replacement and therefore major portion of WEEE is generated in OECD countries. WEEE production per capita was reckoned to be below 1 kg per year in developing countries (India, China), however total absolute volume is enormous due to large population in these countries5. Along with the large volume of imported e-waste into developing countries, the complex composition of e-waste is another sincere matter of concern which may affect the environment and human health if e-wastes are not discarded in an appropriate manner. Landfilling and incineration, in spite of causing high risks to health and environment, are still being carried out in an unprofessional manner for e-waste disposal. Ongondo and coworkers6 mentioned that nearly 82% of 2.25 million tons of e-waste was disposed of in landfills in USA. Solid waste containing toxic metals and organic substances are being landfilled in India due to lack of licensed hazardous waste sites7. Landfill leachate may potentially transport 3 ACS Paragon Plus Environment


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toxic metals and persistent organic pollutants (dioxin, BFRs (brominated flame retardants), polyvinyl chloride, PAHs (polycyclic aromatic hydrocarbons), polychlorinated biphenyls) into food chain and cause chronic health effects. Persistence and weathering of organic contaminants may cause formation of metabolites which could be even more toxic than parent compounds. Ha and coworkers8 investigated the effect of occupational exposure to the workers of an e-waste recycling site in Bangalore and reported high concentrations of rare trace metals such as In, Sb, and Bi in the hair samples of male workers. High concentration of BDE-209 in humans was reported in an e-waste dismantling area of Guangdong, China9. It is also noteworthy that due to lack of monitoring and consumer awareness, e-waste are collected along with municipal waste in rural areas and burned without any sorting in order to reduce the volume before final disposal at unlined landfills10. Combustion typically generates smaller particles (< 2.5μm) and consequently, fine particulate matter strongly entailed into pulmonary and cardiovascular disease7. Lundin and coworkers11 demonstrated significantly high concentration of dioxins in waste incineration ashes which were emitted due to burning activities of e-waste processing unit. Incineration in a high temperature combustion chamber or burning of WEEE into open environment result into release of toxic fumes into the ecosystem and therefore, should be completely prohibited. On the other hand, the complex mixture of polymers (PC (polycarbonate), ABS (acrylonitrile butadiene styrene), PC/ABS Blends, and high impact polystyrene) and precious/heavy metals in e-waste secernates it from other solid waste and also makes it a potential secondary resource to overcome the scarcity of metals. Cui and Zhang12 mentioned that on an average, precious metal content in cell phones, calculators, and PCB (printed circuit board) scraps is more than 70% of the value, whereas TV boards and DVD player contain about 40% precious metals. Environmental concerns and presence of reusable metals/components trigger the need to recover heavy and precious metals from e-waste before disposing off scrap materials into 4 ACS Paragon Plus Environment

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the ecosystem. Recovery of metals from WEEE using conventional pyro-metallurgical processes is widely reported in literature and few industrial plants have also been successfully installed13-14. Pyrometallurgical techniques include incineration, smelting in plasma arc furnace, blast furnace or Cu smelter and roasting however high temperature requirement and emission of toxic fumes to the environment proscribe use of these energy intensive processes. Hydrometallurgical methods are also widely employed to process e-waste due to lesser amount of toxic gas emission compared to pyrometallurgical process, less dust generation, easy implementation in laboratory conditions, lower operational cost and significant metal recovery15-17. Leaching reagents such as cyanide, thiourea, thiosulfate and halide solutions are widely employed to achieve significant metal recovery from WEEE18,19. Leaching is followed by conventional metal recovery methods including precipitation, solvent extraction, adsorption, ion-exchange and electrowinning20-22 to recover individual metals. Although the technical feasibility of hydrometallurgical routes have been proven, these processes are associated with high risks of environmental impact due to toxicity of the reagents used and generation of large amount of by-products. Also, hydrometallurgical processes are not regarded as an economical way to extract valuable components from electronic waste due to large capital cost and high consumption of energy. Recently, biohydrometallurgical methods are being considered a green alternative of existing technologies with a huge potential to minimize economics and energy requirements23,24, nevertheless commercial implementation of this process is still in a stage of infancy due to inherently slow nature of the process, possibility of contamination and sensitivity of microorganisms towards temperature/pH variation25. Limitations associated with the on-going methods for metal recovery and recycling are drawing researcher’s concern towards new technologies based on green chemistry principles. Chelate assisted extraction of heavy metals from industrial waste, soil and other contaminated 5 ACS Paragon Plus Environment


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sites26-28 has been reported recently as a novel green alternative due to effective recycling of chelating agent and nearly closed loop cycle. Therefore, the unique combination of chelationdechelation has been explored in present study for metal extraction from WEEE. 1.1. Chelation Technology: A Novel Green Approach Chelation process works on the concept of coordinative incorporation of a metal ion into a heterocyclic ring structure in which monodentate ligands are replaced by multidentate ones to form a metal-ligand complex28. Chelating agent assisted metal extraction process takes place into two steps : formation of metal–ligand complexes on WEEE surfaces and then kinetic detachment of the complex from WEEE into aqueous solution to maximize the thermodynamic stability of the complex29-32. Mass transfer of liquid extractant from solid– liquid interface to inner zone of the WEEE particles and then mass transfer of metal– ligand complex from interior of WEEE to bulk of the liquid extractant exclusively depend upon external and internal diffusion mechanism. External diffusion involves rapid extraction of metals from superficial sites of the solid surface whereas internal diffusion suggests extraction of metals present on internal sites of particles. Reactions associated with overall extraction process can be categorized into primary and secondary reactions based on the interaction of chelating agents with target metals and other components present on the solid surfaces29. Primary reactions describe metal-ligand interaction at solid–liquid interface by ligand substitution mechanism as shown in eq. (1) - (3). Secondary reactions include readsorption of metal ions by surface complexation (eq. (4)), substitution with other metal ions Mii2+ (eq. (5), (6)) or present as free ions in solution (eq. (7)), depending upon stability constants of metal-chelate complexes, binding capacity of Mim+ on particle surface, and approachability of Miim+ . Primary Reactions: {𝑊𝐸𝐸𝐸 − 𝑂} − 𝑀𝑖𝑚+ + 𝐿𝑛− + 𝐻2 𝑂 ↔ [𝑊𝐸𝐸𝐸] − 𝑂𝐻 + 𝑀𝑖 − 𝐿𝑚+𝑛− + 𝑂𝐻 − 6 ACS Paragon Plus Environment

(1)

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𝑀𝑖 (𝑂𝐻)𝑚 + 𝐿𝑛− ↔ 𝑀𝑖 − 𝐿𝑚+𝑛− + 𝑚𝑂𝐻 −

(2)

𝑀𝑖 𝑂(𝑀𝑖 𝑂)𝑚+2− + 𝐿𝑛− + 𝐻2 𝑂 ↔ 𝑀𝑖 − 𝐿𝑚+𝑛− + 2𝑂𝐻 −

(3)

Secondary Reactions: [𝑊𝐸𝐸𝐸] − 𝑂𝐻 + 𝑀𝑖 𝑚+ − 𝐿𝑛− ↔ [𝑊𝐸𝐸𝐸] − 𝐿𝑛− − 𝑀𝑖 𝑚+ + 𝑂𝐻 −

(4)

[𝑊𝐸𝐸𝐸] − 𝐿 − 𝑀𝑖 + [𝑊𝐸𝐸𝐸] − 𝑂 − 𝑀𝑖𝑖 + 𝐻 + ↔ [𝑊𝐸𝐸𝐸] − 𝐿 − 𝑀𝑖𝑖 + [𝑊𝐸𝐸𝐸] − 𝑂𝐻 + 𝑀𝑖𝑚+

(5)

[𝑊𝐸𝐸𝐸] − 𝐿𝑛− − 𝑀𝑖𝑖 𝑚+ + 𝑂𝐻 − ↔ [𝑊𝐸𝐸𝐸] − 𝑂𝐻 + 𝑀𝑖𝑖 − 𝐿𝑚+𝑛−

(6)

[𝑊𝐸𝐸𝐸] − 𝑂𝐻 + 𝑀𝑖𝑚+ ↔ {[𝑊𝐸𝐸𝐸] − 𝑂} − 𝑀𝑖𝑚+2− + 𝐻 +

(7)

Chelation technology, traditionally been used for metal recovery from soil, has recently been reported as a possible route to bind metals in industrial and electronic waste4,26,27. Chauhan and coworkers33 performed simultaneous extraction of metals from multimetallic spent catalyst using EDTA (ethylenediaminetetraacetic acid) chelating agent and recovered more than 84% of molybdenum (Mo6+) and cobalt (Co2+) in one cycle. More than 96% recovery of chelating agents was reported from aqueous solution of metal-ligand complexes using a novel combination of chelation and dechelation process27. Recently, a comparative analysis of the performance of sulfuric acid (H2SO4) leaching and chelation-dechelation process was performed for the recovery of copper from printed circuit board of computer desktop 4. More than 80% Cu2+ was recovered using EDTA in chelate assisted extraction process whereas acid leaching could not extract even 30% Cu2+ from PCB in absence of any oxidant. The chelation process is carried out at moderate reaction conditions (in terms of temperature and pH) and no any hazardous by-products are liberated. Significant metal extraction, successful recovery and recycling of chelating agent in next chelation cycles, moderate thermodynamic stability and nearly zero discharge to the environment are the major advantages of chelation process 27 which articulate its transcendency over other conventional approaches for WEEE management.



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Chelate assisted extraction of metals from WEEE is still an unexplored area. To the best of our knowledge, only one research paper has been published yet for the extraction of metals from e-waste using chelation-dechelation concept in which ‘OVAT (one variable at a time)’ approach was employed to optimize the process parameters4. Being a heterogeneous chemical reaction, metal-chelate complexation process depends on various process parameters such as reaction pH, molar ratio of reactants, reaction temperature, reaction time, presence of competing ions in aqueous phase and many more. OVAT approach count the effect of only one parameter at a time while keeping all other parameters constant, thus it does not take into account the mutual interaction effect of various process parameters. Therefore, efforts have been pioneered in present study to perform multiparametric analysis of chelate assisted extraction of heavy metals from WEEE by employing RSM (response surface methodology). Optimization of process parameters using RSM includes the investigation of statistically designed combinations for maximizing the target response, estimation of coefficients by fitting the experimental data to the response functions, prediction of response using the developed regression model and checking the adequacy of the model34. Recently, RSM has been employed for the statistical analysis, development of regression model and optimization of process parameters for the extraction of metals from solid waste35-37. In the present study, design of experiments (BBD (box behnken design) and CCD (central composite design) matrix) was coupled with RSM to obtain mathematical and statistical optimization of extraction efficiency using a set of designed experiments for chelate assisted metal extraction process. Mathematical regression model were developed to statistically determine the synergic relationship between process variables. ANOVA studies were performed and 2D/3D contours were prepared in order to optimize the reaction parameters. Ramp functions were drawn based on the desirability >0.95 and collateral experiments were conducted to substantiate adequacy of the developed regression models. 8 ACS Paragon Plus Environment

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2. Experimental Work 2.1.

Raw Materials preparation and characterization

PCB of desktop computer and MB (batteries of mobile phones) were kindly provided by local WEEE collection center to carry out the present study. It is noteworthy that PCBs and MBs contain significant amount of metals and therefore, these two were chosen as potential source of metal recovery in present study, although MB is not included in e-waste classification. All the capacitors, resistors and polymeric materials were removed manually from PCB using pliers. The metal plate was shredded into the small pieces, crushed in the ball mill and then sieved to form homogenous powder of particle size 150µ. Figure 1(a) - 1(c) demonstrate different stages of the PCB dismantling process. MB could not be shredded and crushed directly by applying mechanical pressure. The plastic separator membrane between the electrode sheets gets damaged at high mechanical pressure. These practices also liberate fumes and lead to explosion; therefore it is not recommended for the dismantling of mobile batteries. An alternate safer approach was adopted in present study to avoid any accident or emission of hazardous fumes. One wall of the cell was removed manually and the stacked layers were separated. These stacked layers (roll) were then crushed to get the homogeneous powder of the desired particle size 150µ. Figure 1(d) - 1(f) demonstrate different stages of the mobile batteries dismantling process. Figure 1: Dismantling Stages of (a) Printed circuit board (b) shredded pieces of printed circuit board (c) crushed powder of printed circuit board (150 µ) (d) mobile batteries (MB) (e) stacked sheets (roll) (f) crushed powder of mobile batteries (150 µ). ICP-OES (Inductive coupled plasma optical emission spectrometry) was performed at SpectroLab, New Delhi (INDIA) to determine the metal composition of raw material. 14.7 wt.% copper (Cu2+) and 14.3 wt.% cobalt (Co2+) was found in the sample PCB and MB respectively. Trace amount of other precious metals (Au, Ag, Pd, Pt) were also present in 9 ACS Paragon Plus Environment


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PCB and MB sample, however extraction of precious metals was not covered in the scope of present study. SEM (Scanning electron microscopy) analysis was done with the aid of SEMEVO50 for PCB and SEM-Zeiss for MB at 20kV to investigate the changes in morphology of the sample after chelation experiments. Philips X’pert-1 X-ray diffractometer was employed for XRD (X-ray diffraction) analysis in order to identify the metal phase distribution in PCB/MB samples and their residues. 2.2. Metal Recovery from WEEE A novel green chelation-dechelation concept was employed to recover metals Cu2+ and Co2+ from PCB and MB respectively using EDTA (Fisher Scientific) as chelating agent. Experiments were performed in a batch reactor (500ml round bottom flask with three openings) under atmospheric reflux condition (using condenser) in the similar manner as described in literature4. Extractant solution was prepared by maintaining molar concentration of EDTA for a wide range from 0.25M – 0.75M. Nitric acid (HNO3) (Sigma Aldrich) and sodium hydroxide (NaOH, 0.1N) (Sigma Aldrich) were employed to maintain the pH of the extractant solution. Double distilled water of high purity was employed in all the experiments. Process flow chart for the chelation-dechelation process is shown in Figure 2. Figure 2: Proposed flow chart for chelation-dechelation process. 10 g of e-waste sample was added to the extractant solution and a certain solid to liquid (S/L) ratio was maintained to provide thorough mixing in the reactor. Molar concentration of chelating agent and S/L are interdependent parameters and can be collectively represented as MR (molar ratio of EDTA to metal). MR was varied from 1:1 to 10:1 for chelation experiments in the present study by varying molar concentration and S/L for a wide range. Once the reaction completed, the slurry was filtered through whatmann filter paper of 150 mm diameter in a funnel with porous disc (pore size 40−90 μ, porosity grade 2) using a 10 ACS Paragon Plus Environment

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vacuum pump. Residue was sent for the characterization after providing three times water washing. The filtrate (blue color for PCB; pink color for MB) was subjected to dechelation process in which metal-chelate complex was hydrolyzed by dropwise addition of HNO3 to the filtrate solution and kept still at room temperature for 6 hours. More than 93% EDTA was precipitated whereas metal-salt solutions (Cu(NO3)2 and Co(NO3)2) were present in their respective supernatants. Metal concentration in supernatant solutions was determined with the aid of Hitachi U-2900 spectrophotometer. Recovered EDTA was separated using vacuum filter and was effectively recycled in subsequent extraction cycles after washing with double distilled water (40°C) and drying at 100°C for 2h. More than 64% extraction efficiency was attained after three subsequent cycles of recovered EDTA (data not shown). The entire process starting from preparation of extractant solution, chelation reaction in the batch reactor, filtration, dechelation process and recovery of chelating agent was completed in 16 h. 3. Design of Experiments And Statistical Optimization RSM, a statistical optimization approach, was employed in present study to overcome the limitations of OVAT and to provide elaborated quality of information regarding effect of various process parameters on chelation efficiency. RSM approach proceeds with carrying out design of experiments, followed by evaluating coefficients in a regression model and prediction of response using the model38. It is an effective way to predict the target response affected by a number of input variables with the aim of optimizing the responses. The methodology finds an optimal set of experimental parameters that brings forth a maximum or minimum value of response, and can represent the direct and interactive effects of process parameters through 2D contours and 3D response surfaces38. RSM has been successfully employed in literature for modeling and optimization of ferric sulfate leaching of PCB39, simultaneous extraction of gold (Au3+) and Cu2+ from computer PCB35 and recovery of metals from spent zinc-manganese batteries36. Present study investigates the applicability of 11 ACS Paragon Plus Environment


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BBD and CCD matrices in conjunction with RSM for the statistical optimization of chelate assisted metal extraction efficiency. BBD is a rotatable second-order design which emphasizes the middle points of the variable’s range; therefore reaction does not need to perform at the extreme points37. CCD contains a fractional factorial design in which experiments are performed at a group of star points (-α and +α points) to estimate the curvature. Building upon our previous experimental studies26,33, five independent process parameters (molar concentration of the chelating agent (X1), solid to liquid ratio (S/L) (X2), reaction time (X3), reaction temperature (X4) and reaction pH (X5)) were selected as the prevalent parameters for proposed chelation studies. BBD design matrix at three different levels (-1, 0, 1) was coupled with RSM to investigate extraction of Cu2+ from computer PCB. Total 46 (8 axial, 32 factorial, six-fold repetition of center points) experimental runs were carried out to evaluate the mutual interaction effect of five independent variables on Cu2+ extraction. CCD small factorial design was employed at five different levels (-α, -1, 0, +1, +α) for the statistical optimization of extraction of Co2+ from MB. Total 26 experimental runs (11 factorial points, 10 star points and five-fold repetition of center points) were carried out and percentage extraction of Co2+ was determined. Table 1 lists the coded and experimental values of the independent variables. Table 1: Coded and experimental values of process parameters In the RSM, the quantitative pattern of relationship between desired response and independent input variables could be interpreted as shown in eq. (8): Y = f( x1, x2, x3……….., xn) ± ε

(8)

where Y is the response of the system (metal extraction efficiency in present study), xi’s are input variables (factors) and ε is difference between observed and predicted response (residual). Independent variables are assumed to be continuous and controllable by experiments with negligible errors. If the analysis of variance suggests a significant effect of 12 ACS Paragon Plus Environment

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the overall curvature, further experiments can be carried out to develop a regression model 40. As the experimental domain was made up of all combinations of factors that are possible to carry, the correlation between the metal extraction efficiency (%Y) and the set of independent variables (Xi ; i = 1-5) was incurred by quadratic polynomial equation shown in eq.(9)41: 𝑖=5

𝑌(%) = 𝛽0 + ∑ 𝛽𝑖 𝑋𝑖 + 𝑖=1

𝑖=5

∑ 𝛽𝑖𝑖 𝑋𝑖2 𝑖=1

𝑖=5 𝑖=5

+ ∑ ∑ 𝛽𝑖𝑗 𝑋𝑖 𝑋𝑗 + 𝜖 𝑖=1 𝑖≠𝑗=1

(9) Where Y(%) represents the response i.e. percentage recovery of metals from e-waste ; β0 is a constant; βi is the estimation of main effect of a factor i for the response (linear coefficient); βii is estimation of the second order factor i’s effect for the response (squared coefficient); βij is a cross-product coefficient which defines the estimation of the interaction effect between the factor i and the factor j for the response; ε represents residual error for the proposed model. Results were analyzed by the least-square method and response surfaces were generated to find the optimum reaction conditions for the extraction process. Adequacy of the proposed quadratic model was evaluated by ANOVA (analysis of variance) and regression coefficient. ANOVA42 is a means of validating the mathematical model by using F-value (Fisher’s variation sources)42 which subdivides the total variation of results in two dispersion: model and the experimental error43. Comparing these two variations illustrates whether the variation from the model is significant44. MS (Mean squares) values were obtained by taking the ratio of SS (sum of squares) of each variation source and respective DF (degree of freedom). Fvalue was calculated as the ratio of MS due to the model variation and MS due to error variance. It is a statistically valid measure of how well the factors describe the variation in the data about its mean. Three-dimensional response surfaces and the two-dimensional contour



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plots were drawn using Design Expert 10 (StatEase, USA) software to investigate the interaction effects of process variables and to identify the optimal region for the process. Ramp functions45 were drawn for the proposed model on the basis of desirability function approach in order to localize the well-defined optimal zone for metal extraction. Desirability function approach identifies optimum settings of input variables simultaneously in order to optimize performance levels for target response. This approach was originally introduced by Harrington46 and then modifications were proposed by Derringer and Suich47. The desirability process is carried out in two steps i.e. finding the levels of the independent variables that simultaneously produce the most desirable predicted responses and then maximizing overall desirability with respect to the controllable factors. Each response (Yi) is first converted into an individual desirability function (di) varying over the range 0 ≤ di ≤ 1. If response Yi is at its goal or target, then di = 1, and if the response is outside an acceptable region, di = 0. In that case, design variables are chosen to maximize the overall desirability D using following equation (10)44: 𝐷 = (𝑑1 × 𝑑2 × … … . . 𝑑𝑛 )

1⁄ 𝑛

(10)

4. Results And Discussions 4.1. Regression model of response and ANOVA studies Regression analysis and the estimation of the regression coefficients were performed on the basis of experimental data obtained by design matrix. Design matrices (BBD for PCB; CCD for MB) with the observed and predicted response(s) for each set of reaction parameters are given in supporting information (Table S1 and Table S2 respectively). The observed responses were correlated with independent variables employing multiple regressions through the least-square method to fit the second order polynomial equation. Non-significant factors were eliminated using the stepwise elimination method48,49. ANOVA was performed in order 14 ACS Paragon Plus Environment

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to investigate the adequacy of developed regression models. ANOVA results of observed responses (% Cu2+ (Y1) and % Co2+ (Y2)) are shown here in Table 2. Developed mathematical models to predict the metal extraction efficiency from PCB and MB are given in eq. (11) and (12) respectively. % 𝐂𝐮𝟐+ 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 (𝐘𝟏 ) = +85.33 + (13.24 ∗ X1 ) + (14.36 ∗ X2 ) + (12.50 ∗ X3 ) + (9.78 ∗ X4 ) + (1.56 ∗ X5 ) − (3.07 ∗ X1 ∗ X2 ) − (2.95 ∗ X1 ∗ X3 ) − (2.38 ∗ X1 ∗ X4 ) − (2.12 ∗ X1 ∗ X5 ) − (3.02 ∗ X3 ∗ X4 ) − (8.35 ∗ X12 ) − (8.01 ∗ X22 ) − (7.41 ∗ X32 ) − (3.89 ∗ X42 ) − (1.26 ∗ X52 )

(11)

% 𝐂𝐨𝟐+ 𝐄𝐱𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧 (𝐘𝟐 ) = +86.21 + (11.81 ∗ X1 ) + (4.67 ∗ X2 ) + (13.23 ∗ X3 ) + (7.93 ∗ X4 ) + (3.02 ∗ X5 ) + (0.37 ∗ X1 ∗ X2 ) + (3.56 ∗ X1 ∗ X3 ) − (2.20 ∗ X1 ∗ X4 ) + (1.57 ∗ X1 ∗ X5 ) + (0.068 ∗ X2 ∗ X3 ) − (0.56 ∗ X2 ∗ X4 ) − (1.37 ∗ X2 ∗ X5 ) + (2.65 ∗ X3 ∗ X4 ) + (1.44 ∗ X3 ∗ X5 ) + (2.81 ∗ X 4 ∗ X5 ) − (5.16 ∗ X12 ) − (1.84 ∗ X22 ) − (4.37 ∗ X32 ) − (1.89 ∗ X42 ) − (2.41 ∗ X52 )

(12)

Though, in general, the positive sign indicates synergistic (acting together) effect, whereas negative sign signifies the antagonistic (resistance) effect as written in eq. (11) and eq. (12), here the synergic and antagonistic behavior could be rather explained on the basis of coefficient values associated with linear, quadratic and interaction. The coefficient for interaction term of two variables was observed to be lesser than the corresponding linear variables in the present study. Also, lesser magnitude and negative coefficients for squared variables inferred an antagonistic behavior of process parameters on extraction efficiency. Thus, linear variables were found to have more synergistic effect on extraction efficiency rather the mutual interaction effect of any two variables. The saturation and amplification effects of each linear, squared and combinatorial variable are discussed in more detail in section 4.3. Regression models were considered significant at 95% confidence interval on the basis of high F-value (129.30 for Y1 and 237.61 for Y2) which exceeds the tabulated F-value for both 15 ACS Paragon Plus Environment


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the models at 5% level. Several times greater F-value from unity indicates good acceptability of the variation around its mean value50. It can be depicted from eq. (11) and eq. (12) that % Cu2+ and % Co2+ extraction from WEEE fitted well to quadratic polynomial model. t-test was applied to investigate significance of each regression coefficient and the corresponding pvalues (probability of error values) was calculated to determine whether the association between the response and operating parameters is statistically significant. From the p-values, defined as the smallest level of significance leading rejection of null hypothesis 51, it appears that main effect of each factor and the interaction effects are statistically significant when p < 10-4. All the process parameters exert a significant influence with a positive linear effect on extraction of Cu2+ from PCB which can be substantiated by relatively low p-values of process parameters (Table S3). However, p-value was observed to be considerably high (0.0068) for reaction pH (X5) which indicated less significant effect of pH on extraction efficiency than other process parameters. Interaction terms (X2X3, X2X4, X2X5, X3X5, X4X5) were excluded from the regression model (eq. 11) due to higher p-values (0.2756, 0.2469, 0.9252, 0.3886, 0.6562 respectively) as shown in Table S3. Negative coefficients with smaller magnitude and considerably high p-value (0.0074, 0.0333, and 0.0097 respectively) for the interaction terms (X1X2, X1X3 and X1X4) reflect antagonistic behavior towards Cu2+ extraction efficiency. It could be inferred from the regression model and corresponding p-values for X1X2 interaction that with the increase in concentration of chelating agent beyond a certain limit, Cu2+ extraction efficiency decreases. Kim and coworkers52 suggested that only a small fraction of the chelating agent is effectively utilized to extract metal from contaminated site. Major fraction of chelating agent is freely available in aqueous solution at high MR values and form metal-chelate complexes with other metal cations; therefore, no effective increase in extraction efficiency can be observed beyond a certain S/L when the concentration of reagent is enough for the metal extraction.


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Extraction of Co2+ (Y2) from mobile batteries was predicted using regression model given in eq. (12). It is clearly depicted that all process parameters exert a significant effect on recovery of Co2+ from MB. Also, mutual interaction effect between molar concentration of EDTA and reaction temperature was found to be substantial based on the regression coefficient and corresponding p-value. Reaction temperature demonstrated dominant interaction with reaction time and reaction pH. A wide range of reaction pH was varied (pH = 5 to pH = 13) to investigate the extraction of Co2+ from MB. It was observed that EDTA did not dissolve at acidic pH therefore; extraction efficiency was considerably low at acidic pH. When pH was increased from pH = 7 to pH = 11, no any significant change in extraction efficiency was observed which can be related with the accumulation of insoluble metal hydroxide at alkaline pH. It can be explained by the generation of an autogenous pH due to displacement of hydrogen ion from a protonated form of the chelating agent by metal ion. This autogenous pH depends on the base strength of the counter ions of metal salt. At the most acidic pH value (pH = 1 - 2), the fully protonated H6EDTA2+ form predominates, whereas at the alkaline pH above pH = 9, the fully deprotonated L4− form is prevalent33. Within the pH range 2 - 6, the free form of EDTA is H2L−2 which provides the availability of two protons for displacement by metal ion chelation. EDTA exists in the form of HL−3 between pH = 6 - 8; therefore, the slope of the curve changes to 1, and only one proton can be displaced. Thus, up to pH = 9, the rise of pM with an increase in pH shows slight increase in the degree of metal binding due to hydrogen displacement. Above pH = 9, chelates are formed from fully dissociated chelant L−4, where no hydrogen ions are displaced and the slope of the curve is zero; therefore, above pH = 9, pM is independent of pH. Accordingly, the extraction efficiency of EDTA does not depend on the reaction pH beyond pH = 9; thus, no significant increase was observed in metal extraction at alkaline pH. Jadhao and coworkers4 reported decrease in extraction efficiency with increase in reaction pH due to presence of competing metal cations in e-waste



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which may interfere with the formation of metal-chelate complex at alkaline pH. Relatively higher p-values for the quadratic and interaction terms (shown in Table S4) depict the less significant combinatorial effect of any two variables. Similar observations were also illustrated in eq. (12) where coefficients of linear variables were considerably higher than the interaction/quadratic terms. Regression coefficient of quadratic X5 (-2.41) also substantiated the negative impact of reaction pH at extreme conditions. Results were found to be in concordance with literature53-55. It may be concluded from ANOVA results and proposed regression models that the variance was divided into three different zones i.e. linear, quadratic and interaction in order to assess the adequacy of developed regression model and the relative significance of each term. Linear, quadratic and interaction effects were found to exert a significant synergic/antagonistic effect on the chelate assisted extraction of metals from PCB and MB. Table 2: ANOVA studies of quadratic empirical models developed for % Cu2+ and % Co2+ extraction. The ‘‘Lack of Fit Test’’ compares the residual error to the pure error from replicated design points. Lack of fit was observed to be non-significant which is considered favorable for empirical models. A lower value of 2.80% and 1.51% coefficient of variation indicated a high degree of precision and reliability of the experiments to achieve Y1 and Y2 respectively. High values of the regression coefficients (R2 = 0.99 for Cu2+ and Co2+ recovery) accounted for a good agreement between the experimental and predicted values of the fitted models. The values of regression coefficient implied that >99% of the response variability could be explained by the developed regression model56. Descriptive qualities (adjusted R2 and predictive R2) were also calculated for the developed regression models. A high regression coefficient along with the high value of the adjusted regression coefficient indicates the


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potential of empirical model to satisfactorily describe the system behavior within the investigated range of studied parameters41. Predicted R2 is a measure of how good the model predicts a response value. The adjusted R2 and predicted R2 should be within 0.20 of each other to be in reasonable agreement. If they are not, there may be a problem with either the data or the model44. The values of both parameters were observed to be close to 1 which testified the good quality of proposed models43. Also, difference in the values of adjusted R2 and predicted R2 was observed to be less than 0.07 for both the models which validated the precision of the modified model obtained after stepwise elimination method. 4.2. Error Analysis: A plot between the observed and predicted response for % extraction of Cu2+ and Co2+ is shown in Figure 3(A) and Figure 3(B) respectively. All of the points were equally scattered around the diagonal line, which depicted low discrepancies between experimental and predicted data points. The predicted values of the extraction efficiency lie within ± 4% of the experimentally observed response as demonstrated in Figure 3(A) and Figure 3(B). Figure 3: Graphical representation of comparative analysis of observed and predicted (A) % Cu2+ extraction efficiency (B) % Co2+ extraction efficiency. Error analysis was performed in order to evaluate the goodness of fitting and prediction accuracy of the developed regression models. RMSE (root mean square error) was calculated using following expression shown in eq. (13): 1

(%)𝑅𝑀𝑆𝐸 = (√(( ) ∗ ∑𝑁 𝑖=1 ( 𝑁

𝑌𝑒𝑥𝑝 −𝑌𝑝𝑟𝑒 𝑌𝑒𝑥𝑝

2

) )) ∗ 100

(13)

𝑖

It is a measure of the difference between values predicted by the developed regression model and the values experimentally obtained for the system that is being modeled. RMSE



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aggregates the individual differences (residuals) into a single measure of predictive power29. Lower magnitude of RMSE is desirable for better fitting of the data. Very small magnitude of RMSE (1.5% for Cu2+ extraction and 0.74% for Co2+ extraction) was obtained for both the developed regression models which reasserted the goodness of fitting. Thus, quadratic models shown in eq. (11) and eq. (12) were found to be suitable for modeling the Cu2+ and Co2+ extraction process respectively. 4.3. Localization of optimum conditions 4.3.1. 3D response surfaces The interaction effect of process variables on Cu2+ extraction was investigated using 3D response surface plots and corresponding 2D contours. 3D response surface plots give a visualization of the influence of process parameters on the response and help to get the optimal values of independent variables for maximization of extraction efficiency. Figure 4(A) and Figure 4(B) show the 3D response surfaces and their corresponding contours for chelate assisted extraction of Cu2+ from PCB. These plots demonstrated the effect of two process factors on the target response at a time, while other factors were kept at level zero. The 3D response surface plot and the contour plot in Figure 4(A) gives the (%) extraction of Cu2+ as function of molar concentration of EDTA (X1) and S/L (X2). Molar concentration was varied from 0.25M to 0.75M and S/L was varied from 1/10 to 1/30 in order to keep the MR greater than 1. Based on molar conc. of EDTA in solution and S/L, MR was varied from 1:1 to 10:1. The spherical shape of 3D surface and concentric 2D contour plots depicted the equable effect of both parameters on Cu2+ extraction efficiency. It may be seen from Figure 4(A) that very less extraction efficiency (36.6%) was predicted when both factors were kept at low levels and MR value was equal to 1. It indicates equal proportion of chelating agent and metal in the solution. Cu2+ extraction efficiency increased with an increase in MR value and nearly 93% metal was extracted at MR 10:1 while other parameters were set at zero level 20 ACS Paragon Plus Environment

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for the reaction. Increase in molar concentration of EDTA upto 0.75M (high level) at S/L = 1/10 (low level) resulted into 69% extraction of metal. This behavior is acceptable because an excess amount of chelating agent moves the reaction in a forward direction due to the reversible nature of chelation reaction. Decrease in S/L from 1/10 to 1/30 with a constant molar concentration 0.75M also demonstrated nearly 23% increase in % Cu2+ extraction, however beyond S/L = 1/20, extraction efficiency becomes nearly constant. An exponential increase in metal recovery was observed while moving from MR = 1 to MR 4.3 (low level to center level for both parameters) and nearly 85.3% recovery of Cu2+ metal was reported at MR 4.3 (X1 = 0.5M, X2 = 1/20). Beyond this MR value, only 8% increase in extraction efficiency was observed when both factors were kept at high level (MR = 10) and thus a saturation effect was established above MR 4.3. It could be inferred from the observations that MR 4.3 is the optimum MR value for metal extraction from PCB. The observed 3D response surfaces were in concordance with the developed regression model where negative coefficients of squared variables (X2) and significantly lower coefficient of the interaction variable (X1X2) substantiated that extraction efficiency does not affect a lot beyond a certain MR value and therefore, it is not desirable to go upto high level of both parameters. Kim and coworkers52 also reported negligible increase in extraction efficiency beyond a certain S/L when concentration of chelating agent is present above than MR value 1:1. It may be concluded that the chelate concentration should be above the stoichiometric amount to attain higher percentage of extractable metals; however a saturation zone establishes when concentration of chelating agent is enough for effective metal recovery. Results were found to be in agreement with the literature26,33,52. Figure 4(A): 3D response surface and 2D contour to study interaction effect of X 1 and X2 on extraction of Cu2+ from printed circuit board.



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The response surface of Figure 4(B) illustrated interaction effect of X1 and X5 on extraction of Cu2+ from PCB. Reaction pH was varied from neutral (pH = 7) to alkaline (pH = 11) pH in BBD matrix. It may be seen that with the increase in reaction pH, nearly 6% increase was observed in Cu2+ extraction at low level of X1, thus no any considerable amplification zone was observed. At higher molar concentration of chelating agent, extraction efficiency was found to be constant by varying reaction pH from 7 to 11. Straight lines in the contours represented the nonsignificant behavior of reaction pH. Similar behavior for reaction pH was observed in the ANOVA studies (shown in Table S3) and developed regression model as given in eq. (11). No any appreciable variation was obtained in the coefficients of linear pH (+1.56) and coefficient of interaction parameters (+2.06 for X1X5), whereas the linear coefficient of X1 was found to be relatively high (coefficient value = +13.24). It substantiated the non-significant effect of reaction pH at alkaline pH and therefore neutral pH could be considered a feasible pH for Cu2+ extraction. Chauhan and coworkers33 explained that hydrogen availability for displacement decreases with the shift in pH from acidic to alkaline and consequently, deprotonated forms are prevalent which leads to constant extraction efficiency beyond a certain pH range. A clear saturation zone (elliptical contours) was observed in 2D contours at center level of both process parameters. Moving towards high level of any of these parameters may cause decrease in extraction efficiency. Thus mutual interaction effect may pose an antagonistic effect on extraction efficiency beyond a certain limit of process parameters. Results were found to be in concordance with the literature33,53. Figure 4(B): 3D response surface and 2D contour to study interaction effect of X 1 and X5 on extraction of Cu2+ from printed circuit board. The mutual effect of molar concentration of chelating agent (X1) and reaction temperature (X3) is shown in Figure S1. Semi elliptic contours demonstrated less significant interaction effect between X1 and X3. The BBD matrix indicated < ± 4% deviation between experimental 22 ACS Paragon Plus Environment

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observations and predicted responses at extreme values of molar concentration of chelating agent and reaction temperature (when both factors are at a low level or a high level). 3D response surface and 2D contours to demonstrate the synergic effect between X1 and X4 are also shown in Figure S2 of supporting information. Maximum 93.1% Cu2+ extraction was predicted at high level of X1 within 3h of reaction time, when all other parameters were kept constant at level zero. It was also observed that chelation process takes place at a faster rate in initial reaction hours therefore, nearly 78% Cu2+ was recovered within one hour while as the reaction progresses, a saturation effect was observed and extraction efficiency became nearly constant. Effect of various process parameters on extraction of Co2+ from mobile batteries was investigated using 3D response surface plots and corresponding 2D contours. Figure 5(A) and Figure 5(B) demonstrate the interaction effect of reaction pH with other process parameters using 3D response surfaces and corresponding contours. Interaction effect of reaction pH and S/L is shown in Figure 5(A). It is clearly evident that recovery of Co2+ was less than 90% when both parameters were at their high level. Also when S/L was decreased from 1/20 to 1/40, nearly 10% increase in extraction of Co2+ was observed. Similar observation were obtained when pH was increased from pH = 7 to pH = 11, extraction efficiency was increased from 73% to 81%. These observations led to the conclusion that individual and interaction effect of reaction pH and S/L was trivial as compared to other process parameters on recovery of Co2+ from MB. Small positive linear coefficient in eq. (12) and non-significant pvalue in ANOVA results for the individual parameters (reaction pH and S/L) substantiated the 3D responses obtained from the predicted model. Figure 5(A): 3D response surface and 2D contour to study interaction effect of X 2 and X5 on extraction of Co2+ from mobile batteries.



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Figure 5(B) showed a strong mutual interaction effect of reaction pH with reaction time. Nearly 95% extraction of Co2+ was predicted at high level of reaction time and reaction pH, whereas when both parameters were kept at low level, 22% less extraction of Co2+ was achieved as shown in Figure 5(B). Nearly 10% increase was observed in Co2+ extraction when X4 and X5 were moved to high level from center level, thus saturation effects were not clearly visible at center level of process parameters. High level of reaction time was found to be desirable for higher extraction of Co2+ from MB. Figure 5(B): 3D response surface and 2D contour to study interaction effect of X 4 and X5 on extraction of Co2+ from mobile batteries. Similar 3D response surfaces were obtained to investigate the interaction effect between X1X5 and X3-X5 and are shown in Figure S3 and Figure S4 respectively. It can be inferred from all the 3D response surfaces and 2D contours obtained for % Cu2+ and % Co2+ extraction that mutual interaction effects exist between reaction parameters, though it was not as significant as the individual effect of reaction parameters. Semi-elliptical or straight line contour plots confirmed the less significant mutual interaction effect of process parameters. 4.3.2. Numerical optimization of process parameters The numerical optimization was performed using Design Expert 10 software in order to find the specific point that maximizes the desirability function. By using numerical optimization, a desirable value for each input factor and response can be selected. Therein, the possible input optimizations that can be selected include: the range, maximum, minimum, target, none (for responses) and set so as to establish an optimized output value for a given set of conditions. In this study, the input variables were given specific range values, whereas the response was designed to achieve a maximum. The focus of present study was to maximize the extraction of metals from PCB and MB with recalculating all responsible process parameters using 24 ACS Paragon Plus Environment

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desirability functions. In order to maximize the metal recovery, best possible combinations of reaction parameters were obtained with the help of point prediction feature of the Design Expert 10 software. Desirability varies from 0 to 1 depending upon the nearness of the response toward the objective38. The optimized solutions were found to have desirability of more than 0.95 under the given set of constraints. 15 best combinations with desirability > 0.95 were selected out of 100 available combinations from the Design Expert software and distribution of process parameters was investigated in order to localize the optimal zone of process parameters. The distribution curve for all process parameters and corresponding extraction of Cu2+ is shown in Figure 6. It can be observed from Figure 6 that X3 and X5 process parameters were set towards high level whereas remaining three parameters were localized around the center level. Although, largest variation was observed among the chosen 15 best combinations for reaction time (120 - 170.7 min) and reaction temperature (80 95.2°C), nevertheless process parameters for more than 80% of chosen combinations were localized in a very narrow range which depicted that the process optimum is well defined. Distribution curve suggested the optimum range of process parameters i.e molar concentration of EDTA (0.5-0.6M); S/L (1/19 to 1/23 g/ml); reaction temperature (83-89°C) ; reaction time (130-148 min.) and reaction pH (8-9) to achieve Cu2+ extraction greater than 90% from PCB. No any remarkable variation was observed for reaction pH in all chosen combination and thus, X5 can be considered the least relevant parameter for present study. It can also be inferred that higher reaction temperature may improve the reaction kinetics and therefore it is advisable to go at high level for reaction temperature and time to maximize the extraction efficiency. Figure 6: Distribution curve to define optimal zone of process parameters for extraction of Cu2+ from printed circuit board.



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Ramp graphs for one of those best possible combinations of reaction parameters is also shown in Figure 7 for the validation of the optimized extraction of Cu2+ from PCB. The dot point on the ramp showed horizontal movement of the point and goal satisfaction37. Figure 7: Ramp function for % Cu2+ recovery from printed circuit board. Similar distribution curve and ramp graphs were obtained for the extraction of Co2+ from MB and shown in supporting information (Figure S5 and Figure S6 respectively). All process parameters were observed to lie in a very narrow range and thus well-defined optima was obtained for Co2+ extraction. It could be reasoned out from distribution curve of process parameters for Co2+ extraction that optimal range of molar conc. of EDTA (X1 : 0.33-0.41M); S/L ratio (X2 : 1/25-1/28 g/ml) and reaction pH (X5 : 8.3-8.8) were found close to the zero level of CCD matrix, however optimal zone for reaction temp (X4 : 87-92°C) and reaction time (163-178 min.) were observed to lie towards high level of CCD matrix in order to attain nearly 95% Co2+ extraction from MB. Reaction pH was inferred as the least relevant parameters for metal extraction based on the defined range of pH in the present study. Different treatment combinations were chosen randomly among the series of best possible combinations of process parameters and confirmatory experiments were performed. Experiments were carried out at the nearest possible experimental values and results were found according to the constraint set for the desired extraction efficiency. Reproducibility was investigated by performing each experiment in triplicates and the actual value of the response was calculated as the average of these three trials. Data is given in Table S5 of supporting information. Maximum ± 4% deviation was observed between experimental and predicted extraction efficiency. All of the validation checks substantiated well fitted regression models for the chelate assisted metal extraction process.


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5. Characterization Of Raw Material And Residue SEM analysis and XRD patterns were investigated in order to evaluate the physicochemical properties of raw material (PCB and MB) and their residues obtained after the chelationdechelation experiments. Difference in the morphology of raw material and final residue was investigated by SEM analysis at 20 kV. SEM images of raw materials and residue of PCB and MB are shown here in Figure 8. Pellets (6 mm diameter) of raw material and residue were prepared and mounted on a circular metallic sample holder. A 20-50nm thick gold coating was provided on the samples in order to provide electrical conductivity. Rough surface, clog formation and accumulation of metals on the surface of PCB and MB are clearly evident in Figure 8(A) and 8(C) respectively. SEM image of the residue obtained after the chelation experiments at center level points of design matrices are shown in Figure 8(B) and 8(D) for PCB and MB respectively. A clear surface and void formation indicated the efficient removal of Cu2+ and Co2+ from PCB and MB respectively. Figure 8: SEM images of (A) Printed circuit board: raw material (B) Printed circuit board: residue obtained after chelation experiments (C) Mobile batteries: raw material (D) Mobile batteries: residue obtained after chelation experiments. XRD patterns for e-waste raw materials and their respective chelation residues at center level data points of design matrices are shown in Figure 9. Figure 9(A) demonstrates the XRD patterns for PCB where several small peaks at 2θ of 29.4° (111), 31.3° (111), 36.2° (220), 43.3° (111), 50.2° (200) were observed. These peaks affirmed the presence of Cu2+ metal ion in the PCB raw material. Relatively weak (negligible) diffraction peaks were observed at the same angle for PCB residue obtained after the chelation experiments as shown in Figure 9(A). Similar results were obtained for MB raw material and residue. A strong peak at angle 26.7° was observed for both the samples (MB raw material and MB residues) as shown in



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Figure 9(B), although intensity of MB residue was considerably less than raw material. These observations substantiated the significant removal of Cu2+ and Co2+ from PCB and MB respectively. Figure 9: XRD Patterns of (A) Printed circuit board: raw material and residue (B) Mobile batteries: raw material and residue. 6. Conclusion Present study is a novel effort to recover metals from WEEE by employing a novel green chelation-dechelation concept. Response surface methodology in conjunction with design of experiments was successfully used for the development of quadratic models with regression coefficient greater than 0.99 for spent PCB and MB. Experimental data and predicted data points suggested that chelation process can effectively work even at neutral pH and extraction efficiency decreases with increase in reaction pH beyond a certain limit due to unavailability of hydrogen ions to displace by metal ions. A mutual interaction effect existed between reaction parameters, though it was not as significant as the individual effect of reaction parameters. Optimum range of process parameters i.e molar concentration of EDTA (0.50.6M); S/L ratio (1/19 to 1/23 g/ml); reaction temperature (83-89°C) ; reaction time (130-148 min.) and reaction pH (8-9) was achieved using desirability function approach to achieve Cu2+ extraction greater than 90% from PCB. Similarly, distribution curve suggested optimal range of molar conc. of EDTA (X1: 0.33-0.41M); S/L ratio (X2: 1/25-1/28 g/ml), reaction time (X3: 163-178 min.), reaction temp (X4: 87-92°C) and reaction pH (X5: 8.3-8.8) to attain more than 94% Co2+ extraction from MB. Confirmatory experiments demonstrated maximum ±4% deviation between experimental and predicted extraction efficiency and thus, substantiated well fitted regression models for the chelate assisted metal extraction process. Characterization studies corroborated the successful recovery of metals from electronic scrap.


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The proposed correlation in present study may prove useful to bring up chelation process as an alternative eco-friendly approach in order to overcome the problem of e-waste management and resource scarcity. There are certain issues which need to be addressed to carve-out new territory for metalligand complexation. Future attempts should focus on the sustainability, economics and environmental impact of the process for commercialization of the process. Although, more than 85% extraction of metal(s) was achieved in present study using chelation technology at laboratory scale, however pilot plant studies, are still necessitated to demonstrate it as a feasible process on industrial platform. A pilot plant for chelation-dechelation process should include sequencing operation of various unit operations i.e. comminution circuit, reactor sizing and agitator selection, chelation process in stirred tank reactor, filtration, dechelation with interstage cooling using heat exchanger and separation of metals in solution by filtration. The mathematical regression models would be crucial in designing the pilot plant/commercial plant for the extraction of heavy metals using environmental friendly chelation technology.

Supporting Information Supporting information includes the design matrices for both metals, 3D response surfaces of few process parameters, distribution curve and ramp graphs for Co2+ extraction. This information is available free of charge via the Internet at http: //pubs.acs.org.



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List of Abbreviations: ABS

Acrylonitrile butadiene styrene

ANOVA

Analysis of variance

BBD

Box behnken design

BDE

Brominated diphenyl ether

BFRs

Brominated flame retardants

CCD

Central composite design

DF

Degree of freedom

EDTA

Ethylenediaminetetraacetic acid

EEE

Electronic and electrical equipment

F-value

Fisher’s variation

ICP-OES

Inductive coupled plasma optical emission spectrometry

MB

Mobile batteries

MR value

Molar ratio of EDTA to metal

MS

Mean of squares

OECD

Organization for Economic Co-operation and Development

OVAT

One variable at a time

PAHs

Polycyclic aromatic hydrocarbons

PC

Polycarbonate

PCB

Printed circuit board

p-value

Probability of error values

RMSE

Root mean square error

RSM

Response surface methodology

SEM

Scanning electron microscopy

SS

Sum of squares

WEEE

Waste electronic and electrical equipment

XRD

X-ray diffraction


Page 30 of 43

Page 31 of 43

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Aqueous

Solution

by

Brushite.

Arab.

J.

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Page 38 of 43

List of Tables: Table 1: Coded and experimental values of process parameters. Table 2: ANOVA studies of quadratic empirical models developed for % Cu2+ and % Co2+ extraction.


Page 39 of 43

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List of Figures: Figure 1: Dismantling Stages of (a) Printed circuit board (b) Shredded pieces of printed circuit board (c) Crushed powder of printed circuit board (150 µ) (d) Mobile batteries (MB) (e) Stacked sheets (roll) (f) Crushed powder of mobile batteries (150 µ). Figure 2: Proposed flow chart for chelation-dechelation process. Figure 3: Graphical representation of comparative analysis of observed and predicted (A) %Cu2+ extraction efficiency (B) %Co2+ extraction efficiency. Figure 4(A): 3D response surface and 2D contour to study interaction effect of X1 and X2 on extraction of Cu2+ from printed circuit board. Figure 4(B): 3D response surface and 2D contour to study interaction effect of X 1 and X5 on extraction of Cu2+ from printed circuit board. Figure 5(A): 3D response surface and 2D contour to study interaction effect of X2 and X5 on extraction of Co2+ from mobile batteries. Figure 5(B): 3D response surface and 2D contour to study interaction effect of X4 and X5 on extraction of Co2+ from mobile batteries. Figure 6: Distribution curve to define optimal zone of process parameters for extraction of Cu2+ from printed circuit board. Figure 7: Ramp function for % Cu2+ recovery from printed circuit board. Figure 8: SEM images of (A) printed circuit board: raw material (B) printed circuit board: residue obtained after chelation experiments (C) mobile batteries: raw material (D) mobile batteries: residue obtained after chelation experiments. Figure 9: XRD Patterns of (A) printed circuit board: raw material and residue (B) mobile batteries: raw material and residue. 39 ACS Paragon Plus Environment


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Page 40 of 43

Table 1: Coded and experimental values of process parameters. Design

Parameter

Matrix

Values

No. of experiments

Molar conc.

S/L (g/ml)

Reaction Temp.

Reaction Time

Reaction pH

at each level for

(M) (X1)

(X2)

(°C) (X3)

(min) (X4)

(X5)

individual process parameter Low level (-1)

8

0.25M

1/10

60°C

60 min

7

Center Point (0)

30

0.5M

1/20

80°C

120 min

9

High Level (+1)

8

0.75M

1/30

100°C

180 min

11

Star Point (-α)

1

0.035M

1/11.7

44°C

53 min

5.35

CCD

Low Level (-1)

5

0.2M

1/20

60°C

90 min

7

(Total 26

Center Point (0)

13

0.4M

1/30

80°C

135 min

9

Experiments) High Level (+1)

6

0.6M

1/40

100°C

180 min

11

1

0.763M

1/48.2

116°C

216 min

12.6

BBD (Total 46 Experiments)

Star Point (+α)


Page 41 of 43

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Table 2: ANOVA studies of quadratic empirical models developed for % Cu2+ and % Co2+ extraction. Sum of Source

Mean

Squares

DF

(SS)

p-value

F

Square (MS)

Value

Prob > F

129.30

< 0.0001

Significant

1.34

0.4024

not significant

(%) Cu2+ Extraction from Printed Circuit Board Regression Analysis Model

11496.23

20

574.81

Residual

111.14

25

4.45

Lack of Fit

93.64

20

4.68

Descriptive Statistics Std. Dev.

2.11

R-Squared

0.99

Mean

75.28

Adj R-Squared

0.98

C.V. %

2.80

Pred R-Squared

0.96

PRESS

399.77

Adeq Precision

40.78

(%) Co2+ Extraction from Mobile Batteries Regression Analysis Model

6395.12

20

319.76

Residual

6.73

5

1.35

Lack of Fit

6.1

1

6.1

237.61

< 0.0001

Significant

38.86

0.934

not significant

Descriptive Statistics Std. Dev.

1.16

R-Squared

0.9989

Mean

76.83

Adj R-Squared

0.9947

C.V. %

1.51

Pred R-Squared

0.9231

PRESS

4397.21

Adeq Precision

55.229



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For Table of Contents Only:


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123x84mm (300 x 300 DPI)

ACS Paragon Plus Environment

Statistical Optimization of Heavy Metal (Cu2+ and Co2+) Extraction

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