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Sustainable recycling process for metals recovery from used printed circuit boards (PCBs)
Accepted Manuscript Sustainable recycling process for metals recovery from used printed circuit boards (PCBs)
Deblina Dutta, Rekha Panda, Archana Kumari, Sudha Goel, Manis Kumar Jha PII: DOI: Article Number: Reference:
S2214-9937(17)30164-1 doi:10.1016/j.susmat.2018.e00066 e00066 SUSMAT 66
To appear in:
Sustainable Materials and Technologies
Received date: Revised date: Accepted date:
7 November 2017 9 June 2018 18 June 2018
Please cite this article as: Deblina Dutta, Rekha Panda, Archana Kumari, Sudha Goel, Manis Kumar Jha , Sustainable recycling process for metals recovery from used printed circuit boards (PCBs). Susmat (2018), doi:10.1016/j.susmat.2018.e00066
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ACCEPTED MANUSCRIPT Sustainable recycling process for metals recovery from used printed circuit boards (PCBs) Deblina Duttaa,b, Rekha Pandab, Archana Kumarib, Sudha Goela, Manis Kumar Jhab,* a
Corresponding Author: Tel.: +91-657 2345302; FAX +91-657-2345213
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School of Environmental Science & Engineering, Indian Institute of Technology Kharagpur (IIT), Kharagpur-721302, India b Metal Extraction & Recycling Division, CSIR-National Metallurgical Laboratory, Jamshedpur-831007, India
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E-mail address: [email protected]; [email protected] (M.K. Jha)
ACCEPTED MANUSCRIPT Abstract In comparison to extraction of metals from limited primary sources, the recycling of metals / materials from various alternative resources particularly from metallurgical waste and complex such as E-waste (Electronic waste) are gaining importance in view of energy, purity and
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environmental concern. In all E-waste, PCBs are essential components, which contain nearly
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28% metallic, 23% plastic and 49% ceramic materials in a complex form. Due to tremendous
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increase in e-waste globally, the recycling of PCBs to recover metals are getting importance which will not only mitigate the environmental pollution but will also conserve the natural
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resources and energy. PCBs contain copper (Cu) in major, therefore experiments were carried
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out to optimize different process parameters viz. effect of acid concentration, pulp density, temperature, time, etc. About, 91.58% Cu was found to be leached using 3M HNO 3 maintaining
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75 g/L pulp density at temperature 75 °C and mixing time 120 min. The two stages leaching
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under the similar condition resulted in the recovery of 99.99% of Cu. To optimize the conventional hydrometallurgical process, the Response Surface Methodology (RSM) was also
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studied. The result obtained by using the RSM model will help the researchers to validate the
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data scientifically with less number of experiments. Kinetics of Cu leaching fitted well with the “Chemical reaction control dense constant size cylindrical particles model” i.e. 1-(1-X)1/2 = K ct.
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The leach liquor generated will be further purified by the method of solvent extraction (SX)/ ion exchange (IX) to get purified metallic solution. From the pure solution obtained, the metal/salt could be produced by electro-winning/ precipitation and crystallization, respectively. Keywords: E-waste; PCBs; Recycling; Hydrometallurgy; RSM
ACCEPTED MANUSCRIPT 1. Introduction Scarcity of time and invasion of advancement in the field of technology has compelled the society to replace the old electrical and electronic appliances with the new one having modified and versatile features. In the competition, the old electrical and electronic gadgets finally have
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created their place as e-waste around the world. E-waste can be classified into large Electrical
personal
computers,
refrigerators,
washing
machines,
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Televisions,
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and Electronic Equipments (EEE) and small EEEs depending on their size distribution. microwave
etc.
are
categorized as large equipments while small equipment includes mobile phones, batteries,
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tablets, laptops etc. Irrespective of their sizes, they all contain a common component, known as
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PCBs [1]. These PCBs are complex in nature, and comprises of valuable as well as hazardous metals, which have been studied, analyzed and reported by a number of researchers [2-7]. The
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PCBs of personal computers are rich in metals and measured as a high value waste which
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constitutes about 6% of the total weight of e-waste [4]. Waste PCBs typically contain ~ 28% metallic, ~ 23% plastic and ~ 49% ceramic materials although their actual composition varies
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depending upon their specific variety [5]. Metallic elements such as Cu (20%), Pb (2%), Sn
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(4%), Ni (2%), Fe (8%) and Zn (1%) as well as some precious metals like Ag (0.2%), Au (0.1%) and Pd (0.005%) are also present in PCBs [6]. Waste PCBs can be considered as urban mineral
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resource for recovery of various metals. Open dumping, incineration and landfill of these wastes generate hazardous byproducts and gaseous emissions, which poses serious threat to the human health, environment and sustainable economic growth [7]. Proper management with scientific knowledge regarding generation, collection and proper disposal of these e-wastes can lead to a safe environment, helping in the conservation of natural resources.
ACCEPTED MANUSCRIPT Various researchers have reported the recovery of Cu from PCBs. Acids such as H2 SO4 , HNO 3 , HClO and aqua regia along with oxidants like O 2 , Cl2 , H2 O2 , etc. were used for metal leaching [8]. Effective recovery of Cu and Ni was obtained by the use of Fe3+ ion as oxidant on sulphate leaching of PCBs [9]. Kasper et. al., 2014 [10] reported 95% purity of Cu obtained by using aqua
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regia followed by electro-winning. HCl was used to leach Cu after treating PCBs thermally i.e.
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either by burning or pyrolysis [11]. Some literatures have also been found on pressure oxidative
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leaching in PCBs recycling which provides benefit of high oxygen concentration in solution and fast kinetics [12-13]. Jha et. al., 2011 [13] studied pressure oxidative leaching of Cu after the Practices have been made for the recovery of metals such as
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pretreatment by organic swelling.
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hydrometallurgy [14-18], pyrometallurgy [19], and hybrid of the processes with biotechnology [20, 21]. In comparison to pyrometallurgy, hydrometallurgical processes are easily manageable,
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require low capital cost, energy and have less environmental impact [22].
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For the optimization of parameters for effective recovery and feasibility, the RSM model and plan was tested in different applications. It requires short time comparative to traditional
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optimization process to evaluate all the variables [23]. It decreases the number of experiments
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and its response surface map is used to identify the optimum response variables [24]. Central Composite Design (CCD) is commonly used for optimization and for the analysis of the
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interactions among the various parameters [25]. The results of the experiment were validated by performing the design of experiment [26]. Present research work reports systematic studies for recovery of metals using nitric acid in closed system. In nitrate leaching, the use of closed system prevents the emission of generated NO x to the environment. The research work was carried out using RSM model for process optimization, which requires very short time to evaluate all the variables in less numbers of experiments. The
ACCEPTED MANUSCRIPT designed
experimental model to optimize the leaching process parameters is validated
scientifically. The developed and reported basic studies with all safety measures fulfils all environmental norms and has potential to be translated in industry after scale-up studies and
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meet the techno economics of process.
2. Experimental section
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2.1. Material preparation and characterization
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PCBs are essentially present in all electronic and electrical equipment which contain small parts like relays, capacitors, resistors, batteries etc. comprising of various metals For the experimental
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purpose, waste PCBs of personal computers were collected, depopulated and weighed as given in
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Table 1.
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2.2. Physical beneficiation/ pre-treatment
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The small devices populated on the PCBs were removed by thermal treatment. The naked PCBs which contained majority of Cu was crushed into very small size (~2 mm) using scutter-cutter
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(Make: Hoysung, S. No. 996938005, South Korea) as shown in Fig. 1.
The liberated small
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electronic devices populated on the PCBs will be sent to other recycling company for the further processing. Various separators viz. color separator, size separator, magnetic separator, eddy current separator, air classifier, corona electrostatic separator, etc. will be used for further segregation of small electronic parts. The metallic, non-metallic, plastics, rubber, ceramics fractions obtained after separation will further be processed using pyro-/ hydro-/ elctro-/ hybrid techniques to get pure metal/ salts.
ACCEPTED MANUSCRIPT 2.3. Characterization studies For effective recycling of PCBs characterization and chemical analysis of the samples generated were carried out by Scanning Electron Microscopy (SEM) (Zeiss Evo 60, Germany), X-ray Powder Diffraction (XRD) (PANalytical X Pert Powder, UK), X-ray Fluorescence (XRF) Epsilon
3,
UK)
and
Fourier
Transform
Infrared
spectroscopy
(FTIR)
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(PANalytical
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(Thermoscientific- Nicolet 6700 manufactured by Kimaya Engineers, USA) and Atomic
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Absorption Spectrophotometer (AAS) (Perkin Elmer model, Analyst 200, USA). The chemical reagents like HNO 3, H2 SO4 and HCl used for experiments were of laboratory grade (Grade: GR,
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supplied by Merck, India).
2.4. Leaching procedure
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Three necked closed Pyrex glass reactor fitted with a condenser was used for the leaching of
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metals from PCBs. The NOx generated during the HNO 3 leaching reaction was condensed to form liquid using condenser in closed glass reactor. The experiment was planned to ensure zero
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discharge of any gaseous/liquid waste to the environment. Hot plate (Make: IKA, Germany,
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RPM-1500, Model- RCTBS22) was used to maintain the required temperature during leaching whereas, continuous agitation of the sample was maintained during leaching using magnetic
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stirrer. Based on the characterization studies; various mineral acids were used for the experiments. Various process parameters such as effect of acid concentration, time, temperature, pulp density, etc. were studied. The residue left after leaching was again treated with fresh acid maintaining the same experimental condition to carry out the second stage leaching of the remaining Cu present in the residue. The solution obtained in close loop was analyzed for acid consumed and unused acid remained in the leach liquor. Required amount of concentrated acid
ACCEPTED MANUSCRIPT was added to maintain the concentration 3M as per the flow-sheet presented as Fig. 2. Satisfactory mass balance was obtained for each batch test and leaching experiment in continuous loop.
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3. Results and discussion
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3.1. Characterization of PCBs and leached residues
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XRD has been done for the characterization of rapid phase composition of the material and presented in Fig. 3(a). The result presented depicts that Cu is the main component of PCBs, the
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amount of Cu gets reduced after the dissolution process. XRF analysis was done to find out the
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chemical composition (Table 2) of the elements and their concentration present in the sample. FTIR peaks in Fig. 3(b) (original samples and leached residue) clearly indicate the non-
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leachability of plastic, ceramic and epoxy resin during the acid leaching of the metals. From
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the result presented, it can be explained that the absorption bands around 1200 and 1400 cm-1 are due to the asymmetric stretch vibrations of C-O-C bonds and around 3200 and 3500 cm-1
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are due to the stretching vibration of O-H bonds. SEM was carried out to examine the Fig. 3(c) shows cylindrical form of
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structural characteristics of metal present in waste PCBs.
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PCBs which fit with the validated model of leaching kinetics.
3.2. Leaching studies
Hydrometallurgical process was followed to recover Cu from the crushed PCBs of obsolete computers. Various parameters viz. selection of leachant, effect of acid concentration, pulp density, temperature and time were studied and optimized for maximum recovery of metals. The results obtained are discussed below.
ACCEPTED MANUSCRIPT 3.2.1. Selection of leachant Initially, experiments were carried out using different mineral acids viz. H2 SO 4 , HCl and HNO 3 to observe the effective dissolution of metals from crushed PCBs. The selection of suitable leachant for maximum dissolution of metals is very important and depends on factors such as
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chemical and physical character of the material to be leached, selectivity, reagent cost and its
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ability to be regenerated. It was noticed that the leaching of Cu increased from 1.84% to 62.59%
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using 1M HNO 3 at 75 °C with increase in time 15 min to 120 min maintaining pulp density 75 g/L whereas, the leaching of Cu in H2 SO4 and HCl was very less under same experimental
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condition. Fig. 4(a) shows the leaching of Cu in different mineral acids. It was observed that
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HNO 3 which is a stronger oxidizing agent leaches Cu efficiently compared to HCl and H2 SO4 . It has also considerable advantages over acids like HCl, H2 SO 4 etc. which may provide hindrance
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due to the formation of precipitates. During the process of leaching, the following reaction takes
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place [27]:
Cu + 4 HNO 3 = 2NO 2 + Cu (NO 3 )2 + 2H2 O
(1)
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Thus, HNO 3 was found to be a suitable lixiviant for metal dissolution from waste PCBs because
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of its selectivity, low cost, ability of easy regeneration and reuse [27].
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3.2.2. Effect of acid concentration The effect of acid concentration has an intense effect on metal dissolution and is one of the important factors to be studied for optimizing the condition for the dissolution of metals. Therefore, studies for the leaching of metals from PCBs were studied varying concentration of HNO 3 . Fig. 4(b) depicts the percentage leaching of Cu increases from 52.83% to 91.58% with increase in acid concentration from 0.5 to 3M at 75 °C in 120 min keeping pulp density of 75
ACCEPTED MANUSCRIPT g/L. Leaching of other metals under the similar conditions are also shown in the figure which shows recovery of Pb and Fe to increase from 12.71% to 64.34% and 93.33% to 99.99%, respectively. Increase in acid concentration increases the flux of H+ ion across the particle boundaries and hence increases the rate of reaction [28]. Therefore, 3M HNO 3 was optimized for
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experimental purpose. As leaching in HNO 3 results in formation of NO x , thus all experiments
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were carried out in closed system fitted with a condenser, to condense and recycle the NO x and
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other constituents as liquid to the system.
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3.2.3. Effect of pulp density
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Pulp density plays significant role during the leaching of metals from PCBs, therefore further studies have been carried out varying pulp density 25 to 300 g/L. The obtained results presented
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in Fig. 4(c) shows that the leaching of Cu decreases from 93.45% to 35.55% with the increase in
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pulp density from 25 to 300 g/L. Leaching of other metals like Pb and Fe under the similar conditions also decrease from 69.73% to 21.11% and 99.99% to 15% respectively with increase
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in pulp density. Therefore, pulp density is inversely proportional to leaching of metals. The other
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parameters viz. acid concentration, temperature, time were kept constant 3M HNO 3 , 75 o C, 120 min, respectively. From the above studies, it is concluded that lower pulp density i.e. 100 g/L is
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best for higher dissolution as it allows acid to react with large quantity of sample.
3.2.4. Effect of temperature Experiments were carried out by varying the temperature from 50 to 90 °C at a constant experimental conditions i.e. pulp density of 75 g/L, 3M HNO 3 in 120 min. Result of the leaching reaction shows noticeable effects that increase in solution temperature accelerates the reaction
ACCEPTED MANUSCRIPT kinetics with HNO 3 and resulted in increase in the rate of metal dissolution. The result presented in Fig. 4(d) indicates that the leaching of Cu increased from 54.38% to 93.24% with the rise in temperature from 50 to 90 °C using 3M HNO 3 in 120 min Leaching of Pb and Fe under the similar experimental conditions also increased from 28.78% to 53.78% and 56.67% to 99.99%,
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respectively. The rise in temperature increased the mass transfer coefficient and the diffusivity,
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hence increased dissolution. At 75 °C, 91.58% Cu was found to be leached, thus, there is
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negligible increase in metal leaching in compared to Cu leached at 90 °C. Thus, 75 °C was
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selected keeping in view the techno-economics of the process and energy conservation.
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3.2.5. Effect of time
The effect of time was observed for all sets of experiments. With increase in time from 15 to 180
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min the leaching efficiency of Cu was also found to increase from 23.25 to 94.8% as shown in
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Fig. 4(e). About 99% leaching of Pb and Fe was achieved in 90 min under the similar conditions. As no significant change in metal dissolution was noticed between 120 to 180 min, therefore,
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120 min was considered optimum for maximum dissolution of metals.
3.2.6. Kinetic studies and Arrhenius equation
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The studies for the leaching kinetics were made targeting Cu metal, keeping in mind PCBs contain Cu as major metallic fraction. The temperature was varied to understand the dissolution process of Cu using HNO 3 . All the standard equations of shrinking core models (equation 2 to 6) were tested for reaction from the obtained experimental data of leaching [29].The basis of selecting equation for a model is the highest value of R2 , i.e. the regression coefficient and the constant value of k obtained.
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Film diffusion control dense constant size small particles – all geometries X= K ct
(2)
Film diffusion control dense shrinking spheres 1-(1-X)2/3 = Kct Chemical reaction control dense constant size cylindrical particles model
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(3)
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1-(1-X)1/3 = Kct
(4)
Chemical reaction control dense constant size or shrinking spheres
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1-(1-X)1/2 = Kct
(5)
Ash diffusion control dense constant size-spherical particles (6)
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1-3(1-X)2/3 + 2(1-X) = Kct
Where, K c is the reaction rate constant (min−1 ), t=time (min), and X= fraction of metal reacted
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(% leaching/100).
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In this present study, the leaching kinetics of crushed PCBs has been calculated based on the data
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obtained for Cu dissolution with 1.5M HNO 3 at different temperature maintaining pulp density of 75 g/L (Fig. 5(a)). It was found that data fitted with Chemical reaction control dense constant
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size cylindrical particles model; i.e. 1-(1-X)1/2 = Kct. On the basis of equation for a model,
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highest value of R2 , i.e. the regression coefficient and the constant value of k was obtained. Fig. 5(b) shows the activation energy, Ea = -23.35 kJ/mol determined from the slope of straight line plotted between lnk and 1/T. Thus, the determined activation energy indicates that the leaching of Cu from PCBs using HNO 3 in the range of 333-363 K occurs in constant zone.
ACCEPTED MANUSCRIPT 4. Validation of the leaching process 4.1. Response Surface Methodology for process optimization RSM is an empirical statistical and mathematical technique for optimization study in a complex route. This technique facilitates a group of information from a less number of experiments which This statistical model determines the regression
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makes it different from conventional method.
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model and process conditions by using the data derived from design of experiment (DOE). It
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provides interaction effects between the considered parameters and can establish the combination of levels in order to optimize the process more accurately and also help in the estimation of
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coefficients in mathematical model, response prediction and checking adequacy of the model.
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Thus, this model represents relationship between design variables and response (y). This relationship can be symbolized by the following equation [30, 31].
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y f (" x1.............. xn ") e
(7)
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Where, y is the response, f is the response function, x1 are the independent variables and e is the
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experimental error.
Three coded level (-1, 0, +1) Central Composite Design (CCD) based RSM was done to optimize
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the dissolution of Cu where four parameters like acid concentration, temperature, pulp density
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and time were selected as an independent variable while dissolution percentage of Cu was the output response variable (Table 3). 31 sets of experiment with three replicates were conducted to investigate the combined effect of above mentioned four independent variables in dissolution percentage of Cu. Experimental design was carried out by Minitab 17 and the responses were analyzed by higherorder polynomial quadratic equation to describe the functional relationship between the response, Y and the input variables x1 , x2…………..xk in equation 8 [30, 31].
ACCEPTED MANUSCRIPT k
k
k
i 1
i 1
i j
Y 0 i xi ii xi2 ij xi x j e
(8) The response include the linear terms x1 , x2 , …, xk , square terms x1 2 , x2 2 , .. , xk 2 , and interaction terms x1 x2 , x1 x3 , .. , xk-1 xk . Where, Y represents the predicted response.
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In order to check the accuracy of the fitted model, a series of statistical analysis such as the main and interaction effects, the contour plot and analysis of variance (ANOVA) was examined as
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shown in Table 4. The accuracy and desirability of second order polynomial regression equation
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was evaluated by coefficient of determination (R2 ). Significance test of individual model coefficients involved the determination of the P-value.
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Further optimization process was carried out based on data derived from the single parameter
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study through response surface methodology and DOE derived responses of Cu dissolution percentage as given in Table 5. After getting the observed response values of the different
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regression coefficients of a second-order regression equation for percentage dissolution of Cu
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with different variables such as acid concentration, temperature, pulp density and time were quadratic as suggested by the software given in equation 8.
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Yield = -110.7+44.3 x1 + 0.420 x2 + 0.411 x3 + 73.9 x4 - 4.47 x1 × x1 - 0.00322 x2 × x2 -
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0.00296 x3 × x3 -12.50 x4 × x4 - 0.0276 x1 × x2 -0.0834 x1 × x3 - 1.93 x1 × x4 + 0.00191 x2× x3 + 0.0296 x2× x4 + 0.0129 x3 × x4
(9)
Table 5 shows the ANOVA for response surface of the quadratic model. The model ‘f’ value of the regression model (13.94) of dissolution of Cu implies the model is significant (<0.001). The lack of fit, which indicates the variation of data around the fitted model, shows the insignificant value (‘f’ value 2.49). If the lack of fit shows significant value it means the data are not well fitted with the model. In this consequence our data are well fitted with the model [32].
ACCEPTED MANUSCRIPT R2 value (92.42%) of the model also implies good predictability of the model. It also signifies high correlation between observed and predicted value of Cu dissolution percentage. Further normal probability plot of the residues (Fig. 6) suggested the errors are distributed normally as the residues were fall on a straight line [33].
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Each 3D response surface plot from the predicted model represents the effect of two parameters.
pulp
density,
temperature and
time.
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5. Treatment of leached residues and effluents
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dissolution in 75 °C at 120 min.
Graphical representation shows the
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concentration,
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Fig. 7 a, b, c, d, e and f show correlation between percentage dissolution of Cu, acid
The leached residue left after recovery of metals was processed for Toxicity Characteristic
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Leaching Procedure (TCLP) test before utilization/ disposal to the environment. Results of TCLP
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test for obtained residue was found under the permissible limit and suitable for safe disposal/ utilization as binder in making RCC road, paving blocks, etc. As leaching was carried out in a
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three necked closed Pyrex reactor fitted with a condenser, the NO x generated during leaching
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were condensed and recycled to the system. The acidic effluent generated was collected and sent
disposal.
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to effluent treatment plant (ETP) for neutralization where the pH is maintained at 7 before final
6. Conclusions From the experimental results, it can be concluded that HNO 3 is the best leachant for Cu recovery from PCBs compared to H2 SO 4 and HCl. With 3M HNO 3 , 75 g/L pulp density at 75 °C about 91.58% Cu was leached out in a single stage in 120 min. However, in two stages leaching
ACCEPTED MANUSCRIPT maintaining the experimental condition the recovery of Cu was found to be 99.99%. Conventional hydrometallurgical method required a number of experiments for optimization and validation whereas the development of new optimization methods (RSM) not only saves time but also minimizes the loss of chemicals used for various experiments. Through this model,
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validations can be made with higher leaching efficiency and optimizing various conditions.
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Further, the leaching process will be followed by solvent extraction and electrowinning for
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procuring metal sheets/ metal salts.
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Acknowledgement
Authors acknowledge the joint venture of IIT-Kharagpur and CSIR-NML, Jamshedpur for the e-
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waste recycling project and for giving the permission to publish this article. One of the authors
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Ms. Deblina Dutta, Senior Research Fellow is thankful to IIT-Kharagpur for financial support.
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[22] N. Zhu, Y. Xiang, T. Zang, P. Wu, Z. Dang, P. Li, J. Wu, Bioleaching of metal concentrates of waste printed circuit boards by mixed culture of acidophilic bacteria, J. Hazard. Mater. 192
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(2011) 614-619.
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[23] H.R. Moskowitz, Product optimization: approaches and applications, in: H.J.H. MacFie, D.M.H. Thomson (Eds.), Measurement of food preferences. Springer, Boston (1994) 97-136.
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[24] R. Kumar, R. Singh, N. Kumar, K. Bishnoi, N.R. Bishnoi, Response surface methodology
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approach for optimization of biosorption process for removal of Cr(VI), Ni(II),and Zn(II) ions by immobilized bacterial biomass sp Bacillus brevis, Chem. Eng. J. 146 (2009) 401-407.
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[25] I.A.W. Tan, A.L. Ahmad, B.H. Hameed, Optimization of preparation conditions for
(2008) 462-470.
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activated carbons from coconut husk using response surface methodology, Chem. Eng. J. 137
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[26] H. Yang, J. Liu, J. Yang, Leaching Cu from shredded particles of waste printed circuit boards, J. Hazard. Mater.187 (2011) 393-400. [27] A. Mecucci, K. Scott, Leaching and electrochemical recovery of Cu, lead and tin from scrap printed circuit boards, J. Chem. Technol. Biotechnol. 77 (2002) 449-457. [28] R.A. Sawahbkeh, Hydrometallurgical extraction of zinc from Jordanian electric arc furnace dust, Hydrometallurgy. 104 (2010) 61-65.
ACCEPTED MANUSCRIPT [29] M.K. Jha ,V. Kumar , L. Maharaj , R. J. Singh, Studies on Leaching and Recycling of Zinc from Rayon Waste Sludge, Ind. Eng. Chem. 43 (2004) 1284-1295. [30] R. Bhaumik, N.K. Mondal, Optimizing adsorption of fluoride from water by modified banana peel dust using response surface modeling approach, Appl. Water. Sci. 6 (2016) 115-135.
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[31] K.M. Lee, S.B.A. Hamid, Simple response surface methodology: Investigation on advance
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photocatalytic oxidation of 4-Chlorophenoxyacetic acid using UV-active ZnO photocatalyst.
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Materials. 8 (2015) 339-354.
[32] R.H. Myers, D.C. Montgomery, Response surface methodology, process and product
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optimization using designed experiments, Second Ed., John Wiley and Sons, New York, 2002.
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[33] B.K. Körbahti, M.A. Rauf, Determination of optimum operating conditions of carmine decoloration by UV/H2O2 using response surface methodology, J. Hazard. Mater. 161 (2009)
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281-286.
ACCEPTED MANUSCRIPT List of Tables: Table 1. Weight of PCBs before and after removal of the electronic devices Table 2. XRF data of metals found in crushed PCBs Table 3. Independent variables and experimental range considered for the dissolution of copper
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Table 4. Central composite design with predictive values and their experimental results
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Table 5. ANOVA for the response surface quadratic model
ACCEPTED MANUSCRIPT Table 1. Weight of PCBs before and after removal of the electronic devices Wt. of PCBs (g)
Wt. of depopulated PCBs (g)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
438.50 450.00 462.00 450.50 458.00 464.00 392.00 456.00 501.50 542.00 447.50 471.50 422.50 432.50 488.50
163.50 178.00 177.50 162.50 173.50 192.50 178.50 190.00 190.00 194.50 157.00 158.50 171.50 154.50 165.00
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Sl. No.
ACCEPTED MANUSCRIPT Table 2. XRF data of metals found in crushed PCBs Cu 19.34%
Fe 6.89%
Ni 0.26%
Sn 2.16%
Pb 1.01%
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Metals Conc. unit
Zn 0.11%
ACCEPTED MANUSCRIPT Table 3. Independent variables and experimental range considered for the dissolution of copper Units
Symbol
Low
High
Acid concentration
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x1
1
5
Temperature
°C
x2
25
125
Pulp density
g/L
x3
25
125
Time
hr
x4
1
4
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Process parameters
ACCEPTED MANUSCRIPT Table 4. Central composite design with predictive values and their experimental results
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Yield (Predicted) 39.45 59.42 39.76 56.97 34.91 46.54 39.99 48.87 83.76 96.00 87.02 96.52 80.50 84.41 88.55 89.70 59.40 80.53 77.00 82.60 86.15 74.79 -4.71 80.42 87.86 87.86 87.86 87.86 87.86 87.86 87.86
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x3 x4 Yield (Observed) 50 1 38.77 50 1 58.79 50 1 36.57 50 1 52.78 100 1 30.60 100 1 38.97 100 1 40.08 100 1 43.28 50 3 78.50 50 3 90.11 50 3 88.78 50 3 89.97 100 3 78.88 100 3 76.76 100 3 78.33 100 3 84.57 75 2 62.79 75 2 93.80 75 2 85.48 75 2 90.79 25 2 90.14 125 2 87.46 75 0 0.00 75 4 92.38 75 2 94.45 75 2 93.78 75 2 89.32 75 2 81.34 75 2 93.99 75 2 78.56 75 2 83.58
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x2 50 50 100 100 50 50 100 100 50 50 100 100 50 50 100 100 75 75 25 125 75 75 75 75 75 75 75 75 75 75 75
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x1 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3 3
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Std Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Residual -0.68 -0.63 -3.19 -4.19 -4.31 -7.57 0.08 -5.59 -5.26 -5.90 1.75 -6.54 -1.62 -7.65 -10.22 -5.13 3.39 13.27 8.48 8.19 3.99 12.67 4.71 11.95 6.59 5.92 1.46 -6.52 6.13 -9.30 -4.28
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Degrees of Freedom. Sum of Squares. c Mean Squares.
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a
b
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F-Value 13.94 7.82 0.55 2.26 126.98 0.09 0.81 0.70 0.27 0.10 0.02 6.69 1.36 1.14 52.20 2.49
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Adj MSc 1193.70 669.50 47.00 193.60 10871.10 7.60 69.50 59.60 22.90 8.80 1.70 572.50 116.10 97.60 4469.10 110.40 44.30
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Adj SSb 16711.50 669.50 47.00 193.60 10871.10 7.60 69.50 59.60 22.90 8.80 1.70 572.50 116.10 97.60 4469.10 1104.20 265.70 18081.40
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Source DFa Model 14 x1 1 x2 1 x3 1 x4 1 x1 x2 1 x1 x3 1 x1 x4 1 x2 x3 1 x2 x4 1 x3 x4 1 2 x1 1 2 x2 1 2 x3 1 2 x4 1 Lack-of-Fit 10 Pure Error 6 Total 30 2 2 R = 92.42% , R (adj) = 85.79%
P-Value <0.001 <0.05 0.47 0.15 <0.0001 0.77 0.38 0.42 0.61 0.75 0.89 <0.05 0.26 0.30 <0.0001 0.14
ACCEPTED MANUSCRIPT Highlights
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Hydrometallurgical metal extraction from used PCBs. Optimization of process parameters using RSM model. Leaching in close system prevent gas emission. Recycling process is environmental.
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Figure 1
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Deblina Dutta, Rekha Panda, Archana Kumari, Sudha Goel, Manis Kumar Jha PII: DOI: Article Number: Reference:
S2214-9937(17)30164-1 doi:10.1016/j.susmat.2018.e00066 e00066 SUSMAT 66
To appear in:
Sustainable Materials and Technologies
Received date: Revised date: Accepted date:
7 November 2017 9 June 2018 18 June 2018
Please cite this article as: Deblina Dutta, Rekha Panda, Archana Kumari, Sudha Goel, Manis Kumar Jha , Sustainable recycling process for metals recovery from used printed circuit boards (PCBs). Susmat (2018), doi:10.1016/j.susmat.2018.e00066
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ACCEPTED MANUSCRIPT Sustainable recycling process for metals recovery from used printed circuit boards (PCBs) Deblina Duttaa,b, Rekha Pandab, Archana Kumarib, Sudha Goela, Manis Kumar Jhab,* a
Corresponding Author: Tel.: +91-657 2345302; FAX +91-657-2345213
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*
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School of Environmental Science & Engineering, Indian Institute of Technology Kharagpur (IIT), Kharagpur-721302, India b Metal Extraction & Recycling Division, CSIR-National Metallurgical Laboratory, Jamshedpur-831007, India
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E-mail address: [email protected]; [email protected] (M.K. Jha)
ACCEPTED MANUSCRIPT Abstract In comparison to extraction of metals from limited primary sources, the recycling of metals / materials from various alternative resources particularly from metallurgical waste and complex such as E-waste (Electronic waste) are gaining importance in view of energy, purity and
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environmental concern. In all E-waste, PCBs are essential components, which contain nearly
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28% metallic, 23% plastic and 49% ceramic materials in a complex form. Due to tremendous
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increase in e-waste globally, the recycling of PCBs to recover metals are getting importance which will not only mitigate the environmental pollution but will also conserve the natural
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resources and energy. PCBs contain copper (Cu) in major, therefore experiments were carried
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out to optimize different process parameters viz. effect of acid concentration, pulp density, temperature, time, etc. About, 91.58% Cu was found to be leached using 3M HNO 3 maintaining
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75 g/L pulp density at temperature 75 °C and mixing time 120 min. The two stages leaching
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under the similar condition resulted in the recovery of 99.99% of Cu. To optimize the conventional hydrometallurgical process, the Response Surface Methodology (RSM) was also
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studied. The result obtained by using the RSM model will help the researchers to validate the
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data scientifically with less number of experiments. Kinetics of Cu leaching fitted well with the “Chemical reaction control dense constant size cylindrical particles model” i.e. 1-(1-X)1/2 = K ct.
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The leach liquor generated will be further purified by the method of solvent extraction (SX)/ ion exchange (IX) to get purified metallic solution. From the pure solution obtained, the metal/salt could be produced by electro-winning/ precipitation and crystallization, respectively. Keywords: E-waste; PCBs; Recycling; Hydrometallurgy; RSM
ACCEPTED MANUSCRIPT 1. Introduction Scarcity of time and invasion of advancement in the field of technology has compelled the society to replace the old electrical and electronic appliances with the new one having modified and versatile features. In the competition, the old electrical and electronic gadgets finally have
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created their place as e-waste around the world. E-waste can be classified into large Electrical
personal
computers,
refrigerators,
washing
machines,
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Televisions,
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and Electronic Equipments (EEE) and small EEEs depending on their size distribution. microwave
etc.
are
categorized as large equipments while small equipment includes mobile phones, batteries,
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tablets, laptops etc. Irrespective of their sizes, they all contain a common component, known as
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PCBs [1]. These PCBs are complex in nature, and comprises of valuable as well as hazardous metals, which have been studied, analyzed and reported by a number of researchers [2-7]. The
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PCBs of personal computers are rich in metals and measured as a high value waste which
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constitutes about 6% of the total weight of e-waste [4]. Waste PCBs typically contain ~ 28% metallic, ~ 23% plastic and ~ 49% ceramic materials although their actual composition varies
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depending upon their specific variety [5]. Metallic elements such as Cu (20%), Pb (2%), Sn
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(4%), Ni (2%), Fe (8%) and Zn (1%) as well as some precious metals like Ag (0.2%), Au (0.1%) and Pd (0.005%) are also present in PCBs [6]. Waste PCBs can be considered as urban mineral
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resource for recovery of various metals. Open dumping, incineration and landfill of these wastes generate hazardous byproducts and gaseous emissions, which poses serious threat to the human health, environment and sustainable economic growth [7]. Proper management with scientific knowledge regarding generation, collection and proper disposal of these e-wastes can lead to a safe environment, helping in the conservation of natural resources.
ACCEPTED MANUSCRIPT Various researchers have reported the recovery of Cu from PCBs. Acids such as H2 SO4 , HNO 3 , HClO and aqua regia along with oxidants like O 2 , Cl2 , H2 O2 , etc. were used for metal leaching [8]. Effective recovery of Cu and Ni was obtained by the use of Fe3+ ion as oxidant on sulphate leaching of PCBs [9]. Kasper et. al., 2014 [10] reported 95% purity of Cu obtained by using aqua
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regia followed by electro-winning. HCl was used to leach Cu after treating PCBs thermally i.e.
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either by burning or pyrolysis [11]. Some literatures have also been found on pressure oxidative
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leaching in PCBs recycling which provides benefit of high oxygen concentration in solution and fast kinetics [12-13]. Jha et. al., 2011 [13] studied pressure oxidative leaching of Cu after the Practices have been made for the recovery of metals such as
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pretreatment by organic swelling.
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hydrometallurgy [14-18], pyrometallurgy [19], and hybrid of the processes with biotechnology [20, 21]. In comparison to pyrometallurgy, hydrometallurgical processes are easily manageable,
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require low capital cost, energy and have less environmental impact [22].
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For the optimization of parameters for effective recovery and feasibility, the RSM model and plan was tested in different applications. It requires short time comparative to traditional
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optimization process to evaluate all the variables [23]. It decreases the number of experiments
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and its response surface map is used to identify the optimum response variables [24]. Central Composite Design (CCD) is commonly used for optimization and for the analysis of the
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interactions among the various parameters [25]. The results of the experiment were validated by performing the design of experiment [26]. Present research work reports systematic studies for recovery of metals using nitric acid in closed system. In nitrate leaching, the use of closed system prevents the emission of generated NO x to the environment. The research work was carried out using RSM model for process optimization, which requires very short time to evaluate all the variables in less numbers of experiments. The
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experimental model to optimize the leaching process parameters is validated
scientifically. The developed and reported basic studies with all safety measures fulfils all environmental norms and has potential to be translated in industry after scale-up studies and
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meet the techno economics of process.
2. Experimental section
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2.1. Material preparation and characterization
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PCBs are essentially present in all electronic and electrical equipment which contain small parts like relays, capacitors, resistors, batteries etc. comprising of various metals For the experimental
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purpose, waste PCBs of personal computers were collected, depopulated and weighed as given in
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Table 1.
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2.2. Physical beneficiation/ pre-treatment
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The small devices populated on the PCBs were removed by thermal treatment. The naked PCBs which contained majority of Cu was crushed into very small size (~2 mm) using scutter-cutter
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(Make: Hoysung, S. No. 996938005, South Korea) as shown in Fig. 1.
The liberated small
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electronic devices populated on the PCBs will be sent to other recycling company for the further processing. Various separators viz. color separator, size separator, magnetic separator, eddy current separator, air classifier, corona electrostatic separator, etc. will be used for further segregation of small electronic parts. The metallic, non-metallic, plastics, rubber, ceramics fractions obtained after separation will further be processed using pyro-/ hydro-/ elctro-/ hybrid techniques to get pure metal/ salts.
ACCEPTED MANUSCRIPT 2.3. Characterization studies For effective recycling of PCBs characterization and chemical analysis of the samples generated were carried out by Scanning Electron Microscopy (SEM) (Zeiss Evo 60, Germany), X-ray Powder Diffraction (XRD) (PANalytical X Pert Powder, UK), X-ray Fluorescence (XRF) Epsilon
3,
UK)
and
Fourier
Transform
Infrared
spectroscopy
(FTIR)
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(PANalytical
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(Thermoscientific- Nicolet 6700 manufactured by Kimaya Engineers, USA) and Atomic
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Absorption Spectrophotometer (AAS) (Perkin Elmer model, Analyst 200, USA). The chemical reagents like HNO 3, H2 SO4 and HCl used for experiments were of laboratory grade (Grade: GR,
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supplied by Merck, India).
2.4. Leaching procedure
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Three necked closed Pyrex glass reactor fitted with a condenser was used for the leaching of
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metals from PCBs. The NOx generated during the HNO 3 leaching reaction was condensed to form liquid using condenser in closed glass reactor. The experiment was planned to ensure zero
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discharge of any gaseous/liquid waste to the environment. Hot plate (Make: IKA, Germany,
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RPM-1500, Model- RCTBS22) was used to maintain the required temperature during leaching whereas, continuous agitation of the sample was maintained during leaching using magnetic
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stirrer. Based on the characterization studies; various mineral acids were used for the experiments. Various process parameters such as effect of acid concentration, time, temperature, pulp density, etc. were studied. The residue left after leaching was again treated with fresh acid maintaining the same experimental condition to carry out the second stage leaching of the remaining Cu present in the residue. The solution obtained in close loop was analyzed for acid consumed and unused acid remained in the leach liquor. Required amount of concentrated acid
ACCEPTED MANUSCRIPT was added to maintain the concentration 3M as per the flow-sheet presented as Fig. 2. Satisfactory mass balance was obtained for each batch test and leaching experiment in continuous loop.
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3. Results and discussion
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3.1. Characterization of PCBs and leached residues
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XRD has been done for the characterization of rapid phase composition of the material and presented in Fig. 3(a). The result presented depicts that Cu is the main component of PCBs, the
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amount of Cu gets reduced after the dissolution process. XRF analysis was done to find out the
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chemical composition (Table 2) of the elements and their concentration present in the sample. FTIR peaks in Fig. 3(b) (original samples and leached residue) clearly indicate the non-
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leachability of plastic, ceramic and epoxy resin during the acid leaching of the metals. From
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the result presented, it can be explained that the absorption bands around 1200 and 1400 cm-1 are due to the asymmetric stretch vibrations of C-O-C bonds and around 3200 and 3500 cm-1
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are due to the stretching vibration of O-H bonds. SEM was carried out to examine the Fig. 3(c) shows cylindrical form of
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structural characteristics of metal present in waste PCBs.
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PCBs which fit with the validated model of leaching kinetics.
3.2. Leaching studies
Hydrometallurgical process was followed to recover Cu from the crushed PCBs of obsolete computers. Various parameters viz. selection of leachant, effect of acid concentration, pulp density, temperature and time were studied and optimized for maximum recovery of metals. The results obtained are discussed below.
ACCEPTED MANUSCRIPT 3.2.1. Selection of leachant Initially, experiments were carried out using different mineral acids viz. H2 SO 4 , HCl and HNO 3 to observe the effective dissolution of metals from crushed PCBs. The selection of suitable leachant for maximum dissolution of metals is very important and depends on factors such as
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chemical and physical character of the material to be leached, selectivity, reagent cost and its
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ability to be regenerated. It was noticed that the leaching of Cu increased from 1.84% to 62.59%
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using 1M HNO 3 at 75 °C with increase in time 15 min to 120 min maintaining pulp density 75 g/L whereas, the leaching of Cu in H2 SO4 and HCl was very less under same experimental
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condition. Fig. 4(a) shows the leaching of Cu in different mineral acids. It was observed that
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HNO 3 which is a stronger oxidizing agent leaches Cu efficiently compared to HCl and H2 SO4 . It has also considerable advantages over acids like HCl, H2 SO 4 etc. which may provide hindrance
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due to the formation of precipitates. During the process of leaching, the following reaction takes
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place [27]:
Cu + 4 HNO 3 = 2NO 2 + Cu (NO 3 )2 + 2H2 O
(1)
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Thus, HNO 3 was found to be a suitable lixiviant for metal dissolution from waste PCBs because
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of its selectivity, low cost, ability of easy regeneration and reuse [27].
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3.2.2. Effect of acid concentration The effect of acid concentration has an intense effect on metal dissolution and is one of the important factors to be studied for optimizing the condition for the dissolution of metals. Therefore, studies for the leaching of metals from PCBs were studied varying concentration of HNO 3 . Fig. 4(b) depicts the percentage leaching of Cu increases from 52.83% to 91.58% with increase in acid concentration from 0.5 to 3M at 75 °C in 120 min keeping pulp density of 75
ACCEPTED MANUSCRIPT g/L. Leaching of other metals under the similar conditions are also shown in the figure which shows recovery of Pb and Fe to increase from 12.71% to 64.34% and 93.33% to 99.99%, respectively. Increase in acid concentration increases the flux of H+ ion across the particle boundaries and hence increases the rate of reaction [28]. Therefore, 3M HNO 3 was optimized for
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experimental purpose. As leaching in HNO 3 results in formation of NO x , thus all experiments
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were carried out in closed system fitted with a condenser, to condense and recycle the NO x and
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other constituents as liquid to the system.
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3.2.3. Effect of pulp density
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Pulp density plays significant role during the leaching of metals from PCBs, therefore further studies have been carried out varying pulp density 25 to 300 g/L. The obtained results presented
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in Fig. 4(c) shows that the leaching of Cu decreases from 93.45% to 35.55% with the increase in
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pulp density from 25 to 300 g/L. Leaching of other metals like Pb and Fe under the similar conditions also decrease from 69.73% to 21.11% and 99.99% to 15% respectively with increase
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in pulp density. Therefore, pulp density is inversely proportional to leaching of metals. The other
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parameters viz. acid concentration, temperature, time were kept constant 3M HNO 3 , 75 o C, 120 min, respectively. From the above studies, it is concluded that lower pulp density i.e. 100 g/L is
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best for higher dissolution as it allows acid to react with large quantity of sample.
3.2.4. Effect of temperature Experiments were carried out by varying the temperature from 50 to 90 °C at a constant experimental conditions i.e. pulp density of 75 g/L, 3M HNO 3 in 120 min. Result of the leaching reaction shows noticeable effects that increase in solution temperature accelerates the reaction
ACCEPTED MANUSCRIPT kinetics with HNO 3 and resulted in increase in the rate of metal dissolution. The result presented in Fig. 4(d) indicates that the leaching of Cu increased from 54.38% to 93.24% with the rise in temperature from 50 to 90 °C using 3M HNO 3 in 120 min Leaching of Pb and Fe under the similar experimental conditions also increased from 28.78% to 53.78% and 56.67% to 99.99%,
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respectively. The rise in temperature increased the mass transfer coefficient and the diffusivity,
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hence increased dissolution. At 75 °C, 91.58% Cu was found to be leached, thus, there is
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negligible increase in metal leaching in compared to Cu leached at 90 °C. Thus, 75 °C was
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selected keeping in view the techno-economics of the process and energy conservation.
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3.2.5. Effect of time
The effect of time was observed for all sets of experiments. With increase in time from 15 to 180
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min the leaching efficiency of Cu was also found to increase from 23.25 to 94.8% as shown in
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Fig. 4(e). About 99% leaching of Pb and Fe was achieved in 90 min under the similar conditions. As no significant change in metal dissolution was noticed between 120 to 180 min, therefore,
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120 min was considered optimum for maximum dissolution of metals.
3.2.6. Kinetic studies and Arrhenius equation
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The studies for the leaching kinetics were made targeting Cu metal, keeping in mind PCBs contain Cu as major metallic fraction. The temperature was varied to understand the dissolution process of Cu using HNO 3 . All the standard equations of shrinking core models (equation 2 to 6) were tested for reaction from the obtained experimental data of leaching [29].The basis of selecting equation for a model is the highest value of R2 , i.e. the regression coefficient and the constant value of k obtained.
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Film diffusion control dense constant size small particles – all geometries X= K ct
(2)
Film diffusion control dense shrinking spheres 1-(1-X)2/3 = Kct Chemical reaction control dense constant size cylindrical particles model
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(3)
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1-(1-X)1/3 = Kct
(4)
Chemical reaction control dense constant size or shrinking spheres
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1-(1-X)1/2 = Kct
(5)
Ash diffusion control dense constant size-spherical particles (6)
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1-3(1-X)2/3 + 2(1-X) = Kct
Where, K c is the reaction rate constant (min−1 ), t=time (min), and X= fraction of metal reacted
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(% leaching/100).
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In this present study, the leaching kinetics of crushed PCBs has been calculated based on the data
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obtained for Cu dissolution with 1.5M HNO 3 at different temperature maintaining pulp density of 75 g/L (Fig. 5(a)). It was found that data fitted with Chemical reaction control dense constant
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size cylindrical particles model; i.e. 1-(1-X)1/2 = Kct. On the basis of equation for a model,
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highest value of R2 , i.e. the regression coefficient and the constant value of k was obtained. Fig. 5(b) shows the activation energy, Ea = -23.35 kJ/mol determined from the slope of straight line plotted between lnk and 1/T. Thus, the determined activation energy indicates that the leaching of Cu from PCBs using HNO 3 in the range of 333-363 K occurs in constant zone.
ACCEPTED MANUSCRIPT 4. Validation of the leaching process 4.1. Response Surface Methodology for process optimization RSM is an empirical statistical and mathematical technique for optimization study in a complex route. This technique facilitates a group of information from a less number of experiments which This statistical model determines the regression
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makes it different from conventional method.
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model and process conditions by using the data derived from design of experiment (DOE). It
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provides interaction effects between the considered parameters and can establish the combination of levels in order to optimize the process more accurately and also help in the estimation of
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coefficients in mathematical model, response prediction and checking adequacy of the model.
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Thus, this model represents relationship between design variables and response (y). This relationship can be symbolized by the following equation [30, 31].
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y f (" x1.............. xn ") e
(7)
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Where, y is the response, f is the response function, x1 are the independent variables and e is the
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experimental error.
Three coded level (-1, 0, +1) Central Composite Design (CCD) based RSM was done to optimize
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the dissolution of Cu where four parameters like acid concentration, temperature, pulp density
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and time were selected as an independent variable while dissolution percentage of Cu was the output response variable (Table 3). 31 sets of experiment with three replicates were conducted to investigate the combined effect of above mentioned four independent variables in dissolution percentage of Cu. Experimental design was carried out by Minitab 17 and the responses were analyzed by higherorder polynomial quadratic equation to describe the functional relationship between the response, Y and the input variables x1 , x2…………..xk in equation 8 [30, 31].
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k
k
i 1
i 1
i j
Y 0 i xi ii xi2 ij xi x j e
(8) The response include the linear terms x1 , x2 , …, xk , square terms x1 2 , x2 2 , .. , xk 2 , and interaction terms x1 x2 , x1 x3 , .. , xk-1 xk . Where, Y represents the predicted response.
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In order to check the accuracy of the fitted model, a series of statistical analysis such as the main and interaction effects, the contour plot and analysis of variance (ANOVA) was examined as
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shown in Table 4. The accuracy and desirability of second order polynomial regression equation
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was evaluated by coefficient of determination (R2 ). Significance test of individual model coefficients involved the determination of the P-value.
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Further optimization process was carried out based on data derived from the single parameter
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study through response surface methodology and DOE derived responses of Cu dissolution percentage as given in Table 5. After getting the observed response values of the different
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regression coefficients of a second-order regression equation for percentage dissolution of Cu
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with different variables such as acid concentration, temperature, pulp density and time were quadratic as suggested by the software given in equation 8.
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Yield = -110.7+44.3 x1 + 0.420 x2 + 0.411 x3 + 73.9 x4 - 4.47 x1 × x1 - 0.00322 x2 × x2 -
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0.00296 x3 × x3 -12.50 x4 × x4 - 0.0276 x1 × x2 -0.0834 x1 × x3 - 1.93 x1 × x4 + 0.00191 x2× x3 + 0.0296 x2× x4 + 0.0129 x3 × x4
(9)
Table 5 shows the ANOVA for response surface of the quadratic model. The model ‘f’ value of the regression model (13.94) of dissolution of Cu implies the model is significant (<0.001). The lack of fit, which indicates the variation of data around the fitted model, shows the insignificant value (‘f’ value 2.49). If the lack of fit shows significant value it means the data are not well fitted with the model. In this consequence our data are well fitted with the model [32].
ACCEPTED MANUSCRIPT R2 value (92.42%) of the model also implies good predictability of the model. It also signifies high correlation between observed and predicted value of Cu dissolution percentage. Further normal probability plot of the residues (Fig. 6) suggested the errors are distributed normally as the residues were fall on a straight line [33].
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Each 3D response surface plot from the predicted model represents the effect of two parameters.
pulp
density,
temperature and
time.
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5. Treatment of leached residues and effluents
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dissolution in 75 °C at 120 min.
Graphical representation shows the
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concentration,
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Fig. 7 a, b, c, d, e and f show correlation between percentage dissolution of Cu, acid
The leached residue left after recovery of metals was processed for Toxicity Characteristic
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Leaching Procedure (TCLP) test before utilization/ disposal to the environment. Results of TCLP
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test for obtained residue was found under the permissible limit and suitable for safe disposal/ utilization as binder in making RCC road, paving blocks, etc. As leaching was carried out in a
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three necked closed Pyrex reactor fitted with a condenser, the NO x generated during leaching
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were condensed and recycled to the system. The acidic effluent generated was collected and sent
disposal.
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to effluent treatment plant (ETP) for neutralization where the pH is maintained at 7 before final
6. Conclusions From the experimental results, it can be concluded that HNO 3 is the best leachant for Cu recovery from PCBs compared to H2 SO 4 and HCl. With 3M HNO 3 , 75 g/L pulp density at 75 °C about 91.58% Cu was leached out in a single stage in 120 min. However, in two stages leaching
ACCEPTED MANUSCRIPT maintaining the experimental condition the recovery of Cu was found to be 99.99%. Conventional hydrometallurgical method required a number of experiments for optimization and validation whereas the development of new optimization methods (RSM) not only saves time but also minimizes the loss of chemicals used for various experiments. Through this model,
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validations can be made with higher leaching efficiency and optimizing various conditions.
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Further, the leaching process will be followed by solvent extraction and electrowinning for
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procuring metal sheets/ metal salts.
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Acknowledgement
Authors acknowledge the joint venture of IIT-Kharagpur and CSIR-NML, Jamshedpur for the e-
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waste recycling project and for giving the permission to publish this article. One of the authors
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Ms. Deblina Dutta, Senior Research Fellow is thankful to IIT-Kharagpur for financial support.
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ACCEPTED MANUSCRIPT List of Tables: Table 1. Weight of PCBs before and after removal of the electronic devices Table 2. XRF data of metals found in crushed PCBs Table 3. Independent variables and experimental range considered for the dissolution of copper
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Table 4. Central composite design with predictive values and their experimental results
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Table 5. ANOVA for the response surface quadratic model
ACCEPTED MANUSCRIPT Table 1. Weight of PCBs before and after removal of the electronic devices Wt. of PCBs (g)
Wt. of depopulated PCBs (g)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
438.50 450.00 462.00 450.50 458.00 464.00 392.00 456.00 501.50 542.00 447.50 471.50 422.50 432.50 488.50
163.50 178.00 177.50 162.50 173.50 192.50 178.50 190.00 190.00 194.50 157.00 158.50 171.50 154.50 165.00
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ACCEPTED MANUSCRIPT Table 2. XRF data of metals found in crushed PCBs Cu 19.34%
Fe 6.89%
Ni 0.26%
Sn 2.16%
Pb 1.01%
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Metals Conc. unit
Zn 0.11%
ACCEPTED MANUSCRIPT Table 3. Independent variables and experimental range considered for the dissolution of copper Units
Symbol
Low
High
Acid concentration
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x1
1
5
Temperature
°C
x2
25
125
Pulp density
g/L
x3
25
125
Time
hr
x4
1
4
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Process parameters
ACCEPTED MANUSCRIPT Table 4. Central composite design with predictive values and their experimental results
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Yield (Predicted) 39.45 59.42 39.76 56.97 34.91 46.54 39.99 48.87 83.76 96.00 87.02 96.52 80.50 84.41 88.55 89.70 59.40 80.53 77.00 82.60 86.15 74.79 -4.71 80.42 87.86 87.86 87.86 87.86 87.86 87.86 87.86
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x3 x4 Yield (Observed) 50 1 38.77 50 1 58.79 50 1 36.57 50 1 52.78 100 1 30.60 100 1 38.97 100 1 40.08 100 1 43.28 50 3 78.50 50 3 90.11 50 3 88.78 50 3 89.97 100 3 78.88 100 3 76.76 100 3 78.33 100 3 84.57 75 2 62.79 75 2 93.80 75 2 85.48 75 2 90.79 25 2 90.14 125 2 87.46 75 0 0.00 75 4 92.38 75 2 94.45 75 2 93.78 75 2 89.32 75 2 81.34 75 2 93.99 75 2 78.56 75 2 83.58
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x2 50 50 100 100 50 50 100 100 50 50 100 100 50 50 100 100 75 75 25 125 75 75 75 75 75 75 75 75 75 75 75
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x1 2 4 2 4 2 4 2 4 2 4 2 4 2 4 2 4 1 5 3 3 3 3 3 3 3 3 3 3 3 3 3
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Std Order 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31
Residual -0.68 -0.63 -3.19 -4.19 -4.31 -7.57 0.08 -5.59 -5.26 -5.90 1.75 -6.54 -1.62 -7.65 -10.22 -5.13 3.39 13.27 8.48 8.19 3.99 12.67 4.71 11.95 6.59 5.92 1.46 -6.52 6.13 -9.30 -4.28
ACCEPTED MANUSCRIPT Table 5. ANOVA for the response surface quadratic model
Degrees of Freedom. Sum of Squares. c Mean Squares.
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F-Value 13.94 7.82 0.55 2.26 126.98 0.09 0.81 0.70 0.27 0.10 0.02 6.69 1.36 1.14 52.20 2.49
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Adj MSc 1193.70 669.50 47.00 193.60 10871.10 7.60 69.50 59.60 22.90 8.80 1.70 572.50 116.10 97.60 4469.10 110.40 44.30
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Adj SSb 16711.50 669.50 47.00 193.60 10871.10 7.60 69.50 59.60 22.90 8.80 1.70 572.50 116.10 97.60 4469.10 1104.20 265.70 18081.40
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Source DFa Model 14 x1 1 x2 1 x3 1 x4 1 x1 x2 1 x1 x3 1 x1 x4 1 x2 x3 1 x2 x4 1 x3 x4 1 2 x1 1 2 x2 1 2 x3 1 2 x4 1 Lack-of-Fit 10 Pure Error 6 Total 30 2 2 R = 92.42% , R (adj) = 85.79%
P-Value <0.001 <0.05 0.47 0.15 <0.0001 0.77 0.38 0.42 0.61 0.75 0.89 <0.05 0.26 0.30 <0.0001 0.14
ACCEPTED MANUSCRIPT Highlights
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Hydrometallurgical metal extraction from used PCBs. Optimization of process parameters using RSM model. Leaching in close system prevent gas emission. Recycling process is environmental.
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Figure 1
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