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Ni and Cu recovery by bioleaching from the printed circuit boards of mobile phones in non-conventional medium
Journal of Environmental Management 250 (2019) 109502
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Research article
Ni and Cu recovery by bioleaching from the printed circuit boards of mobile phones in non-conventional medium
T
Mahdokht Arshadi, Sheida Nili, Soheila Yaghmaei∗ Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Bioleaching Mobile phone PCBs Penicillium simplicissimum Non-conventional medium Metal recovery
There is a substantial volume of mobile phone waste every year. Due to the disadvantages of traditional methods, it is necessary to look for biological processes that are more eco-friendly and economical to recover metals from e-waste. Fungi provide large amounts of organic acids and dissolve metals but using sucrose in the medium is not economical. In this paper, the main objective is to find a suitable alternative carbon substrate instead of sucrose for fungi bioleaching of Ni and Cu in printed circuit boards (PCBs) of mobile phones using Penicillium simplicissimum. Four kinds of carbon sources (including sucrose, cheese whey, sugar, and sugar cane molasses) were selected. Also, pH and number of spores in inoculum were optimized by response surface methodology (RSM) for all carbon sources. The results showed the simultaneous maximum recovery of Cu and Ni is not possible. For Cu recovery, sugar is the best economical and simplistic medium instead of sucrose. Maximum recovery of Cu (90%) gained at the pH of 7, 3.3 × 107 spores, and in sugar. The amount of Ni recovery (89%) was highest in molasses, at the pH of 2, and 106 spores. The results proved non-conventional carbon sources improve bioleaching efficiency and the possibility of industrialization.
1. Introduction Waste of electronic and electric instruments (e-waste) has the highest rate of growth in the world; the bulk part of the residues produces in developing countries. E-waste contains dangerous and precious metals simultaneously like As, Pb, Cu, Ni, Au, Ag, Pd, Pt, etc. (Islam and Huda, 2019; Marra et al., 2018). Recent researches show that the amounts of mobile phone wastes grow exponentially. It is reported that there are more than 4.57 billion users of the mobile phone around the world since 2017 (Bauer et al., 2019) and the average life expectancy of mobile phone reduced from 2.9 years in 2011 to 2.21 years in 2018 (Liu et al., 2019). In the latest reports, estimated that at the end of 2015, more than 7 billion mobile phones used worldwide. Around 10 million kilograms of them are discarded yearly. The e-waste contains about 50% of plastics and different metals like gold, silver, copper, nickel, and iron. The concentration of Au and Ag within PCBs is more than ten times rather than natural ores (Arshadi et al., 2018b). Most of the used metals in mobile phone wastes can be reused and recycled using different physical, chemical, and biological methods. Old manual divesting techniques like mechanical treatment (such as crushing and jigging), hydrometallurgical, and pyrometallurgical methods have been used to recover e-waste. These methods result in a high recovery of valuable metals (Ren et al., 2014). But they are not ∗
enough efficient and environmental friendly in every situation (Vera et al., 2013). Physical and chemical technologies consume high energy, they are expensive, and some of the chemicals produced agents influence the environment negatively (Veit et al., 2006). Biotechnological leaching processes which are more eco-friendly, economical, and doesn't require specialized labors than other chemical methods have been used to recover valuable metals from e-waste (Faraji et al., 2018; Marra et al., 2018). In the bioleaching process, organic or inorganic acids produced by microorganisms (including bacteria or fungi) solute metals (Auerbach et al., 2019). Compared to bacteria, fungi can tolerate toxic materials more; they have a shorter lag phase and faster leaching rate. Fungal bioleaching has four main mechanisms: (1) acidolysis (the principal mechanism) in which by producing the organic acids, metals dissolve in mentioned acids; (2) complexolysis where the metals make complexes with produced organic or amino acids; (3) redoxolysis in which organic acids reduce the metals; and (4) bioaccumulation in which the mycelium acts as a sink for the metal ions (Faraji et al., 2018; Mirazimi et al., 2015). By the formation of metal complexes, the toxicity of metal reduces. Aspergillus and Penicillium are the most used heterotrophic fungi in recovering of heavy and worth metals from solid wastes because they can provide large amounts of organic acids (citrate, gluconate, and oxalate) (Faraji et al., 2018).
Corresponding author. E-mail address: [email protected] (S. Yaghmaei).
https://doi.org/10.1016/j.jenvman.2019.109502 Received 28 May 2019; Received in revised form 15 August 2019; Accepted 31 August 2019 0301-4797/ © 2019 Published by Elsevier Ltd.
Journal of Environmental Management 250 (2019) 109502
M. Arshadi, et al.
medium is an important strategy to make production economically viable (Salari et al., 2019). Molasses is a cheap carbon source and a food industry by-product in the process of sugar beet or cane production (Sun et al., 2019). The composition of molasses varies depending on the quality of the raw product. Considering the high sucrose content of the molasses, it would seem to be a natural alternative to synthetic media for P. simplicissium growth and metabolite production (Gojgic-Cvijovic et al., 2019). Sun et al. (2019) showed the economical production of some metabolites using molasses (Sun et al., 2019). Cheese whey is the byproduct of the cheese production with high biochemical oxygen demand. Cheese whey contains about 55% of nutrients in the original milk supports the metabolite production in microbial fermentation (Salari et al., 2019). Sugar has a similar composition to sucrose; it is not pure and contains galactose too. It is noteworthy that the application of fungal bioleaching in solid waste is limited; there are a lot of gaps in this field. In our knowledge, too limited research was found to study bioleaching of e-waste using P. simplicissimum. Especially using sucrose in the medium is not economical. Finding an appropriate substitution instead of sucrose is one of the essential aims of industrializing this biological method (Gojgic-Cvijovic et al., 2019). In this research, extraction of Ni and Cu from mobile phone wastes using P. simplicissimum fungi was investigated. The aim of this study was to find the optimized conditions for growing P. simplicissimum fungus in industrial medium (including sugar cane molasses, cheese whey, and sugar) before the medium scale in the laboratory (bioleaching in bioreactors) and comparison to the principal medium of P. simplicissimum (sucrose as carbon source) using one-step bioleaching method. In this research, the optimal amounts of pH and inoculum size were defined to reach the maximum recovery of goal metals using the central composite design (CCD) of the RSM. The interaction effect and contour plots of the parameters were discussed. Also, to measure the amount of carbon source in non-conventional mediums, the chemical oxygen demand (COD) test was done. The fungi biomass in the selected mediums was measured in the presence and absence of the salts unless sucrose in Bosshard medium to observe the growth of P. simplicissimum. The results showed sugar and molasses respectively for Cu and Ni recovery are more efficient and economical for metal bio-recovery from ewaste.
Bioleaching methods divide into three categories including (1) onestep bioleaching, in which fungus is inoculated to the medium with solid waste, (2) two-step bioleaching, in which the solid waste adds to the medium when the fungus is in its logarithmic growth phase, (3) spent-medium bioleaching, in which the fungus is in the stationary phase; the highest level of organic acids in the medium provide and then the solid phase is added to the biomass (Mirazimi et al., 2015). Choosing an effective method depends on the type of substrate and also the selected microorganism (Amiri et al., 2011). Several physical, chemical, and biological variables affect the bioleaching process (such as carbon and nitrogen source, oxygen supply, pH, temperature, inoculation content, and etc.). These parameters have to be optimized to reach the maximum amount of recovery. pH is one of the main parameters in the bioleaching process (Muddanna and Baral, 2019). Some researchers attend to fungal bioleaching of metals from solid waste utilized Aspergillus niger and P. simplicissimum as the most effective microbes. In a first reported attempt, Brandl et al. (2001) used these fungi to recover metals from e-waste. The microbial growth was inhibited in a higher pulp density of 10 g/l. During the six weeks of the adaptation phase, fungi grew at a concentration of 100/l. Both fungi mobilized Cu and Sn by 65%, Al, Zn, Pb, and Ni more than 95% from ewaste (Brandl et al., 2001). Xia et al. (2018) aimed to study the feasibility of recovery of metals from e-waste by mixed fungal cultures in the stirred tank reactors. At the first step, the fungi adopted to 80 g/l of the e-waste sample. At the end of the bioleaching process, community structure analysis proved that A. niger (28%) and Purpureocillium lilacinum (72%) were the two dominated fungal species. About 56% of Cu, 20% of Pb, 15.7% of Al, 49% of Zn, and 8% of Sn were recovered (Xia et al., 2018). Rasoulnia and Mousavi (2016) studied bioleaching of V and Ni from a vanadium-rich power plant residual ash using A. niger and P. simplicissimum. They monitored fungal growth through measurement of pH and produced organic acids to find the optimum fermentation period. Leaching duration of 7 days for both fungi determined as the optimal condition. They showed V and Ni were recovered respectively 97% and 50% using A. niger. By using P. simplicissimum V and Ni were extracted about 90% and 49% respectively in spent-medium bioleaching (Rasoulnia and Mousavi, 2016). Amiri et al. (2011) applied experimental designs to screen and optimize the bioleaching of spent catalyst by P. simplicissimum. They determined the optimal values for pulp density, sucrose, NaNO3, and yeast extract respectively as 4% w/v, 90 g/l, 2 g/l, and 0.36 g/l which 98% of Mo, 46% of Ni, and 14.3% of Al were extracted. Their results proved the importance of medium composition rather than culture condition to reach maximal Mo recovery in the bioleaching process of the spent catalyst using P. simplicissimum (Amiri et al., 2011). Kim et al. (2016) explored the bioleaching of spent Zn–Mn or Ni–Cd batteries by Aspergillus species. Sucrose and malt extract as the carbon sources were used. They showed the dominant organic acid in sucrose and malt extract were citric acid and oxalic acid, respectively. Also, it was proved the fungal metabolic pathway influenced by the type of media used. Consequently, choosing the proper nutrient (such as carbon source) is essential to reach an efficient recovery (Kim et al., 2016). Muralidhar et al. (2001) studied the production of lipase by Candida cylindracea in 5 different mediums (including olive oil, malt extract, peptone, tween 80, and glucose). A response surface approach was used for optimization and comparison of selected medium for lipase production. The results showed that the optimal yield was about 17.30 U/ ml by glucose and 47.25 U/ml by olive oil. Also, olive oil was selected as the best medium through all mediums to produce C. cylindracea in fermentation (Muralidhar et al., 2001). The used medium influences the fungal metabolite pathway; choosing the proper nutrient source has to be considered for metal recovery (Kim et al., 2016). In microbial fermentation, the cost of fermentation medium makes 30% of the total cost; using a cost-effective
2. Materials and methods 2.1. Preparation of PCBs sample The PCBs of mobile phones wastes were bought from the collection center of e-waste in Tehran, Iran. Complete PCBs changed into balls with the diameter around 2 cm using a hammer mill. Then Ball mill was used to turn 2 cm diameter balls into particles with 75–149 μm diameters. To detect the metal concentration of the mobile phone PCBs, 1 g of the powder digested in a mixture of aqua regia, hydrogen peroxide and hydrofluoric acid with 1:1:3 ratio (Ilyas et al., 2014). This suspension was kept at 150°C until a humid powder obtained. Afterward, 20 ml of aqua regia was added and heated until it boiled. The boiling suspension was filtered and increased volume to 50 mL by DI water. The amounts of dissolved Ag, Au, Cu, Fe, and Ni were measured by inductively coupled plasma optical emission spectrometry (ICP-OES 730-ES, Varian) shown in Table 1. 2.2. Fungi preparation, inoculation and growth conditions P. simplicissimum fungus (PTCC 5129) was prepared from the Biochemistry Research Center of Sharif University, Tehran, Iran in lyophilized form. Sterilized potato dextrose agar plates were used to grow fungus in Petri dishes kept at 30 ̊C for seven days. The spores on the Petri dishes were washed using sterilized distilled water. The spores 2
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The presence of e-waste powder in the medium, as a toxic material, affects the reproduction of fungi negatively (Bahaloo-Horeh et al., 2018); fungi should adapt to the hazardous powder step by step. The pulp density of the mobile phone PCBs sample was increased in the levels of 1 g/l to achieve this aim. Each step required an average of one week and was done at 30°C and 130 rpm. The serial adaptation continued up to 40 g/l of mobile phone PCBs powder. In higher concentrations of mobile phone PCBs powder, the growth of the fungi was weak because the metabolism of fungi was inhibited.
Table 1 Metals content (ppm) of mobile phone PCBs. Metal
Wave length (cm−1)
Concentration (ppm)
Ag Au Cu Fe Ni
328.068 267.594 327.395 259.940 231.604
1470 1484 210000 47090 2790
2.3. Feasibility study of recovery of e-waste using non-conventional mediums The economic value of the mobile phone PCBs has to be calculated and compared with natural ore to gain more insight into the financial advantage of the e-waste. The total economic value of metals in a sample can calculate using ∑ Wti Prti which Wti and Prti are the weight percentage and price of metal i, respectively (Arshadi et al., 2018a). Prti were 5673 $/ton for Cu, 14740 $/ton for Ni, 1505 $/troy ounce for Au, 17.16 $/troy ounce for Ag. Calculating this phrase for the reported metals of Table 1 shows that the economic value of mobile phone PCBs just for Cu, Ni, Ag, and Au is equal to 72849 $/ton of mobile phone PCBs. That is while the value of the top ten mines of the world's most valuable ores is 2096.8 $/ton, in average (Basov, 2017) which emphasizes on the importance of mobile phone PCBs recovery. So, the average total value of mobile phone PCBs is about 35 times higher than the value of the average top ten natural ores and known as a potential source for the recovery of metals. Meantime, the cost of the process represents a critical aspect of the commercial and industrialized of the e-waste recovery. In the fungal bioleaching the cost of fermentation medium makes 30% of the total cost (Salari et al., 2019); using a cheaper carbon source, instead of the commonly used sucrose, might result in a lower cost of the final product. The price of sucrose for biochemistry, sugar; sucrose used in the food industry, cheese whey powder, and sugar cane molasses are about 54 $/kg, 200 $/ton, 1 $/kg, and 140 $/ton respectively. The sucrose is 200 times more expensive than sugar and 386 times than molasses. These prices reported from https://www.alibaba.com/showroom which produced in Iran.
Fig. 1. Material and COD amount.
2.4. Pre-study of non-conventional mediums Fig. 2. COD standard curve.
To find an appropriate substitution instead of sucrose, sugar, sugar cane molasses, and cheese whey as the carbon sources were tried. The COD test was done to compare the amount of carbon source in nonconventional mediums and sucrose. Fig. 1 shows the amount of COD in each medium. The amount of COD is more in sucrose, sugar, molasses, and cheese whey respectively. Fig. 2 shows the COD standard curve. To observe the growth power of P. simplicissimum in the selected medium, eight primarily experiments were done. The presence and absence of the salts in the Bosshard medium (NaNO3, KH2PO4, MgSO4.7H2O, KCl and yeast extract) unless sucrose were studied. Similar to the sucrose concentration in the Bosshard medium, the amount of sugar, molasses, and cheese whey were fixed at 100 g/l. Because of the high density of molasses, it was tried at two concentrations of 10 ml/l and 100 ml/l. All flasks were incubated in 30°C for seven days. After the period, the suspension of biomass, medium, and extracted acids was filtered. The remaining mass was kept in 80°C for 24 h in the oven. Consequently, the biomass was measured (Bahaloo-Horeh et al., 2018). Table 2 shows the mass of produced biomass in different conditions. Due to Table 2, P. simplicissimum can grow in all selected mediums. The amount of produced biomass was higher in molasses (in high concentration), sucrose, sugar, and cheese whey respectively. Adding mineral salts improved the fungi growth in the case of molasses and cheese whey. All of the tried media were selected for future studies;
Table 2 The mass of produced biomass in different conditions. Num.
Medium
Concentration
Mass of biomass(g)
1 2 3 4 5 6 7 8
Sucrose Sugar Cheese whey without the salts Cheese whey with salts Molasses without the salts Molasses with the salts Molasses without the salts Molasses with the salts
100 g/L 100 g/L 100 ml/L 100 ml/L 10 ml/L 10 ml/L 100 ml/L 100 ml/L
0.9837 0.8511 0.2946 0.3615 0.1916 0.4526 1.6703 2.8265
were numbered under a phase-contrast microscope (Olympus, CH–B145-2, Japan). In all experiments, 107 spores were inoculated to 100 ml of the medium. The foremost medium for growing the P. simplicissium fungus contains 100 g of sucrose, 1.5 g NaNO3, 0.5 KH2PO4, 0.025 g MgSO4.7H2O, 0.025 g KCl and 1.6 g yeast extract in 1 l distilled water (Bosshard et al., 1996). Bioleaching experiments were done using 250 ml Erlenmeyer flasks containing 100 ml of the medium. The flasks were incubated at 30°C with shaking incubator (Labcon 5082u, South Africa) at 130 rpm. 3
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Table 3 Experimental design based on central composite design. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Variables
Metal recovery (%) 7
Variables
Metal recovery (%) 7
pH
No. Spores ( × 10 )
Medium
Cu
Ni
Run
pH
No. Spores ( × 10 )
Medium
Cu
Ni
3.2 8.8 3.2 8.8 2 10 6 6 6 6 6 3.2 8.8 3.2 8.8 2 10 6 6 6 6 6
1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05 1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05
Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey
52.8 51.5 48.4 67.5 77.0 49.2 52.5 49.8 59.9 63.7 48.2 11.7 42.6 50.3 61.2 10.0 41.4 57.0 65.0 44.1 33.8 36.0
12.8 2.5 21.6 6.4 63.4 16.5 12 14.3 1.8 13.4 3 26 5 31.3 25.2 27.9 9.7 36.4 0.04 0 4.5 5
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
3.2 8.8 3.2 8.8 2 10 6 6 6 6 6 3.2 8.8 3.2 8.8 2 10 6 6 6 6 6
1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05 1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05
Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses
46.0 36.2 45.7 65.7 45.0 62.8 67.4 47.3 100 97.0 94.3 0 27.3 46.0 18.8 44.0 27.9 60.5 43.4 45.5 43.0 39.7
23.7 20.7 13.6 5 51.9 48.9 21.1 30.2 2.6 1.9 2.3 67.1 7.1 49.1 8.5 72.5 11.1 8.2 7 4.8 5.1 5.6
Table 4 Fitted curves for Cu and Ni recoveries depend on the mediums. Cu (in terms of actual factors) Medium Constant Coefficient Sucrose +80.4 Cheese whey −19.3 Sugar −40.1 Molasses −35.6 Ni (in terms of actual factors) Medium Constant Coefficient Sucrose Cheese whey Sugar Molasses
pH
No. Spores
−7.1 +16.3 +32.9 +18.9
+1.5 −3.6 +1.3 +9.3
+85.6 +78.1 +104.8 +156.7
* * * *
10−8 10−7 10−6 10−7
No. Spores * pH
pH2
No. Spores2
+5.1* 10−8 −5.0 * 10−8 +7.5 * 10−8 −1.4 * 10−7
+0.3 −0.8 −2.9 −1.1
−2.9 *10−15 +9.0 * 10−15 −1.77* 10−14 −5.1*10−16
pH
No. Spores
pH2
No. Spores2
−23.7 −15.4 −28.4 −38.8
+8.4 * 10−8 −7.7 * 10−7 −5.5 * 10−7 −3.4* 10−7
+1.6 +1.1 +2.3 +2.5
−2.67 *10−16 +6.8*10−15 +5.0 *10−15 +2.7*10−15
significant parameters. There are three main steps in this method: i) designing and doing experiments, ii) modeling of RSM by regression and iii) optimization. All the equations in Design-Expert solve by the technique of least squares. The matrix was described in Eq. (1) should be solved to explain the behavior of the system using a cubic equation.
adding the mineral salts was preferred. 2.5. Bioleaching experiments All of the experiments were done in autoclaved 250 mL Erlenmeyer containing 100 ml of the defined medium. After sterilization, 1 ml of the inoculation suspension including 107 spores of P. simplicissimum was inoculated to the medium. The pulp density was constant and fixed on 1 g of powder of mobile phone waste in the 100 ml of the medium (1% w/v). The Erlenmeyer flasks were kept in a rotary shaker incubator at 30°C and 130 rpm. One-step bioleaching was selected; the e-waste powder and fungus inoculation suspension added in medium simultaneously. One-step bioleaching is more practical because this method is simplistic and takes less time than other methods of bioleaching (Amiri et al., 2011). All experiments were done at the same time under the same condition of inoculation.
y ⎛ 1 ⎞ ⎛ 1 x11 x12 y2 ⎜ . ⎟ = ⎜ 1. x.21 x22 . . ⎜⎜ . ⎟⎟ ⎜⎜ . . y x x 1 n 1 n2 n ⎝ ⎠ ⎝
. . . . .
. x1k ⎞ ⎛ β0 ⎞ ε ⎛ 1⎞ . x2k ⎜ β1 ⎟ ε2 ⎟ ⎜ + .⎟ . .. ⎜ . ⎟ . . ⎟ . . x nk ⎟ ⎜ ⎟ ⎜ εn ⎟ ⎠ ⎝ βk ⎠ ⎝ ⎠
(1)
The total number of experiments are given by 2n + 2n + n 0 ; where n is the number of the selected parameters for optimization () Arshadi et al., 2019. In this study, CCD with two numeric factors including pH (in the range of 2–10) and number of spores (between 106-108) with five levels (-α, −1, 0, +1, +α) and the type of carbon sources (including sucrose, sugar, sugar cane molasses, and cheese whey) as the categorical parameter were used to optimize the recovery of Cu and Ni from mobile phone PCBs using P. simplicissimum. α is the distance of the axial point from the center, which can calculate by α = 2^(n/4) (n is the number of factors). There are eight non-center points and three center points for each medium.
2.6. Design of experiments and optimization After previous experiments for recognizing the ability of new carbon sources in fungus growth, an experimental design was defined to estimate the effects of physical parameters on metal recovery efficiencies. RSM is one of the mathematical and statistical methods to optimize the processes. This technique is used in the sake of assessment of different 4
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(C + CAM ) × Vs R (%) = ⎛ MS × 100⎞ PD × MC ⎠ ⎝
(2)
CMS and CAM show respectively the amount of released metals in the solution and concentration of metal ions absorbed by fungi. VS presents the volume of solution fixed at 100 ml. PD and MC are respectively pulp density and metal concentration. 3.1. Fitting models of Cu and Ni recoveries Results of 44 experiments were analyzed by CCD to estimate the best-fitted curve and factors. Table 4 shows the third-order cubic equations coefficients for Cu and Ni recovery. Because the estimated models using coded parameters are not user-friendly and contain three levels of the mediums simultaneously; the equations in terms of actual factors (shown in Table 4) were provided. These equations can be used to make predictions about the response for given levels of each parameter. The models in terms of coded factors can be used to estimate Cu and Ni recovery for given levels of each parameter. All analysis within the software is based on the coded equations contained cubic terms while they do not appear in the equations in terms of actual factors. To find the equation from Table 4, first, choose Cu or Ni, then find the medium and write the coefficients. For instance, Ni recovery in sugar medium is presented by Eq. (3).
Ni %= +104.8 − 28.4*pH − 5.5* 10−7*No. of Spores + 2.3* pH2 + 5.0*10−15* No. Spores
2
(3)
According to Table 4, the effect of the number of spores is much lower than pH and carbon sources. Even the removal of the number of spores has no significant effect on both responses. pH is a significant parameter (Rasoulnia et al., 2016) for both Cu and Ni recovery. pH has a negative effect on Ni recovery. Amiri et al. (2012) studied the bioleaching of spent refinery catalyst using A. niger. They showed the pH parameter has a negative effect for MO, Al, and Ni (Amiri et al., 2012). The negative coefficient of pH suggests acidolysis as the principal leaching mechanisms for the related statement (Muddanna and Baral, 2019). An essential field in fungal leaching is based on acidolysis which the protonation of oxygen atoms covering the surface of metallic compounds. Oxygen and protons are associated with water, then the metal release and detach from the surface (Burgstaller and Schinner, 1993). Eq. (4) shows the producing nickel ions in acidolysis reaction, pH decreasing increases Ni recovery; following Table 4 for Ni recovery the pH coefficient is negative for all mediums.
NiO + 2H+ → Ni 2 + + H2 O
The pH coefficient for Cu recovery in the mediums of sugar, molasses, and cheese whey is positive. The other essential field of fungal leaching is complexolysis which metal complexes and chelates are formed. Organic acids are the most prominent metabolite secretes by fungi help to leach metal ions. Type and concentration of produced organic acids significantly affect the leaching of metals ions (Deng et al., 2013). Complexolysis leads to the solubilization of the metal ions as like as, a complex of oxalic acid with magnesium, iron, and aluminum, a complex of tartaric acid with aluminum, calcium, and magnesium and a complex of citric acid with calcium and magnesium. Finally, metal ions which are stabilized in comploxolysis, solubilized into solution by acidolysis. The bellow equation shows a complexolysis reaction that produces nickel citrate (Bahaloo-Horeh et al., 2018) emphasizes that the alkaline condition is more beneficial for running reaction.
Fig. 3. Normal plot of residuals (a) Cu recovery (b) Ni recovery. Table 5 ANOVA table and responses for Cu and Ni recoveries. Cu%
Ni%
Source Mean squares df Sum of squares Model 652.90 23 15016.73 Reduced Cubic Models, R-Squared = 0.8381 Significant parameters: Medium, A2, A2C, B2C Source Mean squares df Sum of squares Model 654.53 19 12436.02 Reduced Cubic Models, R-Squared = 0.8002 Significant parameters: pH, AC, A2
F-Value 4.50
F-Value 5.06
(4)
P-Value 0.0006
P-Value 0.0001
3. Results
Ni 2 + + C6 H8 O7 → Ni (C6 H5 O7)− + 3H+ RSM suggested 44 experiments. All of them were done at 30 °C and 130 rpm. The experimental design of 44 tests and their responses are summarized in Table 3. The amount of metal recovery was calculated using Eq. (2).
(5)
The effect of the culture medium is very important, by changing medium the coefficients change significantly. Fig. 3 parts (a) and (b) show the normal plot of residuals respectively for Cu and Ni recovery which are normally distributed. As they 5
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Fig. 4. 2D recovery plots of Cu, Horizontal axis is number of spores in inoculum and vertical axis shows pH (a) Sucrose (b) Cheese whey (c) Sugar (d) Molasses.
cheese whey, and molasses in all of the tried conditions is about 60% while this amount for sugar reaches to 100%. Simple sugars are called monosaccharides and include glucose (also known as dextrose), fructose, and galactose. Sucrose is a carbon source for fungi, hydrolysis to glucose and fructose. Molasses is 50% sugar by dry weight, predominantly sucrose, but contains significant amounts of glucose, fructose, and lactose. So, the main difference between sugar, sucrose, and molasses is the presence of galactose. The chemical formula of glucose, fructose, and galactose is C6H12O6. They are isomers. Muddanna and Baral studied the bioleaching from spent catalyst using A. niger. They showed within three days of A. niger inoculation to the medium the sucrose hydrolysis complete. They proved the fungi consume sucrose, fructose, and glucose in the medium respectively (Muddanna and Baral, 2019). Galactose is an attractive industrial carbon source. It is reported some strains consume galactose much more slowly compared to glucose (Moktaduzzaman et al., 2015); helps to substrate utilization for fungi and lead to producing a higher amount of metabolite continuously. The predominant sugar of cheese whey is lactose. Hossein et al. (1984) studied the effect of sugar source on metabolite production by fungi that was more in the presence of sucrose, glucose, fructose, and lactose respectively (Hossain et al., 1984). According to Fig. 1, the growth of fungi is limited in cheese whey. Fig. 4 shows the effect of the initial number of spores and pH on Cu recovery. All parts express that alkaline condition can be used if the number of spores is in the maximum amount. Rasoulnia et al. (2016) measured the P. simplicissimum growth in a pH range of 2–11. They showed the P. simplicissimum colony diameters in the pH range of 3–7 were similar and in the maximum amount. But the fungal growth was retarded in higher and lower pH range. The maximum fungal growth gained at a pH of 4 (Rasoulnia et al., 2016). Consequently, at the notfavorable condition, more spores have to be inoculated for sufficient growth.
lie on the diagonal line, validate no deviation of the variance (Arshadi and Mousavi, 2014). 3.2. Statistical analysis Analysis of Variance (ANOVA) was used to analyze the data statistically. The ANOVA suggested cubic models which predict the Cu and Ni recoveries for all selected mediums shown in Table 5. In this table, the sum of squares is the summation of squared deviations from the mean. The df is the degree of freedom used to estimate the value of statics of freedom for terms and subsets of the model (Mirazimi et al., 2013). Term of the mean square is used to show the associated variance can be calculated by dividing the sum of squares by the df. The third parameter is F-value, shows the comparison of the associated variance with the residual variance. It computed by dividing the mean square for the term by the mean square for the residuals (Arshadi and Mousavi, 2014). Higher F-value is a more acceptable variation of mean values and more significance of the factors (Mirazimi et al., 2013). Amount of P-value less than 0.0500 (0.0002 for Cu and 0.0001 for Ni) indicates that the model terms are significant. High amounts of R-squared prove these models (reduced cubic model) are appropriate for these data. The confidence level of these models are 95%, so it is trustable (Amiri et al., 2012). 3.3. 2D and 3D recovery plots 3.3.1. Cu recovery Fig. 4 shows the response surface of Cu recovery for different used carbon sources. Part (a), (b), (c), and (d) are drawn respectively, for sucrose, cheese whey, sugar, and molasses. According to Table 3, the maximum amount of Cu recovery is observed in Fig. 4(c) for the sugar medium. The maximum recovery of Cu in the medium of sucrose, 6
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Fig. 5. 2D recovery plots of Ni, Horizontal axis is number of spores in inoculum and vertical axis shows pH. (a) Sucrose (b) Cheese whey (c) Sugar and (d) Molasses.
fungi. Also by increasing the number of spores in the inoculum, lack of carbon source is the main reason for decreasing recovery because, after the specific growth rate, fungi started to use themselves as a carbon source when the condition is unfavorable (Rasoulnia and Mousavi, 2016).
Fig. 4(a) shows the effect of pH and inoculation size on Cu recovery in the sucrose medium is not as effective as changing the medium; almost the Cu recovery in all conditions is the same. In the presented model for Cu recovery (Table 4) in sucrose medium, the coefficient of pH is negative. The maximum leaching efficiency of Cu recovery in sucrose medium was 68%. Probably for Cu recovery in sucrose medium both acidolysis and complexolysis are significant as same as each other. In Fig. 4(b) the recovery plot of Cu recovery in cheese whey is illustrating. In alkaline condition with high numbers of spores, the Cu recovery reached to the maximum amount. By reducing pH Cu recovery decreases. However in acidic condition by increasing the numbers of spores in the inoculum, the recovery increases as far as increasing the numbers of spores in inoculum results in the lack of carbon source for growing the fungi, they start to use themselves as food and the recovery is less than alkaline condition (Rasoulnia and Mousavi, 2016). If the carbon source is sugar, as it is demonstrated in Fig. 4 (c) and the equation of modeling sugar in Table 4, the recovery trend is quite different from other carbon sources. The highest value of Cu recovery is in neutral condition with moderate numbers of spores in the inoculum. The highest value of Cu recovery is in neutral condition with moderate numbers of spores in the inoculum. If the solution is alkaline or acidic with low or high numbers of spores in the inoculum, the less amount of Cu will be recovered; due to increasing toxicity of medium for growing fungi. In Fig. 4(d), acidic condition with low numbers of spores lead to low recovery while by increasing the number of spores in inoculum in acidic medium, producing of organic acids is more and the recovery is more too. In alkaline condition, increasing the number of spores in inoculum decreases the recovery. Generally, for Cu recovery in all kind of carbon sources and alkaline condition recovery reaches the least amount around fewer than 30% that is a reason for the high toxicity of alkaline medium for growing
3.3.2. Ni recovery Fig. 5 shows the response surface of Ni recovery for different used carbon sources. Part (a), (b), (c), and (d) are drawn respectively for sucrose, cheese whey, sugar, and molasses. Almost in all of the figures and tables, the Ni recovery is less than Cu recovery (Fig. 4) in the same condition. There are some reasons. First, the amount of Cu is more than the other elements. Cu concentration is about 75 times more than Ni concentration in the mobile phone PCBs (Table 1). Second, with attention to the Ksp Ni makes stable salts; Cu ions are more stable in the solution (Spence and Soderstrom, 1999). Oxalic acid (a produced metabolites by P. simplicissimum) precipitates a fraction of released Ni and forms nickel oxalate which has low solubility or acts as an inhibitor for the process; The following equation shows the nickel oxalate formation (Biswas et al., 2013) that is slightly soluble in water and oxalic acid; it is soluble in strong acid and decreases Ni recovery while the formation of copper oxalate shows no significant effect on Cu recovery rather than Ni.
NiO + HO2 C. CO2 H → NiC(O2 C. O2) + 2H+
(6)
Third, the lower Ni recovery compared to Cu recovery relate to the highly toxic effect of Ni over fungi (Muddanna and Baral, 2019). Fig. 5 shows for all used carbon sources low pH is more benefit for Ni recovery. It was reported that the pH range higher than 7 shows an inhibitory effect on P. simplicissimum (Rasoulnia et al., 2016). Muddanna and Baral (2019) showed that acidolysis is the dominant leaching mechanisms for Ni recovery. They proved lesser the pH higher 7
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Fig. 6. Overlay plots for simultaneous recovery of Cu and Ni. Table 6 Point prediction and verification of the models at the optimum conditions. Response (%)
Target
Predicted recovery (%)
Confirmation experiments (%)
95% CI low
95% CI high
Desirability (%)
Cu Ni
Maximum recovery of Cu/Ni recover is none. Maximum recovery of Ni/Cu recover is none.
89% 88.8%
90% 89%
84.6% 84.3%
93.5% 93.2%
100 100
lower recovery of metals.
Ni recovery efficiency (Muddanna and Baral, 2019). In this figure, the highest recovery of Ni recovery around 65% is in mediums with molasses as a carbon source which is independent of the number of spores in the inoculum. Cane molasses are acidic and rarely show an alkaline reaction (Olbrich, 2006); according to dominant leaching mechanisms for Ni recovery, molasses shows the highest leaching efficiency. In other carbon sources, the highest recovery of Ni is around 40% in acidic condition. The initial pH of cheese whey medium (about 6.2) and its lower COD (rather than other media) cause
3.4. Optimization experiments The principal purpose of this process is to find the best carbon source instead of sucrose and optimize the conditions to scale-up the process and make that more economical. After doing all of the experiments and analyzing the effect of selected variables on Cu and Ni recoveries, the software suggested the simultaneous maximum recovery 8
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molasses, and cheese whey. P. simplicissimum can grow in all selected medium and the most biomass produced in molasses (in high concentration), sucrose, sugar, and cheese whey respectively. Adding mineral salts improve the fungi growth in the case of molasses and cheese whey. Two third-order cubic equations were suggested, to model Cu and Ni recovery. pH is a significant parameter for both responses with a negative effect on Ni recovery; proves acidolysis is the dominant leaching mechanisms for Ni recovery; lesser the pH higher Ni recovery efficiency. The pH coefficient for Cu recovery is positive shows complexolysis as an essential mechanism for Cu leaching. The overlay plots showed that the simultaneous maximum recovery of Cu and Ni is not possible. The accessible Ni recovery is less than Cu recovery in the same condition. Maximum recovery of Cu (90%) was gained at the pH of 7, the number of spores of 3.3 × 107, and in the sugar medium. The Ni recovery (89%) was maximized in the molasses medium, at the pH of 2, and the number of spores of 106. Sterilization is probably the other main limitation of fungal bioleaching that increases the cost of the process. Future efforts are also required to decrease sterilization cost or omitting the sterilization needs. The research findings suggest the fungal bioleaching of e-waste (as a profitable technology) and using cheap carbon sources (such as molasses and sugar) to avoid the losses of resources and limit the impacts on the environment as promoted by the circular economy concept.
of Cu and Ni is not possible. The overlay plots for sugar and molasses medium are shown in Fig. 6. The effect of pH and the number of spores on Cu and Ni recovery are shown in Fig. 6 that delineate the common regions of responses in a predefined recovery range. Fig. 6(a) shows the overlay plot for both Cu and Ni recovery of more than 43% in the sugar medium. The highlighted region is the area in which the recovery of more than 43% is possible for both responses; if the leaching efficiency is selected more than 45% for both Cu and Ni, disappears; the simultaneous recovery of Cu and Ni more than 45% in sugar medium is not possible. But if the recovery of Ni decreases to more than 7% in Fig. 6(b); the highlighted region is presented even in the Cu recovery of more than 90%. This finding confirms Figs. 4(c) and 5(c); the region related to the maximum recovery of Cu (Fig. 4(c)) and Ni (Fig. 5(c)) in sugar medium is not as same as each other. Fig. 6(c) is drawn in the molasses medium, expresses the overlay plot for simultaneous Cu and Ni recovery of more than 56%. The software predicted for same recovery of Cu and Ni more than 58% there is no any possible condition. If the recovery of Cu reduces to more than 5% in Fig. 6(d); the highlighted region is presented even in the Ni recovery of more than 75%. Figs. 4(d) and 5(d) confirm the overlay plots in the molasses medium. The impossibility of the simultaneous recovery of copper and nickel may have some reasons. First, as mentioned before Cu concentration is much more than Ni concentration (according to Table 1). By increasing the amount of substrate in the left side of the equation, the tendency of the reaction increases for substrate consumption. Second, this is related to the optimal condition for the responses. As previously was mentioned in 3.2. section, the principal mechanism for Ni recovery is acidolysis while comploxelosis is the main for Cu recovery. Increasing pH shows a positive effect on Cu recovery while reduces Ni recovery. The significant difference between Ni and Cu concentration and different optimal condition for them lead to the impossibility of simultaneous recovery of Cu and Ni. Because the maximum simultaneously recovery of Cu and Ni is not possible, to reach the highest possible recovery of each response, the optimal condition for Cu and Ni have to be investigated under the situation that the other answer is not considered. These criteria are designed by the software. In the case where the goals of Cu and Ni recovery were selected respectively as the maximum and none; the optimal condition was suggested as pH of 7, spore number of 3.3 × 107, and in the sugar medium. In the case where the goals of Ni and Cu recovery were selected respectively as the maximum and none; the optimal condition was suggested as pH of 2, spore number of 106, and in the molasses medium. An experiment was done using the optimal conditions suggested by the models compared with the results of the models, shown in Table 6 to validate the models. The experimental results were within the low and high confidence levels that confirm the validity of the models. Cu and Ni recovery were estimated respectively by the models about 89% and 88% in the selected optimal condition. Performing experiments in the laboratory have given 90% of Cu recovery and 89% of Ni recovery. The experimental results were very close to the predicted data confirmed the accuracy of the model's prediction and the optimized conditions.
Acknowledgments The authors are grateful to Stat-Ease, Minneapolis, MN, USA, for provision of the Design-Expert 10 0.0 0.4 package. This project has been conducted by the deputy of research and technology of Sharif University of Technology (Award Number QA: 970713) in Iran. The authors are thankful to Pars Charkhesh Asia Company for supplying the PCBs. References Amiri, F., Mousavi, S.M., Yaghmaei, S., 2011. Enhancement of bioleaching of a spent Ni/ Mo hydroprocessing catalyst by Penicillium simplicissimum. Separ. Purif. Technol. 80, 566–576. Amiri, F., Mousavi, S.M., Yaghmaei, S., Barati, M., 2012. Bioleaching kinetics of a spent refinery catalyst using Aspergillus Niger at optimal conditions. Biochem. Eng. J. 67, 208–217. Arshadi, M., Mousavi, S.M., 2014. Simultaneous recovery of Ni and Cu from computerprinted circuit boards using bioleaching: statistical evaluation and optimization. Bioresour. Technol. 174, 233–242. Arshadi, M., Yaghmaei, S., Mousavi, S.M., 2018a. Content evaluation of different waste PCBs to enhance basic metals recycling. Resour. Conserv. Recycl. 139, 298–306. Arshadi, M., Yaghmaei, S., Mousavi, S.M., 2018. Study of plastics elimination in bioleaching of electronic waste using Acidithiobacillus ferrooxidans. Int. J. Environ. Sci. Technol. 1–14. Accepted Manuscript. https://link.springer.com/article/10.1007/ s13762-018-2120-1. Arshadi, M., Yaghmaei, S., Mousavi, S.M., 2019. Optimal electronic waste combination for maximal recovery of Cu-Ni-Fe by Acidithiobacillus ferrooxidans. J. Clean. Prod. 240https://doi.org/10.1016/j.jclepro.2019.118077. In press. Auerbach, R., Ratering, S., Bokelmann, K., Gellermann, C., Brämer, T., Baumann, R., Schnell, S., 2019. Bioleaching of valuable and hazardous metals from dry discharged incineration slag. An approach for metal recycling and pollutant elimination. J. Environ. Manag. 232, 428–437. Bahaloo-Horeh, N., Mousavi, S.M., Baniasadi, M., 2018. Use of adapted metal tolerant Aspergillus Niger to enhance bioleaching efficiency of valuable metals from spent lithium-ion mobile phone batteries. J. Clean. Prod. 197, 1546–1557. Basov, V., 2017. These 10 Mines Have the World's Most Valuable Ore (Mining.com). . Bauer, J., O'Mahony, C., Chovan, D., Mulcahy, J., Silien, C., Tofail, S.A.M., 2019. Thermal effects of mobile phones on human auricle region. J. Therm. Biol. 79, 56–68. Biswas, S., Dey, R., Mukherjee, S., Banerjee, P.C., 2013. Bioleaching of nickel and cobalt from lateritic chromite overburden using the culture filtrate of Aspergillus Niger. Appl. Biochem. Biotechnol. 170, 1547–1559. Bosshard, P.P., Bachofen, R., Brandl, H., 1996. Metal leaching of fly ash from municipal waste incineration by Aspergillus Niger. Environ. Sci. Technol. 30, 3066–3070. Brandl, H., Bosshard, R., Wegmann, M., 2001. Computer-munching microbes: metal leaching from electronic scrap by bacteria and fungi. Hydrometallurgy 59, 319–326. Burgstaller, W., Schinner, F., 1993. Leaching of metals with fungi. J. Biotechnol. 27, 91–116. Deng, X., Chai, L., Yang, Z., Tang, C., Wang, Y., Shi, Y., 2013. Bioleaching mechanism of heavy metals in the mixture of contaminated soil and slag by using indigenous Penicillium chrysogenum strain F1. J. Hazard Mater. 248–249, 107–114.
4. Conclusion Extraction of Ni and Cu in non-conventional mediums including sugar, cheese whey, and sugar cane molasses instead of sucrose from PBCs of mobile phones, were optimized using P. simplicissimum by RSM. Also, Numerical parameters (including pH and number of spores) were studied. The metal analysis showed that the average total value of mobile phone PCBs is about 35 times higher than the value of the average top ten natural ores and known as a potential source for the recovery of metals. The sucrose is 200 times more expensive than sugar and 386 times more than molasses. The pretreatment experiments showed that the amount of COD is respectively more for sucrose, sugar, 9
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approach for the comparison of lipase production by Candida cylindracea using two different carbon sources. Biochem. Eng. J. 9, 17–23. Olbrich, H., 2006. The Molasess. Biotechnologie-Kempe GmbH. Rasoulnia, P., Mousavi, S.M., 2016. V and Ni recovery from a vanadium-rich power plant residual ash using acid producing fungi: Aspergillus Niger and Penicillium simplicissimum. RSC Adv. 6, 9139–9151. Rasoulnia, P., Mousavi, S.M., Rastegar, S.O., Azargoshasb, H., 2016. Fungal leaching of valuable metals from a power plant residual ash using Penicillium simplicissimum: evaluation of thermal pretreatment and different bioleaching methods. Waste Manag. 52, 309–317. Ren, Z., Xiao, X., Chen, D., Bi, X., Huang, B., Liu, M., Hu, J., Peng, P., Sheng, G., Fu, J., 2014. Halogenated organic pollutants in particulate matters emitted during recycling of waste printed circuit boards in a typical e-waste workshop of Southern China. Chemosphere 94, 143–150. Salari, M., Sowti Khiabani, M., Rezaei Mokarram, R., Ghanbarzadeh, B., Samadi Kafil, H., 2019. Preparation and characterization of cellulose nanocrystals from bacterial cellulose produced in sugar beet molasses and cheese whey media. Int. J. Biol. Macromol. 122, 280–288. Spence, J.R., Soderstrom, M.D., 1999. Practical aspects of copper solvent extraction from acidic leach liquors. In: G.V.J. II (Ed.), Copper Leaching, Solvent Extraction, and Electrowinning Technology. Society for Mining, Metallurgy, and Exploration Inc. (SME), U.S.A. Sun, Y., Xu, Z., Zheng, Y., Zhou, J., Xiu, Z., 2019. Efficient production of lactic acid from sugarcane molasses by a newly microbial consortium CEE-DL15. Process Biochem. 81, 132–138. Veit, H.M., Bernardes, A.M., Ferreira, J.Z., Tenório, J.A.S., Malfatti, C.d.F., 2006. Recovery of copper from printed circuit boards scraps by mechanical processing and electrometallurgy. J. Hazard Mater. 137, 1704–1709. Vera, M., Schippers, A., Sand, W., 2013. Progress in bioleaching: fundamentals and mechanisms of bacterial metal sulfide oxidation-part A. Appl. Microbiol. Biotechnol. 97, 7529–7541. Xia, M., Bao, P., Liu, A., Wang, M., Shen, L., Yu, R., Liu, Y., Chen, M., Li, J., Wu, X., Qiu, G., Zeng, W., 2018. Bioleaching of low-grade waste printed circuit boards by mixed fungal culture and its community structure analysis. Resour. Conserv. Recycl. 136, 267–275.
Faraji, F., Golmohammadzadeh, R., Rashchi, F., Alimardani, N., 2018. Fungal bioleaching of WPCBs using Aspergillus Niger: observation, optimization and kinetics. J. Environ. Manag. 217, 775–787. Gojgic-Cvijovic, G.D., Jakovljevic, D.M., Loncarevic, B.D., Todorovic, N.M., Pergal, M.V., Ciric, J., Loos, K., Beskoski, V.P., Vrvic, M.M., 2019. Production of levan by Bacillus licheniformis NS032 in sugar beet molasses-based medium. Int. J. Biol. Macromol. 121, 142–151. Hossain, M., Brooksl, J.D., Maddox, S., 1984. The effect of the sugar source on citric acid production by Aspergillus niger. Appl. Microbiol. Biot 19 (6), 393–397. https://link. springer.com/article/10.1007%2FBF00454376. Ilyas, S., Lee, J.-c., Kim, B.-s., 2014. Bioremoval of heavy metals from recycling industry electronic waste by a consortium of moderate thermophiles: process development and optimization. J. Clean. Prod. 70, 194–202. Islam, M.T., Huda, N., 2019. Material flow analysis (MFA) as a strategic tool in E-waste management: applications, trends and future directions. J. Environ. Manag. 244, 344–361. Kim, M.-J., Seo, J.-Y., Choi, Y.-S., Kim, G.-H., 2016. Bioleaching of spent Zn–Mn or Ni–Cd batteries by Aspergillus species. Waste Manag. 51, 168–173. Liu, J., Bai, H., Zhang, Q., Jing, Q., Xu, H., 2019. Why are obsolete mobile phones difficult to recycle in China? Resour. Conserv. Recycl. 141, 200–210. Marra, A., Cesaro, A., Rene, E.R., Belgiorno, V., Lens, P.N.L., 2018. Bioleaching of metals from WEEE shredding dust. J. Environ. Manag. 210, 180–190. Mirazimi, S.M.J., Abbasalipour, Z., Rashchi, F., 2015. Vanadium removal from LD converter slag using bacteria and fungi. J. Environ. Manag. 153, 144–151. Mirazimi, S.M.J., Rashchi, F., Saba, M., 2013. Vanadium removal from roasted LD converter slag: optimization of parameters by response surface methodology (RSM). Separ. Purif. Technol. 116, 175–183. Moktaduzzaman, M., Galafassi, S., Capusoni, C., Vigentini, I., Ling, Z., Piškur, J., Compagno, C., 2015. Galactose utilization sheds new light on sugar metabolism in the sequenced strain Dekkera bruxellensis CBS 2499. FEMS Yeast Res. 15. Muddanna, M.H., Baral, S.S., 2019. Leaching of nickel and vanadium from the spent fluid catalytic cracking catalyst by reconnoitering the potential of Aspergillus Niger associating with chemical leaching. Journal of Environmental Chemical Engineering 7, 103025. Muralidhar, R.V., Chirumamila, R.R., Marchant, R., Nigam, P., 2001. A response surface
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Journal of Environmental Management journal homepage: www.elsevier.com/locate/jenvman
Research article
Ni and Cu recovery by bioleaching from the printed circuit boards of mobile phones in non-conventional medium
T
Mahdokht Arshadi, Sheida Nili, Soheila Yaghmaei∗ Chemical and Petroleum Engineering Department, Sharif University of Technology, Tehran, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Bioleaching Mobile phone PCBs Penicillium simplicissimum Non-conventional medium Metal recovery
There is a substantial volume of mobile phone waste every year. Due to the disadvantages of traditional methods, it is necessary to look for biological processes that are more eco-friendly and economical to recover metals from e-waste. Fungi provide large amounts of organic acids and dissolve metals but using sucrose in the medium is not economical. In this paper, the main objective is to find a suitable alternative carbon substrate instead of sucrose for fungi bioleaching of Ni and Cu in printed circuit boards (PCBs) of mobile phones using Penicillium simplicissimum. Four kinds of carbon sources (including sucrose, cheese whey, sugar, and sugar cane molasses) were selected. Also, pH and number of spores in inoculum were optimized by response surface methodology (RSM) for all carbon sources. The results showed the simultaneous maximum recovery of Cu and Ni is not possible. For Cu recovery, sugar is the best economical and simplistic medium instead of sucrose. Maximum recovery of Cu (90%) gained at the pH of 7, 3.3 × 107 spores, and in sugar. The amount of Ni recovery (89%) was highest in molasses, at the pH of 2, and 106 spores. The results proved non-conventional carbon sources improve bioleaching efficiency and the possibility of industrialization.
1. Introduction Waste of electronic and electric instruments (e-waste) has the highest rate of growth in the world; the bulk part of the residues produces in developing countries. E-waste contains dangerous and precious metals simultaneously like As, Pb, Cu, Ni, Au, Ag, Pd, Pt, etc. (Islam and Huda, 2019; Marra et al., 2018). Recent researches show that the amounts of mobile phone wastes grow exponentially. It is reported that there are more than 4.57 billion users of the mobile phone around the world since 2017 (Bauer et al., 2019) and the average life expectancy of mobile phone reduced from 2.9 years in 2011 to 2.21 years in 2018 (Liu et al., 2019). In the latest reports, estimated that at the end of 2015, more than 7 billion mobile phones used worldwide. Around 10 million kilograms of them are discarded yearly. The e-waste contains about 50% of plastics and different metals like gold, silver, copper, nickel, and iron. The concentration of Au and Ag within PCBs is more than ten times rather than natural ores (Arshadi et al., 2018b). Most of the used metals in mobile phone wastes can be reused and recycled using different physical, chemical, and biological methods. Old manual divesting techniques like mechanical treatment (such as crushing and jigging), hydrometallurgical, and pyrometallurgical methods have been used to recover e-waste. These methods result in a high recovery of valuable metals (Ren et al., 2014). But they are not ∗
enough efficient and environmental friendly in every situation (Vera et al., 2013). Physical and chemical technologies consume high energy, they are expensive, and some of the chemicals produced agents influence the environment negatively (Veit et al., 2006). Biotechnological leaching processes which are more eco-friendly, economical, and doesn't require specialized labors than other chemical methods have been used to recover valuable metals from e-waste (Faraji et al., 2018; Marra et al., 2018). In the bioleaching process, organic or inorganic acids produced by microorganisms (including bacteria or fungi) solute metals (Auerbach et al., 2019). Compared to bacteria, fungi can tolerate toxic materials more; they have a shorter lag phase and faster leaching rate. Fungal bioleaching has four main mechanisms: (1) acidolysis (the principal mechanism) in which by producing the organic acids, metals dissolve in mentioned acids; (2) complexolysis where the metals make complexes with produced organic or amino acids; (3) redoxolysis in which organic acids reduce the metals; and (4) bioaccumulation in which the mycelium acts as a sink for the metal ions (Faraji et al., 2018; Mirazimi et al., 2015). By the formation of metal complexes, the toxicity of metal reduces. Aspergillus and Penicillium are the most used heterotrophic fungi in recovering of heavy and worth metals from solid wastes because they can provide large amounts of organic acids (citrate, gluconate, and oxalate) (Faraji et al., 2018).
Corresponding author. E-mail address: [email protected] (S. Yaghmaei).
https://doi.org/10.1016/j.jenvman.2019.109502 Received 28 May 2019; Received in revised form 15 August 2019; Accepted 31 August 2019 0301-4797/ © 2019 Published by Elsevier Ltd.
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medium is an important strategy to make production economically viable (Salari et al., 2019). Molasses is a cheap carbon source and a food industry by-product in the process of sugar beet or cane production (Sun et al., 2019). The composition of molasses varies depending on the quality of the raw product. Considering the high sucrose content of the molasses, it would seem to be a natural alternative to synthetic media for P. simplicissium growth and metabolite production (Gojgic-Cvijovic et al., 2019). Sun et al. (2019) showed the economical production of some metabolites using molasses (Sun et al., 2019). Cheese whey is the byproduct of the cheese production with high biochemical oxygen demand. Cheese whey contains about 55% of nutrients in the original milk supports the metabolite production in microbial fermentation (Salari et al., 2019). Sugar has a similar composition to sucrose; it is not pure and contains galactose too. It is noteworthy that the application of fungal bioleaching in solid waste is limited; there are a lot of gaps in this field. In our knowledge, too limited research was found to study bioleaching of e-waste using P. simplicissimum. Especially using sucrose in the medium is not economical. Finding an appropriate substitution instead of sucrose is one of the essential aims of industrializing this biological method (Gojgic-Cvijovic et al., 2019). In this research, extraction of Ni and Cu from mobile phone wastes using P. simplicissimum fungi was investigated. The aim of this study was to find the optimized conditions for growing P. simplicissimum fungus in industrial medium (including sugar cane molasses, cheese whey, and sugar) before the medium scale in the laboratory (bioleaching in bioreactors) and comparison to the principal medium of P. simplicissimum (sucrose as carbon source) using one-step bioleaching method. In this research, the optimal amounts of pH and inoculum size were defined to reach the maximum recovery of goal metals using the central composite design (CCD) of the RSM. The interaction effect and contour plots of the parameters were discussed. Also, to measure the amount of carbon source in non-conventional mediums, the chemical oxygen demand (COD) test was done. The fungi biomass in the selected mediums was measured in the presence and absence of the salts unless sucrose in Bosshard medium to observe the growth of P. simplicissimum. The results showed sugar and molasses respectively for Cu and Ni recovery are more efficient and economical for metal bio-recovery from ewaste.
Bioleaching methods divide into three categories including (1) onestep bioleaching, in which fungus is inoculated to the medium with solid waste, (2) two-step bioleaching, in which the solid waste adds to the medium when the fungus is in its logarithmic growth phase, (3) spent-medium bioleaching, in which the fungus is in the stationary phase; the highest level of organic acids in the medium provide and then the solid phase is added to the biomass (Mirazimi et al., 2015). Choosing an effective method depends on the type of substrate and also the selected microorganism (Amiri et al., 2011). Several physical, chemical, and biological variables affect the bioleaching process (such as carbon and nitrogen source, oxygen supply, pH, temperature, inoculation content, and etc.). These parameters have to be optimized to reach the maximum amount of recovery. pH is one of the main parameters in the bioleaching process (Muddanna and Baral, 2019). Some researchers attend to fungal bioleaching of metals from solid waste utilized Aspergillus niger and P. simplicissimum as the most effective microbes. In a first reported attempt, Brandl et al. (2001) used these fungi to recover metals from e-waste. The microbial growth was inhibited in a higher pulp density of 10 g/l. During the six weeks of the adaptation phase, fungi grew at a concentration of 100/l. Both fungi mobilized Cu and Sn by 65%, Al, Zn, Pb, and Ni more than 95% from ewaste (Brandl et al., 2001). Xia et al. (2018) aimed to study the feasibility of recovery of metals from e-waste by mixed fungal cultures in the stirred tank reactors. At the first step, the fungi adopted to 80 g/l of the e-waste sample. At the end of the bioleaching process, community structure analysis proved that A. niger (28%) and Purpureocillium lilacinum (72%) were the two dominated fungal species. About 56% of Cu, 20% of Pb, 15.7% of Al, 49% of Zn, and 8% of Sn were recovered (Xia et al., 2018). Rasoulnia and Mousavi (2016) studied bioleaching of V and Ni from a vanadium-rich power plant residual ash using A. niger and P. simplicissimum. They monitored fungal growth through measurement of pH and produced organic acids to find the optimum fermentation period. Leaching duration of 7 days for both fungi determined as the optimal condition. They showed V and Ni were recovered respectively 97% and 50% using A. niger. By using P. simplicissimum V and Ni were extracted about 90% and 49% respectively in spent-medium bioleaching (Rasoulnia and Mousavi, 2016). Amiri et al. (2011) applied experimental designs to screen and optimize the bioleaching of spent catalyst by P. simplicissimum. They determined the optimal values for pulp density, sucrose, NaNO3, and yeast extract respectively as 4% w/v, 90 g/l, 2 g/l, and 0.36 g/l which 98% of Mo, 46% of Ni, and 14.3% of Al were extracted. Their results proved the importance of medium composition rather than culture condition to reach maximal Mo recovery in the bioleaching process of the spent catalyst using P. simplicissimum (Amiri et al., 2011). Kim et al. (2016) explored the bioleaching of spent Zn–Mn or Ni–Cd batteries by Aspergillus species. Sucrose and malt extract as the carbon sources were used. They showed the dominant organic acid in sucrose and malt extract were citric acid and oxalic acid, respectively. Also, it was proved the fungal metabolic pathway influenced by the type of media used. Consequently, choosing the proper nutrient (such as carbon source) is essential to reach an efficient recovery (Kim et al., 2016). Muralidhar et al. (2001) studied the production of lipase by Candida cylindracea in 5 different mediums (including olive oil, malt extract, peptone, tween 80, and glucose). A response surface approach was used for optimization and comparison of selected medium for lipase production. The results showed that the optimal yield was about 17.30 U/ ml by glucose and 47.25 U/ml by olive oil. Also, olive oil was selected as the best medium through all mediums to produce C. cylindracea in fermentation (Muralidhar et al., 2001). The used medium influences the fungal metabolite pathway; choosing the proper nutrient source has to be considered for metal recovery (Kim et al., 2016). In microbial fermentation, the cost of fermentation medium makes 30% of the total cost; using a cost-effective
2. Materials and methods 2.1. Preparation of PCBs sample The PCBs of mobile phones wastes were bought from the collection center of e-waste in Tehran, Iran. Complete PCBs changed into balls with the diameter around 2 cm using a hammer mill. Then Ball mill was used to turn 2 cm diameter balls into particles with 75–149 μm diameters. To detect the metal concentration of the mobile phone PCBs, 1 g of the powder digested in a mixture of aqua regia, hydrogen peroxide and hydrofluoric acid with 1:1:3 ratio (Ilyas et al., 2014). This suspension was kept at 150°C until a humid powder obtained. Afterward, 20 ml of aqua regia was added and heated until it boiled. The boiling suspension was filtered and increased volume to 50 mL by DI water. The amounts of dissolved Ag, Au, Cu, Fe, and Ni were measured by inductively coupled plasma optical emission spectrometry (ICP-OES 730-ES, Varian) shown in Table 1. 2.2. Fungi preparation, inoculation and growth conditions P. simplicissimum fungus (PTCC 5129) was prepared from the Biochemistry Research Center of Sharif University, Tehran, Iran in lyophilized form. Sterilized potato dextrose agar plates were used to grow fungus in Petri dishes kept at 30 ̊C for seven days. The spores on the Petri dishes were washed using sterilized distilled water. The spores 2
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The presence of e-waste powder in the medium, as a toxic material, affects the reproduction of fungi negatively (Bahaloo-Horeh et al., 2018); fungi should adapt to the hazardous powder step by step. The pulp density of the mobile phone PCBs sample was increased in the levels of 1 g/l to achieve this aim. Each step required an average of one week and was done at 30°C and 130 rpm. The serial adaptation continued up to 40 g/l of mobile phone PCBs powder. In higher concentrations of mobile phone PCBs powder, the growth of the fungi was weak because the metabolism of fungi was inhibited.
Table 1 Metals content (ppm) of mobile phone PCBs. Metal
Wave length (cm−1)
Concentration (ppm)
Ag Au Cu Fe Ni
328.068 267.594 327.395 259.940 231.604
1470 1484 210000 47090 2790
2.3. Feasibility study of recovery of e-waste using non-conventional mediums The economic value of the mobile phone PCBs has to be calculated and compared with natural ore to gain more insight into the financial advantage of the e-waste. The total economic value of metals in a sample can calculate using ∑ Wti Prti which Wti and Prti are the weight percentage and price of metal i, respectively (Arshadi et al., 2018a). Prti were 5673 $/ton for Cu, 14740 $/ton for Ni, 1505 $/troy ounce for Au, 17.16 $/troy ounce for Ag. Calculating this phrase for the reported metals of Table 1 shows that the economic value of mobile phone PCBs just for Cu, Ni, Ag, and Au is equal to 72849 $/ton of mobile phone PCBs. That is while the value of the top ten mines of the world's most valuable ores is 2096.8 $/ton, in average (Basov, 2017) which emphasizes on the importance of mobile phone PCBs recovery. So, the average total value of mobile phone PCBs is about 35 times higher than the value of the average top ten natural ores and known as a potential source for the recovery of metals. Meantime, the cost of the process represents a critical aspect of the commercial and industrialized of the e-waste recovery. In the fungal bioleaching the cost of fermentation medium makes 30% of the total cost (Salari et al., 2019); using a cheaper carbon source, instead of the commonly used sucrose, might result in a lower cost of the final product. The price of sucrose for biochemistry, sugar; sucrose used in the food industry, cheese whey powder, and sugar cane molasses are about 54 $/kg, 200 $/ton, 1 $/kg, and 140 $/ton respectively. The sucrose is 200 times more expensive than sugar and 386 times than molasses. These prices reported from https://www.alibaba.com/showroom which produced in Iran.
Fig. 1. Material and COD amount.
2.4. Pre-study of non-conventional mediums Fig. 2. COD standard curve.
To find an appropriate substitution instead of sucrose, sugar, sugar cane molasses, and cheese whey as the carbon sources were tried. The COD test was done to compare the amount of carbon source in nonconventional mediums and sucrose. Fig. 1 shows the amount of COD in each medium. The amount of COD is more in sucrose, sugar, molasses, and cheese whey respectively. Fig. 2 shows the COD standard curve. To observe the growth power of P. simplicissimum in the selected medium, eight primarily experiments were done. The presence and absence of the salts in the Bosshard medium (NaNO3, KH2PO4, MgSO4.7H2O, KCl and yeast extract) unless sucrose were studied. Similar to the sucrose concentration in the Bosshard medium, the amount of sugar, molasses, and cheese whey were fixed at 100 g/l. Because of the high density of molasses, it was tried at two concentrations of 10 ml/l and 100 ml/l. All flasks were incubated in 30°C for seven days. After the period, the suspension of biomass, medium, and extracted acids was filtered. The remaining mass was kept in 80°C for 24 h in the oven. Consequently, the biomass was measured (Bahaloo-Horeh et al., 2018). Table 2 shows the mass of produced biomass in different conditions. Due to Table 2, P. simplicissimum can grow in all selected mediums. The amount of produced biomass was higher in molasses (in high concentration), sucrose, sugar, and cheese whey respectively. Adding mineral salts improved the fungi growth in the case of molasses and cheese whey. All of the tried media were selected for future studies;
Table 2 The mass of produced biomass in different conditions. Num.
Medium
Concentration
Mass of biomass(g)
1 2 3 4 5 6 7 8
Sucrose Sugar Cheese whey without the salts Cheese whey with salts Molasses without the salts Molasses with the salts Molasses without the salts Molasses with the salts
100 g/L 100 g/L 100 ml/L 100 ml/L 10 ml/L 10 ml/L 100 ml/L 100 ml/L
0.9837 0.8511 0.2946 0.3615 0.1916 0.4526 1.6703 2.8265
were numbered under a phase-contrast microscope (Olympus, CH–B145-2, Japan). In all experiments, 107 spores were inoculated to 100 ml of the medium. The foremost medium for growing the P. simplicissium fungus contains 100 g of sucrose, 1.5 g NaNO3, 0.5 KH2PO4, 0.025 g MgSO4.7H2O, 0.025 g KCl and 1.6 g yeast extract in 1 l distilled water (Bosshard et al., 1996). Bioleaching experiments were done using 250 ml Erlenmeyer flasks containing 100 ml of the medium. The flasks were incubated at 30°C with shaking incubator (Labcon 5082u, South Africa) at 130 rpm. 3
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Table 3 Experimental design based on central composite design. Run
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
Variables
Metal recovery (%) 7
Variables
Metal recovery (%) 7
pH
No. Spores ( × 10 )
Medium
Cu
Ni
Run
pH
No. Spores ( × 10 )
Medium
Cu
Ni
3.2 8.8 3.2 8.8 2 10 6 6 6 6 6 3.2 8.8 3.2 8.8 2 10 6 6 6 6 6
1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05 1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05
Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Sucrose Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey Cheese whey
52.8 51.5 48.4 67.5 77.0 49.2 52.5 49.8 59.9 63.7 48.2 11.7 42.6 50.3 61.2 10.0 41.4 57.0 65.0 44.1 33.8 36.0
12.8 2.5 21.6 6.4 63.4 16.5 12 14.3 1.8 13.4 3 26 5 31.3 25.2 27.9 9.7 36.4 0.04 0 4.5 5
23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44
3.2 8.8 3.2 8.8 2 10 6 6 6 6 6 3.2 8.8 3.2 8.8 2 10 6 6 6 6 6
1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05 1.5 1.5 8.6 8.6 5.05 5.05 0.1 10 5.05 5.05 5.05
Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Sugar Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses Molasses
46.0 36.2 45.7 65.7 45.0 62.8 67.4 47.3 100 97.0 94.3 0 27.3 46.0 18.8 44.0 27.9 60.5 43.4 45.5 43.0 39.7
23.7 20.7 13.6 5 51.9 48.9 21.1 30.2 2.6 1.9 2.3 67.1 7.1 49.1 8.5 72.5 11.1 8.2 7 4.8 5.1 5.6
Table 4 Fitted curves for Cu and Ni recoveries depend on the mediums. Cu (in terms of actual factors) Medium Constant Coefficient Sucrose +80.4 Cheese whey −19.3 Sugar −40.1 Molasses −35.6 Ni (in terms of actual factors) Medium Constant Coefficient Sucrose Cheese whey Sugar Molasses
pH
No. Spores
−7.1 +16.3 +32.9 +18.9
+1.5 −3.6 +1.3 +9.3
+85.6 +78.1 +104.8 +156.7
* * * *
10−8 10−7 10−6 10−7
No. Spores * pH
pH2
No. Spores2
+5.1* 10−8 −5.0 * 10−8 +7.5 * 10−8 −1.4 * 10−7
+0.3 −0.8 −2.9 −1.1
−2.9 *10−15 +9.0 * 10−15 −1.77* 10−14 −5.1*10−16
pH
No. Spores
pH2
No. Spores2
−23.7 −15.4 −28.4 −38.8
+8.4 * 10−8 −7.7 * 10−7 −5.5 * 10−7 −3.4* 10−7
+1.6 +1.1 +2.3 +2.5
−2.67 *10−16 +6.8*10−15 +5.0 *10−15 +2.7*10−15
significant parameters. There are three main steps in this method: i) designing and doing experiments, ii) modeling of RSM by regression and iii) optimization. All the equations in Design-Expert solve by the technique of least squares. The matrix was described in Eq. (1) should be solved to explain the behavior of the system using a cubic equation.
adding the mineral salts was preferred. 2.5. Bioleaching experiments All of the experiments were done in autoclaved 250 mL Erlenmeyer containing 100 ml of the defined medium. After sterilization, 1 ml of the inoculation suspension including 107 spores of P. simplicissimum was inoculated to the medium. The pulp density was constant and fixed on 1 g of powder of mobile phone waste in the 100 ml of the medium (1% w/v). The Erlenmeyer flasks were kept in a rotary shaker incubator at 30°C and 130 rpm. One-step bioleaching was selected; the e-waste powder and fungus inoculation suspension added in medium simultaneously. One-step bioleaching is more practical because this method is simplistic and takes less time than other methods of bioleaching (Amiri et al., 2011). All experiments were done at the same time under the same condition of inoculation.
y ⎛ 1 ⎞ ⎛ 1 x11 x12 y2 ⎜ . ⎟ = ⎜ 1. x.21 x22 . . ⎜⎜ . ⎟⎟ ⎜⎜ . . y x x 1 n 1 n2 n ⎝ ⎠ ⎝
. . . . .
. x1k ⎞ ⎛ β0 ⎞ ε ⎛ 1⎞ . x2k ⎜ β1 ⎟ ε2 ⎟ ⎜ + .⎟ . .. ⎜ . ⎟ . . ⎟ . . x nk ⎟ ⎜ ⎟ ⎜ εn ⎟ ⎠ ⎝ βk ⎠ ⎝ ⎠
(1)
The total number of experiments are given by 2n + 2n + n 0 ; where n is the number of the selected parameters for optimization () Arshadi et al., 2019. In this study, CCD with two numeric factors including pH (in the range of 2–10) and number of spores (between 106-108) with five levels (-α, −1, 0, +1, +α) and the type of carbon sources (including sucrose, sugar, sugar cane molasses, and cheese whey) as the categorical parameter were used to optimize the recovery of Cu and Ni from mobile phone PCBs using P. simplicissimum. α is the distance of the axial point from the center, which can calculate by α = 2^(n/4) (n is the number of factors). There are eight non-center points and three center points for each medium.
2.6. Design of experiments and optimization After previous experiments for recognizing the ability of new carbon sources in fungus growth, an experimental design was defined to estimate the effects of physical parameters on metal recovery efficiencies. RSM is one of the mathematical and statistical methods to optimize the processes. This technique is used in the sake of assessment of different 4
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(C + CAM ) × Vs R (%) = ⎛ MS × 100⎞ PD × MC ⎠ ⎝
(2)
CMS and CAM show respectively the amount of released metals in the solution and concentration of metal ions absorbed by fungi. VS presents the volume of solution fixed at 100 ml. PD and MC are respectively pulp density and metal concentration. 3.1. Fitting models of Cu and Ni recoveries Results of 44 experiments were analyzed by CCD to estimate the best-fitted curve and factors. Table 4 shows the third-order cubic equations coefficients for Cu and Ni recovery. Because the estimated models using coded parameters are not user-friendly and contain three levels of the mediums simultaneously; the equations in terms of actual factors (shown in Table 4) were provided. These equations can be used to make predictions about the response for given levels of each parameter. The models in terms of coded factors can be used to estimate Cu and Ni recovery for given levels of each parameter. All analysis within the software is based on the coded equations contained cubic terms while they do not appear in the equations in terms of actual factors. To find the equation from Table 4, first, choose Cu or Ni, then find the medium and write the coefficients. For instance, Ni recovery in sugar medium is presented by Eq. (3).
Ni %= +104.8 − 28.4*pH − 5.5* 10−7*No. of Spores + 2.3* pH2 + 5.0*10−15* No. Spores
2
(3)
According to Table 4, the effect of the number of spores is much lower than pH and carbon sources. Even the removal of the number of spores has no significant effect on both responses. pH is a significant parameter (Rasoulnia et al., 2016) for both Cu and Ni recovery. pH has a negative effect on Ni recovery. Amiri et al. (2012) studied the bioleaching of spent refinery catalyst using A. niger. They showed the pH parameter has a negative effect for MO, Al, and Ni (Amiri et al., 2012). The negative coefficient of pH suggests acidolysis as the principal leaching mechanisms for the related statement (Muddanna and Baral, 2019). An essential field in fungal leaching is based on acidolysis which the protonation of oxygen atoms covering the surface of metallic compounds. Oxygen and protons are associated with water, then the metal release and detach from the surface (Burgstaller and Schinner, 1993). Eq. (4) shows the producing nickel ions in acidolysis reaction, pH decreasing increases Ni recovery; following Table 4 for Ni recovery the pH coefficient is negative for all mediums.
NiO + 2H+ → Ni 2 + + H2 O
The pH coefficient for Cu recovery in the mediums of sugar, molasses, and cheese whey is positive. The other essential field of fungal leaching is complexolysis which metal complexes and chelates are formed. Organic acids are the most prominent metabolite secretes by fungi help to leach metal ions. Type and concentration of produced organic acids significantly affect the leaching of metals ions (Deng et al., 2013). Complexolysis leads to the solubilization of the metal ions as like as, a complex of oxalic acid with magnesium, iron, and aluminum, a complex of tartaric acid with aluminum, calcium, and magnesium and a complex of citric acid with calcium and magnesium. Finally, metal ions which are stabilized in comploxolysis, solubilized into solution by acidolysis. The bellow equation shows a complexolysis reaction that produces nickel citrate (Bahaloo-Horeh et al., 2018) emphasizes that the alkaline condition is more beneficial for running reaction.
Fig. 3. Normal plot of residuals (a) Cu recovery (b) Ni recovery. Table 5 ANOVA table and responses for Cu and Ni recoveries. Cu%
Ni%
Source Mean squares df Sum of squares Model 652.90 23 15016.73 Reduced Cubic Models, R-Squared = 0.8381 Significant parameters: Medium, A2, A2C, B2C Source Mean squares df Sum of squares Model 654.53 19 12436.02 Reduced Cubic Models, R-Squared = 0.8002 Significant parameters: pH, AC, A2
F-Value 4.50
F-Value 5.06
(4)
P-Value 0.0006
P-Value 0.0001
3. Results
Ni 2 + + C6 H8 O7 → Ni (C6 H5 O7)− + 3H+ RSM suggested 44 experiments. All of them were done at 30 °C and 130 rpm. The experimental design of 44 tests and their responses are summarized in Table 3. The amount of metal recovery was calculated using Eq. (2).
(5)
The effect of the culture medium is very important, by changing medium the coefficients change significantly. Fig. 3 parts (a) and (b) show the normal plot of residuals respectively for Cu and Ni recovery which are normally distributed. As they 5
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Fig. 4. 2D recovery plots of Cu, Horizontal axis is number of spores in inoculum and vertical axis shows pH (a) Sucrose (b) Cheese whey (c) Sugar (d) Molasses.
cheese whey, and molasses in all of the tried conditions is about 60% while this amount for sugar reaches to 100%. Simple sugars are called monosaccharides and include glucose (also known as dextrose), fructose, and galactose. Sucrose is a carbon source for fungi, hydrolysis to glucose and fructose. Molasses is 50% sugar by dry weight, predominantly sucrose, but contains significant amounts of glucose, fructose, and lactose. So, the main difference between sugar, sucrose, and molasses is the presence of galactose. The chemical formula of glucose, fructose, and galactose is C6H12O6. They are isomers. Muddanna and Baral studied the bioleaching from spent catalyst using A. niger. They showed within three days of A. niger inoculation to the medium the sucrose hydrolysis complete. They proved the fungi consume sucrose, fructose, and glucose in the medium respectively (Muddanna and Baral, 2019). Galactose is an attractive industrial carbon source. It is reported some strains consume galactose much more slowly compared to glucose (Moktaduzzaman et al., 2015); helps to substrate utilization for fungi and lead to producing a higher amount of metabolite continuously. The predominant sugar of cheese whey is lactose. Hossein et al. (1984) studied the effect of sugar source on metabolite production by fungi that was more in the presence of sucrose, glucose, fructose, and lactose respectively (Hossain et al., 1984). According to Fig. 1, the growth of fungi is limited in cheese whey. Fig. 4 shows the effect of the initial number of spores and pH on Cu recovery. All parts express that alkaline condition can be used if the number of spores is in the maximum amount. Rasoulnia et al. (2016) measured the P. simplicissimum growth in a pH range of 2–11. They showed the P. simplicissimum colony diameters in the pH range of 3–7 were similar and in the maximum amount. But the fungal growth was retarded in higher and lower pH range. The maximum fungal growth gained at a pH of 4 (Rasoulnia et al., 2016). Consequently, at the notfavorable condition, more spores have to be inoculated for sufficient growth.
lie on the diagonal line, validate no deviation of the variance (Arshadi and Mousavi, 2014). 3.2. Statistical analysis Analysis of Variance (ANOVA) was used to analyze the data statistically. The ANOVA suggested cubic models which predict the Cu and Ni recoveries for all selected mediums shown in Table 5. In this table, the sum of squares is the summation of squared deviations from the mean. The df is the degree of freedom used to estimate the value of statics of freedom for terms and subsets of the model (Mirazimi et al., 2013). Term of the mean square is used to show the associated variance can be calculated by dividing the sum of squares by the df. The third parameter is F-value, shows the comparison of the associated variance with the residual variance. It computed by dividing the mean square for the term by the mean square for the residuals (Arshadi and Mousavi, 2014). Higher F-value is a more acceptable variation of mean values and more significance of the factors (Mirazimi et al., 2013). Amount of P-value less than 0.0500 (0.0002 for Cu and 0.0001 for Ni) indicates that the model terms are significant. High amounts of R-squared prove these models (reduced cubic model) are appropriate for these data. The confidence level of these models are 95%, so it is trustable (Amiri et al., 2012). 3.3. 2D and 3D recovery plots 3.3.1. Cu recovery Fig. 4 shows the response surface of Cu recovery for different used carbon sources. Part (a), (b), (c), and (d) are drawn respectively, for sucrose, cheese whey, sugar, and molasses. According to Table 3, the maximum amount of Cu recovery is observed in Fig. 4(c) for the sugar medium. The maximum recovery of Cu in the medium of sucrose, 6
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Fig. 5. 2D recovery plots of Ni, Horizontal axis is number of spores in inoculum and vertical axis shows pH. (a) Sucrose (b) Cheese whey (c) Sugar and (d) Molasses.
fungi. Also by increasing the number of spores in the inoculum, lack of carbon source is the main reason for decreasing recovery because, after the specific growth rate, fungi started to use themselves as a carbon source when the condition is unfavorable (Rasoulnia and Mousavi, 2016).
Fig. 4(a) shows the effect of pH and inoculation size on Cu recovery in the sucrose medium is not as effective as changing the medium; almost the Cu recovery in all conditions is the same. In the presented model for Cu recovery (Table 4) in sucrose medium, the coefficient of pH is negative. The maximum leaching efficiency of Cu recovery in sucrose medium was 68%. Probably for Cu recovery in sucrose medium both acidolysis and complexolysis are significant as same as each other. In Fig. 4(b) the recovery plot of Cu recovery in cheese whey is illustrating. In alkaline condition with high numbers of spores, the Cu recovery reached to the maximum amount. By reducing pH Cu recovery decreases. However in acidic condition by increasing the numbers of spores in the inoculum, the recovery increases as far as increasing the numbers of spores in inoculum results in the lack of carbon source for growing the fungi, they start to use themselves as food and the recovery is less than alkaline condition (Rasoulnia and Mousavi, 2016). If the carbon source is sugar, as it is demonstrated in Fig. 4 (c) and the equation of modeling sugar in Table 4, the recovery trend is quite different from other carbon sources. The highest value of Cu recovery is in neutral condition with moderate numbers of spores in the inoculum. The highest value of Cu recovery is in neutral condition with moderate numbers of spores in the inoculum. If the solution is alkaline or acidic with low or high numbers of spores in the inoculum, the less amount of Cu will be recovered; due to increasing toxicity of medium for growing fungi. In Fig. 4(d), acidic condition with low numbers of spores lead to low recovery while by increasing the number of spores in inoculum in acidic medium, producing of organic acids is more and the recovery is more too. In alkaline condition, increasing the number of spores in inoculum decreases the recovery. Generally, for Cu recovery in all kind of carbon sources and alkaline condition recovery reaches the least amount around fewer than 30% that is a reason for the high toxicity of alkaline medium for growing
3.3.2. Ni recovery Fig. 5 shows the response surface of Ni recovery for different used carbon sources. Part (a), (b), (c), and (d) are drawn respectively for sucrose, cheese whey, sugar, and molasses. Almost in all of the figures and tables, the Ni recovery is less than Cu recovery (Fig. 4) in the same condition. There are some reasons. First, the amount of Cu is more than the other elements. Cu concentration is about 75 times more than Ni concentration in the mobile phone PCBs (Table 1). Second, with attention to the Ksp Ni makes stable salts; Cu ions are more stable in the solution (Spence and Soderstrom, 1999). Oxalic acid (a produced metabolites by P. simplicissimum) precipitates a fraction of released Ni and forms nickel oxalate which has low solubility or acts as an inhibitor for the process; The following equation shows the nickel oxalate formation (Biswas et al., 2013) that is slightly soluble in water and oxalic acid; it is soluble in strong acid and decreases Ni recovery while the formation of copper oxalate shows no significant effect on Cu recovery rather than Ni.
NiO + HO2 C. CO2 H → NiC(O2 C. O2) + 2H+
(6)
Third, the lower Ni recovery compared to Cu recovery relate to the highly toxic effect of Ni over fungi (Muddanna and Baral, 2019). Fig. 5 shows for all used carbon sources low pH is more benefit for Ni recovery. It was reported that the pH range higher than 7 shows an inhibitory effect on P. simplicissimum (Rasoulnia et al., 2016). Muddanna and Baral (2019) showed that acidolysis is the dominant leaching mechanisms for Ni recovery. They proved lesser the pH higher 7
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Fig. 6. Overlay plots for simultaneous recovery of Cu and Ni. Table 6 Point prediction and verification of the models at the optimum conditions. Response (%)
Target
Predicted recovery (%)
Confirmation experiments (%)
95% CI low
95% CI high
Desirability (%)
Cu Ni
Maximum recovery of Cu/Ni recover is none. Maximum recovery of Ni/Cu recover is none.
89% 88.8%
90% 89%
84.6% 84.3%
93.5% 93.2%
100 100
lower recovery of metals.
Ni recovery efficiency (Muddanna and Baral, 2019). In this figure, the highest recovery of Ni recovery around 65% is in mediums with molasses as a carbon source which is independent of the number of spores in the inoculum. Cane molasses are acidic and rarely show an alkaline reaction (Olbrich, 2006); according to dominant leaching mechanisms for Ni recovery, molasses shows the highest leaching efficiency. In other carbon sources, the highest recovery of Ni is around 40% in acidic condition. The initial pH of cheese whey medium (about 6.2) and its lower COD (rather than other media) cause
3.4. Optimization experiments The principal purpose of this process is to find the best carbon source instead of sucrose and optimize the conditions to scale-up the process and make that more economical. After doing all of the experiments and analyzing the effect of selected variables on Cu and Ni recoveries, the software suggested the simultaneous maximum recovery 8
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molasses, and cheese whey. P. simplicissimum can grow in all selected medium and the most biomass produced in molasses (in high concentration), sucrose, sugar, and cheese whey respectively. Adding mineral salts improve the fungi growth in the case of molasses and cheese whey. Two third-order cubic equations were suggested, to model Cu and Ni recovery. pH is a significant parameter for both responses with a negative effect on Ni recovery; proves acidolysis is the dominant leaching mechanisms for Ni recovery; lesser the pH higher Ni recovery efficiency. The pH coefficient for Cu recovery is positive shows complexolysis as an essential mechanism for Cu leaching. The overlay plots showed that the simultaneous maximum recovery of Cu and Ni is not possible. The accessible Ni recovery is less than Cu recovery in the same condition. Maximum recovery of Cu (90%) was gained at the pH of 7, the number of spores of 3.3 × 107, and in the sugar medium. The Ni recovery (89%) was maximized in the molasses medium, at the pH of 2, and the number of spores of 106. Sterilization is probably the other main limitation of fungal bioleaching that increases the cost of the process. Future efforts are also required to decrease sterilization cost or omitting the sterilization needs. The research findings suggest the fungal bioleaching of e-waste (as a profitable technology) and using cheap carbon sources (such as molasses and sugar) to avoid the losses of resources and limit the impacts on the environment as promoted by the circular economy concept.
of Cu and Ni is not possible. The overlay plots for sugar and molasses medium are shown in Fig. 6. The effect of pH and the number of spores on Cu and Ni recovery are shown in Fig. 6 that delineate the common regions of responses in a predefined recovery range. Fig. 6(a) shows the overlay plot for both Cu and Ni recovery of more than 43% in the sugar medium. The highlighted region is the area in which the recovery of more than 43% is possible for both responses; if the leaching efficiency is selected more than 45% for both Cu and Ni, disappears; the simultaneous recovery of Cu and Ni more than 45% in sugar medium is not possible. But if the recovery of Ni decreases to more than 7% in Fig. 6(b); the highlighted region is presented even in the Cu recovery of more than 90%. This finding confirms Figs. 4(c) and 5(c); the region related to the maximum recovery of Cu (Fig. 4(c)) and Ni (Fig. 5(c)) in sugar medium is not as same as each other. Fig. 6(c) is drawn in the molasses medium, expresses the overlay plot for simultaneous Cu and Ni recovery of more than 56%. The software predicted for same recovery of Cu and Ni more than 58% there is no any possible condition. If the recovery of Cu reduces to more than 5% in Fig. 6(d); the highlighted region is presented even in the Ni recovery of more than 75%. Figs. 4(d) and 5(d) confirm the overlay plots in the molasses medium. The impossibility of the simultaneous recovery of copper and nickel may have some reasons. First, as mentioned before Cu concentration is much more than Ni concentration (according to Table 1). By increasing the amount of substrate in the left side of the equation, the tendency of the reaction increases for substrate consumption. Second, this is related to the optimal condition for the responses. As previously was mentioned in 3.2. section, the principal mechanism for Ni recovery is acidolysis while comploxelosis is the main for Cu recovery. Increasing pH shows a positive effect on Cu recovery while reduces Ni recovery. The significant difference between Ni and Cu concentration and different optimal condition for them lead to the impossibility of simultaneous recovery of Cu and Ni. Because the maximum simultaneously recovery of Cu and Ni is not possible, to reach the highest possible recovery of each response, the optimal condition for Cu and Ni have to be investigated under the situation that the other answer is not considered. These criteria are designed by the software. In the case where the goals of Cu and Ni recovery were selected respectively as the maximum and none; the optimal condition was suggested as pH of 7, spore number of 3.3 × 107, and in the sugar medium. In the case where the goals of Ni and Cu recovery were selected respectively as the maximum and none; the optimal condition was suggested as pH of 2, spore number of 106, and in the molasses medium. An experiment was done using the optimal conditions suggested by the models compared with the results of the models, shown in Table 6 to validate the models. The experimental results were within the low and high confidence levels that confirm the validity of the models. Cu and Ni recovery were estimated respectively by the models about 89% and 88% in the selected optimal condition. Performing experiments in the laboratory have given 90% of Cu recovery and 89% of Ni recovery. The experimental results were very close to the predicted data confirmed the accuracy of the model's prediction and the optimized conditions.
Acknowledgments The authors are grateful to Stat-Ease, Minneapolis, MN, USA, for provision of the Design-Expert 10 0.0 0.4 package. This project has been conducted by the deputy of research and technology of Sharif University of Technology (Award Number QA: 970713) in Iran. The authors are thankful to Pars Charkhesh Asia Company for supplying the PCBs. References Amiri, F., Mousavi, S.M., Yaghmaei, S., 2011. Enhancement of bioleaching of a spent Ni/ Mo hydroprocessing catalyst by Penicillium simplicissimum. Separ. Purif. Technol. 80, 566–576. Amiri, F., Mousavi, S.M., Yaghmaei, S., Barati, M., 2012. Bioleaching kinetics of a spent refinery catalyst using Aspergillus Niger at optimal conditions. Biochem. Eng. J. 67, 208–217. Arshadi, M., Mousavi, S.M., 2014. Simultaneous recovery of Ni and Cu from computerprinted circuit boards using bioleaching: statistical evaluation and optimization. Bioresour. Technol. 174, 233–242. Arshadi, M., Yaghmaei, S., Mousavi, S.M., 2018a. Content evaluation of different waste PCBs to enhance basic metals recycling. Resour. Conserv. Recycl. 139, 298–306. Arshadi, M., Yaghmaei, S., Mousavi, S.M., 2018. Study of plastics elimination in bioleaching of electronic waste using Acidithiobacillus ferrooxidans. Int. J. Environ. Sci. Technol. 1–14. Accepted Manuscript. https://link.springer.com/article/10.1007/ s13762-018-2120-1. Arshadi, M., Yaghmaei, S., Mousavi, S.M., 2019. Optimal electronic waste combination for maximal recovery of Cu-Ni-Fe by Acidithiobacillus ferrooxidans. J. Clean. Prod. 240https://doi.org/10.1016/j.jclepro.2019.118077. In press. Auerbach, R., Ratering, S., Bokelmann, K., Gellermann, C., Brämer, T., Baumann, R., Schnell, S., 2019. Bioleaching of valuable and hazardous metals from dry discharged incineration slag. An approach for metal recycling and pollutant elimination. J. Environ. Manag. 232, 428–437. Bahaloo-Horeh, N., Mousavi, S.M., Baniasadi, M., 2018. Use of adapted metal tolerant Aspergillus Niger to enhance bioleaching efficiency of valuable metals from spent lithium-ion mobile phone batteries. J. Clean. Prod. 197, 1546–1557. Basov, V., 2017. These 10 Mines Have the World's Most Valuable Ore (Mining.com). . Bauer, J., O'Mahony, C., Chovan, D., Mulcahy, J., Silien, C., Tofail, S.A.M., 2019. Thermal effects of mobile phones on human auricle region. J. Therm. Biol. 79, 56–68. Biswas, S., Dey, R., Mukherjee, S., Banerjee, P.C., 2013. Bioleaching of nickel and cobalt from lateritic chromite overburden using the culture filtrate of Aspergillus Niger. Appl. Biochem. Biotechnol. 170, 1547–1559. Bosshard, P.P., Bachofen, R., Brandl, H., 1996. Metal leaching of fly ash from municipal waste incineration by Aspergillus Niger. Environ. Sci. Technol. 30, 3066–3070. Brandl, H., Bosshard, R., Wegmann, M., 2001. Computer-munching microbes: metal leaching from electronic scrap by bacteria and fungi. Hydrometallurgy 59, 319–326. Burgstaller, W., Schinner, F., 1993. Leaching of metals with fungi. J. Biotechnol. 27, 91–116. Deng, X., Chai, L., Yang, Z., Tang, C., Wang, Y., Shi, Y., 2013. Bioleaching mechanism of heavy metals in the mixture of contaminated soil and slag by using indigenous Penicillium chrysogenum strain F1. J. Hazard Mater. 248–249, 107–114.
4. Conclusion Extraction of Ni and Cu in non-conventional mediums including sugar, cheese whey, and sugar cane molasses instead of sucrose from PBCs of mobile phones, were optimized using P. simplicissimum by RSM. Also, Numerical parameters (including pH and number of spores) were studied. The metal analysis showed that the average total value of mobile phone PCBs is about 35 times higher than the value of the average top ten natural ores and known as a potential source for the recovery of metals. The sucrose is 200 times more expensive than sugar and 386 times more than molasses. The pretreatment experiments showed that the amount of COD is respectively more for sucrose, sugar, 9
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