Assessment of plastic waste materials degradation through near infrared spectroscopy

Waste Management 82 (2018) 71–81

Contents lists available at ScienceDirect

Waste Management journal homepage: www.elsevier.com/locate/wasman

Assessment of plastic waste materials degradation through near infrared spectroscopy Ayah Alassali a, Silvia Fiore b,⇑, Kerstin Kuchta a a TUHH – Hamburg University of Technology, Institute of Environmental Technology and Energy Economics, Waste Resources Management, Harburger Schlossstr. 36, 21079 Hamburg, Germany b DIATI (Department of Environment, Land and Infrastructures Engineering), Politecnico di Torino, 24, corso Duca degli Abruzzi, 10129 Turin, Italy

a r t i c l e

i n f o

Article history: Received 17 May 2018 Revised 2 October 2018 Accepted 6 October 2018 Available online xxxx Keywords: Ageing Degradation Model Plastic Waste Recyclability

a b s t r a c t Plastic waste is a relevant challenge for waste management sector and further technological means have to be urgently researched. The evaluation of plastic waste quality through non-destructive, cost-effective and mature technologies could be without any doubt a key issue. This study is aimed at the assessment of Near Infrared (NIR) spectroscopy for the generation of global degradation-prediction models able to forecast plastic ageing. The degradation of Polyethylene terephthalate (PET), Acrylonitrile Butadiene Styrene (ABS), Polypropylene (PP) and Polyethylene (PE) was achieved by thermal ageing (at 85 °C, 105 °C and 120 °C and durations ranging from 4 to 504 h), to simulate environmental outdoor conditions. Experimental data obtained for each plastic material were elaborated through partial least square (PLS) regression to obtain empirical models. For all inspected plastic materials, a good correspondence between the variation in absorbance units and the change in chemical bonds vibrations was observed. The PLS models were afterwards calibrated (taking into account the different ageing conditions; first separately then including the ageing factors combined). A high accuracy (R2 equal to 0.85–1.00) was observed in predicting ageing for PET and ABS, while the correspondence showed a 30% decrease for PE and PP. This study proves that NIR spectroscopy can be recommended as an effective tool to investigate plastics degradation, with some limitations for specific polymers that need further investigations. Ó 2018 Elsevier Ltd. All rights reserved.

1. Introduction Plastic material is widely used in manufacturing and daily applications (Miller, 1996; Andrady and Neal, 2009; Hopewell et al., 2009; Lithner et al., 2011), with a global annual production of 322 million tonnes in 2015 (Plastic Europe, 2016). It was reported that the annual global oil production representing the cost for assembly of plastic raw material in 2009 was about 4%, with an additional equivalent 4% of global oil consumption as energy to convert the plastic materials into end-products (Wu et al., 2013; Gourmelon, 2015; Nkwachukwu et al., 2013). One of the main concerns associated with the extensive applications of plastics is the generated waste; about half of the produced plastics are used for single-use disposable applications (Hopewell et al., 2009) and particularly in form of flexible films for domestic and industrial use (Horodytska et al., 2018). About two thirds of the total plastic waste are generated from municipal solid waste, whereas the second plastic waste stream arises from the supply ⇑ Corresponding author. E-mail address: [email protected] (S. Fiore). https://doi.org/10.1016/j.wasman.2018.10.010 0956-053X/Ó 2018 Elsevier Ltd. All rights reserved.

and industrial sectors (Perugini et al., 2005; Hannequart, 2004). Plastic waste embodies 11%-wt of the total waste (Deterre and Feller, 2014); in 2014 alone, nearly 26 million tonnes of postconsumer plastics waste ended in the official waste streams, where about 31%-wt was disposed in landfills (Plastic Europe, 2016). Plastic waste has to be properly managed to overcome associated environmental damages (Wu et al., 2013). In order to contribute to the optimization of plastic waste management, an integrated strategy of waste minimization, reuse and recovery should be considered, since the processes of recycling and recovery enable waste to become a resource (Worrell and Reuter, 2014), to achieve closed production cycles (circular-economy). In order to apply the best management strategies of the plastic waste, developing tools to contribute in the analysis of the plastic waste stream flows is needed, in order to control the extensive and expanding use of plastic material, as well as to detect the flow of those streams (Scarascia-Mugnozza et al., 2008). Due to the continuous and increasing generation of plastic waste, plastic recycling was extensively studied in the past decade (Jenseit et al., 2003; Perugini et al., 2005; Briassoulis et al., 2013). Both, material and energy recovery can be performed on plastic

72

A. Alassali et al. / Waste Management 82 (2018) 71–81

waste streams, with differently implemented collection-recycling schemes (Hahladakis et al., 2018; Rigamonti et al., 2014). In addition to reducing the quantity of waste to landfill, plastic recycling would also reduce the total environmental burden (Ross and Evans, 2003). EU strategy about plastic waste recycling and recovery started in 1994 by setting minimum recycling rates, which were increased (Lazarevic et al., 2010; Plastic Europe, 2016). In 2017, the European Commission confirmed the goal of ensuring that all plastic packaging is recyclable by 2030 (Plastic Europe, 2016). Post-consumer plastic waste material recovery options strongly depend on the required quality of the recovered material and on the perspective of a local or global market (Sevigné-Itoiz et al., 2015). For instance, halogenated flame-retardants could be a liability for both material recovery (Pivnenko et al., 2017) and energy recovery, due to the consequent generation of hydrogen halides and dioxin, however it was demonstrated that dehalogenation of plastic waste could be possible (Cagnetta et al., 2018; Shen et al., 2016). This study focused on the mechanical recycling of plastics, since it supports the circular-economy concept, by recovering plastic waste for reuse in manufacturing plastic products via mechanical means (Al-Salem et al., 2009). Recycling is highly challenging, and it can be only performed on a single-polymer plastic waste; by increasing the complexity of the contamination among different materials, the difficulty of recycling increases (Al-Salem et al., 2009). Firstly, compared to virgin plastic, recycled plastic showed deterioration in its physical and mechanical qualities (Perugini et al., 2005). The deterioration in the mechanical properties and material quality affects the reusability of the recycled product (Al-Salem et al., 2009). Secondly, the existence of impurities, including pigments, inks, adhesives and food residues, results in decreasing the quality of the recycled product (Fleming, 1992; Hopewell et al., 2009). However, mechanical recycling even if strongly dependent on the quality of the recycled plastic, was demonstrated as the recovery option allowing the highest greenhouse gas savings, particularly with the perspective of local market (Sevigné-Itoiz et al., 2015), with minimized energy consumption in comparison to new plastic production (Lazarevic et al., 2010). From the process scheme viewpoint, mechanical recycling of plastic includes consequent phases of sorting, shredding, washing, melting and/or regranulation that could be combined in different outlines. After the sorting step, plastic waste could be either directly melted and then moulded into a new shape, or melted after being shredded into flakes and then processed into granules called regranulate (Siddique et al., 2008). Obviously, the mechanical recycling processes expose plastic waste to heat, mechanical stress and photo-oxidation, which could eventually reverse the chemical reactions used in polymer formation (i.e. polymer addition, polymerization and poly-condensation) (Al-Salem et al., 2009). Following the degradation implied by the recycling process, recycled plastics experience quality deterioration during their new use; the negative impacts of natural weathering on polymer physical quality were broadly discussed (Basfar and Ali, 2006; Al-Salem et al., 2009). In order to evaluate plastics’ quality deterioration, it is necessary to assess the changes in the polymer’s mechanical and chemical properties and to understand their effects on plastic quality. Some physical properties, such as discoloration, loss of volatile components, and loss of mechanical strength have been identified to detect the polymer degradation. Basically, thermoplastic polymers’ mechanical and physical properties (i.e., tensile strength, elasticity and colour) could be considered as properties to measure the shift obtained due to degradation. For instance, the degradation behaviour decreases tensile strength and impact strength (Gupta et al., 2008). On the other hand, the crystallization behaviour increases young modulus and yield stress. Therefore, the under-

standing of the degradation and crystallization is of scientific and technological significance for plastic waste recycling (Yin et al., 2015). On the ground of the alteration in chemical properties of degraded plastics, low-molecular volatile compounds are generated from oxygenated fragments of the original polymer, which get trapped in the polymer; in its solid state. These contaminants may diffuse through the melt and hinder an effective reprocessing, affecting the product’s quality as well as the processing itself (Ragaert et al., 2017). Literature has shown successful plastictype and color-based sorting techniques (Ragaert et al., 2017; Ip et al., 2018; Stessel, 2012; Al-Salem, 2009; Al-Salem et al., 2009; Hopewell et al., 2009). However, to our knowledge, there isn’t any literature available about the quali/quantitative evaluation of plastic waste degradation. This study is aimed at the assessment of Near Infrared (NIR) spectroscopy for the generation of degradation models able to predict plastic ageing. NIR spectroscopy was chosen due to its fast analysis, high accuracy and non-destructive features (Pasquini, 2003; Macho and Larrechi, 2002). The concept is based on providing energy to the molecules from a source (i.e. NIR radiation), which applies photon energy in the energy range between 2.65
1019 to 7.96
1020 J, resulting in vibrating, stretching or bending of the chemical bonds of molecules in polymers (Pasquini, 2003). This research’s approach has two main goals: the first was to perform a qualitative evaluation, based on the investigation of differences in NIR spectral data, which could be linked to polymer deterioration. The second goal was a quantitative analysis through building partial least square (PLS) models, based on selected wavelengths, from the experimental data. This study is focused on the analysis of the degradation of polyolefins, which are the most common materials for packaging and foils, products with a short lifespan, yet contributing with relevant fractions to plastic waste. Polypropylene (PP) and low density polyethylene (LDPE) were found as the most common plastic types in municipal solid waste, followed by polyethylene terephthalate (PET), polystyrene (PS) and high density polyethylene (HDPE) (Dahlbo et al., 2018). In details, the following four polymeric materials were chosen in this study: LDPE and PET due to their extensive application in packaging (Vasile et al., 2005; World Economic Forum et al., 2016) as well as their availability for recycling; Acrylonitrile Butadiene Styrene (ABS), a plastic which is mostly used in electric equipment and automotive applications (Balart et al., 2005; Peeters et al., 2015); PP, a thermoplastic polymer used in a wide variety of applications, including packaging and labelling, automotive parts, textiles, stationery and containers of various types (Plastic Europe, 2016). The properties and applications of the selected polymers are summarized in Table 1, as well as their chemical structures.

2. Materials and methods 2.1. Raw plastic materials In this study, only virgin and pure polymers were studied, due to the need of processing the material under controlled conditions, with the final purpose of achieving a comprehensive model, able to reliably and consistently estimate its degradation. Furthermore, the decision to exclude real plastic waste samples from ageing procedure was related to the great number of unknown variables, such as the wide-range of additives, granulometry, moisture and type of pigments. These variables could significantly affect the quality of the plastic, influencing the produced NIR spectrum in an unpredictable way. The pure and virgin polymers considered in the research were:

73

A. Alassali et al. / Waste Management 82 (2018) 71–81 Table 1 Plastics description and applications (adapted from Bashford, 1996, Abenojar et al., 2009, Roedel, 1953, Ngai and Roland, 1993, Hunt et al., 2015; Singh et al., 2017). Resin code

Polymer name and formula

General properties

Applications

01

Polyethylene terephthalate

Clear, tough, high chemical resistance, high tensile impact strength, processability, thermal stability.

Packaging, bottles, textiles, films, foils, detergents containers, tubes.

04

Low-density polyethylene ½CH2  CH2 n Polypropylene 2 3 CH  CH2  4 5 j CH3 n Others: SAN, PC, Nylon, Acrylonitrile-butadiene-styrene (ABS)

Transparent, ease of processing, low cost, solvent resistant and higher impact strength. Resistance to chemicals, abrasion and hot water, low water adsorption, good electrical capacity.

Packaging, film, flexible containers, bottles, caps, toys, pipes and tubes. Fibres, tapes, toys, domestic equipment, food packaging, heating pipes, electrical goods.

e.g. ABS: Good mechanical properties, heat and chemical resistance, high impact strength, good electrical insulator

Automotive and appliance components, computers, toys, luggage cases,

05

07

– Polyethylene terephthalate (PET), in a shape of granules, from NEOPET 82 FR - INEOS Olefins and Polymers Europe. – Low density polyethylene (LDPE) in a shape of cylindrical granules, from INEOS Olefins and Polymers Europe. – Acrylonitrile-butadiene-styrene (ABS), in a shape of cylindrical granules, from POLYLAC PA-747 – CHI MEI CORPORATION. – Polypropylen-homopolymer (PP) in a shape of pellets, from Olefins and Polymers Europe.

tðacceleratedÞ ¼ t real
Q10ðT aging T ambient Þ=10

ð1Þ

where;

2.2. Thermal ageing procedure Raw polymers were aged by thermal stress in a Binder oven at different temperatures (85 °C, 105 °C and 120 °C) and for different times (from 4 to 504 h) (see Table 2). 200 g samples were prepared in triplicates for each ageing (time-temperature combination), obtaining 12 series of samples. In order to obtain a homogeneous ageing from durations above 24 h, each sample was mixed once a day. At the end of the thermal procedure, the samples were sealed and directly sent to NIR analysis to avoid further chemical degradation or oxidation effects. The here-described ageing procedure was designed to simulate the thermo-oxidative degradation of plastics during their use,

Table 2 Details of thermal ageing procedure. Series

Samples

Temperature [°C]

Exposure time [h]

1-PET 2-PET 3-PET 1-ABS 2-ABS 3-ABS 1-PE 2-PE 3-PE 1-PP 2-PP 3-PP

PET PET PET ABS ABS ABS LDPE LDPE LDPE PP PP PP

85 105 120 85 105 120 85 105 120 85 105 120

A A A A A A B B B A A C

A: 4, 24, 48, 96, 144, 240, 336, 408, 504. B: 4, 24, 48, 96, 144, 240, 408. C: 4, 24, 48, 96, 144, 192, 240.

assuming that the accelerated thermo-oxidative ageing corresponds roughly to doubling of the ageing rate for each increase of 10 °C (Shimada and Kabuki, 1968; Boldizar and Möller, 2003). Eq. (1) was applied for ageing time calculation, where the value of Q10 in this case is 2. For instance, the use period of 1 year at room temperature (20 °C) could be simulated by an ageing time of about 72 h at 90 °C following Eq. (1).

t(accelerated): accelerated ageing time in days, t(real): the real ageing time applied, using accelerated conditions in days, Q10: accelerated ageing factor (here 2 is considered (Murray et al., 2013)), T(aging): the ageing temperature applied in the treatment process (°C), T(ambient): the ambient temperature (°C). 2.3. Near infrared spectroscopy analysis Near infrared (NIR) spectroscopy analysis was conducted through a Bruker Optics FT-NIR spectrometer MPA (MultiPurpose Analyser) on 100 g samples. The spectral range covered the NIR window, from 12500 cm1 till 4000 cm1 and the measurements were obtained in absorbance units. Each of the samples was scanned three times after being reshuffled and compacted. After spectra collection, the modelling procedure was realised using OPUS software. The ‘‘Quant 2 Method” option was used to apply a PLS regression on the data. 2.4. Model quantitative analysis and statistical evaluation of experimental data A two-step procedure was adopted to elaborate experimental data through partial least square (PLS) regression approach: 1. Temperature was fixed and ageing duration changed 2. Both factors (ageing temperature as well as ageing time) were considered

74

A. Alassali et al. / Waste Management 82 (2018) 71–81

The PLS models were created accordingly and statistical analysis was applied by calculating R2 value and Root Mean Square Error of Cross-Validation (RMSECV). R2 value describes the percentage of the total variation of the data that is described by the regression line (see Eq. (2)). RMSECV (see Eq. (3)) is useful for the characterization of the model quality.

Pn 

R2 ¼ 1 

0

2

i¼1 yi  yi  Pn   2 i¼1 yi  yi

RMSECV ¼

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi uP  un  0 2 u y  yi ti¼1 i n

ð2Þ

ð3Þ

where: yi : given value (measured) 0 yi : correspondent value on the regression line (estimated) 

yi : mean of the given values n : number of data In order to include both factors in a linear model, Eq. (1) was used, once to calculate the real ageing time as it was experimentally applied and a second time as the calibrated PLS models suggested, when both, time and temperature factors were included. All ageing series for each polymer (see Table 2) were combined, to achieve two-component models. After the achievement of the ageing time for the experimental and the model-estimated data, a linear regression was drawn to present model-estimated ageing time versus real ageing time for each polymer. The relation was assessed by calculating R2 value and model’s bias through calculating the mean error value (ME) as illustrated in Eq. (4) (Walther and Moore, 2005). Furthermore, the model’s accuracy was also applied as an evaluation parameter, by calculating mean square error (MSE), shown in Eq. (5). n 0 1X ME ¼ ðy  yi Þ n i¼1 i

MES ¼

n  0 2 1X y  yi n i¼1 i

ð4Þ

ð5Þ

Where; yi : real ageing time (using experimental values) 0 yi : estimated ageing time (using model’s estimated values) 3. Results and discussion 3.1. Thermal ageing The temperatures were chosen to not exceed the melting temperature of the material (i.e. 109–125 °C for LDPE, 110–125 °C for ABS, 265 °C for PET (Beyler and Hirschler, 2002) and 160–166 °C for PP (Shubhra et al., 2013)). However for ABS a temperature of 120 °C was applied, first to apply homogeneous conditions to all samples and also to test on ABS the effect of harsh conditions. This resulted in partial melting and sticking of the granules. Furthermore, ABS granules underwent a change in colour from pale yellow to light brown when either of the two applied parameters was increased (i.e. the exposure time as well as the ageing temperature) (see Fig. 1). As was reported by (Burn et al.; Tiganis et al., 2002), ABS discolouration was at the surface only, with fading and slight yellowing occurring initially. The depth of discolouration increased with exposure time. The increase in the colour intensity is due the coupling of radical scavengers with degradation peroxy radicals (Faucitano et al., 1996). Furthermore, degradation and subsequent discolouration in the polymer granules is controlled by the rate of oxygen diffusion through the polymer (Davis et al., 2004). The same was observed for LDPE (see Fig. 2), yet it was less significant in comparison to ABS. Conversely, PET and PP granules did not experience any particular optical changes; there was no colour change at any of the selected heating conditions (see Figs. 3 and 4). 3.2. NIR analysis NIR analysis of raw and aged polymers was aimed at finding a correlation between the spectrum of a specific material and its thermal degradation. The realisation of this purpose was possible due to the application of chemometrics (Frank and Friedman, 1993). NIR region (see Fig. 5) includes wavenumbers between approximately 12,500 and 4000 cm1 (or the interval of wavelengths between 800 and 2500 nm),

Fig. 1. (a) ABS treated at 105 °C, from the left: for 24 h, 240 h and 504 h. (b) ABS treated at 120 °C, from the left: for 24 h, 240 h and 504 h.

A. Alassali et al. / Waste Management 82 (2018) 71–81

75

Fig. 2. LDPE treated at 105 °C, from the left: for 24 h, 48 h, 204 h and 617 h.

Fig. 3. PET, from the left: at 85 °C for 408 h, 105 °C for 240 h and at 120 °C for 336 h.

Fig. 4. PP, from the left: at 105 °C for 24 h, 105 °C for 204 h and at 105 °C for 480 h.

positioning between visible and mid-infrared region. NIR monitores the overtones and IR monitors the fundamental bands. Although the latter is by far more intense than overtones, NIR analysis was applied due to its high sensitivity photo conductive detectors (Wilks, 2006). NIR is a suitable method to identify and determine OH and NH groups, OH bond indicates degradation in polymeric material, yet when IR is applied, the water molecules could interfer in the obtained results (Kasaai, 2008). As was reported by Fischer and Eichhorn (1998), the application of in-line NIR spectroscopy and the use of chemometric calibration is a suitable quantitative in-line process analysis, which was used in that study for multi-component polymer melts. This extends the industrial application of plastic quality assessment for recycling purposes. 3.2.1. Qualitative analysis Qualitative analysis involved visual comparisons between NIR spectra obtained under different conditions. The spectra were also compared with an available spectral library, in order to identify chemical bonds/chemical groups in the analysed sample, on the grounds of the vibrational motion of polymer’s chemical bonds (Blanco and Villarroya, 2002). The peaks positions, expressed in wavenumber values and absorbance units, differ from one material to another (Huth-Fehre et al., 1995). The NIR spectra of virgin LDPE,

PP, ABS and PET (see Fig. 5) exhibited specific peaks positions for each material related to the correspondent absorbance value. ABS and PET have absorbance peaks shown in the wavenumber region between 4000 cm1 and 4600 cm1, which were not observed for PP and PE. The NIR spectra of each polymer were studied to define chemical groups responsible for the generated peaks (see Table 3). The spectra of each polymer were analysed to compare the spectra of raw materials to that of the aged ones. For PET, the spectra of the unaged sample were compared to the spectra of samples aged at 105 °C for 240 h and at 120 °C for 408 h; the same configurations in terms of shape, but not in terms of absorbance units, were observed. The tendency shows a minimal, yet detectable decrease in the absorbance units with increasing the intensity of ageing. The distinction is not visible in the whole spectra, but only in some specific wavenumber ranges (in correspondence of spectrum peaks around 7073 cm1 and 5245 cm1). Thermal degradation results in scission of the chain of the ester linkage, providing a decrease in the molecular weight (explaining the decrease happening at 5245 cm1, which is representing C@O stretching energy of the ester group) (Venkatachalam et al., 2012). Moreover, the second wavenumber detecting spectral absorbance unit change (i.e. 7073 cm1) is representing CH stretching and CH deformation in the aromatic ring.

76

A. Alassali et al. / Waste Management 82 (2018) 71–81

Fig. 5. NIR spectra of virgin PE, PP, ABS and PET.

For ABS, NIR spectra of the aged material shifted up when compared to the raw material, where the shifting occurred on the whole spectral wavelength, except for wavelengths above 10,500 cm1. This could be explained by the material discoloration happening on the surface of the ABS sample, as was previously dis-

cussed. In order to apply a qualitative comparison on the spectral change between aged and unaged samples, spectral pretreatment, (i.e., base-line subtraction) had to be applied, allowing for a uniform base-line for all ABS samples. The spectra of samples treated at 85, 105 and 120 °C for 240 h, showed a decrease of

A. Alassali et al. / Waste Management 82 (2018) 71–81 Table 3 NIR spectra qualitative assessment for PE, PP, ABS and PET. Polymer

Wavelength (cm1)

Group in NIR

PE

10724.80 8563.77 8224.66 7173.79 7063.28 5773.52

CH2 third overtone of symmetrical stretching CH2 second overtone of asymmetrical stretching CH2 second overtone of symmetrical stretching (CH2 Methylene) 2X CH-stretching + CHdeformation combination First overtone of asymmetrical stretching of a methyl group ACH first overtone CH2 first overtone of symmetrical stretching @CH stretching + C@C stretching (the combination region)

PP

ABS

5664.05 5495.79 4966.49 4872.10 4667.01 10952.10 8652.11 8388.58 7161.45 5795.32 5486.29 5063.56 8746.50 8316.78 7067.90 6107.64 5960.06 5699.85 5248.07 5143.33 4666.55 4610.69 4575.12 4478.52 4340.05

PET

4260.81 4043.25 8861.98 8516.96 7326.32 7072.93 6022.94 5863.06 5245.21 5118.70 4690.45 4636.56 4580.38 4432.69 4291.31 4244.13 4177.62 4090.92

ACH third overtone ACH3 second overtone of asymetrical stretching ACH3 second overtone of symetrical stretching (CH2 Methylene) 2X CH-stretching + CHdeformation combination ACH3 first overtone of asymetrical stretching ACH2 first overtone of symmetrical stretching CH-stretching + CH-deformation Vinyl group second overtone ACH2 second overtone of symmetrical stretching (CH2 methylene) 2X CH-stretching + CHdeformation combination (C@C) vinyl group first overtone @CH stretching ACH first overtone Second overtone of CONH @CH stretching + C@C stretching combination of alkene (C@C alkene) CH2 stretching + C@C stretching CH2 stretching + C@C stretching combination of a vinyl group CH stretching + CH deformation of CH2 methyl group CH2 stretching + CH2 deformation of a vinyl group CH + CAC combination Second overtone of aromatic CH CH2 methyl second overtone of asymmetric stretching (CH2 methylene) 2X CH stretching + CH deformation combination region (CH aromatic) 2X CH-stretching + CH-deformation combination and methylene CH2 combination (C@C) vinyl group first overtone CH2 methyl first overtone of asymmetric stretching 2X C@O stretching (COOR ester) C@O second overtone @CH stretching + C@C stretching CH2 stretching + @CH2 deformation (CHO aldehide) CH stretching + C@O stretching CH3; combination of CH stretching + CH deformation (CH2 methyl) CH stretching + CH deformationcombination CH + CAC combination

absorbance with increasing the temperature at wavenumbers 8746, 8317, 7068, 5248, 5143 and 4261 cm1. However, an increase in absorbance was observed for the wavenumbers 6108, 5960 and 5700 cm1. This means that there was a decrease in the energy presented by the vinyl group second overtone, ACH2 second overtone of symmetrical stretching, CAH stretching, CH2 stretching, and C@C stretching. However, there was an increase in the CAO stretching vibration, @CH stretching of alkene group and in the ACH first overtone. These results were confirmed by literature (Wypych, 2013). However, there was a reduction in the absorbance units in the bands attributed to the C-H deformation

77

vibrations of poly-1,2-butadiene, poly-trans-1,4-butadiene, and to the band corresponding to C@C stretching vibrations. On the grounds of the gathered results, it may be affirmed that the degradation products of polybutadiene promote the oxidation of styrene segments to oxidation products, which are responsible for ABS discoloration (Wypych, 2013). Furthermore, the spectral behavior at wavenumbers lower than 4600 cm1 was not uniform to be included in the evaluation process. NIR spectra of PE samples aged at 85, 105 and 120 °C for 24 h were compared; the NIR spectra for wavenumbers 96675, 8564, 7174, 7063, 6952 and 5884 cm1 were higher for samples treated at higher temperatures, yet it was the opposite for wavenumbers 8225, 5246 and 5225 cm1. This means higher energies obtained by possible oxidative reactions (Gulmine et al., 2003) and hydrolytic degradation (Massey et al., 2007). At wavelength of 5884, the increase could be explained by the formation of methyl groups during the degradation. However, the energy decrease happening at wavenumber 8225 could be attributed to the deformation of CH2. For PP, the absorbance of aged samples was generally similar to non-aged, yet there were absorbance peaks higher for samples aged considering longer times and higher temperatures. Comparing the NIR spectra of samples aged for 240 h, at 85, 105 and 120 °C, higher absorbance was detected with increasing the applied temperature, specifically at wavenumbers of  8300, 5795 to 5900, and 5486 cm1. This could be explained by the higher energies obtained from CH3 first and second overtone and CH2 first and second overtone due to chain rupture, resulting in smaller chains with more methyl and methylene groups. PP degradation is expected to produce intermolecular oxygen bonds as for PE, as per the obtained spectra, with harsher ageing conditions, clearer peaks were observed in the region 5216–5284 cm1. 3.2.2. Evaluation of the spectral analysis In view of the molecular level, the thermo – oxidative degradation of the studied polymers, from a chemical point of view, is a process accomplished with exposing the composite to elevated temperatures in the presence of oxygen. The consequence results in a variation in the polymeric structure, due to the oxidation of the compound. PE and PP are chemically more stable than PET and ABS, as indicated by the chemical structure (see Table 1). PE and PP contain carbon and hydrogen atoms only, with carbon-carbon single bonds. Accordingly, if there are chemical changes that might be evidenced in NIR spectroscopy, they are very slow. The degradation of both, PE and PP could be initiated with thermal, photo or oxidation activities, resulting in chain breakage and a formation of radicals, which are easily oxidized. Hence, the NIR analysis of significantly aged samples should indicate an increase in C@C, C@O or CAOH bonds, in addition to the increase in the concentration of methyl groups (ending of the chains). On the other hand, ABS contains C@C and C„N bonds. As was reported by Tiganis et al. (2002), degradation of the elastomeric polybutadiene phase (i.e., containing C@C) in ABS is initiated by hydrogen abstraction from the carbon a to unsaturated bonds, generating hydroperoxide radicals, producing carbonyl and hydroxyl products. Thermal degradation of the styrene–acrylonitrile phase (i.e., containing C@C and C„N) in ABS also takes place by thermo-oxidative degradation, yet less significantly. In PET, the aromatic ring connected to a short aliphatic chain provides stiffness to the molecule. The lack of segmental mobility in the polymer chains results in relatively high thermal stability (Venkatachalam et al., 2012). However, the thermal degradation of PET was reported to take place through intramolecular back-biting leading to cyclic oligomers of up to three units in size, and chain scission through

78

A. Alassali et al. / Waste Management 82 (2018) 71–81

a b-C-H hydrogen transfer reaction, resulting in vinyl ester and acid end-groups (Holland and Hay, 2002).

3.2.3. Quantitative analysis The PLS chemometric algorithm was used to derive the empirical spectroscopic models for each polymeric material (PET, ABS, LDPE and PP) after being aged differently (i.e., different heating time and temperatures). This procedure simultaneously reduced the amount of the spectral data and tried to find a regression over the data. The dependent variable was the absorbance at a number of wavelengths, while the independent variables were the properties identified for the study: time of exposure and temperature in this case. The quantitative analysis had the aim of finding a measurable relationship between spectral data and changes in sample properties, through Partial Least Square (PLS) regression. The final goal was the construction of calibration models able to predict exposure times and mechanical properties of the considered plastic material, in relation to the absorbance spectra collected. For each series (described in Table 2) temperature was fixed and the ageing time changed; it was defined that PLS models with R2 values higher than 0.94 would be accepted with no limitations. As a general rule, R2 values higher than 0.90 indicate a very good correlation, allowing the application of the model for different purposes, including quality insurance as was discussed by (Krapf, 2013). However, series 3PET, 1-PE and 1-PP had R2 values equal to 0.80, 0.77 and 0.83 respectively, indicating a good correlation, yet with limitations in possible application. After processing the data (see Table 4), even better fitting models were obtained (see Fig. 6). R2 values were optimized applying the PLS model-suggested pre-treatment method as well as the model-suggested wavenumbers as described in Table 4. The different pre-treatment method applied in every and each aging-model was semi-automated, where the PLS model provided suggestions to improve the aging model (by proposing data treatment methods and sellecting regions of wavenumbers), after which, the operator optimized the sellection based on the obtained statistical evaluation. R2 values increased for all described series; the higher the R2 value (closer to 1.0), the more reliable the prediction model (Legates and McCabe, 1999). The RMSECV values were higher for series 3-PET, 1-PE and 1-PP (see Fig. 7.) Applying data pretreatment resulted in lower RMSECV values, showing an optimiza-

Fig. 6. R2 values of PLS models built based on the NIR spectra of the different presented polymers, before and after data processing.

Fig. 7. Root Mean Square Error of Cross-Validation (RMSECV) of the PLS models of different series, before and after processing.

tion in the generated ageing models (Moriasi et al., 2007). However, each of series 3-PET, 1-PE and 1-PP showed more limitations in the application for data prediction in comparison to the other series. Generally, it can be observed that the PLS model built based on time changing while keeping the temperature constant shows a good tool for ageing-time prediction.

Table 4 Description of data processing method and selected wavelengths. Polymer

Series

Pre-treatment Applied

PET

1-PET

Subtraction of constant offset

ABS

2-PET 3-PET 1-ABS

Multiplicative scatter correlation Baseline normalization First derivative

2-ABS 3-ABS

First derivative and baseline normalization First derivative and baseline normalization

1-PE

No pre-treatment

2-PE 3-PE

Multiplicative scatter correlation Baseline normalization

1-PP

Multiplicative scatter correlation

2-PP

Min-Max normalization

3-PP

Min-Max normalization

PE

PP

Spectra wavelengths considered (cm1) From

To

9403.8 5450.2 9403.8 7502.2 6800.2 5450.2 7502.2 7502.2 5450.2 9403.8 5450.2 9403.8 7506.0 5454.0 7502.2 5450.2 9403.8 5450.2 6102.0

7498.3 4597.7 6098.2 4597.7 6098.2 4597.7 6098.2 6098.2 4597.7 7498.3 4246.7 4597.7 6094.3 4242.9 6098.2 4597.7 6098.2 4597.7 4597.7

A. Alassali et al. / Waste Management 82 (2018) 71–81

79

Fig. 8. Calculated real ageing time versus calculated model-proposed ageing time: (a) PET, (b) ABS, (c) PE and (d) PP.

3.2.4. Calibrated real ageing time models The quantitative model of the NIR spectra was applied, including two ageing factors (i.e. temperature and time). The data processing method suggested for each polymer was selected based on the model’s proposal and the evaluation of the statistical figures. The aim was to provide an automated process, which is able to do an online and continuous optimization of the data. For PET, the PLS model built from NIR spectra (after data processing by applying multiplicative scatter correlation and controlled on the suggested wavelength ranges: 6800–6098 cm1 and 5450– 4598 cm1) showed R2 value of 0.87 for time component and 0.78 for temperature component. When both parameters were included in calculating the ageing time (see Eq. (1)), the real ageing time (applying real ageing time and temperature) was presented against model-proposed aging time (model predicted ageing time and temperature) and R2 value of the linear relation was raised to 0.87 (see Fig. 8a). The relation between model-based values and experimental values has a ME (bias) of 0.13 (indicating a minor model underestimation (Moriasi et al., 2007)), and a MSE (accuracy) value of 14.25 was calculated. For ABS, the applied data pre-treatment methods were first derivation and standard normal variance on the wavelength ranges (9404 to 5446 cm1 and 4602 to 4247 cm1). The R2 values obtained were 0.95 and 0.98 for time component and temperature component, respectively. The R2 value of the combined factors, when the real ageing time is presented against the modelproposed ageing time, was 0.97 (see Fig. 8b), with a minor bias value of -0.06 and a model MSE of 3.25, indicating an excellent correlation of the data. About PE, PLS ageing model showed a poor correlation for time component (i.e., R2 value of 0.53), while it was significantly higher for the temperature component (R2 = 0.92), noting that these values were obtained after data processing (applying subtraction of a constant offset for the wavelength ranges (9404–8451 cm1 and 4602–4247)). As seen in Fig. 8c, the collinearity of the relation

between real ageing time and model-proposed ageing time was relatively low (0.65), allowing for the application of approximate calibration only, as was proposed by (Krapf, 2013). PE ageing model in comparison to real ageing data showed a negligible ME value (0.06), indicating good model estimation, however the calculated accuracy was 12.62. Furthermore, the collinearity of the PP’s PLS ageing model, including both factors (time and temperature) was similarly low (R2 = 0.60, see Fig. 8d), yet this value is sufficient for a rough screening (Krapf, 2013). The individual R2 values for time component and temperature component were 0.67 and 0.92 respectively. This suggested that applying PLS model to predict the temperature component alone for both polymers, for PE and PP, is more accurate than using it for time component prediction or the calculated ageing time prediction. The ME value calculated indicates a very minimal model overestimation (0.72), yet the calculated accuracy of the PP’s model shows the highest value in comparison to the other studied polymers, with MSE value of 17.76.

4. Conclusions In this paper, NIR analysis was used to develop an automated system for polymer degradation and polymer quality assessment. NIR spectra showed that absorbance qualitatively changed at some wavenumbers (depending on the studied polymer) when thermal ageing was applied. These changes were connected to the severity of the applied aging (time and temperature), especially for PET and ABS. Yet, although the variance in the spectra of PE and PP was detectable, it was less obvious in respect to the predictable degradation components. This is due to the chemical stability of PE and PP; even if changes were happening, they were very slow to be efficiently detected by NIR spectroscopy. To conduct a quantitative analysis, the PLS chemometric algorithm was used to derive the empirical spectroscopic models

80

A. Alassali et al. / Waste Management 82 (2018) 71–81

for each polymeric material (PET, ABS, LDPE and PP) after being aged at different heating time and temperatures. Generally, when temperature was set constant and ageing time changed, the generated linear models for all series (mentioned in Table 2.) were strongly fitting the experimental data, except for series 3-PET (PET treated at 120 °C), series 1-PP (PP treated at 85 °C) and series 3-PP (PP treated at 120 °C), which showed lower R2 values (<0.85). After the application of data pre-treatment (processing) and the selection of model-proposed wavenumbers, the R2 values of all series increased, where the increase was mostly significant for the series with lower R2 values. Due to the satisfactory R2 values obtained (0.85–1.00), the data quality can be described consistently with literature (Kilbride et al., 2006). Nevertheless, with ABS samples (series 1-ABS, 2-ABS and 3-ABS) it was evident that data pre-treatment didn’t make particular differences in any of the generated models. All the coefficients of determination (R2) were higher than 98%, which represented an excellent value, with a correlated quite good RMSECV. The reason might be the regular and quantifiable change in the polymer’s chemical structure with the increase in the aging severity, which could be detected by NIR spectroscopy. In order to generate a global prediction model, the main results of the PLS models were presented applying a factor-combination parameter (i.e., ageing time). In all the models, the calibration was able to detect an increase in the ageing time, which is connected to degradation. The global ageing models for ABS and PET were accurately predictive, where the calculated statistical performance measures were appropriate. For PE and PP, the calculated model’s performance measures were similarly robust, yet the R2 values of the model-ageing to real ageing relation were relatively low (0.6). In conclusion, this study proved that NIR spectroscopy may be considered as an effective tool to investigate plastic waste degradation, with some limitations for specific polymers that need further investigations in the future. Acknowledgements The authors equally contributed to all phases of the research and of the writing process. This research did not receive any specific grant from funding agencies in the public, commercial, or notfor-profit sectors. The authors declare no conflict of interest. The authors gratefully acknowledge the contribution of Arianna Prette in supporting the experimental activities of the research. References Abenojar, Juana, Torregrosa-Coque, Rafael, Martínez, Miguel Angel, MartínMartínez, José Miguel, 2009. Surface modifications of polycarbonate (PC) and acrylonitrile butadiene styrene (ABS) copolymer by treatment with atmospheric plasma. Surf. Coat. Technol. 203 (16), 2173–2180. Al-Salem, S.M., 2009. Establishing an integrated databank for plastic manufacturers and converters in Kuwait. Waste Manage. (Oxford) 29 (1), 479–484. Al-Salem, S.M., Lettieri, P., Baeyens, J., 2009. Recycling and recovery routes of plastic solid waste (PSW). A review. Waste Manage. 29 (10), 2625–2643. Andrady, Anthony L., Neal, Mike A., 2009. Applications and societal benefits of plastics. Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 364 (1526), 1977–1984. Balart, Rafael, Lopez, Juan, García, David, Salvador, M Dolores, 2005. Recycling of ABS and PC from electrical and electronic waste. Effect of miscibility and previous degradation on final performance of industrial blends. Eur. Polym. J. 41 (9), 2150–2160. Basfar, A.A., IdrissM, Ali K., 2006. Natural weathering test for films of various formulations of low density polyethylene (LDPE) and linear low density polyethylene (LLDPE). Polym. Degrad. Stab. 91 (3), 437–443. Bashford, D.P., 1996. Thermoplastics. Directory and databook. Springer Science & Business Media. Beyler, Craig L., Hirschler, Marcelo M., 2002. Thermal decomposition of polymers. SFPE Handbook of Fire Protection Engineering 2, 111–131. Blanco, M., Villarroya, I.N.I.R., 2002. NIR spectroscopy. A rapid-response analytical tool. TrAC Trends Anal. Chem. 21 (4), 240–250. Boldizar, Antal, Möller, Kenneth, 2003. Degradation of ABS during repeated processing and accelerated ageing. Polym. Degrad. Stab. 81 (2), 359–366. Briassoulis, D., Hiskakis, M., Babou, E., 2013. Technical specifications for mechanical recycling of agricultural plastic waste. Waste Manage. 33 (6), 1516–1530.

Burn, B., Tiganis, P., Davis, L.S., Gotama, J. The mechanism of photo-oxidative degradation of acrylonitrile-butadiene-styrene (abs) resins used in pipes. Available online at . Cagnetta, Giovanni, Zhang, Kunlun, Zhang, Qiwu, Huang, Jun, Yu, Gang, 2018. Mechanochemical pre-treatment for viable recycling of plastic waste containing haloorganics. Waste Manage. (Oxford) 75, 181–186. Dahlbo, Helena, Poliakova, Valeria, Mylläri, Ville, Sahimaa, Olli, Anderson, Reetta, 2018. Recycling potential of post-consumer plastic packaging waste in Finland. Waste Manage. (Oxford) 71, 52–61. Davis, Paul, Tiganis, B.E., Burn, L.S., 2004. The effect of photo-oxidative degradation on fracture in ABS pipe resins. Polym. Degrad. Stab. 84 (2), 233–242. Deterre, Rémi; Feller, Jean-François, 2014. Environmental Impact of Polymers. 1., Auflage. Edited by Thierry Hamaide. New York, NY: John Wiley & Sons (ISTE). Faucitano, A.B.A.U.D.P., Buttafava, A.B.A.U.D.P., Camino, G.B.A.U.D.T., Greci, L.B.A.U. D.A., 1996. Photo-oxidation and stabilization of polymers. Trends Polym. Sci. 3 (4), 92–98. Fischer, D., Eichhorn, K.-J., 1998. Process analysis of polymers by NIR spectroscopy. Analusis 26 (4), 58–60. Fleming, Richard A., 1992. Design for Recycling. A Plastic Bottle Recycler’s Perspective. Partnership for Plastics Progress. Frank, LLdiko E., Friedman, Jerome H., 1993. A statistical view of some chemometrics regression tools. Technometrics 35 (2), 109–135. Gourmelon, Gaelle, 2015. Global Plastic Production Rises, recycling lags. In: New Worldwatch Institute analysis explores trends in global plastic consumption and recycling. Recuperado de . Gulmine, J.V., Janissek, P.R., Heise, H.M., Akcelrud, L., 2003. Degradation profile of polyethylene after artificial accelerated weathering. Polym. Degrad. Stab. 79 (3), 385–397. Gupta, Samik, Pallavi, M.B., Som, Abhijit, Krishnamurthy, Raja, Bhowmick, Anil K., 2008. Anomalous mechanical behavior upon recycling of poly (phenyleneether)-based thermoplastic elastomer. Polym. Eng. Sci. 48 (3), 496–504. Hahladakis, John N., Purnell, Phil, Iacovidou, Eleni, Velis, Costas A., Atseyinku, Maryann, 2018. Post-consumer plastic packaging waste in England. Assessing the yield of multiple collection-recycling schemes. Waste Manage. (Oxford) 75, 149–159. Hannequart, J.P., 2004. Good practice guide on waste plastics recycling. A guide by and for local and regional authorities. Holland, B.J., Hay, J.N., 2002. The thermal degradation of PET and analogous polyesters measured by thermal analysis–Fourier transform infrared spectroscopy. Polymer 43 (6), 1835–1847. Hopewell, Jefferson, Dvorak, Robert, Kosior, Edward, 2009. Plastics recycling. Challenges and opportunities. Philos. Trans. Roy. Soc. Lond. B: Biol. Sci. 364 (1526), 2115–2126. Horodytska, O., Valdés, F.J., Fullana, A., 2018. Plastic flexible films waste management – a state of art review. Waste Manage. (Oxford). https://doi.org/ 10.1016/j.wasman.2018.04.023. Hunt, Emily J., Zhang, Chenlong, Anzalone, Nick, Pearce, Joshua M., 2015. Polymer recycling codes for distributed manufacturing with 3-D printers. Resour. Conserv. Recycl. 97, 24–30. Huth-Fehre, Th., Feldhoff, R., Kantimm, Th., Quick, L., Winter, F., Cammann, K., et al., 1995. NIR-Remote sensing and artificial neural networks for rapid identification of post consumer plastics. J. Mol. Struct. 348, 143–146. Ip, Karine, Testa, Mariapaola, Raymond, Anne, Graves, Stephen C., Gutowski, Timothy, 2018. Performance evaluation of material separation in a material recovery facility using a network flow model. Resour. Conserv. Recycl. 131, 192–205. Jenseit, Wolfgang, Stahl, Hartmut, Wollny, Volrad, Wittlinger, R., 2003. Recovery options for plastic parts from end-of-life vehicles. An eco-efficiency assessment. Edited by Brussels APME. Institute for applied ecology. Darmstadt, Germany. Available online at . Kasaai, Mohammad R., 2008. A review of several reported procedures to determine the degree of N-acetylation for chitin and chitosan using infrared spectroscopy. Carbohydr. Polym. 71 (4), 497–508. Kilbride, C., Poole, J., Hutchings, T.R., 2006. A comparison of Cu, Pb, As, Cd, Zn, Fe, Ni and Mn determined by acid extraction/ICP–OES and ex situ field portable X-ray fluorescence analyses. Environ. Pollut. 143 (1), 16–23. Krapf, Lutz Christian, 2013. Evaluation of near infrared spectroscopy to estimate process parameters in anaerobic digestion of agricultural feedstocks. Technische Universität München, Germany. Lazarevic, David, Aoustin, Emmanuelle, Buclet, Nicolas, Brandt, Nils, 2010. Plastic waste management in the context of a European recycling society. Comparing results and uncertainties in a life cycle perspective. Resour. Conserv. Recycl. 55 (2), 246–259. Legates, David R., McCabe, Gregory J., 1999. Evaluating the use of ‘‘goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour. Res. 35 (1), 233–241. Lithner, Delilah, Larsson, Åke, Dave, Göran, 2011. Environmental and health hazard ranking and assessment of plastic polymers based on chemical composition. Sci. Total Environ. 409 (18), 3309–3324. Macho, S., Larrechi, M.S., 2002. Near-infrared spectroscopy and multivariate calibration for the quantitative determination of certain properties in the petrochemical industry. TrAC Trend. Anal. Chem. 21 (12), 799–806. Massey, S., Adnot, A., Rjeb, A., Roy, D., 2007. Action of water in the degradation of low-density polyethylene studied by X-ray photoelectron spectroscopy. Express Polym. Lett. 1 (8), 506–511. Miller, Edward, 1996. Introduction to plastics and composites. Mech. Propert. Eng. Appl. (76)

A. Alassali et al. / Waste Management 82 (2018) 71–81 Moriasi, Daniel N., Arnold, Jeffrey G., van Liew, Michael W., Bingner, Ronald L., Harmel, R Daren., Veith, Tamie L., 2007. Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50 (3), 885–900. Murray, K.A., Kennedy, J.E., McEvoy, B., Vrain, O., Ryan, D., Cowman, R., et al., 2013. The effects of high energy electron beam irradiation in air on accelerated aging and on the structure property relationships of low density polyethylene. Nucl. Instrum. Meth. B 297, 64–74. Ngai, K.L., Roland, C.M., 1993. Chemical structure and intermolecular cooperativity. Dielectric relaxation results. Macromolecules 26 (25), 6824–6830. Nkwachukwu, Onwughara Innocent, Chima, Chukwu Henry, Ikenna, Alaekwe Obiora, Albert, Lackson, 2013. Focus on potential environmental issues on plastic world towards a sustainable plastic recycling in developing countries. Int. J. Indus. Chem. 4(1), 1–13. Pasquini, Celio, 2003. Near infrared spectroscopy. Fundamentals, practical aspects and analytical applications. J. Braz. Chem. Soc. 14 (2), 198–219. Peeters, Jef R., Vanegas, Paul, Kellens, Karel, Wang, Feng, Huisman, Jaco, Dewulf, Wim, Duflou, Joost R., 2015. Forecasting waste compositions. A case study on plastic waste of electronic display housings. Waste Manage. (Oxford) 46, 28–39. Perugini, Floriana, Mastellone, Maria Laura, Arena, Umberto, 2005. A life cycle assessment of mechanical and feedstock recycling options for management of plastic packaging wastes. Environ. Prog. 24 (2), 137–154. Pivnenko, Kostyantyn, Granby, Kit, Eriksson, Eva, Astrup, Thomas Fruergaard, 2017. Recycling of plastic waste. Screening for brominated flame retardants (BFRs). Waste Manage. (Oxford) 69, 101–109. Plastic Europe, 2016. Plastics—the Facts 2016 An Analysis of European Plastics Production, Demand and Waste Data. Association of Plastic Manufacturers Brussels, Brussels, pp. 1–38. Ragaert, Kim, Delva, Laurens, van Geem, Kevin, 2017. Mechanical and chemical recycling of solid plastic waste. Waste Manage. 69, 24–58. Rigamonti, L., Grosso, M., Møller, J., Martinez Sanchez, V., Magnani, S., Christensen, T.H., 2014. Environmental evaluation of plastic waste management scenarios. Resour. Conserv. Recycl. 85, 42–53. Roedel, M.J., 1953. The molecular structure of polyethylene. I. Chain branching in polyethylene during polymerization1. J. Am. Chem. Soc. 75 (24), 6110–6112. Ross, Stuart, Evans, David, 2003. The environmental effect of reusing and recycling a plastic-based packaging system. J. Cleaner Prod. 11 (5), 561–571. Scarascia-Mugnozza, G., Sica, C., Picuno, P., 2008. optimisation of the management of agricultural plastic waste in Italy using a geographical information system. Acta Horticulturae 801 (Part 1), 219–226. Sevigné-Itoiz, Eva, Gasol, Carles M., Rieradevall, Joan, Gabarrell, Xavier, 2015. Contribution of plastic waste recovery to greenhouse gas (GHG) savings in Spain. Waste Manage. (Oxford) 46, 557–567.

81

Shen, Yafei, Zhao, Rong, Wang, Junfeng, Chen, Xingming, Ge, Xinlei, Chen, Mindong, 2016. Waste-to-energy. Dehalogenation of plastic-containing wastes. Waste Manage. (Oxford) 49, 287–303. Shimada, Junichi, Kabuki, Kimiaki, 1968. The mechanism of oxidative degradation of ABS resin. Part I. The mechanism of thermooxidative degradation. J. Appl. Polym. Sci. 12 (4), 655–669. Shubhra, Quazi T.H., Alam, A.K.M.M., Quaiyyum, M.A., 2013. Mechanical properties of polypropylene composites. A review. J. Thermoplast. Compos. Mater. 26 (3), 362–391. Siddique, Rafat, Khatib, Jamal, Kaur, Inderpreet, 2008. Use of recycled plastic in concrete – A review. Waste Manage. (Oxford) 28 (10), 1835–1852. Singh, Narinder, Hui, David, Singh, Rupinder, Ahuja, I.P.S., Feo, Luciano, Fraternali, Fernando, 2017. Recycling of plastic solid waste. A state of art review and future applications. Compos. B Eng. 115, 409–422. Stessel, Richard I., 2012. Recycling and Resource Recovery Engineering. Principles of Waste Processing. Springer Science & Business Media. Tiganis, B.E., Burn, L.S., Davis, Paul, Hill, A.J., 2002. Thermal degradation of acrylonitrile–butadiene–styrene (ABS) blends. Polym. Degrad. Stab. 76 (3), 425–434. Vasile, Cornelia, Pascu, Mihaela, 2005. Practical guide to polyethylene: iSmithers Rapra Publishing. Venkatachalam, S.G.S.V.J.R.P., Nayak, Shilpa G., Labde, Jayprakash V., Gharal, Prashant R., Rao, Krishna, Kelkar, Anil K., 2012. Degradation and recyclability of poly (ethylene terephthalate). Polyester: InTech, 75–98. Walther, Bruno A., Moore, Joslin L., 2005. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance. Ecography 28 (6), 815–829. Wilks, P., 2006. NIR versus mid-IR How to choose. Spectroscopy 21 (4), 43–46. World Economic Forum, Ellen MacArthur Foundation and McKinsey & Company, 2016. The New Plastics Economy, Rethinking the Future of Plastics. Available online at . Worrell, Ernst, Reuter, Markus A., 2014. Chapter 1 – Recycling. A Key Factor for Resource Efficiency. In: Handbook of Recycling. Boston: Elsevier, pp. 3–8. Wu, Guiqing, Li, Jia, Xu, Zhenming, 2013. Triboelectrostatic separation for granular plastic waste recycling – a review. Waste Manage. (Oxford) 33 (3), 585–597. Wypych, George, 2013. 14 – Data on specific polymers. In: Handbook of Material Weathering. 5th ed. Elsevier, Oxford, pp. 351–546. Yin, Shi, Tuladhar, Rabin, Shi, Feng, Shanks, Robert A., Combe, Mark, Collister, Tony, 2015. Mechanical reprocessing of polyolefin waste. A review. Polym. Eng. Sci. 55 (12), 2899–2909.