Pyrolysis kinetics and synergistic effect in co-pyrolysis of Samanea saman seeds and polyethylene terephthalate using thermogravimetric analyser

Bioresource Technology 289 (2019) 121608

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Pyrolysis kinetics and synergistic effect in co-pyrolysis of Samanea saman seeds and polyethylene terephthalate using thermogravimetric analyser

T

Ranjeet Kumar Mishra, Abhisek Sahoo1, Kaustubha Mohanty

⁎

Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, India

GRAPHICAL ABSTRACT

ARTICLE INFO

ABSTRACT

Keywords: Lignocellulosic biomass Waste plastic Kinetic analysis Model-free methods Synergistic effect Thermodynamic analysis

This work deals with co-pyrolysis of polyethylene terephthalate (PET) with Samanea saman seeds (SS) to understand the kinetics and synergistic effects between two different feedstocks. SS and PET were blended in different ratios (1:1, 3:1 and 5:1) and iso-conversional models such as Kissinger-Akahira-Sunose (KAS), Friedman method (FM), Starink (ST), Ozawa-Flynn-Wall method (OFW), and Coats-Redfern method (CR) were used to calculate the kinetic parameters. Results substantiate assumed hypothesis that blending of SS and PET at 3:1 provided higher synergistic effect and RMS value, which in turn indicated maximum formation of hot volatiles during pyrolysis. Kinetic analysis confirmed that individual SS and PET required higher activation energy while blended SS and PET at 3:1 ratio required lower activation energy to start the reaction. The thermodynamic and kinetic analysis confirmed that biomass had complex reaction kinetics which depends on reaction rate as well as its order.

1. Introduction Energy is a critical factor for the existence of human civilization and the development of new technologies. The rapid increase in population and change in lifestyle, exponentially increase energy demand. To fulfill this energy demand, fossil fuels resources are insufficient and unsustainable; given their limited availability and their major contribution to anthropogenic global warming (Burra and Gupta, 2018a). The near uncontrolled increase in solid waste generation is a direct fallout

of rapid increase in population and changing lifestyles. Waste materials are divided into two major categories: fossil-based wastes and bio-based wastes. Waste plastics are one of the major fossil based wastes. The extremely slow rate of waste plastics decomposition demands huge areas of land for the disposal. The global consumption and production of plastic wastes increased for more than 50 years. The global waste production was about 299 million in 2013, which is 4% higher than the previous year 2012 and is expected to rise in the future (2050) (Gourmelon, 2015). According to Central Pollution Control Board of

Corresponding author. E-mail address: [email protected] (K. Mohanty). 1 Centre for Energy Engineering, Central University of Jharkhand, Ranchi 835205, India. ⁎

https://doi.org/10.1016/j.biortech.2019.121608 Received 5 April 2019; Received in revised form 1 June 2019; Accepted 3 June 2019 Available online 06 June 2019 0960-8524/ © 2019 Elsevier Ltd. All rights reserved.

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India (CPCB) (2015), about 15,342 tons of waste plastic is produced every day in the country, out of which, 9205 tons were reported to be recycled and leaving 6137 tons uncollected and littered (Gupta et al., 2015). Polyethylene (PE), polyvinyl chloride (PVC), polystyrene (PS), polypropylene (PP), polyethylene terephthalate (PET or PETE), and nylons are the major waste plastics (Burra and Gupta, 2018a). Plastics are used in the food industry majorly for packaging. These plastics are non-degradable and rapidly filling the landfills. Moreover, it is inherently heterogeneous, which implies the low degree of recycling value, less economic value while making its separation from other wastes, a major challenge. From the energy loss perspective, if assumed that the heating values of PET and waste plastics are the same, then an astounding amount of 2.8 quads of energy equivalent is being sent to the landfills (Burra and Gupta, 2018a). Alternatively, this figure can be interpreted as a substantial portion of the fossil fuel (naphtha or natural gas) required for polymer production, is actually discarded as waste in landfills. This is the major reason for the imbalance in the productiondisposal cycle since production is continuously increasing per year. These waste plastics can be used for different applications such as fuel and chemicals by applying suitable processes that enhance the net economic cost. Biomass such as forest residue, non-edible seeds, agricultural residue, waste paper, and yard waste are renewable as well as sustainable and are also considered as carbon neutral. The blending of biomass with plastic can alter the quality and quantity of fuel by converting it into sustainable clean energy (Wang et al., 2017). Conversion of bio-based material through biological route has been in practice since decades. However, the thermochemical pathway is more efficient for the conversion of biomass and waste plastics into renewable fuels (Burra and Gupta, 2018a). Combustion, gasification, liquefaction (hydrothermal), and pyrolysis are the major thermochemical techniques. These thermal processes involve fragmentation of higher molecular weight compounds into lower molecular weight compounds by a continuous supply of heat. In this process, hot volatile products are released due to rapid decomposition of the material which produced different types of gases such as hydrogen, carbon dioxide, carbon monoxide, methane, and a trace amount of other higher series of hydrocarbons. Before opting for pyrolysis of biomass and plastic, knowledge of the kinetic parameters involved is essential in aiding minimization of process parameters. Rain tree is a fast-growing tropical tree under the family Fabaceae. The fruit (pod) is well known as cow tamarind or monkey pod and often browsed by ruminants. A full-grown tree can produce 450–600 kg pods in a year. Dry ripe pods can be stored for a long period under normal condition (Rath et al., 2014). The availability of the seeds in India or across the world is still unknown. Kinetic analysis of materials in a thermogravimetric analyzer (TGA) is widely accepted and adapted (Damartzis et al., 2011; Mishra and Mohanty, 2018b). Non-isothermal and isothermal techniques are used to describe the TGA pyrolysis reaction kinetics. The non-isothermal technique produced less error (depends on the biomass, its composition and adopted models usually 2–4%) than the isothermal technique since it needs holding time and rates (Heydari et al., 2015). Moreover, the shorter investigational time and less experimentational data required in non-isothermal conditions, confer a definite advantage over isothermal conditions (Mishra and Mohanty, 2018b). Since kinetic analysis continuously utilizes a complete range of temperatures, it consequently minimizes the probable error in thermochemical induction methods. Furthermore, non-isothermal techniques are again categorized into two parts: model fitting and model-free methods, where the minimum error produced by the model-free method, makes it the method of choice (Damartzis et al., 2011; Mishra and Mohanty, 2018b). Model-free non-isothermal methods are considered the more efficient route to understand the pyrolysis reaction kinetics. Lower heating rates are used in this model to avoid possible error since a higher heating rate created turbulence and needed higher temperature (∼900 °C) (Damartzis et al., 2011; Mishra and Mohanty, 2018b). In this method, the kinetic parameters

are determined by assuming the function of temperature and heating rate. The Model free method was used at multiple heating rates while assuming negligible mass loss and allowing selection of different types of reaction mechanism in the direction of the reaction. Number of researchers have explained kinetic analysis of biomass as a single-step reaction model, or a two-step reaction model (Babu and Chaurasia, 2003), and even as multipseduo components of the three-step model (Sharma et al., 2016), and the distributed activation energy model (DAEM) (Ceylan and Kazan, 2015; Mishra and Mohanty, 2018b). Recently, co-pyrolysis of various types of feedstocks (biomass, plastics, waste tire, sewage sludge, etc.) gained more attention due to its inherent advantages such as easy availability, low cost, and sustainability. Co-pyrolysis of polypropylene with cellulose, showed a reduction in activation energy and char yield, than during individual pyrolysis. The alteration may arise due to H-abstraction in polypropylene leading to the radical formation from cellulose. Additionally, the eOH from cellulose re-joining with polypropylene oligomer radicals, to form long chain alcohols, along with biomass char catalyzed propylene pyrolysis (Părpăriţă et al., 2014). Due to the effective synergistic effect, increase in syngas and hydrogen gas were observed during the co-gasification of pinewood with various plastic wastes such as polypropylene, polycarbonate, polyethylene, and PETE (Ahmed et al., 2011; Burra and Gupta, 2018b). The analysis of co-pyrolysis synergies, with delineation of reactants, intermediary compounds, and final products is essential; given the commercial viability of the combined pyrolysis of lignocellulose biomass and plastic wastes. The present study deals with the kinetic analysis and synergistic effect of SS and PET at different blend ratios. TGA was used to understand the effect of heating rates, the composition of biomass, reaction rate and individual contribution of constituents. This study will help the readers to understand the importance of blending of biomass with plastic towards enhancing total products yield. This study also deals with fostering energy sustainability in the future from increased use of waste plastics and waste biomass. In this study, five model-free methods such as Kissinger-Akahira-Sunose (KAS), Friedman method (FM), Starink (ST), Ozawa-Flynn-Wall method (OFW), and Coats-Redfern method (CR) were used to calculate the kinetic parameters of pure SS and PET along with different blends (1:1, 3:1 and 5:1) at dynamic heating rates (10 °C min−1, 30 °C min−1 and 50 °C min−1). Furthermore, thermodynamic parameters such as Enthalpy, Entropy, and Gibes free energy were also determined to understand the pyrolysis behavior of SS and PET. 2. Materials and methods 2.1. Sample collection and preparation Samanea saman seeds (SS) were collected from Meghalaya state, India while polyethylene terephthalate (PET) was collected from the Indian Institute of Technology Guwahati (IITG) campus (used and disposed water bottles). The collected biomass was sundried for 100 h, based on the prevalent atmospheric conditions and placed in airtight plastic containers to prevent moisture absorption. The collected plastic bottles (PET) were washed with hot water to remove the extraneous elements and dried for 48 h, at ambient temperature. The biomass was pulverized in knife mill followed by a hammer mill to desired particle size (< 1 mm) while PET was fed to a cutting mill followed by CryoMill to sieved an average particle size of (< 1 mm). 2.2. Physicochemical characterization of biomass and plastic Physicochemical characterization of biomass and plastic is the primary step to predict its bioenergy potential in the production of fuel and value-added chemicals. The proximate analysis involves moisture, volatile matter, ash content, and fixed carbon while ultimate analysis involves a percentage of carbon, hydrogen, oxygen, nitrogen, and 2

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sulphur. ASTM standards (E-871, D1102-84) were used for proximate analysis. Moisture was determined by placing 1 g of biomass/plastic into a hot air oven for one h at 105 °C. The final versus the initial weight of the sample provided the percentage of moisture. The volatile matter was determined by placing 1 g oven-dried biomass/plastic into muffle furnace at 925 ± 10 °C for 7 min. After 7 min, the sample was removed from the muffle furnace and placed in a desiccator for isothermal cooling. The difference between the final and initial weight provided the percentage of volatile matter. Ash content was determined by placing 1 g of oven dried biomass/plastic into muffle furnace for 4 h at 575 ± 10 °C. After the desired time, the sample was removed from the muffle furnace and placed in a desiccator. To obtained accurate results, this process was repeated until the constant weight was obtained. The final and initial weight difference indicated the percentage of ash. The amount of fixed carbon (FC) was calculated by the following equation.

FC (%) = 100

(Mc (%) + VM (%) + As (%))

for the measurement of effective synergistic effect. Higher RMS values indicate more intense synergistic effect. The RMS value can be calculated by the following equation,

n

(4)

Fourier transform infrared spectroscopy (FTIR, Perkin Elmer, Spectrum-two, US) was used for identification of various functional groups present in SS, PET as well as blends. The samples were thoroughly mixed with dried KBr powder in the ratio of 1:100, and then placed in the sample holder. Scanning was done in the range of 400 cm−1–4000 cm−1 with the step size of 4 cm−1 at a scanning rate of 40. Complete mixing of samples with KBr is essential to get clear and accurate peaks.

(1)

2.6. Kinetic theory Biomass is a complex mixture of various compounds such as hemicellulose, cellulose, lignin, extractive and small amount of inorganic content, where every single constituent has its own thermal decomposition range. Therefore, predicting an exact pyrolysis kinetic reaction is rather difficult. However, a general overall pyrolysis reaction mechanism can be written as;

Thermal analysis of the biomass/plastic was carried out in a thermogravimetric analyzer (NETZSCH, TG 209 F1 Libra). A desired amount of biomass/plastic (9 mg) was taken in the crucible and placed in the TGA furnace. The furnace heating ranged from 25 °C–900 °C at 10 °C min−1 heating rate with a constant flow rate of inert gas (40 mL min−1 of N2). The pyrolysis experiments were carried out at three different heating rates (10 °C min−1, 30 °C min−1, and 50 °C min−1) and each heating rate experiment was carried out for thrice for the reproducibility of accurate results.

Biomass

k (t )

Vollatiles (gas + tar ) + Char ( solid residue )

(5)

Conversion of biomass from solid state to volatile, rate of reaction is written as;

dx = k f (x ) dt

(6)

where, x = rate of conversion within sample and t = time. Further rate of reaction is defined by Arrhenius equation as:

2.4. Synergistic effect and root mean square (RMS) value

Ea RT

k = k0 e

An effect arising between two substances/materials that produces an effect greater than the sum of their individual effects is known as a synergistic effect. This may arise due to the interaction of molecules of both substances especially carbon and hydrogen (Clarke, 2008). The synergistic effect of SS and PET along with blends such as SS + PET (1:1), SS + PET (3:1) and SS + PET (5:1) were calculated by using the experimental and theoretical mass loss of SS, PET, SS + PET (1:1), SS + PET (3:1) and SS + PET (5:1); based on the equation:

(7)

where k represents reaction rate constant, E = activation energy expressed (kJ mol−1) R = Gas constant (8.314 J mol-1 K−1), ko = preexponential factor (min−1), and T = Absolute temperature (K). The conversion of sample is the function of temperature, therefore, conversion is defined as:

m0 m0

x=

(2)

mt mf

(8)

By solving the Eqs. (6)–(8),

where Wexp.represents an experimental mass loss of the sample, W represents theoretical weight loss of samples, Wcalc. = theoretical mass loss of the sample based on the weight averaged sum of the isolated samples, which is calculated as:

Wcalc . = x p WP + x C WC

i i Wcalcu )/ Wcalcd ]2

2.5. FTIR analysis

2.3. Thermal analysis

Wcalc .

i [(Wexp tl

where n represents a number of ΔW points. Further, to identify the optimum loading of biomass to plastics, thermal pyrolysis of SS and PET at (1:1, 3:1, and 5:1) were carried out in a semi-batch reactor at 500 °C temperature, 80 °C min−1 heating rates, and 100 mL min−1 nitrogen gas flow rates.

where VM represents the percentage of volatile matter, Mc represents the percentage of moisture, FC represents the percentage of fixed carbon and As represents the percentage of ash. The ultimate analysis was carried out in an elemental analyzer (Euro EA3000, Euro Vector, Italy). The heating value of samples were determined by using oxygen bomb calorimeter (Parr, Model: 1341 Plain Jacket Calorimeter). The bulk density of biomass/plastic were determined by using digital balance and graduated cylinder. All the experiments were repeated thrice and average data is reported. The extractive content of the biomass was determined using a Soxhlet apparatus where hexane and ethanol were used as a solvent while thermogravimetric analyzer (TGA) was used for determination of the chemical composition of biomass.

W = Wexp.

n i=1

RMS =

dx = ko e dt

( RTE ) (1

x )n

(9)

The heating rate is an important process parameter of pyrolysis. Therefore, the heating rate (β) is written as

(3)

where, x p and x C represent the mass ratio of the SS, PET, and blends. WP and WC represent the mass loss of SS, PET, and blends at the same experimental conditions. It was reported that the positive value of W accelerated formation of volatiles, while the negative value of W inhibited the formation of volatiles, which was defined as less than zero. In addition, the root mean square (RMS) value of the ΔW can be utilized

=

dT dT dx = × dt dx dt

(10)

After solving Eqs. (9) and (10),

g( ) =

3

x 0

dx = f (x )

T 0

A

e

(E / RT )

dT

(11)

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R.K. Mishra, et al.

g( ) =

AE R

x 0

2

u

e udu =

AE p (x ) R

of the straight line while the frequency factor was calculated by using intercept.

(12)

where g ( ) = integral conversion and x = RT . However, p (x ) has no exact solution; therefore, the solution can be achieved through numerical approximation method. p (x ) is varied corresponded to the type of approximation selected for simplifying. E

2.6.5. Coats-Redfern method (CR) The Coats-Redfern model is an integral method generally used for calculation of order of reaction (n) and pre-exponential factor (A) (Damartzis et al., 2011). In this study, the CR model was used to calculate the frequency factor at a heating rate of 10 °C min−1. When selecting the order of reaction (n), the rate of reaction can be written as,

2.6.1. Kissinger–Akahira–Sunose (KAS) Kissinger–Akahira–Sunose (KAS) is an iso-conversional method employ to calculate the kinetic energy of the material. By applying an approximation of p (x ) = x 2e x in Eq. (12) and solving;

ln

T2

= ln

AE Rg (x )

E RT

dx A ( E) = e RT (1 dT

(13)

(1

( )

1

ln

(14)

2.315

(15)

2.6.3. Friedman method The first and more general iso-conversional method is the Friedman method, which is used for kinetic analysis of samples. This method is based on the differential methods which minimize the probability of error. The Friedman equation is written as:

E + ln (Af (x )n) RT

Kinetic plot between ln

cept ofln(A f ( )n) .

( ) vs. 1/T provides slope dx dt

TS

= CS

T1.8

= CS

ln

T

e( RT ) dT E

(21)

(1

x )1 n)

n

AR 1 E

2RT E

E RT

(for n

2RT E

E RT

(for n = 1)

1)

(22)

ln(1 x ) T2 AR 1 E

(22)

1

(1 T 2 (1

x )1 n)

n

= ln

AR E

E RT

(for n

1)

(16) E RT

(23)

and inter-

ln

ln(1 x ) T2

= ln

AR E

E RT

(for n = 1)

(24)

where n represents the order of reaction, T represents absolute temperature (K), β represents heating rate (°C min−1), R represents Gas constant (J mol−1K−1), E represents activation energy (kJ mol−1), and A represents pre-exponential factor (min−1) respectively. The kinetic

plot between ln 1 versesT

{

1

(1 x )1 n T 2 (1 n )

}

versus

2RT E

1 T

for n ≠ 1 and ln

{

ln(1 x ) T2

}

{ (1 ) } . < < 1, intercepts will become ln { }. Thus, using

for (n = 1) provides slope

If assume that

E R

and intercept ln

AR E

2RT E

AR E

this intercept, calculation of pre-exponential factor and order of reaction can be carried out.

(17)

The change in the enthalpy, Gibbs free energy, frequency factor, and entropy can be calculated by thermodynamic parameters. The change in enthalpy (ΔH) indicated nature of reaction process (endothermic or exothermic) and change in entropy (ΔE) indicated reactivity of reaction system, however, the change in Gibbs free energy (ΔG) indicated how far the system is from its thermodynamic equilibrium. Thermodynamic parameters such as pre-exponential factor (A), Enthalpy (ΔH), Gibbs energy (ΔG) and change in Entropy (ΔS) were calculated by the

E RT

(18)

Eq. (18) is known as Starink method. The activation energy and preexponential factor were calculated by plotting a linear graph between ln 1.8 and 1 . The activation energy was calculated by using the slope

( )

T 0

2.7. Thermodynamic analysis (TD)

BE RT

1.0037

A

=

However, the term < < 1; hence, it can be excluded. With this assumption simplifying Eqs. (21) and (22), the equation will now become:

By putting S = 1.8 and B = 1.003 in Eq. (15) can be written as

ln

n

2RT E

2.6.4. Starink method Starink model is considered as non-isothermal model-free method and slightly different from the KAS and OFW models. ST method function of conversion value and does not provide a single kinetic parameter. It was also reported that it produced higher accuracy of activation energy compared to other model-free methods such as KAS, OFW, FM (Gai et al., 2013). Ozawa's method is less accurate than Kissinger's method due to one order of magnitude, however, Kissinger's method delivers 0.05% of error, while ST method produces five times more accurate results (Starink, 1996). The equation derived from approximation can be written as,

ln

x )1 n

T 2 (1

= ln

T

=

1

ln

The activation energy and pre-exponential factor were calculated by plotting graph betweenLn ( ) verses 1 .

dx ln dt

(1 1

= ln

E 0.457 RT

(20)

An integral part of Eq. (21) has no exact solution. Therefore, by applying asymptotic series and neglecting higher order terms, solution will become

By substituting Doyle’s approximation in Eq. (12),

AE Ln ( ) = Ln Rg (x )

dx A ( E) = e RT dt x )n Integrating Eq. (20) gives

2.6.2. Ozawa–Flynn–Wall (OFW) Ozawa–Flynn–Wall (OFW) method is known as the model-free method used to calculate activation energy and frequency factor of material by using Doyle’s approximation which is:

2.315 + 0.457x

(19)

On arranging equation, it becomes

Eq. (13) is known as Kissinger–Akahira–Sunose (KAS) model. The kinetic plot between ln 2 versus 1 will give slope and intercepts T T which are further used to calculate activation energy and frequency factor.

p (x ) =

x )n

T

4

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following equations.

3.2. Thermal analysis

× E × e ( RTm ) R Tm2 E

A =

H = E

S =

(26)

RTm

Go = E + R × Tm × ln

KB Tm hA

Go

H

Thermal analysis of biomass was carried out in a TGA analyzer. From results, it was found that the biomass underwent three major stages of degradation such as drying stage, devolatilization or active pyrolytic stage and char formation or passive pyrolytic stage. Hazelnut husk biomass was studied by Ceylan and Topçu, (2014) (Ceylan and Topçu, 2014) and different types of sawdust were studied by Mishra and Mohanty (2018b) (Mishra and Mohanty, 2018b) in a TGA analyzer and reported that these biomass degraded through three major stages. At the first stage, moisture and light volatile matter evaporated upto 150 °C. In the second stage (150 °C−600 °C), the continuous supply of heat fragmented higher molecular weight compounds into lower molecular weight compounds. In this stage, cellulose, and hemicellulose majorly decomposed and released maximum hot volatiles which involved condensable and non-condensable gases, therefore this stage is known as the active pyrolytic stage. Finally, at the third stage (> 600 °C), lignin decomposed at a slower rate at high temperature (> 500 °C) aided by the presence of hydroxyl phenolic compounds which increased the thermal stability of biomass (Mishra and Mohanty, 2018b). Moreover, the presence of a higher percentage of lignin in sample resulted in the formation of char which can be utilized in different applications (Mishra and Mohanty, 2018d). From DTG thermograph (Fig. 1) of SS biomass, it was confirmed that the first peak was due to the removal of water and light volatile compounds. Further, the second peak appeared due to the decomposition of hemicellulose, the third and fourth peaks were due to the decomposition of cellulose. However, maximum decomposition occurred during 350 °C−500 °C. The thermal results confirmed that 3.70% and 0.18% decomposition occurred in the first stage, 68.61% and 80.60% happened in the second stage and finally, 2.22% and 3.70% in the third stage took place for SS

(25)

Tm

(27) (28)

where A is the pre-exponential factor (s−1), Tm is the maximum peak decomposition temperature in (K), KB is Boltzmann constant, and h is Plank constant 6.626 × 10 34 Js . 3. Results and discussion 3.1. Physicochemical characterization of SS and PET The physicochemical characterization of biomass (SS) and plastic waste (PET) were compared with other biomass such as neem seeds (Mishra and Mohanty, 2018d), jatropha seed (Kongkasawan et al., 2016), mustard stalk (Raj et al., 2015) and plastic waste such as polyethylene terephthalate (Chen et al., 2017). The proximate results confirmed that the biomass and plastic had higher volatile matter (79.98−91.74%) and lower ash content (0.19−4.11%) than other reported biomass and plastic. Higher volatile matter and lower ash content confirmed that the ignition efficiency of fuel will be easy. Moreover, the heating value of fuel displays inverse proportionality with ash content, which means lower the ash content higher the heating value (Doshi et al., 2014; Mishra and Mohanty, 2018a). Furthermore, higher ash content increases the processing cost and disposal problems, while there is a reduction in combustion and conversion efficiency (Sait et al., 2012). In addition, the presence of higher volatile matter confirmed the formation of hot volatiles during pyrolysis. Moisture was found to be 5.32% which was lower than its threshold limit (< 10%), made SS more suitable for pyrolysis. The biomass SS has higher fixed carbon (10.77%) than PET (8.04%) which can be utilized for various applications such as solid fuel, fertilizers, soil amendments, bio-adsorbents for water and wastewater treatment, making of carbon nanotubes, etc., (Mishra and Mohanty, 2018d). Further, elemental analysis of SS and PET revealed higher carbon content (49−65.80%) along with lower nitrogen and sulphur content which is significant in comparison to other reported biomass and plastics. The lower amount of nitrogen (6.30%) and sulphur indicated the formation of SOx and NOx will be lower during combustion. In addition, lower sulphur percentage indicates corrosion will be lower during boiler and engine application. Presence of higher percentage carbon and hydrogen showed the higher heating value of fuel since heating value depends on the elemental composition of samples. The molar ratio of SS and PET displays higher H/C and O/C ratio which indicates both samples have higher biofuel reactivity (Mishra and Mohanty, 2018a) than other reported biomass. Further, SS and PET samples have higher gross heating value (17.21 MJ kg−1 and 24.50 MJ kg−1 for SS and PET respectively). The bulk density of SS was higher (664 kg m−3) than PET (453 kg m−3) which indicated that the storage and transportation of SS biomass will be easier than PET. The results confirmed that SS biomass had higher extractive contents (31%) which indicate that the production of liquid fuel will be higher during pyrolysis (Mishra and Mohanty, 2018d). The chemical analysis of biomass SS showed a higher percentage of hemicellulose (29%), cellulose (31%) and a lower percentage of lignin (10%) which made SS more attractive for pyrolysis. The present characterization results are in good agreement with other reported biomass.

Fig. 1. Thermal analysis of (a) SS biomass (b) PET. 5

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Fig. 2. Effect of heating rates on biomass SS, PET and their blends at different heating rates (10 °C min−1, 30 °C min−1, and 50 °C min−1).

Fig. 3. Effect of dynamic heating rates (10 °C min−1, 30 °C min−1, and 50 °C min−1) on DTG profile of SS, PET and different blends.

and PET respectively.

pattern. The alteration in the TGA curve occurred due to the formation of thermal lag (temperature gradient) throughout the cross-section of biomass since biomass is a poor conductor of heat. At lower temperature, temperature profile along the cross-section of biomass was assumed to be linear as the outer surface, and the inner core of the biomass achieved the same temperature at a specific time, as enough time was allowed for heating. On the other hand, at higher heating rate, the temperature profile was markedly different from the inner core to the outer core along the cross-section of biomass. This was probably due to short residence time which did not allow enough time for interaction

3.3. Effect of heating rates The thermal decomposition profile of SS, PET and their blends (1:1, 3:1 and 5:1) were studied in TGA analyzer with dynamic heating rates (10 °C min−1, 30 °C min−1, and 50 °C min−1) under non-isothermal condition and shown in Figs. 2 and 3. From Fig. 2, it was confirmed that with increasing heating rates, thermal decomposition profile shifted at higher temperature zone without altering thermal decomposition 6

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Fig. 4. Variation trends of the ΔW versus pyrolytic temperature with (a) different blends of SS + PET and (b) root mean square value of different blends of SS + PET.

between two particles. Therefore, the evolution of volatile matters reduced at higher rate of heating (Maiti et al., 2007). In addition, the effect of the heating rate of the secondary reactions of the primary pyrolysis products such as tar and high molecular weight compounds may be the other possible reason. Higher heat and mass transfer occurred at a lower heating rate inside the particle surface. Moreover, lower heating rates reduced the turbulence which in turn projects a sharp TG profile. The dynamic increase in heating results in a maximum rate of decomposition because of the increase in thermal energy. From DTG thermograph (Fig. 3), it was noticed that peak temperature shifted to the higher temperature zones (185 °C, 190 °C, 193 °C for SS; 375 °C, 399 °C, 411 °C for PET; 167 °C, 180 °C, 195 °C for SS + PET (1:1); 170 °C, 180 °C, 192 °C for SS + PET (3:1) and 176 °C, 184 °C, 197 °C for SS + PET (5:1)) respectively. Similar profiles were reported by Mishra and Mohanty (2018b) and Chandrasekaran et al. (2017). Mishra and Mohanty (2018a–d) demonstrated that with increasing heating rates from 5 °C min−1, 10 °C min−1, 15 °C min−1, 20 °C min−1, and 25 °C min−1 the decomposition peak shifted to higher temperature region. Chandrasekaran et al., (2017) reported pyrolysis of Prosopis juliflora in a TGA analyzer at multiple heating rates 2 °C min−1, 5 °C min−1, 10 °C min−1, 15 °C min−1, 20 °C min−1, and 25 °C min−1 and concluded that with increasing heating rates decomposition peak temperature shifted to higher temperature region (315 °C, 328 °C, 339 °C, 345 °C, 348 °C, and 356 °C, respectively). From the TGA curve, it was also noticed that with increasing heating rates, decomposition temperature ranges of cellulose, hemicellulose, and lignin also increased. Form results it was noticed that overall volatile conversion of SS in the active zone was found (67.70%, 68.06%, 68.15%) at heating rates of 10 °C min−1, 30 °C min−1, and 50 °C min−1. Similar pattern was also found for PET (80.46%, 80.56%, 80.66%), SS + PET (1:1) (70.15%, 70.93%, 70.97%) SS + PET (3:1) (68.05%, 68.13%, 68.16%) and SS + PET (5:1) (66.41%, 66.95%, 67.00%) respectively. TGA curve at 10 °C min−1 confirmed that pure biomass SS had 67.71% decomposition while PET had 80.46% decomposition, however, the blend of SS + PET (3:1) significantly increased (68.05%) the decomposition in the active stage. From the results, it was also noticed that with an increase in heating rates volatile products also increased. However, at lower heating rates total residence time increased, giving rise to the formation of secondary reactions such as re-polymerization and recondensation which finally lead to the formation of char (Maiti et al., 2007). The degradation reaction kinetic is very complicated for the biomass, which formed resistance at lower heating rates, while at higher heating rates this resistance was probably reduced due to higher heat and mass transfer between the materials which again favored higher conversion. It is worthy to mention that the above hypothesis is valid for a particular biomass, its size as well as operating conditions (Maiti et al., 2007).

3.4. Synergistic effect and root mean square (RMS) value The calculation of synergistic effect becomes vital when two different materials are mixed and pyrolyzed. The synergistic effect deals with the positive or negative interaction between particles of materials. From Fig. 4(a), a negative synergistic effect was noticed up to temperature 480 °C while beyond temperature 500 °C positive synergistic effect was noticed which confirmed that more hot volatiles was released during pyrolysis at temperature > 500 °C. Furthermore, blend of SS + PET (1:1) showed the more synergistic effect as compared to SS + PET (3:1) and SS + PET (5:1) respectively, due to higher carbon and hydrogen ratio or higher interaction between particles of biomass and plastic waste. The blending ratio SS + PET (1:1) was not suitable for pyrolysis because it produced lower liquid yield (34 wt%) and very viscous fluid (almost semi-solid) which almost choked the condenser although the calorific value and carbon content were higher. Similarly, blend of SS + PET (5:1) was also not suitable for pyrolysis since it resulted in inferior fuel properties, however, blend of SS + PET (3:1) resulted in good quality fuel (lower viscosity, moisture, and higher heating value, and carbon content) with significant yield (54.24 wt%). Therefore, the blend of SS + PET (3:1) was considered as the optimum ratio for pyrolysis. Further, root mean square value indicated intense synergistic effect between SS and PET. From Fig. 4(b), it was confirmed that the blend of SS + PET (3:1) was more effective than SS + PET (1:1) and SS + PET(5:1). Our synergistic results are in good agreement with other studies (Hu et al., 2016; Meng et al., 2015; Mishra et al., 2019). 3.5. FTIR analysis The presence of the useful functional groups in SS and PET was determined by using FTIR. The adsorption bond 3200 cm−1–3739 cm−1 ascribed with eOH stretching vibration which confirmed the presence of water, aromatic, phenol, acid, protein and alcohol impurities (Chintala et al., 2017; Doshi et al., 2014). The adsorption bond 2925 cm−1 ascribed with C-H2 asymmetric alkanes while peak 2745 cm−1 attributed to axial deformation of CeH showed the presence of alkene (Doshi et al., 2014; Mishra & Mohanty, 2018c). Further, peak 1745 cm−1 attributed with C]O vibration showed the presence of ester and carboxylic acid while 1664 cm−1–1536 cm−1 attributed with C]C aromatic ring that indicated the presence of lignin and protein in biomass (Chintala et al., 2017; Sharma et al., 2004). Peak 1367 cm−1 confirmed the presence of CeH and aliphatic CeH stretching in methyl and phenol. Peak 1039 cm−1 showed the presence of acid and alkenes (Himmelsbach et al., 2002). Furthermore, the adsorption bond 701 cm−1 –500 cm−1 showed the presence of monoaromatics and alkenes. Similarly, for PET, peak 2240 cm−1 –2000 cm−1 7

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Table 1 Kinetic parameters obtained from model-free methods for SS, PET and their blends. SS KAS

FWO

Friedman

Starink

Conversion

Ea (kJ/mol)

R2

A (min−1)

Ea (kJ/mol)

R2

A (min−1)

Ea (kJ/mol)

R2

A (min−1)

Ea (kJ/mol)

R2

A (min−1)

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Avg.

123.24 145.10 156.64 164.70 192.48 257.91 253.96 212.28 188.28

0.9953 0.9918 0.9949 0.9907 0.992 0.9994 0.9964 0.9993

8.07800E + 08 4.62137E + 10 1.51081E + 11 2.74017E + 11 3.03415E + 13 3.10718E + 18 2.09366E + 17 1.13229E + 13 4.14574E + 17

125.11 146.38 157.81 165.86 192.60 255.14 249.64 205.09 187.20

0.9959 0.9928 0.9955 0.9918 0.9928 0.9994 0.9963 0.9986

1.41729E + 15 6.60993E + 16 2.07262E + 17 3.69604E + 17 3.23761E + 19 2.00131E + 24 1.01028E + 23 3.43146E + 18 2.62797E + 23

139.15 150.39 169.61 178.95 204.08 280.94 262.35 254.04 204.93

0.9895 0.9928 0.9962 0.9918 0.988 0.9999 0.9923 1

2.18281E + 12 7.59917E + 12 1.55563E + 14 4.12094E + 14 2.3376E + 16 1.12491E + 22 3.87486E + 19 4.47246E + 17 1.41104E + 21

123.62 145.45 156.99 165.06 192.78 258.00 253.90 211.83 188.45

0.9953 0.992 0.995 0.9908 0.992 0.9994 0.9964 0.9992

3.26226E + 10 8.90360E + 11 1.84084E + 12 2.34984E + 12 1.93173E + 14 1.50366E + 19 7.46371E + 17 2.77108E + 13 1.9729E + 18

0.9998 0.9989 0.9971 0.9952 0.9939 0.9957 0.9977 0.9961

5.67285E + 11 1.17923E + 13 2.9612E + 13 5.1906E + 13 2.14597E + 14 1.69413E + 14 5.56027E + 13 1.99336E + 13 6.91779E + 13

FWO 211.37 226.44 230.83 233.57 241.36 239.93 233.66 227.25 230.55

0.9998 0.999 0.9974 0.9957 0.9945 0.9962 0.9979 0.9965

6.68025E + 17 1.23558E + 19 3.03543E + 19 5.26855E + 19 2.06386E + 20 1.66797E + 20 5.8575E + 19 1.76261E + 19 6.8181E + 19

Friedman 208.58 229.17 233.78 234.13 236.50 226.35 224.46 212.16 225.64

0.9947 0.9959 0.992 0.9827 0.9825 0.9937 0.9935 0.9992

7.83834E + 13 4.9248E + 15 1.66481E + 16 2.00554E + 16 3.12716E + 16 5.58901E + 15 3.51082E + 15 2.85678E + 14 1.02955E + 16

Starink 211.08 226.75 231.26 234.05 242.15 240.58 233.94 228.50 231.03

0.9998 0.9989 0.9971 0.9953 0.994 0.9958 0.9977 0.9961

2.44E + 13 2.41E + 14 3.78E + 14 4.63E + 14 1.41E + 15 8.46E + 14 2.11E + 14 5.54E + 13 4.54217E + 14

0.9824 0.9531 0.9392 0.9266 0.9629 0.9801 0.9626 0.9762

4.540E + 08 2.207E + 10 1.562E + 11 4.375E + 10 2.501E + 10 3.169E + 10 2.068E + 10 7.827E + 09 3.84563E + 10

FWO 125.69 151.83 168.71 176.50 180.28 183.75 183.35 178.45 168.57

0.9858 0.9601 0.9416 0.9349 0.9692 0.9846 0.9705 0.9818

5.418E + 14 2.219E + 16 9.231E + 16 7.431E + 16 5.787E + 16 6.718E + 16 4.49E + 16 1.123E + 16 4.6317E + 16

Friedman 137.11 153.93 163.41 177.29 188.28 205.61 201.63 200.74 178.50

0.9854 0.9545 0.9213 0.9262 0.9546 0.9894 0.9801 0.9751

2.141E + 11 1.305E + 12 1.48E + 12 4.684E + 12 2.866E + 13 4.417E + 14 1.518E + 14 3.533E + 13 8.31438E + 13

Starink 125.68 152.40 168.16 174.56 176.80 180.62 180.10 177.38 166.96

0.9868 0.9538 0.9331 0.9275 0.9636 0.9806 0.9635 0.9769

1.766E + 10 4.163E + 11 8.86E + 11 3.888E + 11 1.697E + 11 1.619E + 11 8.058E + 10 2.178E + 10 2.67834E + 11

0.9999 0.9987 0.999 0.99 0.9914 0.9939 1 1

8.48E + 06 3.7E + 07 2.3E + 08 9.5E + 08 1.4E + 09 5.3E + 07 1.9E + 07 2.43E + 06 3.36E + 07

FWO 104.40 117.67 130.86 143.58 151.46 139.16 136.47 126.03 131.20

0.9997 0.9988 0.9982 0.9921 0.9924 0.9949 0.9999 1

1.41323E + 13 7.9777E + 13 4.17381E + 14 6.32348E + 14 2.65241E + 15 1.05321E + 14 3.17401E + 13 2.70858E + 12 4.91978E + 14

Friedman 102.69 118.4 140.61 142.16 150.47 154.97 152.43 146.75 138.56

1 0.9964 0.9975 0.9911 0.9876 1 0.9942 0.996

3.7221E + 08 3.8924E + 09 9.4631E + 09 9.1501E + 10 1.3485E + 11 1.4905E + 11 5.1283E + 10 4.3388E + 09 5.5604E + 10

Starink 103.43 115.57 129.21 141.74 149.30 137.04 135.35 127.47 129.88

0.9998 0.9987 0.9989 0.9903 0.9915 0.994 1 1

3.28E + 08 7.19E + 08 2.76E + 09 8.12E + 09 8.98E + 09 2.54E + 08 6.81E + 07 6.16E + 06 2.65E + 09

0.9842 0.9904 0.9918 0.9888 0.9937 0.9866 0.9849 0.9801

5.72E + 07 1.61E + 08 1.811E + 09 6.092E + 09 3.879E + 11 1.283E + 11 1.241E + 10 1.161E + 09 6.72E + 10

FWO 111.75 122.85 138.83 150.97 177.19 174.87 169.09 161.11 150.83

0.9819 0.9922 0.9942 0.9924 0.9957 0.9876 0.9869 0.9828

6.327E + 13 2.487E + 14 2.375E + 15 8.649E + 15 4.652E + 17 1.254E + 17 1.381E + 16 9.325E + 14 7.70961E + 16

Friedman 112.66 128.75 142.09 154.97 186.77 179.62 179.05 178.71 157.82

0.9793 0.9904 0.994 0.9884 0.9862 0.9947 0.9970 0.9625

3.115E + 09 3.932E + 10 2.806E + 11 1.382E + 12 1.79E + 14 1.929E + 13 6.643E + 12 7.224E + 11 2.59218E + 13

Starink 112.09 121.93 138.40 150.10 176.84 175.63 169.35 163.32 150.95

0.984 0.9906 0.9921 0.9892 0.9939 0.9867 0.9851 0.9804

2.156E + 09 3.038E + 09 2.141E + 10 5.158E + 10 2.455E + 12 6.007E + 11 4.447E + 10 2.985E + 09 3.9772E + 11

PET 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 Avg.

KAS 210.70 226.41 230.92 233.72 241.83 240.26 233.59 228.26 230.71

SS + PET (1:1) KAS 0.1 125.46 0.2 152.21 0.3 171.76 0.4 174.05 0.5 176.09 0.6 179.96 0.7 179.42 0.8 176.96 Avg. 166.98 SS + PET (3:1) KAS 0.1 103.14 0.2 115.14 0.3 128.80 0.4 141.28 0.5 148.79 0.6 136.57 0.7 135.00 0.8 127.42 Avg. 129.51 SS + PET (5:1) KAS 0.1 111.93 0.2 121.62 0.3 138.12 0.4 149.75 0.5 176.51 0.6 175.43 0.7 169.09 0.8 163.30 Avg. 150.71

attributed with eCOOe group which showed the presence of ester (Chintala et al., 2017). From FTIR results, it was confirmed that both samples are good feedstock’s for pyrolysis.

Mishra & Mohanty, 2018b). The average activation energies, calculated from KAS, OFW, FM and ST methods were 188.28 kJ mol−1, 187.20 kJ mol−1, 204.93 kJ mol−1, 188.45 kJ mol−1 for SS, 230.71 kJ mol−1, 230.55 kJ mol−1, 225.64 kJ mol−1, 231.03 kJ mol−1 for PET, 166.98 kJ mol−1, 168.57 kJ mol−1, 178.50 kJ mol−1, for SS + PET (1:1), 129.51 kJ mol−1, 166.96 kJ mol−1 131.20 kJ mol−1, 138.56 kJ mol−1, 129.88 kJ mol−1 for SS + PET (3:1), and 150.71 kJ mol−1, 150.83 kJ mol−1, 157.82 kJ mol−1, 150.95 kJ mol−1 for SS + PET (5:1) respectively. The correlation coefficient was also found to be higher than 0.95 which implied bestfitted value with experimental data (Table 1). It was also noticed that

3.6. Kinetic analysis Iso-conversational model-free methods such as KAS, OFW, Friedman, Starink, and Coats-Redfern were used to determine the kinetic parameters such as activation energy, frequency factor, and order of the reaction. During curve fitting conversion value higher than 0.8 did not fitted well due to low correlation value (Damartzis et al., 2011; 8

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Fig. 5. Variation of activation energy with conversion value.

the activation energy changed with the conversion which implied high degree of probability to present single step reaction (Mishra and Mohanty, 2018b). From results (Table 1), it was noticed that the activation energy calculated from the Friedman model was higher than others except PET. FM is an efficient and prominent model used for kinetic parameters because it is interconnected to a simple differential form of kinetic rate law (Heydari et al., 2015). It includes no oversimplified approximation, given its association to the differential method, which is only applicable for integral data (Heydari et al., 2015). The average activation energy reported by FM was higher than others such as KAS, OFE, and ST because reaction kinetics and reaction mechanism are probably less than homogenous. The average activation energy calculated from FM was lower than other models for PET because the maximum decomposition occurred within 600 °C (0.6 conversion value); beyond 600 °C there was nothing to decompose therefore activation energy was reduced. The variation of activation energy with respect to conversion value from 0.1 to 0.8 is reported in Table 1. The minimum amount of energy required to start the process or reaction is known as activation energy. Activation energy corresponds to pyrolysis reaction kinetics and reaction mechanism. A higher value of activation energy represents a slower reaction (Mishra and Mohanty, 2018b). Moreover, the reactivity of fuel can be calculated from the activation energy since the reactivity of any fuel has a significant effect on pyrolysis (Gai et al., 2013). In addition, it helps in the optimization of various process parameters and designing of new pyrolyzer (Mishra and Mohanty, 2018b). Pyrolysis process is a continuous process and with an increase in temperature, hot volatiles increases due to higher heat and mass transfer between materials. From results (Table 1), it can be seen that individual pyrolysis of SS and PET required higher average activation energy (188.28 kJ mol−1, 187.20 kJ mol−1, −1 −1 204.93 kJ mol , and 188.45 kJ mol for SS, 230.71 kJ mol−1, 230.55 kJ mol−1, 255.64 kJ mol−1, 231.03 kJ mol−1 for PET for KAS, OFW, FM and ST model. Moreover, individual activation energy corresponded to conversion value, which implies lower decomposition reaction. However, blends of SS and PET have lower average activation energy which confirmed that blending of SS and PET reduced the need of higher energy (Table 1). Among blends, 3:1 required lower activation energy (129.51 kJ mol−1, 131.20 kJ mol−1, 138.56, 129.88 kJ mol−1 for KAS, OFW, FM, and ST model respectively) to start the reaction

which implied that this blend can decompose materials at higher rates than other combinations. CR model works on single heating rate and usually used for estimation of the order of reaction. Recently, this model was used for calculation of pyrolysis reaction mechanism by adopting various models such as diffusion, Avrami, power law, etc., Therefore, in this study, all the calculations were carried out at 10 °C min−1. It is very essential to mention that the order of reaction and frequency factor obtained from the model-free methods has no physical significance and was only used for fitting parameters (Damartzis et al., 2011). The order of reaction was calculated from CR methods by trial and error by placing various value of the order of reaction (n) (n = 0, 1, 2, 3, 4,…). The activation energy was found to be 25.76 kJ mol−1, 22.56 kJ mol−1, 24.90 kJ mol−1, 22.20 kJ mol−1 and 23.18 kJ mol−1 at n = 1 while 34.50 kJ mol−1, 340.55 kJ mol−1, 16.46 kJ mol−1, 21.06 kJ mol−1 and 22.09 kJ mol−1 at n not 1 for SS, PET, SS + PET (1:1), SS + PET (3:1), SS + PET (5:1) respectively. The order of reaction was found to be 1.7, 2.3, 0.1, 0.88 and 0.9 for SS, PET, SS + PET (1:1), SS + PET (3:1), SS + PET (5:1) respectively. The results confirmed that blend 3:1 required lower activation energy to initiate the reaction than other blends including individual materials as well. From the results, it was also noticed that with an increase in the order of reaction, activation energy increased. The activation energy of PET was found to be higher than others. It is noticeable that the energy required to breakdown the structure of plastic was higher than the biomass. The decomposition of biomass starts at a lower temperature than plastics. Further, the decomposition of biomass went through three main stages and the activation energy needed was found to be lower than that required for plastics. Moreover, these differences significantly explain the structural difference in biomass and plastics (Çepelioğullar and Pütün, 2013). The correlation coefficient (R2) was also found to be significant (> 0.95) which showed the best-fitted curve. The calculated average activation energies results are in good agreement with other reported studies (Mishra and Mohanty, 2018b; Varma and Mondal, 2016). The obtained apparent activation energy was compared with other biomass such as Prosopis juliflora (Chandrasekaran et al., 2017), hazelnut husk (Ceylan and Topçu, 2014), walnut shell, and peach stones (Özsin and Pütün, 2017) and plastics such as polystyrene, peach stones/ polystyrene, walnut shell/ polystyrene (Özsin and Pütün, 2017), 9

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3.7. Thermodynamic analysis (TD)

Table 2 Thermodynamic analysis of SS, PET and their blends. Material

Conversion

A (1/s)

ΔH (kJ/ mol)

ΔG0 (kJ/ mol)

ΔS (J/mol.K)

SS

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

4.52E + 05 4.32E + 07 4.77E + 08 2.55E + 09 7.92E + 11 5.38E + 17 2.32E + 17 4.04E + 13

118.66 140.49 152.03 160.10 187.82 253.04 248.94 206.87

208.66 207.86 207.48 207.23 206.46 205.01 205.09 205.99

−150.73 −112.82 −92.86 −78.93 −31.22 80.42 73.43 1.47

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

9.61E + 16 2.41E + 10 3.63E + 11 7.93E + 11 1.28E + 12 5.21E + 12 3.97E + 12 1.26E + 12 4.92E + 11

183.49 205.15 220.82 225.33 228.12 236.22 234.65 228.01 222.57

206.72 249.18 248.76 248.64 248.57 248.37 248.41 248.57 248.71

−38.90 −61.74 −39.17 −32.69 −28.67 −17.03 −19.29 −28.83 −36.66

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

1.67E + 12 1.91E + 04 2.49E + 06 4.3E + 07 1.4E + 08 2.1E + 08 4.1E + 08 3.8E + 08 2.3E + 08

225.11 119.97 146.69 162.45 168.85 171.09 174.91 174.39 171.67

248.65 242.42 241.32 240.76 240.55 240.47 240.35 240.37 240.45

−33.01 −178.21 −137.72 −113.97 −104.34 −100.98 −95.24 −96.02 −100.11

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

1.76E + 08 7.00E + 04 9.10E + 04 1.60E + 06 2.2E + 07 1.1E + 08 8.27E + 06 5.80E + 06 1.11E + 06

161.25 98.48 110.62 124.26 136.79 144.35 132.09 130.40 122.52

240.84 208.80 208.25 207.70 207.24 206.98 207.40 207.47 207.76

−115.82 −185.36 −164.03 −140.19 −118.37 −105.23 −126.54 −129.49 −143.23

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

1.80E + 07 6.11E + 04 4.99E + 05 1.70E + 07 2.01E + 08 5.6E + 10 4.3E + 10 1.1E + 10 3.2E + 09

124.94 107.21 117.05 133.52 145.22 171.96 170.75 164.47 158.44

207.70 205.40 204.99 204.37 203.97 203.17 203.21 203.38 203.56

−139.05 −167.23 −149.77 −120.67 −100.07 −53.16 −55.28 −66.28 −76.85

1.42E + 10

146.08

204.01

−98.67

Average PET

Average SS + PET(1:1)

Average SS + PET(3:1)

Average SS + PET(5:1)

Average

Thermodynamic analysis such as Enthalpy, Entropy, Gibbs free energy and frequency factor were calculated using apparent activation energy calculated from ST methods. ST methods provide more accurate activation energy than other model-free methods (Gai et al., 2013) therefore this model was used for TD calculation. All the thermodynamic parameters were calculated at 10 °C min−1 and listed in Table 2. The frequency factor calculated using ST model varied from 7.00E + 03 s−1–5.21E + 12 s−1. The variation of frequency factor (101–1017 s−1) from 0.1 to 0.8 indicated the complex composition and complex reaction occurred during thermal degradation of materials. The lower value of frequency factor (A > 109) implied surface reaction in most of the cases, but it does not correspond to the surface area. The lower value of the frequency factor implied closed junction (very near to activated complex) (Yuan et al., 2017). Further, the higher value of the frequency factor (A ≥ 109) implied a simple complex (losses junction) (Yuan et al., 2017). The apparent activation energy was probably limited with rotation compared to the initial reagent when the frequency factor varied from 1010–1012 s−1. For an individual molecule, the activated complex is expected to increase by size and interaction will be more intense. The value of the frequency factor increased with increase in conversion value which indicated the reliability of calculated activation energy value. The amount of energy exchanged during a chemical reaction is known as enthalpy. The enthalpy difference implies the energy difference between the activated complex and the reagent (Xu and Chen, 2013). From the results (Table 2), enthalpy increased with increase in conversion value. The activation energy compared with enthalpy showed a little potential energy barrier (4.96 kJ mol−1, 5.93 kJ mol−1, 5.71 kJ mol−1, 4.95 kJ mol−1 and 4.88 kJ mol−1 for SS, PET, SS + PET (1:1), SS + PET (3:1) and SS + PET (5:1) respectively which indicated the viability of reaction at the given conditions. The slight difference between the activation energy and enthalpy calculated in this study replicates that the formation of the activation complex is being favored (Xu and Chen, 2013). Enthalpy is the energy consumed during the thermochemical conversion of materials to produce varied end products such as liquid, gas, and biochar. The activation energy was close enough to enthalpy (4 kJ mol−1–6 kJ mol−1), which implied that by expending a small amount of energy, product formation will be achieved. The obtained results are in good agreement with other reported studies (Khan et al., 2016; Mehmood et al., 2017; Xu and Chen, 2013). The entropy value of SS, PET, and their blends at 1:1, 3:1 and 5:1 had more negative (−185.36 J mol−1 K−1) entropy and less positive value (80.00 J mol−1 K−1). The negative value of entropy implied the degree of disorderness of the products is much lower than the biomass. In this condition, the material showed little reactivity and take more time to form an activated complex. On the other side, the higher value of entropy implies that the material is too far from its own thermodynamic equilibrium. In this situation, the reactivity is higher and implies reduced the time to form the activated complex which results in short reaction times (Turmanova et al., 2008). The calculated results have similarity with other studies (Khan et al., 2016; Mehmood et al., 2017; Xu and Chen, 2013). Gibbs energy implies the amount of energy available from the material. The change in Gibbs free energy implies that the total energy increase of the system at the approach of the reagents and the formation of the activated complex (Turmanova et al., 2008). The Gibbs free energy alters from 205.99 kJ mol−1–208.66 kJ mol−1, 248.71 kJ mol−1 –249.18 kJ mol−1, 240.45 kJ mol−1–242.42 kJ mol−1, 207.76 kJ mol−1–208.80 kJ mol−1 and 203.56 kJ mol−1–205.40 kJ mol−1 for SS and PET and their blend ratios 1:1, 3:1 and 5:1 respectively. Similar results were also found for rice straw and chicken manure (Xu and Chen, 2013).

paulownia wood/polypropylene (3:1), paulownia wood /polyvinyl chloride (3:1), paulownia wood/poly-ethylene terephthalate, (3:1) (Chen et al., 2017) and cellulose and polypropylene (80:20) etc., (Suriapparao et al., 2014). The calculated apparent activation energy has similarity with other reported studies. It was noticed that there was a little variation in activation energy with other reported studies because of different biomass composition. Furthermore, different mathematical calculations and experimental conditions may be other possible reasons. It was also noticed that the calculated activation energy increased with increase in conversion value upto 50% and then started decreasing due to the decomposition of individual components of biomass such as cellulose, hemicellulose, lignin, extractive and other miner components. Additionally, adopting different types of approximation might be the other possible reason (Mishra and Mohanty, 2018b). The alteration in the activation energy versus conversion value along with standard deviation is presented in Fig. 5. From this figure, it was clear that ST model had lower possibility of error than other models. Furthermore, alteration in the activation energy with respect to models occurred due to adopted approximations.

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4. Conclusion

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Co-pyrolysis of PET with SS was examined in this study to understand the kinetics and synergistic effects. Physicochemical characterization of SS and PET showed its potential for the production of fuel and value-added chemical. Blending of SS and PET at 3:1 resulted in higher synergistic and RMS value which indicated the maximum formation of hot volatiles. The kinetic analysis confirmed that individual SS and PET required higher activation energy than their blend at 3:1. Further, change in enthalpy indicated nature of the reaction, change in Gibbs energy implies the amount of energy available from the materials and change in entropy indicated reactivity of reaction system. The results confirmed that this biomass as well as biomass of similar nature can be blended with PET as well as other waste plastics so as to produce better quality and quantity of fuel in a more sustainable way. Acknowledgments The authors would like to thank the analytical laboratory, Department of Chemical Engineering and Centre for Energy, IIT Guwahati for TGA and heating value analysis. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biortech.2019.121608. References Ahmed, I., Nipattummakul, N., Gupta, A., 2011. Characteristics of syngas from co-gasification of polyethylene and woodchips. Appl. Energy 88 (1), 165–174. Babu, B., Chaurasia, A., 2003. Modeling, simulation and estimation of optimum parameters in pyrolysis of biomass. Energy Convers. Manage. 44 (13), 2135–2158. Burra, K., Gupta, A., 2018a. Kinetics of synergistic effects in co-pyrolysis of biomass with plastic wastes. Appl. Energy 220, 408–418. Burra, K., Gupta, A., 2018b. Synergistic effects in steam gasification of combined biomass and plastic waste mixtures. Appl. Energy 211, 230–236. Çepelioğullar, Ö., Pütün, A.E., 2013. Thermal and kinetic behaviors of biomass and plastic wastes in co-pyrolysis. Energy Convers. Manage. 75, 263–270. Ceylan, S., Kazan, D., 2015. Pyrolysis kinetics and thermal characteristics of microalgae Nannochloropsis oculata and Tetraselmis sp. Bioresour. Technol. 187, 1–5. Ceylan, S., Topçu, Y., 2014. Pyrolysis kinetics of hazelnut husk using thermogravimetric analysis. Bioresour. Technol. 156, 182–188. Chandrasekaran, A., Ramachandran, S., Subbiah, S., 2017. Determination of kinetic parameters in the pyrolysis operation and thermal behavior of Prosopis juliflora using thermogravimetric analysis. Bioresour. Technol. 233, 413–422. Chen, L., Wang, S., Meng, H., Wu, Z., Zhao, J., 2017. Synergistic effect on thermal behavior and char morphology analysis during co-pyrolysis of paulownia wood blended with different plastics waste. Appl. Therm. Eng. 111, 834–846. Chintala, V., Kumar, S., Pandey, J.K., Sharma, A.K., Kumar, S., 2017. Solar thermal pyrolysis of non-edible seeds to biofuels and their feasibility assessment. Energy Convers. Manage. 153, 482–492. Clarke, S., 2008. Composition of essential oils and other materials. Churchill Livingstone 123–229. Damartzis, T., Vamvuka, D., Sfakiotakis, S., Zabaniotou, A., 2011. Thermal degradation studies and kinetic modeling of cardoon (Cynara cardunculus) pyrolysis using thermogravimetric analysis (TGA). Bioresour. Technol. 102 (10), 6230–6238. Doshi, P., Srivastava, G., Pathak, G., Dikshit, M., 2014. Physicochemical and thermal characterization of nonedible oilseed residual waste as sustainable solid biofuel. Waste Manage. 34 (10), 1836–1846. Gai, C., Dong, Y., Zhang, T., 2013. The kinetic analysis of the pyrolysis of agricultural residue under non-isothermal conditions. Bioresour. Technol. 127, 298–305. Gourmelon, G. 2015. Global plastic production rises, recycling lags. New Worldwatch Institute analysis explores trends in global plastic consumption and recycling.

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