Pyrolysis of Saccharum munja: Optimization of process parameters using response surface methodology (RSM) and evaluation of kinetic parameters

Journal Pre-proof Pyrolysis of Saccharum munja: Optimization of process parameters using response surface methodology (RSM) and evaluation of kinetic parameters

Mohit Kumar, P.K. Mishra, S.N. Upadhyay PII:

S2589-014X(19)30222-1

DOI:

https://doi.org/10.1016/j.biteb.2019.100332

Reference:

BITEB 100332

To appear in:

Bioresource Technology Reports

Received date:

25 September 2019

Revised date:

4 October 2019

Accepted date:

4 October 2019

Please cite this article as: M. Kumar, P.K. Mishra and S.N. Upadhyay, Pyrolysis of Saccharum munja: Optimization of process parameters using response surface methodology (RSM) and evaluation of kinetic parameters, Bioresource Technology Reports(2019), https://doi.org/10.1016/j.biteb.2019.100332

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Journal Pre-proof Pyrolysis of Saccharum munja: Optimization of process parameters using response surface methodology (RSM) and evaluation of kinetic parameters Mohit Kumar a, P.K. Mishra b, S.N. Upadhyay c,* Department of Chemical Engineering &Technology, IIT (BHU) Varanasi Varanasi 221005, India c,*

[email protected] (Corresponding author), a [email protected] , b [email protected]

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ABSTRACT Saccharum munja, a lingo-cellulosic biomass, is a perennial grass that grows in Africa,

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Australia, South America and the Indian subcontinent. It can serve as an abundant source of

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renewable energy. Its thermochemical characteristics and thermal degradation behaviour have

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been investigated for the first time. It has high volatile matter (80.70%), low fixed carbon

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(10.91%), and good HHV (19.67MJ/kg). Its C, H, O, and N contents are 63.29, 7.84, 27.19, and 2.34%, respectively. The TGA/DTA analyses and pyrolysis are performed at a heating

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rate of 15oC/min up to 1000oC. The central composite design (CCD) and response surface methodology have been used to optimize the effects of temperature and time on the bio-oil

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yield. At the optimum conditions (T= 525oC, t = 60min), the bio-oil yield has been found to be 46.00%. The oxygenated aliphatic and aromatic compounds majorly comprise the bio-oil. Kinetic parameters are evaluated using multiple-linear regression and Coats-Redfern methods. Keywords: Saccharum munja; TGA/DTG analysis; Multiple-linear regression and CoatsRedfern method; Bio-oil

Journal Pre-proof 1. Introduction The renewable energy resources have attracted global attention as cleaner and environment friendly substitutes of fossilized carbonaceous fuels. Out of various renewable energy resources such as hydro, solar, wind, non-fossilized carbonaceous biomasses, etc., the virgin and waste biomasses have the advantage of their abundant availability and net zero carbon foot-print (Saidur et al., 2011). Agricultural wastes (rice husk, wheat straw, etc.), agro-

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industrial wastes (bagasse, de-oiled cakes, etc.), forestry wastes (wood chips, sawdust, and

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bark), virgin biomass like forest waste and perennial wild grasses like Saccharum munja (MJ), etc. and domestic solid waste and sludge from wastewater treatment plants have

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reasonably high energy content (Go et al., 2019).

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Saccharum munja a widely growing perennial grass forms the part of tall Savannah grasses

It grows in Africa, Australia, Indian sub-continent and South

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and Genus Saccharum.

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that cover around 14.0 million sq. km. of global landmass. It belongs to the Kingdom Plantae

America on higher and well drained areas. In the Indian sub-continent it grows in the dry

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areas of the northern part of India (Assam, Punjab, Uttarakhand, Uttar Pradesh, Sunderban and other delta areas), and in some parts of Nepal, Pakistan and Afghanistan (Rahar et al., 2011). It is drought tolerant species of grass and is easy to grow hence it is the biologically fruitful local colonizers of abandoned mines. It is burnt periodically for controlling its overgrowth and taking care of the dead stalk resulting in atmospheric pollution. Depending upon its habitat, it can be about 1 to 2m in height (Singh et al., 2014). It has a broad root arrangement that binds the soil and rock structures, and grows into a thick cluster with high biomass tufts. The grass is long, panicles silky, green in color that after cutting turns greenish-brown (Rahar et al., 2010, 2011). Its leaves are sharp like a blade and can cut the mouth and tongue hence animals do not graze it. Saccharum munja is utilized primarily as a cheap material for covering rooftops and making partition walls of huts, ropes, hand fans,

Journal Pre-proof bins, brooms, and cover for protecting crops. A useful fiber can be extracted from the upper leaf sheets of the flowering culms and is used for making a variety of household items by villagers in the Indian sub-continent. Different parts (flowers, stem, root and adjacent soil) of Saccharum munja contain Na, K, Ca, Mg, and Fe in the form of chlorides, bicarbonates, chlorides, phosphate and sulfate which contribute to its medicinal properties. The plant has been used to stop bleeding and in fever and inflammation. Its different parts have been used to treat burning sensations, thirst, herpes,

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dyspepsia, dyscaria, erysipelas, urinary complaints and eye diseases (Rahar et al., 2010). Its

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root is used in dysuria, giddiness and vertigo and is an active ingredient of several Ayurvedic

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formulations (Rahar et al., 2011).

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A few researchers have made efforts to extract chemicals of medicinal importance from S. munja, but to the best of our knowledge no attempt has been made to study its biological and

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syngas.

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thermochemical degradation to assess its efficacy as a feed-stock for obtaining biogas and

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The present work has focussed on the study of thermochemical behaviour and pyrolysis of (Saccharum munja) for the first time. Results of physicochemical characterization (proximate and ultimate analyses, calorific value, and FTIR, TGA and DTG analyses), biochemical characterization (hemicellulose, cellulose and lignin analysis) and pyrolysis are reported and discussed in this paper. Pyrolysis has been performed using a fixed-bed reactor under inert atmosphere to collect the liquid product and evaluate its yield. Examination of liquid product (bio-oil) was performed using GCMS, FTIR and HNMR. Kinetic study of pyrolysis process has been executed using the TGA data. Kinetic parameters (activation energy, preexponential factor and reaction order) are evaluated using multiple linear regressions (MLR) and Coats – Redfern (CR) methods.

Journal Pre-proof 2. Material and Methods 2.1. Biomass collection and sample preparation Samples of Saccharum munja (MJ) biomass were collected from Lakhimpur Kheri (28.1651° N, 80.6327° E) district of Uttar Pradesh, India. These were mixed properly and washed first with tap water and then with distilled water to remove the surface dust, adsorbed salts, decayed biomass, etc. and dried in ambient air for 70 to 72h. The air dried biomass was

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placed in an air oven at 600C for 36 h to remove the surface moisture and then pulverized

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using a pulverizer (Philadelphia, USA, Model 2). The powdered sample was sieved to get

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particles of 177 µm. (-60 + 85 mesh) size. The dry powder sample thus obtained was placed in zipper polybags for physico-chemical characterization and pyrolysis and bio-oil

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production. 2.2. Physicochemical characterization

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The moisture (MC), volatile matter (VM), fixed carbon (FC) and ash (Ash) contents were

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determined using ASTM protocols as mentioned earlier (Kumar et al., 2019). Each measurement was carried out in triplicate and the mean values are reported. The value of fixed carbon (FC) was calculated by using equation: 𝐹𝑖𝑥𝑒𝑑 𝑐𝑎𝑟𝑏𝑜𝑛(%) = 100 − {𝑀𝐶(%) + 𝑉𝑀(%) + 𝐴𝑠ℎ 𝑐𝑜𝑛𝑡𝑒𝑛𝑡(%)

(1)

The Elemental Analyzer (Euro EA 3000, Italy) was used for the estimation of carbon (C), hydrogen (H), nitrogen (N), and sulfur (S) contents.The oxygen (O) content was estimated by the difference using equation:

𝑂(%) = 100 − {𝐶(%) + 𝐻(%) + 𝑁(%) + 𝑆(%)

(2)

Journal Pre-proof The higher heating value (MJ/kg) was estimated using a bomb calorimeter (Model no. RSB 3, Rajdhani Scientific Inst. Co., New Delhi, India). The cellulose, hemicellulose and lignin contents were determined using the van Soest method (Bledzki et al., 2010; Soest et al., 1991). 2.3. TGA and DTG experiments The

thermal

degradation

under

nitrogen

atmosphere

was

performed

using

a

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thermogravimetric analyzer (TGA-50, Shimadzu) from the ambient temperature to 1000oC at

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three heating rates of 10, 15, and 20oC/min. The nitrogen flow rate was maintained at 100

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mL/min throughout the TGA experiments. The DTG data were generated using TGA data with the help of origin pro software. Out of the three heating rates used least mass of residual

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weight was obtained for the heating rate of 15oC/min. The kinetic analysis of TGA/DTG data

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2.4. Kinetic analysis

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and the fixed-bed pyrolysis experiments were performed at this heating rate only..

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The thermal degradation processes is non-linear in nature therefore it displays different stages of pyrolysis which are described using different models (Sriram and Swaminathan, 2018). The TGA/DTG data for different stages are used to evaluate the kinetic parameters (reaction order, activation energy, and pre-exponential factor) of solid state reactions indirectly. These parameters are useful for understanding the thermal conversion occurring during pyrolysis. Three different methods are generally used for this purpose. The first approach is based on the multiple linear regression (MLR) analysis of the thermo-gravimetric data obtained at a single heating rate. The second approaches are based on the use of iso-conversional models proposed by various workers (Kumar et al., 2019) and require thermo-gravimetric at least at three different heating rates. The third approach involves fitting of reaction based model proposed by Coates and Redfern (1964) using TGA/DTG data obtained at a single heating

Journal Pre-proof rate (Naqvi et al., 2019). The thermo-gravimetric data obtained for S. munja in this work were analyzed using MLR and Coats and Redfern’s model based methods. A brief account of these two methods is given below for local reference. 2.5.1. Multiple linear regression (MLR) method Single heating rate data of TGA was used to evaluate the kinetic triplets using the MLR method described by Duvvuri et al. (1938) and also used by several other workers (Kumar et

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al., 2019; Mansaray and Ghaly, 2015; and Yin and Goh, 2015). It permits the evaluation of

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activation energy (E), pre-exponential factor (A) and reaction order (n). The essential steps of

𝑑𝛼 = 𝐾(𝑇)𝛼 𝑛 𝑑𝑡

(3)

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−

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MLR method are given below. The global kinetics of pyrolysis reaction can be expressed as:

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Where 𝛼 = conversion ratio and is defined as (𝑤𝑡 − 𝑤𝑓 )⁄(𝑤𝑜 − 𝑤𝑓 ), 𝑤0 = mass of biomass

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before thermal degradation, 𝑤𝑓 = final mass, 𝑤𝑡 = mass of the sample at time t, K(T) = rate constant, and n = reaction order. Eq. (3) can be modified using Arrhenius rate equation as: 𝑑𝛼

−𝐸

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− 𝑑𝑡 = 𝐴 𝑒𝑥𝑝 ( 𝑅𝑇 ) 𝛼 𝑛

(4)

where A = pre- exponential factor (min-1), R = universal gas constant (8.314J/mol.K), T= absolute temperature (K), and E = activation energy (kJ/mol). Combining Eq. (3) and (4) one gets: −1

𝑑𝑤

𝑤0 −𝑤𝑓 𝑑𝑡

−𝐸

𝑤𝑡 −𝑤𝑓

= 𝐴 𝑒𝑥𝑝 ( 𝑅𝑇 ) (𝑤

0 −𝑤𝑓

𝑛

)

(5)

Integration of Eq. (5) gives:

𝑙𝑛 (𝑤

−1

0 −𝑤𝑓

𝑑𝑤

𝐸

𝑤𝑡 −𝑤𝑓

) = 𝑙𝑛(𝐴) − (𝑅𝑇) + 𝑛 𝑙𝑛 (𝑤 𝑑𝑡

0 −𝑤𝑓

)

(6)

Journal Pre-proof Here (dw/dt) is the rate of change of mass. The linearized form of Eq. (6) can be written as: 𝑦 = 𝐵 + 𝐶𝛼 + 𝐷𝑧

where 𝑦 = 𝑙𝑛 (𝑤

−1

𝑑𝑤

0 −𝑤𝑓 𝑑𝑡

(7) 𝑤𝑡 −𝑤𝑓

) ; 𝛼 = 1/𝑇 ;𝑍 = 𝑙𝑛 (𝑤

0 −𝑤𝑓

);𝐵 = ln(𝐴);𝐶 =

−𝐸 𝑅

; 𝐷 = 𝑛.

The coefficients B, C and D were calculated by regression analysis of the TGA data. Values of B, C and D were used to compute A, E and n, respectively.

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2.5.2. Coats-Redfern (CR) method

𝑑𝑎

= 𝐾(𝑇). 𝑓(𝑎)

(8)

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𝑑𝑡

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According to this method the rate of change in conversion is expressed as:

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Here the reaction rate constant varies with temperature and the reaction model which itself is a function of level of conversion, respectively. The level of conversion can be defined as: 𝑤 −𝑤

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𝑎 = 𝑤 0−𝑤 𝑡 0

𝑓

(9)

relation

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It is interesting to note here that the conversion, a is related to the conversion ratio, α by the

𝑤 −𝑤

𝑎 = 𝑤 0−𝑤 𝑡 =1-α 0

𝑓

(10)

Equations (3) and (8) differ in sign and the value of order of reaction n, which is equal to 1 in case of Eq. (8). Combine Eq. (8) with Arrhenius equation for K(T) gives: 𝑑𝑎 𝑑𝑡

−𝐸

= 𝐴 𝑒𝑥𝑝 ( 𝑅𝑇 ) . 𝑓(𝑎)

(11)

In the above equation temperature is a function of time; therefore a term heating rate can be defined as:

Journal Pre-proof 𝛽=

𝑑𝑇

𝑑𝑇 𝑑𝑎

= 𝑑𝑎 . 𝑑𝑡

𝑑𝑡

(12)

Combining Eq. (11) and Eq. (12) gives: 𝑑𝑎 𝑑𝑇

𝐴

−𝐸

= 𝛽 𝑒𝑥𝑝 ( 𝑅𝑇 ) . 𝑓(𝑎)

(13)

Integrating Eq. (13) from 𝑎 = 0 𝑡𝑜 𝑎 𝑎𝑛𝑑 𝑇 = 0 𝑡𝑜 𝑇 gives: 𝑎 𝑑𝑎

𝑇

𝐴

−𝐸

= 𝛽 ∫0 𝑒𝑥𝑝 ( 𝑅𝑇 ) 𝑑𝑇 𝑓(𝑎)

(14)

of

𝑔(𝑎) = ∫0

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where the term 𝑔(𝑎)represents the integral reaction model.

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Equation (14) can be solving for kinetic analysis by using two different approaches. The first

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approach uses the model free kinetic analysis in which no reaction model is required and the

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second approach uses a reaction model and is known as the model fitting method. The model fitting method was proposed by Coats and Redfern (1964) for solving Eq. (14) for

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various values of 𝑔(𝑎) to elucidate the reaction mechanism and obtain the values of E and A.

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The integral form of Eq. (14) can be written as: 𝐴𝑅𝑇 2

𝑔(𝑎) =

𝛽𝐸

(1 −

2𝑅𝑇 𝐸

−𝐸

) . 𝑒𝑥𝑝 ( 𝑅𝑇 )

(15)

Taking logarithmic of Eq. (15) and rearrangement gives: 𝑔(𝑎)

𝑙𝑛 (

𝑇2

𝐴𝑅

) = 𝑙𝑛 (𝛽𝐸) (1 −

2𝑅𝑇 𝐸

𝐸

) − 𝑅𝑇

(16)

If the value of E is high, the term 2RT/E << 1, Eq. (16) can be written as: 𝑔(𝑎)

𝑙𝑛 (

𝑇2

𝐴𝑅

𝐸

) = 𝑙𝑛 (𝛽𝐸) − 𝑅𝑇

(17)

Journal Pre-proof 𝑔(𝑎)

The slope gives the activation energy and intercept of plot between 𝑙𝑛 (

𝑇2

) versus 1/𝑇

gives the value of pre-exponential factor. The values of 𝑔(𝑎) are reported by (Islam et al., 2015; Vyazovkin et al., 2011). 2.5. Pyrolysis experiments The pyrolysis of MJ biomass was carried out in a fixed bed tubular reactor made of stainless steel (length: 520 mm; I.D. 44 mm). Non-isothermal heating of the biomass sample was

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carried out in an electrically heated vertical tube furnace (Fig. 1). The furnace temperature

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was controlled using a PID (proportional-integral-derivative) controller. Ten pyrolysis

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experiments were performed on the basis of the design expert software-response surface

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methodology (RSM). Two experimental parameters (temperature from 450oC to 600oC and reaction time from 30 to 60min) were selected for pyrolysis at a constant heating rate of

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15oC/min. 20g of the biomass sample was taken in the reactor for each run and nitrogen gas

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was introduced from the top of the reactor to create an inert atmosphere inside it. Nitrogen also acted as the carrier gas. The condensable product was collected from the bottom of the

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reactor in a conical flask placed in an ice-bath. The non-condensable gases were vented off. When the temperature reached the set pyrolysis temperature, the pyrolysis was continued for a known reaction time to collect all the condensable products in the form of liquid (bio-oil). The residual biomass left after the pyrolysis in the reactor was evaluated as the bio-char. The yields of pyrolysis products (bio-oil and biochar) were calculated using the formula: 𝐵𝑖𝑜 𝑜𝑖𝑙 (𝑔)

𝐵𝑖𝑜 𝑜𝑖𝑙 𝑌𝑖𝑒𝑙𝑑 (𝑤𝑡 %) = 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑓𝑒𝑑 (𝑔) 𝑥100 𝐵𝑖𝑜 𝑐ℎ𝑎𝑟 (𝑔)

𝐵𝑖𝑜 𝑐ℎ𝑎𝑟 𝑌𝑖𝑒𝑙𝑑 (𝑤𝑡 %) = 𝐵𝑖𝑜𝑚𝑎𝑠𝑠 𝑓𝑒𝑑 (𝑔) 𝑥100 The gas yield was calculated using:

(18)

(19)

Journal Pre-proof 𝐺𝑎𝑠 𝑌𝑖𝑒𝑙𝑑 (𝑤𝑡 %) = 100 − {𝐵𝑖𝑜 𝑜𝑖𝑙 𝑦𝑖𝑒𝑙𝑑 (𝑤𝑡 %) + 𝐵𝑖𝑜 𝑐ℎ𝑎𝑟 𝑦𝑖𝑒𝑙𝑑 (𝑤𝑡 %)} 𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 (%) = 100 − {𝐵𝑖𝑜 𝑐ℎ𝑎𝑟 𝑦𝑖𝑒𝑙𝑑 (𝑤𝑡 %)}

(20) (21)

2.6. Experimental Design The response surface methodology (RSM) is an optimization strategy based on mathematical and statistical techniques for determining the relation between independent variables and

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their responses (Kilic et al., 2014). It is commonly used to know the optimum condition of the processes. It was first presented by Box and Wilson in 1951 (Abnisa et al., 2011). Two

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important variables (temperature and heating time) that affect the pyrolysis process were

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studied for the optimization of liquid product (bio-oil) yield by using the central composite

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design (CCD) method. It is a very appropriate method for modelling a quadratic surface with

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least number of experiments along with interaction between the variables. In the Design Expert Software Version 11 (StatEase, USA), CCD consists of 2 to 50 numeric variables.

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Each numeric variable is a set of 5 levels: plus and minus alpha (axial points), plus and minus 1(factorial points) and the middle point. If the categorical factors are added, the central

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composite design will be duplicated for every combination of the categorical factor levels. In this study, the bio-oil yield was optimized and the product obtained under optimum condition was characterized. As per the central composite design, the required number of experiments was calculated using Eq. (22). 𝑁 = 2𝑛 + 2𝑛 + 𝑛𝑐 = 22 + 2 × 2 + 2 = 10

(22)

where N = total number of necessary experiments and n = number of factors (process variables).

Journal Pre-proof After performing all the experiments, the results were fitted into a second degree model (Eq. 23) to find out the process response as a function of the independent variable (Kumar and Singh, 2014). 𝑌 = 𝛽0 + ∑𝑛𝑖=1 𝛽𝑖 𝑋𝑖 + ∑𝑛𝑖=1 𝛽𝑖𝑖 𝑋𝑖2 + ∑𝑛𝑖=1 ∑𝑛𝑗>1 𝛽𝑖𝑗 𝑋𝑖 𝑋𝑗

(23)

where Y = predicted response; n = the number of experiments; and 𝛽0,𝛽𝑖 , 𝛽𝑖𝑖 and 𝛽𝑖𝑗 are the constant, linear, quadratic and interaction coefficients, respectively; and Xi and Xj are the

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independent factors.

The results of optimization of bio-oil yield was analysed using ANOVA to came up with the

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experimental design, data analysis, building of quadratic model equation and 3D plots on the

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basis of two factors- temperature and residence time. In addition, it also provided F and P

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values where F values give the information about variation in the response and P-values indicate the assurance of statistical significance. The R2, adjusted R2 and predicted R2 values

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were noted for the optimization of process parameters.

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2.7. Characterization of Bio-oil

The pyrolysis product (bio-oil) obtained under the optimized conditions was characterized using FTIR and 1HNMR and GC-MS techniques. The FTIR (Model: Nicolet iS5, Thermo Electron Scientific Instruments LLC) spectroscopic analysis was employed to know the functional groups present in the liquid constituents of bio-oil. The 1HNMR was carried out using the high resolution nuclear magnetic resonance spectrometer (Burker Bio Spin international AG, Model: AVH D 500 AVANCE III HD 500 MHɀ One Bay NMR Spectrometer). 1HNMR solvent was prepared using deuterated DMSO (dimethyl sulfoxide). The gas chromatography–mass spectrometric (GCMS) was carried out using GC–MS (QP201

Journal Pre-proof Shimadzu, USA, equipped with Rxi-5 Sil MS column (30 m X 0.25 mm. X 0.25μm film thickness) to know the % of various organic compounds. 3. Results and Discussion 3.1. Thermochemical characteristics The thermochemical properties of MJ are compared with published data for rice husk

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(Mallick et al., 2018) and sugar cane leaves (Saccharum officinarum L) which belongs to the same family as MJ (Kumar et al., 2019) in Table 1. The storage space and transportation cost

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of a biomass are dependent upon its bulk density. It also has a direct influence on the

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behavior of biomasses in the course of thermochemical conversion processes in packed and

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fluidized beds (Kumar et al., 2019). The bulk density of MJ was found to be 166kg/m3 which

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is comparable with that of sugar cane leaves. The moisture content (4.50%) is lower than the limiting value for pyrolysis (Ahmad et al., 2017; Mishra and Mohanty, 2018a). The high

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value of volatile matter (80.70%) and low value of ash content (3.89%) indicate its better energy recovery potential and ignitibility. The biomass with high VM content is a suitable

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candidate for direct use as a fuel and feed-stock for thermal conversion because higher VM leads to a higher quantity of condensable liquid (bio-oil) yield. The MJ has 63.29% carbon, 7.84% hydrogen, 2.34% nitrogen and no sulphur. The higher carbon and hydrogen content and, absence of sulfur and low nitrogen content make it an excellent feedstock for thermal conversion route (combustion, gasification and pyrolysis). Absence of sulphur and low nitrogen will result in SOx free and low fuel NOx emissions during combustion and pyrolysis. S. munja has 35.10% cellulose, 38.90% hemicellulose, and 17.90% lignin and is comparable to that of sugar cane leaves (Kumar et al., 2019a). The calorific value (HHV) was found to be 19.67MJ/kg, indicating it to be a good biomass for gasification and pyrolysis.

Journal Pre-proof 3.2. TGA and DTG analysis The TGA profile of Saccharum munja in the temperature range of 17 to 1000oC under nitrogen atmosphere (nitrogen flow, 100mL/min) is shown in Fig. 2. The DTG data calculated using the TGA data with the help of origin software are also depicted in Fig. 2. The thermal decomposition of the MJ biomass displays the typical characteristics of a lignocellulosic biomass pyrolysis (Pehlivan et al., 2017; Kumar et al., 2019). It is seen from

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Fig. 3 that the maximum weight loss occurred in the temperature range of 250 to 500oC, the

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typical characteristic range for cellulose and hemi-cellulose degradation. It is also seen that similar to other lingo-cellulosic biomasses, the thermal decomposition of Saccharum munja

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comprise three characteristic zones of decomposition: moisture evaporation, devolatilization

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and char formation (Doshi et al., 2014; Kumar et al., 2019; Mishra and Mohanty, 2018b). In

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the first zone of pyrolysis (up to 240oC), in which drying and degradation of some light volatile components take place, only 8.32% weight loss is observed. In the second zone (240

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– 500oC), known as the active pyrolysis zone, the maximum weight loss (73.33%) has been

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noticed. In this zone the higher weight loss is due to the simultaneous thermal decomposition of cellulosic and hemi-cellulosic polymers and conversion of higher molecular weight decomposition products into the smaller ones. In this zone the first peak at 310oC is attributed to the thermal decomposition of hemi-cellulosic components. The second peak at 360oC corresponds to the degradation of cellulose present in the biomass. The decomposition of cellulosic and hemi cellulosic components in the second zone is higher due to the extra heat provided by the hot gaseous and volatiles produced formed earlier. The third zone (from 500 to 1000oC) is due to the lignin degradation where approximately 11.88% weight loss takes place. No sharp peak is observed in this zone, therefore it can be concluded that the lignin degrades at a very slow rate over a wide temperature range due to the higher thermal stability of its constituent molecules (phenolic and other hydroxyl bearing aromatic molecules). After

Journal Pre-proof the completion of the pyrolysis process at the end of 1000oC, 6.46% mass was obtained as the residue. 3.3. Kinetic Analysis 3.3.1. Multiple linear regression (MLR) method The reaction order (n), pre-exponential factor (A) and activation energy (E) were estimated

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using the TGA data obtained at the heating rate of 15oC/min with the help of MLR method. The thermal degradation profile (Fig. 2) of S. munja sample shows 3 distinct zones. The

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kinetic parameters were calculated for these three zones separately and are reported in Table

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2. The values of calculated E, A and n for pyrolysis in Zone I are 36.30kJ/mol, 1.06 ×

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104min-1, and 3, respectively. The value of correlation coefficient R2 was nearly unity in Zone II. In this zone mainly cellulose and hemicellulose decompose together with a small fractions

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of lignin (Kumar et al., 2008). The values of E, A and n in this zone are 49.05kJ/mol, 3 × 103,

na

and ~1, respectively. The degradation of lignin took place over a wide range of temperature and continued up to 1000oC. The value of E = 36.82kJ/mol obtained for Zone III is lower

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than that for Zone II and slightly higher than for Zone I. It can be attributed to the fact that all the hemi-cellulose and cellulose present in the biomass have degraded up to Zone II and most of the lignin has degraded in Zone III. The order of reaction in this zone was observed approximately half and pre-exponential factor was 4×103min-1. 3.3.2. Coats-Redfern method The results of kinetic analysis obtained using Coats-Redfern method are presented Table 3 together with expressions for usual reaction mechanisms for the solid state degradation reaction. The reaction mechanism can be easily classified in to three categories. The first one is the reaction order based models (F1 –F3) and represents the reaction pathways for the rate

Journal Pre-proof determining chemical reaction. Second is the diffusion controlled (D1-D4) reaction mechanism in which reaction is propagated by the diffusion of solid state material and is due to the morphological change in the components of char. Finally, the reaction kinetics is controlled by the phase boundary (R2-R3). The activation energy calculation was also carried out for three distinct zones of degradation discussed earlier. It can be seen from Table 3 that in Zone I (from 30 to 240oC) the activation energy (E) was the lowest(11.14kJ/mol) with the pre-exponential factor (A) 1.14×104min-1 for the contracting area (R2) mechanism and the

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highest E and A were 81.38kJ/mol and 3.21×103min-1 respectively for the second order (F2)

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mechanism. The correlation coefficient (R2) was observed closer to unity for the third order

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(F3) mechanism with E of 21.92kJ/mol and A of 1.18×101min-1; therefore it is the suitable

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reaction mechanism for pyrolysis in Zone I. As the temperature increased and reached to the second zone, the correlation coefficient (R2) was found to be closer to one for the second

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order mechanism and the E and A were found to be 51.61kJ/mol and 4.24×100min-1,

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respectively. In Zone II, the E and A were found to be130.90kJ/mol and 4.44×103min-1 for the three way transport (D3) mechanism. In this zone, the highest correlation coefficient (R2)

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was observed for the first order (F1) mechanism with activation energy 45.20kJ/mol and exponential factor 5.59×103min-1. The average activation energies in Zones I, II, and III were found to be 29.18, 89.52 and, 61.63kJ/mol, respectively. The lowest activation energy in Zone I is attribute to the volatilization of moisture and smaller volatile molecules. It can be observed that the activation energy increases with temperature in Zone II because in this zone maximum degradation of biomass takes place and all the volatile matter in the waste are volatilized which are responsible for bio-oil formation. On further increase in temperature the activation energy decreases in Zone III because the degradation of only remaining residue and char takes place (Ceylan et al., 2014; Maurya et al., 2016).

Journal Pre-proof A comparison of the values of energy of activation obtained with the help of MLR and CoatsRedfern methods shows a similar trend. In both the cases, the E values for Zone II are higher than those for Zones 1 and III. This is consistent with the decomposition of hemi-cellulose and cellulose in this zone. However, the E values obtained with the help of Coats and Redfern model are consistently higher than those obtained with the MLR method for all three zones. 3.4. Optimization of Bio-oil Yield

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The response surface methodology (RSM) was used for the design and statistical analysis of

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bio-oil yield through the pyrolysis in a fixed bed reactor. The CCD design was used for the

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experimental results with two process variables (temperature and residence time). Total 10 experiments were performed, using the software on the basis of experimental conditions. The

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CCD experimental design matrix and results using two variables are presented in Table 4.

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The relation between predicted (mathematically calculated) and actual response

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(experimental) is shown in Fig. 3. The experimental wt% of bio-oil yield varied between 40.60 and 46.00%, which satisfies the model prediction (from 40.47 to 45.87%) with very

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close agreement with the experimental values. Under the optimized conditions (temperature of 525oC and reaction time of 60min) the maximum bio-oil yield is 45.87 wt% and the experimental bio-oil yield is 46.00%. This result shows that the predicted and experimental yields are very close as well as correlation coefficient (R2=0.997) is close to unity, which indicates a close fit of the model with the real data. 3.6. ANOVA analysis The results of variance analysis (ANOVA) of the response of bio-oil yield and for the second order equation are presented in Table 5. The ANOVA investigation was utilized to decide the best fitted quadratic model. The acceptance of the model is based on the F- and p-values which are known as Fischer test and probability value, respectively. A larger F-value shows

Journal Pre-proof that the terms are significant and this is attributed to a greater reliability of the model. A pvalue (less than 0.05) describes that the model is how much significant (Abnisa et al., 2011; Kumar and Singh, 2014; Kılıc et al., 2014). According to ANOVA analysis of bio-oil yield from the pyrolysis of Saccharum munja, the F-value estimation of 276.11 infers that the model is noteworthy. P-values less than 0.0500 indicate that the model terms are significant. For this condition A, B, AB, A2, B2 are significant model terms. The P-values more than 0.1000, demonstrate that the model terms are not critical. Likewise, F-value of 1.38 suggests

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that the lack-of-fit is not noteworthy in respect to the pure error. Non-significant lack-of-fit is

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useful for the model fitting. The model representing the relationship between the two

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independent variables and the selected response (bio-oil yield) are expressed by Eq. (24).

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𝐵𝑖𝑜 − 𝑜𝑖𝑙 𝑦𝑖𝑒𝑙𝑑(𝑤𝑡. %) = 44.91 + 1.14𝐴 + 0.33𝐵 − 0.70𝐴𝐵 + 3.30𝐴2 + 0.92𝐵 2

(24)

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where A and B represent the temperature and residence time, respectively. This coded

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equation is helpful in recognizing the relative impact of the components by looking at the factor coefficients. The statistical analysis for bio-oil yield indicates a standard deviation

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(SD) of 0.16 and which indicates a good model fitting. For the bio-oil yield the mean response is 43.48%. The co-efficient of value (CV %) is 0.37, which ascribes that the model is reliable and reproducible. The predicted co-efficient of variation (R2) of 0.9730 is close to the adjusted R2 of 0.9935 as the difference is less than 0.2. Signal to noise ration measure the degree of accuracy. A ratio more than 4 is recommendable. In this investigation the ratio 43.424 shows a significant signal, therefor this model can be utilized to explore the design space. 3.7. 3D and Contour Plots of Optimization Figure 4(a) shows the 3D response surface plot and relates the interaction effect among process variables and product yield within the range of experimental conditions provided by

Journal Pre-proof the design expert software. It is very useful to understand the different surface shapes for different process variables and effectiveness on the product yield. Similar to 3D plot, contour plot (Fig. 4(b)) is also showing the interaction among process variables but it is more straightforward to understand than earlier (Kılıc et al., 2014). These figures indicate that the relation between the temperature and residence time in 3D and 2D response surfaces plots. It can be seen from Fig.4 (a) and (b), that the maximum bio-oil yield was 46.00 wt. % at 60min

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of reaction time.

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3.8. Physicochemical Properties of Bio-oil

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The bio-oil was a dark brown acidic liquid (pH = 2.9) having a typical smoky smell. The density of bio-oil was 980kg/m3. The viscosity was found to be (0.42Pa.s), which is higher

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than that for traditional transportation fuels. Higher viscosity of a fuel (bio-oil) affects

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pumping and injection of fuel in engine (Cheng et al., 2017; Nayan et al., 2013). The acid

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number, an important parameter of bio-oil and was found to be (276.47mg KOH/g). The higher acid number indicates its corrosive nature. The calorific value was fund to be

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23.28MJ/kg. A higher calorific value is desirable for of a liquid-fuel. 3.9. FTIR Spectra of S. munja and Bio-oil The FTIR spectrum showing functional groups of organic compounds present in S. munja is shown in Fig. 5. The peak in the range of wave number 3400 – 3500cm-1 indicate presence of phenolic, alcoholic and water molecules (Kumar et al., 2019). The peak at 2917cm-1 is due to the presence of asymmetric and symmetric methylene stretching (Biswas et al., 2017). Peaks in the range of 2800-3000cm-1 indicate the presence of C-H vibration (Collins and Ghodke, 2018). The peak at 1633cm-1 is attributed to the C-H and C = O stretching of carboxylic acid and derivatives of esters. The peak corresponding to the wave number of 1246cm-1 is due to

Journal Pre-proof the C-H stretching in cellulose. The peak at 1056cm-1 is due to the C-N stretching of amine. Peak around 585cm-1 is due to the C-C stretching of aromatic ring. The FTIR spectrum of the bio-oil is also shown in Fig. 5. The peak at 3424cm-1 indicates the presence of –OH stretching vibration and it is attributed to alcohol, acid, and phenol (David and Kopac, 2018; Nayan et al., 2013). Peak at 2958cm−1 indicates the presence of C–H stretching vibration which is ascribed to alkane and saturated aliphatic groups (Bordoloi et

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al., 2015). The peaks between 1630 and 1740cm-1 are attributed to C=O stretching vibrations

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which are due to the presence of aldehyde, ketone, carboxylic acid and esters. Peak at 1400cm−1 indicates the existence of C–H bending vibration, CH2, and CH3 which indicate the

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existence of the alkanes group. Peaks at 1250, 1056 and 670cm−1 indicate the presence of an

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aromatic compound in the bio-oil (Mishra and Mohanty, 2018c).

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It is seen that the spectra of S. munja and bio-oil obtained from it are nearly similar and differ

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only in the area under various peaks. This indicates that most of the original constituents of S. munja are present in bio-oil. A few new peaks observed in the case of bio-oil indicate the

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formation of some new compounds.

3.10. 1H Nuclear Magnetic Resonance (NMR) Spectra The 1HNMR spectra of the produced bio-oil sample from Saccharum munja pyrolysis is useful for getting the information about the approximate ratios of the chemical environment of the proton (Biswas et al., 2017; Mullen et al., 2009). Like FTIR the results of 1HNMR spectra are reported in terms of peak assigned to their corresponding functional groups. The integrated peak areas of selected peaks of 1HNMR spectra are summarised in Table 7. The unfiled region of 1HNMR spectra (0.5 to 1.5ppm) indicates the protons of aliphatic and alkyl group bearing molecules. The next region (1.8 to 2.8ppm) corresponds to the proton of allylic group present in carbonyls (ketones, esters, aldehydes, acids, and amides), alkenes or

Journal Pre-proof aromatics with the double bonded functional groups. The spectra of the region of 2.8 to 4.5ppm correspond to the protons present in alcohol, ethers and esters that contain oxygen single bonds and CH2 attached to two aromatic rings. Peaks in the region of 6.5 to 8.5ppm correspond to the proton of aldehyde group. 3.11. GCMS Analysis The bio-oil produced at optimized conditions was diluted with the ethanol before GCMS

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analysis. The peaks observed in the chromatogram were compared with the NIST library to

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identify the organic components present which are listed in Table 8. The peaks of

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chromatograms showed that the derived bio-oil is a complex blend of exceptionally oxygenated and non-oxygenated natural mixes like aliphatic hydrocarbon, aromatics

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hydrocarbon, phenolic, alcoholic, ketonic, aldehydes, carboxylic acids, amide group etc.

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(Balagurumurthy et al., 2015; Mishra and Mohanty, 2018c). Complexity of product is due to

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the decomposition of the complex constituents of biomass- cellulose, hemicellulose and lignin and in situ interaction between various degradation products (David and Kopac, 2018).

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Various chemical functional groups present in the bio-oil are shown in the Fig. 5. It can be seen from Table 8 that the major percentage areas are for compounds such as: cis-9hexadecenal (13.78%), phenol, 2-methoxy- (10.56%), (E)-9-octadecenoic acid ethyl ester (6.74%), 3-methylcyclopentane-1, 2-dione (3.92%), n-hexadecanoic acid (3.63%) and linoleic acid ethyl ester (3.06%). These chemicals are useful in various applications. Nhexadecanoic acid is useful chemical for sodium palmitate manufacture and can be used as an anti-inflammatory agent. Several researchers have reported that the lignin is responsible for benzene, phenols, cresols and various other aromatic compounds (Chatterjee et al., 2018). Interaction of cellulosic and hemi cellulosic component prompts the formation of carbonyl group and carboxylic group compounds and interaction of hemicellulose and lignin is

Journal Pre-proof responsible for phenols and its derivatives. In addition, the extractive content of the biomass leads to the formation of alkanes in bio-oil. 4. Conclusion The thermochemical characteristics and thermal degradation behaviour of S. munja are comparable to common lingo-cellulosic biomasses. The highest activation energy (49.05kJ/mol) was obtained in Zone II and the lowest (36.30kJ/mol) in Zone I. The thermal

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degradation in Zone II followed a first order reaction. A surface response model was

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developed using temperature and time as independent variables. The ANOVA analysis

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indicated that the model was significant (low p-value ˂ 0.0001 and high F value). The highest bio-oil yield (46.0%) was obtained at 60min and 525oC. The predicted and experimental

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Acknowledgement

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yields were close to each other.

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The authors are thankful to the Department of Chemical Engineering & Technology and the

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CIFC of the Institute for providing necessary facilities for this research work. One of the authors (MK) is grateful to MHRD, New Delhi, GoI for the award of a research fellowship. References

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Journal Pre-proof Mishra, R.K., Mohanty, K., 2018c. Thermocatalytic conversion of non-edible Neem seeds towards clean fuel and chemicals. J. Anal. Appl. Pyrolysis 134, 83–92. https://doi.org/10.1016/j.jaap.2018.05.013 Mishra, R.K., Mohanty, K., 2018b. Bioresource Technology Pyrolysis kinetics and thermal behavior of waste sawdust biomass using thermogravimetric analysis. Bioresour. Technol. 251, 63–74. https://doi.org/10.1016/j.biortech.2017.12.029

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pyrolysis of neem seed. Fuel 103, 437–443. https://doi.org/10.1016/j.fuel.2012.08.058 Pehlivan, E., Özbay, N., Yargıç, A.S., Şahin, R.Z., 2017. Production and characterization of chars from cherry pulp via pyrolysis. J. Environ. Manage. 203, 1017–1025. https://doi.org/10.1016/j.jenvman.2017.05.002 Rahar, S., Nagpal, N., Swami, G., Nagpal, M.A., Kapoor, R., 2011. Pharmacognostical Studies of saccharum munja Roxb. Root. Int. J. PharmTech Res. 3, 792–800. Rahar, S., Navneet, N., Gaurav, S., Manisha, A., Suraj, B., Suraj, B., Shwali, S., Shwali, S., Shwali, S., 2010. Medicinal Aspects of Saccharum munja. Res. J. Pharm. Technol. 3, 636–639.

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ower petal using thermogravimetric studies. Bioresour. Technol. 265, 236–246. https://doi.org/10.1016/j.biortech.2018.05.043 Vyazovkin, S., Burnham, A.K., Criado, J.M., Pérez-Maqueda, L.A., Popescu, C., Sbirrazzuoli, N., 2011. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochim. Acta 520, 1–19. https://doi.org/10.1016/j.tca.2011.03.034 Yin, C., Goh, B., 2015. Environmental Effects Thermal Degradation of Rice Husks in Air and Nitrogen : Thermogravimetric and Kinetic Analyses Thermal Degradation of Rice Husks in Air and Nitrogen : Thermogravimetric and Kinetic Analyses. energy sources, part A Recover. Util. Environ. Eff. 7036. https://doi.org/10.1080/15567030903586048

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Journal Pre-proof Title: Pyrolysis of Saccharum munja: Optimization of process parameters using response surface methodology (RSM) and evaluation of kinetic parameters (Bio-resource Technology Reports)

Tables Table 1 Physicochemical properties of Saccharum munja Sugar cane leaves (Kumar et al., 2019) 5.61 77.33 6.38 10.67 7.25 142.9

8.70 60.21 19.70 11.39 5.286 -

76.83 8.19 0.59 14.39 -

38.50 4.79 1.01 36.10 -

42 44 17 18.08

21.22 ± 2.0 39.15 ± 2.0 13.10 ± 1.5 26.53 15.61

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na

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Proximate analysis (wt. %) Moisture content 4.5 ± 0.21 Volatile Matter 80.7 ± 0.49 Ash Content 3.89 ± 0.12 Fixed Carbon 10.91 ± 0.16 VM/FC 7.40 ± 0.38 3 Bulk density (kg/m ) 166 ± 1.5 Ultimate analysis (wt. %) C 63.29 H 7.84 N 2.34 O 27.19 S H/C 1.49 O/C 0.32 Compositional analysis (wt. %) Hemi-cellulose 38.90 ± 0.33 Cellulose 35.10 ± 0.29 Lignin 17.70 ± 0.71 Extractives 8.30 19.67 Calorific value (MJ/kg)

Rice husk (Mallick et al., 2018)

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This work

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Analyses

Table 2 Kinetic parameters using multiple linear regression method Zone I

Zone II

T

A

E

(oC)

min-1

kJ/mol

30 240

1.06 36.30 × 104

n

3

Zone III

T

A

E

(oC)

min-1 kJ/mol

240500

3× 103

49.05

n

T

A

E

(oC)

min-1

kJ/mol

1.18 500- 4 × 1000 103

36.82

n

0.3

Journal Pre-proof Table 3 Kinetic analysis of MJ using coats –Redfern method at heating rate of 15oC/min Mechanism

g(a)

Zone I

Zone II

Zone III

E (kJ/mol)

A(min-1)

E (kJ/mol)

A(min-1)

E (kJ/mol)

A(min-1)

Reaction order based models −𝑙𝑛(1 − 𝑎)

14.34

2.00×103

68.65

1.32

45.20

5.59×103

Second order (F2)

1/(1 − 𝑎)

81.38

3.21×103

51.61

4.24

35.12

4.34×103

1/(1 − 𝑎)2

21.92

1.18×101

115.47

2.38×105

87.03

2.43×100

One way transport (D1)

𝑎2

29.14

8.50×101

104.71

1.28×102

68.99

1.14×103

Two way transport (D2)

(1 − 𝑎)𝑙𝑛(1 − 𝑎) + 𝑎

32.04

5.00×101

115.13

6.47×102

78.79

5.28×100

4.40×101

130.90

4.44×103

92.65

3.18×100

24.51

1.50×103

104.31

2.67×101

75.54

2.80×103

ro

Third order (F3)

of

First order (F1)

GinstlingBrounshtein (D4)

[1-(2/3) a] -(1-a)2/3

36.04

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1⁄ 2 3]

Three way transport (D3)

-p

Diffusion models

Geometrical contraction models Contracting area (R2)

[1 − (1 − 𝑎)]1⁄2

11.14

1.14×104

55.49

4.65×101

33.97

5.72×104

Contracting volume (R3)

[1 − (1 − 𝑎)]1⁄3

12.13

1.50×104

59.45

2.96×102

37.43

5.22×104

Journal Pre-proof Table 4 The CCD experimental design matrix and results Actual variables A:Temperature B:Residence o ( C) time (min)

Response Predicted bio-oil Experimental yield (wt. %) bio-oil yield (wt. %) 43.01 43.00

600

60

2

450

45

40.47

40.60

3

600

45

42.75

42.65

4

525

30

45.80

45.70

5

450

30

40.66

40.65

6

600

30

44.34

44.45

7

450

60

42.12

42.00

8

525

45

44.91

45.00

9

525

60

45.87

46.00

10

525

45

44.91

44.80

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Table 5 ANOVA analysis of bio-oil using response surface quadratic model Mean

F-value

Prob >F

Remarks

Significant

Square

35.58

5

7.12

276.11

< 0.0001

A-Temperature

7.82

7.82

303.42

< 0.0001

1

B-Residence time

8.76

1

8.76

379.82

0.0004

AB

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Model

1.96

1

1.96

76.05

0.0010

25.47

1

25.47

988.02

< 0.0001

1.98

1

1.98

76.86

< 0.0009

Residual

0.1031

4

0.0258

Lack of Fit

0.0831

3

0.0277

1.38

0.5241

Pure Error

0.0200

1

0.0200

Cor Total

43.69

9

A² B²

Not significant

Journal Pre-proof Table 6 Organic compounds in bio-oil detected by GCMS Retention time (RT) 7.697

Compound identified in Bio-oil

Molecular formula C5H8O

8.008

Methyl ester of 3-hydroxy - 4methyl-pentanoic acid

C7H14O3

0.37

8.684

3,3-Diethoxy-2-butanone

C8H16O3

0.39

8.805

2-Furancarboxaldehyde, 5methyl-

C6H6O2

8.920

2-Butanone, 1-(acetyloxy)-

C6H10O3

9.010

Cyclohexanol, 2-methyl-, propionate, trans-

9.743

2-Cyclopenten-1-one, 2,3dimethyl-

10.083

2-Methyl-3-hexanone

10.871

Area ,% 4.36

0.87

1.41

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Cyclopentanone

Molecular structure

1.69

C7H10O

0.43

C7H14O

2.39

3-Methylcyclopentane-1,2-dione

C6H8O2

3.92

10.991

5-Hydroxy-2-heptanone

C7H14O2

0.63

11.614

3,5-Dimethyl cyclopentenolone

C7H10O2

0.85

11.960

2-Furaldehyde diethyl acetal

C9H14O3

1.70

12.158

2-Octen-4-ol

C8H16O

0.48

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C10H18O2

Journal Pre-proof Phenol, 2-methoxy-

C7H8O2

10.56

12.816

2-Hexen-1-ol, (E)-

C6H12O

0.31

13.449

2-Cyclopenten-1-one, 3-ethyl-2hydroxy-

C7H10O2

1.60

15.497

Creosol

C8H10O2

1.45

15.617

Dodecane

C12H26

0.31

17.709

n-Propyl benzoate

C10H12O2

17.872

Phenol, 4-ethyl-2-methoxy-

19.916

Phenol, 2,6-dimethoxy-

21.128

Tetradecane

24.263

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12.541

1.06

1.12

C8H10O3

0.72

C14H30

0.43

Benzene,1,2,3-trimethoxy-5methyl-

C10H14O3

0.32

33.989

n-Hexadecanoic acid

C16H32O2

3.63

34.527

Hexadecanoic acid, ethyl ester

C18H36O2

2.39

36.630

9-Octadecenoic acid (Z)-, methyl ester

C19H36O2

0.58

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C9H12O2

Journal Pre-proof cis-9-Hexadecenal

C16H30O

13.78

37.456

Linoleic acid ethyl ester

C20H36O2

3.06

37.540

(E)-9-Octadecenoic acid ethyl ester

C20H38O2

6.74

37.857

Octadecanoic acid, Ethyl ester

C20H40O2

0.99

38.289 39.489

Linoelaidic acid Ethyl 9-hexadecenoate

C18H32O2 C18H34O2

38.642

Cyclopropaneoctanoic acid, 2hexyl-, methyl ester

39.324

cis-11-Eicosenoic acid

39.694

Methyl 19-methyl-eicosanoate

40.295

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37.322

0.57

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C18H34O2

0.34 1.58

1.94

C22H44O2

0.39

9,12-Octadecadienoic acid (Z,Z)-

C18H32O2

0.33

40.949

7-Tetradecenal, (Z)-

C14H26O

0.95

42.092

9,12-Octadecadienoic acid (Z,Z)-, 2-hydroxy-1(hydroxymethyl)ethyl ester

C21H38O4

0.75

Jo ur

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C20H38O2

Fig. 1 Block diagram of experimental set-up for pyrolysis

Journal Pre-proof Fig. 2 TGA and DTG analysis at the heating rate of 15oC/min Fig. 3 Plot between predicted and actual values of bio-oil yields Fig. 4(a) 3D response surface plot showing the effect of temperature and residence time on liquid yield (b) 2D contour plot showing the effect temperature and residence time on liquid yield

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Fig. 5 FTIR spectra of Saccharum munja and bio-oil

Journal Pre-proof

Conflict of Interest

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Authors have no conflict of interests, whatsoever.

Journal Pre-proof Graphical abstract

Highlights Saccharum munja, a widely growing perennial grass is a potential source of energy.



Thermal degradation of Saccharum munja has been investigated for the first time.



Saccharum munja has high content of volatile matter and oxygenated compounds



RSM has been used to optimize the temperature and time for maximum bio-oil yield.



Multiple linear regression and Coats-Redfern method for thermal kinetics analysis.

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

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5