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Physical and chemical characterization of various Indian agriculture residues for biofuels production TIRATH RAJ, Manali Kapoor, Ruchi Gaur, Jayaraj Christopher, Bhawna Yadav Lamba, Deepak Kumar Tuli, and RAVINDRA KUMAR Energy Fuels, Just Accepted Manuscript • Publication Date (Web): 27 Apr 2015 Downloaded from http://pubs.acs.org on April 27, 2015

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Energy & Fuels

Physical and chemical characterization of various Indian agriculture residues for biofuels production

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Tirath Raj,1 Manali Kapoor,1 Ruchi Gaur,1 J. Christopher,2 Bhawna Lamba,3 Deepak K. Tuli,1 Ravindra Kumar*1 1

DBT-IOC Centre for Advanced Bioenergy Research, Research & Development Centre, Indian Oil Corporation Limited, Sector-13, Faridabad-121007, India 2 Analytical Division, Research & Development Centre, Indian Oil Corporation Limited, Sector-13, Faridabad-121007, India 3 College of Engineering, Department of Chemistry, University of Petroleum & Energy Studies, Village & P.O. Bidholi, Prem Nagar, Dehradun (UA)-248007, India

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*

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*

Author to whom correspondence should be addressed:

Dr. Ravindra Kumar Research Manager DBT-IOC Centre for Advance Bioenergy Research R & D Centre, Indian Oil Corporation Ltd. Sector-13, Faridabad-121007 India Phone 0129-2294463 Fax: 01292286221 Email: [email protected]

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1 ACS Paragon Plus Environment

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Abstract

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Lignocellulosic material (LCM) has been considered as a potent feedstock for biofuel production

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either as gaseous fuel, liquid and/or solid fuel to meet the energy demands. Conversion of

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lignocellulosic materials to biofuels is possible mainly by two processes, i.e. thermochemical and

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biochemical. For overall efficiency of processes designed to convert the lignocellulosic materials

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into the desired biofuel, it is important to understand the characteristics of these lignocellulosic

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components. The present study aims for physicochemical characterization of common

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lignocellulosic agricultural residues available in India, i.e. rice straw, rice husk, cotton stalk,

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wheat straw, bagasse, corn stover, sorghum stalk, mustard stalk, corn cob and jatropha pruning.

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Physical and chemical characterization of lignocellulosic samples is carried out by higher heating

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value, crystallinity index, thermal properties, CHNS/O analysis, FTIR, metal analysis and

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compositional analysis. Among all the biomass samples analyzed, corn cob has the highest

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content of cellulose and hemicellulose, i.e. 61.2% (w/w), making it the most potent feedstock for

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production of biofuels using biochemical process, whereas cotton stalk has relatively higher

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thermochemical potential due to its higher heating value (19.2 MJ/kg). Rice husk and rice straw

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have highest ash content, i.e. 17.4 and 13.7% (w/w), respectively, indicating a significant amount

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of undesirable material.

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Keywords: Biofuel, Characterization, Lignin, Polysaccharides, Thermal analysis

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1 Introduction

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Lignocellulosic material (LCM) is the widely available material across the globe, producing

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approximately 220 billion ton annually.1 LCM includes agricultural crop residue, forestry waste,

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aquatic plants and other energy crops.2 In India, total surplus agricultural LCM available in 2013

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was estimated to be 382.7 million metric tons (MMT) annually which could transform into 129.6

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billion liters of ethanol (Table 1).1 Being renewable in nature, LCM can help to mitigate the

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greenhouse gas emission. As the emission produced using biofuels are compensated during their

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growth phase, it can help to fulfill the sustainable economic development. There are mainly two

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technology platforms, which can convert the biomass into biofuels, i.e. thermochemical and

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biochemical.3,4 Thermochemical process includes the gasification and pyrolysis.5 Gasification

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converts the biomass to syngas as a source of energy and this process is carried out at a very high

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temperature in the presence of limited and controlled amount of air,6 whereas pyrolysis is carried

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out at relatively lower temperature in the absence of air producing pyrolysis oil upgradable to

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liquid fuels.7 Biochemical process is yet another option by which biomass can be converted to

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bioethanol. This technology comprises step like pretreatment, which makes structural

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polysaccharides accessible for enzymatic hydrolysis producing fermentable sugars, thus

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pretreated biomass on downstream processing using saccharification and fermentation can be

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converted to bioethanol. Both these processes have the potential to convert biomass to biofuels.

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However, physical and chemical properties of biomass differ significantly and have a profound

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impact on the biofuels production process and potential.8

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Lignocellulosic material comprises mainly of three biopolymers: cellulose (25-55%),

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hemicelluloses (8-25%) and lignin (10-35%) besides ash and extractives.9 Cellulose is a semi


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crystalline, a linear homo polymer of anhydro-D-glucose units linked together by β-(1-4)-

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glycosidic bonds.10 Hydrogen bonding between these cellulose fibrils is responsible for

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formation of highly ordered crystalline regions that are not accessible to water or any hydrolytic

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enzymes.3, 11 Crystalline cellulose accounts for approximately 50-80% of the total cellulose, the

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remainder being composed of more disorganized amorphous form. Hemicellulose is a polymer

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of predominantly pentose sugars like arabino-glucouronoxylan, arabino-4-O-methyl-glucurono-

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xylan, glucourono-xylan, arabino-xylan and galacto-arabino-glucorono-xylan.11-12 Lignin is a

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high molecular weight phenylpropan biopolymer of coniferyl, coumaryl and sinapyl alcohols and

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intimately imbricated to cellulose and hemicellulose which makes it complex structural material

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and recalcitrant providing protection to plants against biotic and abiotic stresses.3 Understanding

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the physical and chemical properties of the biomass can help the technologist to design the

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process parameters either for thermochemical or biochemical process.

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India is a vast country and has the advantage of being tropical, with very large area under

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agriculture and forestry. Northern part of India is particularly rich in agriculture and thus

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produces the majority of surplus agricultural residue. LCM is a complex matrix of a number of

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components and no single analytical technique caters to decipher its structural network. The

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present study aims for systematic and comprehensive assessment of physical and chemical

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properties of lignocellulosic agricultural residues available in north India (commonly found in

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Asia), i.e. rice straw (RS), rice husk (RH), cotton stalk (CS), wheat straw (WS), sugarcane

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bagasse (SCB), corn stover (CRS), sorghum stalk (SS), mustard stalk (MS), corn cob (CC) and

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jatropha pruning (JP). In this study, a series of laboratory analytical procedures (LAPs)

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developed by the National Renewable Energy Laboratory (NREL) are applied to perform the


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compositional analysis of all biomass residues. Compositional analysis comprising extractives,

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structural polysaccharides, acid insoluble lignin (AIL), acid soluble lignin (ASL), protein, ash

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with metal content has been undertaken. Moreover, higher heating value, crystallinity index,

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CHNS/O analysis, thermogravimetry (TG) and differential thermogravimetry (DTA) analysis are

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also studied. FTIR analysis of all biomass samples was also carried out. These studies have

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enabled us to compare the structural features of these LCM for the first time. The data obtained

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will provide a better understanding of their potential as biofuels feedstock’s for both the said

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platforms.

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Thermogravimetry (TG) and differential themogravimetric analysis (DTA) of biomass provide

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information on thermal decomposition profile of respective components which can be used to

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follow the physicochemical changes that occur during pretreatment processes.13 This technique is

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also helpful to determine the percentage of volatile matter and fixed carbon. Crystallinity of

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cellulose is often evaluated using CrI value determined from XRD patterns. Crystallinity is

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regarded as a key characteristic of cellulose substrates because amorphous cellulose can be

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hydrolyzed enzymatically much more rapidly than crystalline cellulose.10 Higher crystallinity

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may be the result of a more packed cellulose structure in biomass hence results in higher

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chemical and thermal stability. Fourier transform spectroscopy (FTIR) is a widely used tool for

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qualitative and quantitative determination of the chemical constituents of biomass and crystalline

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nature of biomass. Calorific value is the most important property of a fuel which determines the

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energy value of a fuel and can be determined experimentally using bomb calorimeter or can be

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calculated from ultimate and/or proximate analyses results from the Dulong equation.14 These

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physicochemical properties of biomass have significant impact on the choice of technology and

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to determine the feasibility of the overall process.


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Raveendran et al. (1995, 1996) 15,16 reported the studies on the pyrolysis characteristic of

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biomass and effect of mineral matter present in biomass on the pyrolysis characteristics, product

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distribution and properties of various kinds of biomass. However, the current investigation report

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aims at the physicochemical characterization of North Indian agricultural residue to assess the

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biofuel potential through both biochemical and thermochemical process.

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2 Materials and methods

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Xylose, glucose, cellobiose, galactose, arabinose, furfural, 5-hydroxymethylfurfural (HMF)

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and microcrystalline cellulose were of analytical grade, procured from Sigma-Aldrich, India.

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Analytical grade acetic acid, sulfuric acid and calcium carbonate were obtained from Fisher

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Scientific, India. These chemicals were used without any further purification. Ten biomass

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samples, namely rice (Oryza sativa) straw (RS), rice husk (RH), cotton (Gossypium arboreum)

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stalk (CS), wheat (Triticum aestivum) straw (WS), sugarcane (Saccharum officinarum) bagasse

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(SCB), corn (Zea mays) stover (CRS), sorghum (Sorghum bicolor) stalk (SS), mustard (Brassica

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campestris) stalk (MS) and corn (Zea mays) cob (CC) were collected from Mathura (27.28°N

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77.41°E) in UP (North India) at the time of harvesting in the year 2011. Mathura has an average

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elevation of 174 meters. The climate of Mathura is tropical extreme with hot summer

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temperatures rising beyond 44 °C, and cold winters with temperature dipping down to 5 °C. The

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average rainfall is 595 mm, mostly during the monsoons from July to September. Jatropha

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(Jatropha curcas) prunings were collected from the campus of Indian Oil Corporation Ltd.,

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Faridabad in the month of April 2012. All these residues were collected at the time of harvesting

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so these are fully matured and most of the moisture is removed by the natural process during 6 ACS Paragon Plus Environment

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ripening of the crops in the fields. The aim is to bring all the residues to the same level of

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maturity as these would be available in plenty for the production of biofuels. All samples were

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milled to ~2 mm size using a knife mill. All the biomass samples were air dried at ~30 °C to

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bring the moisture content in the range of 8-10% (w/w) and then stored in plastic bags at 25 °C

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till further analysis.

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Moisture content of biomass samples were determined using a standard protocol

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developed by NREL (NREL/TP-510-42620)17 using an infrared drier from Sartorius MA-150C,

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Germany. Volatile matter in the biomass samples were determined using protocol as given in

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ASTM D317.18 For this, ~1 g of biomass was placed in muffle furnace at 950 °C for 7 min in a

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quartz crucible. The crucible were removed from furnace and placed in a desiccator till cooled to

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room temperature. The loss of weight was measured as volatile matter present in the biomass and

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data is summarized in Table 2. Ash content was analyzed using NREL (NREL/TP-510-42622)

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standard protocol.19 For this, 1 g of biomass was burned in air and then heated at 575±25 °C in

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muffle furnace for 4-6 h. Thereafter, crucible was removed from furnace and placed in desiccator

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to cool to room temperature.

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Higher heating values of biomass samples were determined by following standard

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protocol20 using Leco bomb calorimeter (Leco corporation, S.No. 603-300-500), St. Joseph MI,

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USA. Higher heating values of biomass samples were also calculated theoretically by using the

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following Eq. 1.21 %% ℎ ℎ / = 0.42 ∗ 8080 ∗ % + 34500 ∗ "% − $ &'( + 2240 ∗ )* +,. 1 8

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Where C, carbon; H, hydrogen; O, oxygen and S is sulfur.


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Differential thermal analyses (DTA) of biomass samples were carried out using

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(NETZSCH STA 449F1, Jupiter, USA) thermogravimetric analyser under nitrogen atmosphere

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with a flow rate of 20 ml/min. For this, 5mg of biomass was heated under nitrogen atmosphere

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from 30-800 °C with heating rate of 10 °C/min and weight loss was recorded vis-à-vis

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temperature.22

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X-ray diffraction measurement of biomass samples were performed on Rigaku XRD

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Panalytical (Netherland), X-pert pro diffractometer set at 40 kV, 30 Ma respectively. The XRD

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patterns with monochromatic Cu Kα radiation (λ=1.5406 Å) were recorded over the angular

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range of 2θ=30-50° with a scan step size of 0.003°. Approximately 100 mg of powered biomass

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samples were pressed into disks at 200 kgf cm-2 for 30 s. The background intensity without the

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sample was subtracted from the intensity obtained for respective samples. Crystallinity index

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(CrI) of biomass samples were calculated according to the empirical method as given in Eq. 2

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proposed by Segal, et al. (1962).23 .% = /

.001 − .23.0 4 ∗ 100 +,. 2 .001

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Where CrI is the crystallinity index, (I002) is the highest peak intensity (002) at an angle of

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diffraction 2θ=22.4° and (I16.0) is the intensity diffraction for amorphous cellulose at 2θ=16.3°.

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Elements such as CHNS/O were analyzed in Vario EL III CHNS elemental analyzer.

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Elements present in the ash were determined by inductively coupled plasma absorbance emission

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spectroscopy (ICP AES) using a standard method.24 These elements were converted to their

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respective oxides. FTIR spectra of biomass samples were recorded using Prestige-21, Model

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No.A21004802514, FTIR instrument. All spectra were recorded in the absorbance mode from an


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accumulation of 128 scans at 4 cm−1 resolution in 4000–400 cm−1 range. Samples were analyzed

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by grinding with KBr (1:100, w/w) and pressing into pellets in drift mode.

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Extractives analysis in biomass samples is carried out using Milli Q water and 95% ethanol

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as solvent following a standard protocol LAP NREL/TP-510-4261925 by using Buchi multiple

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soxhlet extraction unit (Buchi Labortechnik, Model No. 1000133155), Switzerland.

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Compositional analysis of biomass samples were performed by two-step acid hydrolysis method

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developed by NREL, USA (NREL/TP-510-42618). 300 mg of dried biomass in 3 ml of 72%

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H2SO4 was incubated at 30°C while shaking at 300 rpm for 1 h. The material was diluted to 4%

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H2SO4 with distilled water and autoclaved at 121 °C for 1 h. The reaction was quenched by

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placing samples into an ice bath before neutralization. The hydrolysate (20 ml) was neutralized

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using lime to pH 5, clarified through 0.22 µm filter and subjected to sugar analysis using HPLC

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(Waters, Switzerland) fitted with Bio-Rad Aminex HPX-87P column using water as mobile

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phase (0.6 ml min-1, column temperature 75 °C) and Refractive Index detector. Degradation

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products (HMF, furfural and acetic acid) were analyzed using Water’s HPLC equipped with a

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Bio-Rad Aminex HPX-87H column and a UV detector using 0.05M H2SO4 as mobile phase (0.6

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ml min-1, column temperature 50 °C). Acid insoluble lignin (AIL) was calculated gravimetrically

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after ash correction as dry weight percentage of the samples. The crude protein content was

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estimated by the equation: % protein =% N *6.2 nitrogen factor (NF).26

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Statistical analysis was performed by one-way ANOVA followed Tukey's HSD post hoc

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tests using JMP software (SAS, US) and statistical significance were determined at 0.05 level (p

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< 0.05).

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3 Results and discussion 9 ACS Paragon Plus Environment

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3.1 The proximate composition, ultimate composition and higher heating value

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The proximate and ultimate composition and higher heating value of all biomass samples are

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shown in Table 2, which includes moisture content, volatile matter, fixed carbon and ash content,

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whereas ultimate analysis includes CHNS/O analysis. Fixed carbon indicates the extent of non-

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volatile organic matter present in the biomass and high heating value. CS contains highest fixed

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carbon (16.6%), which corresponds to the highest heating value (19.2 MJ/kg) whereas, CC has

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the lowest fixed carbon, i.e. 4.2% and hence lower heating value (13.3 MJ/kg). High heating

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value (19.2 MJ/kg) for CS is attributed to the presence of high hydrogen (6.4%) and carbon

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(46.8%) whereas, the lower heating value of CC was attributed to the presence of low hydrogen

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(5.9%) and carbon (44.2%).

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Volatile matter signifies the presence of volatile components in the biomass residue, which

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eliminate all organic moieties like cellulose, hemicellulose and most of the lignin as well as the

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moisture from the residue. Data summarized in Table 2, shows that CC has the highest volatile

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matter (80%) and lowest fixed carbon (4.2%), while WS has the lowest volatile matter (63%)

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and highest fixed carbon (15.9%). In general biomass with high volatile matter produces high

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quantities of bio oil and syngas, whereas fixed carbon increases the bio char production via

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thermochemical processes.8 Non-volatiles contain char with high content of heating value and

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ash forming components like silica and a part of lignin. RH, RS and WS shows lower volatile

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matter as these have much higher ash content (10.5 to 17.4%) whereas, CC, SCB and JP shows

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higher volatility due to lower ash content as shown in Table 2.

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CHNS/O analysis of biomass is given in Table 2 and is used for calculation of higher heating

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value using Eq.1. Carbon in all the samples is present in the range of 37.1-46.8%. Hydrogen lies

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in the range of 5.5-6.7% and oxygen as calculated by the difference method is in the range of


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36.0-49.4%. Nitrogen and sulfur are found in the range of 0.2-1.2 and 0.04-0.9% respectively.

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The higher content of oxygen in CC was responsible for the higher volatile matter (80%) but it

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lowers the higher heating value (13.3 MJ/Kg).

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In biomass, ash content may originate from the biomass itself, e.g. nutrient that the plant absorb

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from the water or the soil during its growth (Na, K, Fe etc.) or during harvesting, e.g. soil

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collected along with biomass. Ash content signifies the presence of metals but also inorganic

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materials like P, mostly in the form of oxides in the biomass residues. Estimation of the ash is

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necessary before processing the biomass for biofuel production as it may significantly impact the

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conversion of biomass to biofuels, especially in the biochemical process,27 moreover, these

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metals may deactivate the catalyst being used in the thermochemical process.28 Ca, Mg and K are

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the most abundant metals found in most of the biomass residues. Difference in metal elements

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might be due to the different physical demands for the mineral nutrition during the growth of

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plants.

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The metal composition of ash is presented in Table 3. During ash determination, all the

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metals are converted to their oxides, even though remain in a different form in the agricultural

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residues. Table 2 shows that RS and RH have higher ash content, i.e. 13.7% and 17.4%

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respectively as compared to other biomass which may be due to the presence of high content of

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silica (Table 3). A similar observation was reported by Binod et al. (2010).6 In literature, it is

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mentioned that enzymatic hydrolysis of RS, RH, WS and other biomass were affected by metal

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cations (Mg2+, K+, Ca2+, Al3+, Mn2+, Cu2+, Fe3+ and Zn2+) by affecting the cellulase enzyme

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activities.28 β-glucosidase activity was reported to be promoted by Ca2+ and Mg2+, while

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inhibited by K+. Data in Table 3 shows the presence of significant amount of metals which need

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to be taken into account before processing the biomass for biofuels production.


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The C/H ratio for each biomass is in the range of 5.8-7.8%. On C/H basis, it is observed that RS,

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WS, CS, JP and CRS have high energy content as they are rich in C/H ratio, whereas CC has a

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low higher heating value (13.3 MJ/kg) due to low C/H ratio as given in Table 2. Higher heating

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value of CS (19.2 MJ/kg) and JP (17.9 MJ/kg) shows their potential for the production of

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biofuels using thermochemical process due to presence of high amount of carbon 46.8 and 45.9%

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with high C/H ratio respectively.

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3.2 Thermal properties analysis Thermogravimetric analysis (TGA) provides the loss of weight in biomass with an

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increase in temperature as shown in Figure SI-1. TGA is basically an indicator for the

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optimization of process conditions for gasification and pyrolysis. Moreover, it is an indicator of

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the contents of moisture, cellulose, hemicellulose and lignin present in biomass. Thermal

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decomposition occurs in the order of hemicellulose, cellulose and lignin.13 From the analysis of

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these superimposed DTA curve that TGA-DTA curves were almost similar (Figure 1S-1 and

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Figure 2) and hence showing the similar components in different amount.

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Decomposition of lignocellulosic biomass is a complex process and involves a series of

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competitive and/or consecutive reaction and mainly occurs in four stages. First stage corresponds

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to drying phase in which unbound moisture is lost from 30-150 °C across different biomass

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samples, corresponding to 4-12% weight loss as given in Table 4. Maximum degradation

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temperature (Tmax) is observed from 52 to 70 °C. Second stage, contributes the depolymerization

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of hemicellulose from 150-350 °C with 43-54% due to the degradation, primarily a part of

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hemicellulose, cellulose and bound water. Tmax of hemicellulose in various samples is observed

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from 225.4 °C (SCB) to 287.9 °C (WS). The third stage is for cellulose degradation from 275-


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350 °C with 30-53% losses. Tmax of cellulose in various samples is observed from 323.6 (RS) to

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353 °C (SCB) (Table 4). Fourth stage involves the degradation of lignin from 330-550 °C with

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10-34% weight loss as given in Table 4. Lignin is composed of three kinds of benzene propane

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units, being heavily cross-linked and having very high molecular weight and hence has high

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thermal stability as compare to cellulose and hemicellulose. No prominent Tmax is observed for

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lignin, it may be due to the nonspecific and a very large variation of structural features and

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slower rate of decomposition.

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Higher Tmax shows higher thermal stability, which varies from one biomass to another

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depending upon the chemical constituents and their chemical structures.29 Hence, the thermal

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stability of all the three components varies significantly. Among the three major components, the

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hemicellulose is easiest to decompose due to branched structural features with short side chains

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and shows broad range of Tmax for different samples. Similar observation was reported by Wu et

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al. (2009).30 In contrast, cellulose is associated with semi-crystalline structure which makes it

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thermally more stable. The sharper Tmax shows abrupt weight loss of biomass observed for

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cellulose. That may be due to the highly specific and highly ordered structure of cellulose.

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3.3 XRD analysis

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Crystallinity index (CrI) was measured by the ratio of the intensity of the main crystalline

292

plane (002) at 22.4° and the amorphous at 16.3° of 2θ.23 X-ray diffractogram obtained for

293

biomass samples are given in Figure 3 and quantitative crystallinity was estimated by using Eq.

294

2. Table 4 summarizes the CrI for different biomass samples as calculated by peak intensity

295

method. The peak at I002 indicates the presence of crystalline material in the biomass and the

296

higher CrI is mainly due to lower amount of amorphous material, i.e. hemicellulose and lignin.31

297

It has been reported in the literature that strong crystalline arrangement of cellulose retards


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

298

enzymatic hydrolysis leading to lower yield of fermentable sugar as well as ethanol.31 Highest

299

and lowest CrI were found in RS (61.9%) and CC (43%) respectively. This shows that RS could

300

be less prone to enzymatic digestion than other biomass, whereas CC could be more susceptible

301

to enzymatic digestion.

302

CrI and DTA (Tmax) of cellulose are reported in the same order, i.e. increasing CrI leads

303

to increase in Tmax.29 However, in the current study, the results obtained from Figure 2 and 3, as

304

discussed above, show that the CrI and the Tmax in some cases follow the reverse order. These

305

results are contrary to cellulose and are unexplainable. However, crystallinity is dependent on the

306

degree of polymerization, content of cellulose, hydrogen bonding and the ordered structure. The

307

crystallinity discussed above only signifies the volume of ordered structure and hence other

308

factors also need a better understanding. Factors which possibly impact the CrI are being studied

309

in our laboratory.

310

3.4 Compositional analysis

311

Compositional analysis of all biomass samples is summarized in Table 5, revealing that

312

extractives, cellulose, hemicellulose, protein and lignin content vary significantly from one

313

biomass to another. Analysis of water extractives indicates a complex mixture of soluble

314

mineral, proteins and non-structural carbohydrates, etc. whereas, ethanol extracts indicate the

315

presence of proteins and fatty acids, etc. (characterization data is not reported here). Ethanol

316

extractives are in the range of 0.8-3.4% as given in Table 5. Cellulose is in the range of 28.3%

317

(CRS) to 39.5% (MS), whereas, hemicelluloses, 13.9% (RH) to 29.0% (CC) (Table 5). CRS has

318

lowest cellulose (28.3%) content and maximum hemicellulose is found in CC (29.0%) followed

319

by RS (19.9%) and CS (19.2%), as given in Table 5.


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320

Energy & Fuels

Lignin plays very important role in the biomass as a structural component which provide

321

tensile strength and rigidity to biomass residues.32 The AIL is in the range of 11.9% (RS) to

322

26.4% (RH), whereas, ASL is 1.4-2.5%. Maximum ASL is in SCB (2.7%) and minimum in RH

323

(1.4%) as given in Table 5. The protein content is in the range of 1.3 to 5.4% of dry biomass. All

324

these components except moisture will be beneficial to produce more biofuels by

325

thermochemical platform, whereas for biochemical, only structural carbohydrates are useful. An

326

ideal biomass for bio ethanol production should have higher amount of cellulose and

327

hemicellulose content than lignin and ash content. The highest content of sugars present in CC

328

(61.2%) and CS (58.6%) makes them a material of choice for bioethanol production.

329

CrI of RS (61.9%) was found to be higher than CC (43%) and this was due to the

330

difference in their hemicellulose and lignin contents. Hemicellulose and lignin contents in RS are

331

19.9 and 13.2%, respectively whereas in CC, 29.0 and 18.4% respectively (Tables 4 and 5).

332

However, Tmax of RS (323.6 °C) is lower than CC (332.3 °C). In general, higher the CrI, higher

333

is the Tmax.29 In the current study, the results obtained from Figure 2 and 3 show that the CrI and

334

the Tmax for some LCM do not follow the same trend. This is due to the effect of degree of

335

polymerization, cellulose and hemicellulose contents, hydrogen bonding and the ordered

336

structure on crystallinity and thermal properties of biomass.10

337 338 339

3.5 Fourier transform infrared spectroscopy FTIR spectrum shown in Figure SI-2 reveals that the peak at ~3388 cm-1 is originated due to

340

stretching of O-H groups for intra-molecular hydrogen bonds between cellulose chains. Two

341

bands at around 2920 and 2850 cm−1, related to asymmetric and symmetric methylene stretching

342

in the spectra of all of the LCM. The band at ~1629 cm-1 is due to water in the amorphous region


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

343

and 1053, 1035 and ~1157 cm-1 is attributed to the characteristic of C-O-C stretching (Ist two)

344

and C-O antisymmetric bridge stretching respectively. The bands around 1420-1430 cm−1 is

345

associated with the amount of the crystalline cellulose and the intensity of this band is more

346

pronounced in RS, MS and CS as compared to others due to high cellulose content (Table 4 and

347

Figure SI-2). Band due to vibration of β-glycoside linkage in cellulose is detected as a sharp peak

348

at ~896 cm-1 which represents the C1-H deformation with a ring vibration contribution for

349

amorphous nature. The bands at ~1246, ~1375 and ~1157 cm-1 are assigned to C-H stretching,

350

CH2 wagging and C-O stretching respectively in cellulose. Absorption bands at ~1629, ~1508

351

cm-1 are originated from lignin, which includes the aromatic skeleton vibrations involving both

352

C-C stretching. C=C of aromatic skeletal vibration in lignin appears in the region of 1500-1700

353

cm-1 and C-H symmetric and asymmetric stretching bands appears in the region of ~2900 cm-1.33

354

Stretching vibrations at 1246 and 1732 cm−1 is due to C=O and C-O bonds of the acetyl ester

355

units present in hemicelluloses. The intensity of 1732 cm−1 band is more pronounced in CC with

356

reduced intensity at1420-1430 cm−1 bands due to high hemicellulose content (29.0%) and low

357

cellulose content (32.2%) resulting in its lower crystallinity (43%), which is supported by XRD

358

also. However, RS, RH, MS and SS have high crystallinity (~ 58.5-62%) due to decreased

359

intensity of 1246 and 1732 cm−1 bands and increased intensity of 1420-1430 cm−1 bands (Table

360

4 and Figure SI-2).

361

4 Conclusion

362

The physicochemical characterization of all these biomass shows that CS, RS and MS are the

363

potential candidates for biofuel production due to higher heating value, devolatilization, cellulose

364

and hemicellulose content and are thus good for both the conversion platforms. Although, many

365

of these biomass are found with a higher amount of inorganic salt yet, all of the biomass show


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Energy & Fuels

366

good potential for biofuels production. The highest content of polysaccharide present in CC and

367

CS makes them a material of choice for bioethanol production. The higher heating value of CS

368

and JP make them suitable for thermochemical conversion to biofuels, which may fulfill the

369

biofuels demand. This is the first systematic report on physicochemical characterization of ten

370

biomass residues available in north India. This study provides the baseline of the future work on

371

biomass to biofuels. The impact of these physicochemical properties on the biochemical process

372

for production of ethanol is in progress.

373

Acknowledgements

374

The authors would like to thank the Department of Biotechnology (DBT) and Indian Oil

375

Corporation for supporting this work carried out at DBT-IOC Centre for Advanced Bio Energy

376

Research. Dr. E. Ramu is thanked for FTIR.

377

Supplementary information available: Fig. SI-1: Thermogravimetric analysis of

378

biomass samples; Fig. SI-2: FTIR spectrum of biomass samples.

379

This information is available free of charge via the Internet at http://pubs.acs.org/.

380 381

References

382 383 384 385 386 387 388 389 390 391 392 393 394 395 396

1. Kumar, A.; Kumar, N.; Baredar, P.; Shukla, A., A review on biomass energy resources, potential, conversion and policy in India. Renewable and Sustainable Energy Reviews 2015, 45, 530-539. 2. Haghighi Mood, S.; Hossein Golfeshan, A.; Tabatabaei, M.; Salehi Jouzani, G.; Najafi, G. H.; Gholami, M.; Ardjmand, M., Lignocellulosic biomass to bioethanol, a comprehensive review with a focus on pretreatment. Renewable and Sustainable Energy Reviews 2013, 27, 7793. 3. Naik, S.; Goud, V. V.; Rout, P. K.; Jacobson, K.; Dalai, A. K., Characterization of Canadian biomass for alternative renewable biofuel. Renewable Energy 2010, 35 (8), 1624-1631. 4. Saxena, R. C.; Adhikari, D. K.; Goyal, H. B., Biomass-based energy fuel through biochemical routes: A review. Renewable and Sustainable Energy Reviews 2009, 13 (1), 167178. 5. Damartzis, T.; Zabaniotou, A., Thermochemical conversion of biomass to second generation biofuels through integrated process design-A review. Renewable and Sustainable Energy Reviews 2011, 15 (1), 366-378. 17 ACS Paragon Plus Environment

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397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440

6. Binod, P.; Sindhu, R.; Singhania, R. R.; Vikram, S.; Devi, L.; Nagalakshmi, S.; Kurien, N.; Sukumaran, R. K.; Pandey, A., Bioethanol production from rice straw: An overview. Bioresource Technology 2010, 101 (13), 4767-4774. 7. Pasangulapati, V.; Ramachandriya, K. D.; Kumar, A.; Wilkins, M. R.; Jones, C. L.; Huhnke, R. L., Effects of cellulose, hemicellulose and lignin on thermochemical conversion characteristics of the selected biomass. Bioresource Technology 2012, 114, 663-669. 8. Jahirul, M.; Rasul, M.; Chowdhury, A.; Ashwath, N., Biofuels Production through Biomass Pyrolysis —A Technological Review. Energies 2012, 5 (12), 4952-5001. 9. Kapoor, M.; Raj, T.; Vijayaraj, M.; Chopra, A.; Gupta, R. P.; Tuli, D. K.; Kumar, R., Structural features of dilute acid, steam exploded, and alkali pretreated mustard stalk and their impact on enzymatic hydrolysis. Carbohydrate Polymers 2015, 124, 265-273. 10. Sasmal, S.; Goud, V. V.; Mohanty, K., Characterization of biomasses available in the region of North-East India for production of biofuels. Biomass and Bioenergy 2012, 45, 212-220. 11. Godin, B.; Lamaudière, S.; Agneessens, R.; Schmit, T.; Goffart, J.-P.; Stilmant, D.; Gerin, P. A.; Delcarte, J., Chemical Composition and Biofuel Potentials of a Wide Diversity of Plant Biomasses. Energy & Fuels 2013, 27 (5), 2588-2598. 12. Yu, Y.; Lou, X.; Wu, H., Some Recent Advances in Hydrolysis of Biomass in HotCompressed Water and Its Comparisons with Other Hydrolysis Methods†. Energy & Fuels 2007, 22 (1), 46-60. 13. Ramiah, M. V., Thermogravimetric and differential thermal analysis of cellulose, hemicellulose, and lignin. Journal of Applied Polymer Science 1970, 14 (5), 1323-1337. 14. Kumar, J. V.; Pratt, B. C., Determination of calorific values of some renewable biofuels. Thermochimica Acta 1996, 279, 111-120. 15. Raveendran, K.; Ganesh, A.; Khilar, K. C., Influence of mineral matter on biomass pyrolysis characteristics. Fuel 1995, 74 (12), 1812-1822. 16. Raveendran, K.; Ganesh, A.; Khilar, K. C., Pyrolysis characteristics of biomass and biomass components. Fuel 1996, 75 (8), 987-998. 17. Sluiter, A.; Hames, B.; Ruiz, R.; Scarlata, C.; Sluiter, J., Determination of structural carbohydrates and lignin in biomass. Laboratory Analytical Procedures (LAP), National Renewable Energy Laboratory (NREL), Golden, Co.p. 1-15. 18. Milne, T.; Brennan, A.; Glenn, B. H., Sourcebook of methods of analysis for biomass and biomass conversion processes. Springer: 1990.p. 300-371. 19. Sluiter, A., Determination of Ash in Biomass: Laboratory Analytical Procedure (LAP): National Renewable Energy Laboratory: 2008. Golden, Co.p. 1-5. 20. Parikh, J.; Channiwala, S. A.; Ghosal, G. K., A correlation for calculating HHV from proximate analysis of solid fuels. Fuel 2005, 84 (5), 487-494. 21. Kathiravale, S.; Muhd Yunus, M. N.; Sopian, K.; Samsuddin, A.; Rahman, R., Modeling the heating value of Municipal Solid Waste. Fuel 2003, 82 (9), 1119-1125. 22. Biagini, E.; Barontini, F.; Tognotti, L., Devolatilization of Biomass Fuels and Biomass Components Studied by TG/FTIR Technique. Industrial & Engineering Chemistry Research 2006, 45 (13), 4486-4493. 23. Segal, L.; Creely, J.; Martin, A.; Conrad, C., An empirical method for estimating the degree of crystallinity of native cellulose using the x-ray diffractometer. Tex Res J 1962, 29, 786 - 794.


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441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488

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24. Carrier, M.; Loppinet-Serani, A.; Denux, D.; Lasnier, J.-M.; Ham-Pichavant, F.; Cansell, F.; Aymonier, C., Thermogravimetric analysis as a new method to determine the lignocellulosic composition of biomass. Biomass and Bioenergy 2011, 35 (1), 298-307. 25. Sluiter, A.; Ruiz, R.; Scarlata, C.; Sluiter, J.; Templeton, D., Determination of extractives in biomass. Golden, Co.p. 1-12. 26. Li, H.; Foston, M. B.; Kumar, R.; Samuel, R.; Gao, X.; Hu, F.; Ragauskas, A. J.; Wyman, C. E., Chemical composition and characterization of cellulose for Agave as a fast-growing, drought-tolerant biofuels feedstock. RSC Advances 2012, 2 (11), 4951-4958. 27. Bin, Y.; Hongzhang, C., Effect of the ash on enzymatic hydrolysis of steam-exploded rice straw. Bioresource Technology 2010, 101 (23), 9114-9119. 28. Foust, T.; Aden, A.; Dutta, A.; Phillips, S., An economic and environmental comparison of a biochemical and a thermochemical lignocellulosic ethanol conversion processes. Cellulose 2009, 16 (4), 547-565. 29. Poletto, M.; Heitor, L.; Zattera, A. J., Native Cellulose: Structure, Characterization and Thermal Properties. Materials 2014, 7 (9), 6105-6119. 30. Wu, Y. M.; Zhao, Z. I.; Li, H. B.; He, F., Low temperature pyrolysis characteristics of major components of biomass. Journal of Fuel Chemistry and Technology 2009, 37 (4), 427432. 31. Bansal, P.; Hall, M.; Realff, M. J.; Lee, J. H.; Bommarius, A. S., Multivariate statistical analysis of X-ray data from cellulose: A new method to determine degree of crystallinity and predict hydrolysis rates. Bioresource Technology 2010, 101 (12), 4461-4471. 32. Munir, S.; Daood, S. S.; Nimmo, W.; Cunliffe, A. M.; Gibbs, B. M., Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresource Technology 2009, 100 (3), 1413-1418. 33. Goshadrou, A.; Karimi, K.; Lefsrud, M., Characterization of ionic liquid pretreated aspen wood using semi-quantitative methods for ethanol production. Carbohydrate Polymers 2013, 96 (2), 440-449. 34. White, E. M., Woody biomass for bioenergy and biofuels in the United States: a briefing paper. DIANE Publishing: 2010.


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

489 490

Table 1. Annual surplus biomass residue in India.1 Annual availability Theoretical ethanol potential* (Million metric tons/yr) (Billion liters) cotton stalk 52.9 17.9 Maize cob 27 9.1 Mustard stalk 8.7 2.9 Paddy straw 170 57.6 sugar cane bagasse 12.1 4.1 wheat sraw 112 38 Total 382.7 129.6 *Calculated as per BRDB, 2008: 1 ton dried biomass gives approximately 89.5 gallons of cellulosic ethanol34 Biomass

491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 20 ACS Paragon Plus Environment

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530 531

Energy & Fuels

Table 2. Proximate and ultimate analysis of various biomassa Analysis RS RH Proximate Analysis (% dry wt., w/w) Moisture 10.0 9.8 VM 65.0 64.0 Ash 13.7 17.4 b Fixed Carbon 11.3 8.8 Ultimate analysis (% dry wt., w/w) C 38.8A 39.8A H 6.7A 5.7B A N 0.2 0.5A c A O 38.8 39.8A S 0.2 0.2 C/H 5.8 6.9 Higher heating value (MJ/kg) Experimental 16.2 15.5 Calculated (Dulongs) 15.4 14.9 0.57 0.42 Std. Dev.

CS

WS

SCB

CRS

SS

MS

JP

CC

8.9 71.0 3.5 16.6

10.6 63.0 10.5 15.9

10 76.0 4.4 9.6

8.3 73.0 11.1 7.9

8.7 66.0 8.8 16.5

9.7 70.0 7.9 12.3

8.5 71.0 5.1 15.4

10.2 80.0 5.7 4.2

46.8B 6.4A 0.3A 46.8B 0.2 7.4

41.7C 5.0C 0.4A 41.7C 0.3 7.1

43.2D 6.2A 0.4A 43.2D 0.8 6.9

45.7B 6.3A 0.4A 45.7B 0.5 7.3

44.4D 6.2A 0.5A 44.4D 0.9 7.1

43.8D 5.9A 0.3A 43.8D 0.3 7.4

45.9B 5.9A 0.9B 45.9B 0.04 7.8

44.2D 5.9A 0.4A 44.2D 0.08 7.6

19.2 17.3 1.34

17.4 15.2 1.56

17.7 15.5 1.56

17.9 18.0 0.07

17.1 16.9 0.14

17.6 15.9 1.20

17.9 16.4 1.06

13.3 15.5 1.56

a

All experiments were done in triplicate and the mean is reported here. Statistical significance has been determined for those components which are present in higher amount. Values in the same row with different superscripts letters indicate significant difference at P≤0.05. b% of fixed carbon calculated by difference of moisture, ash & volatile matter. c % of O calculated from the difference of CHNS and ash.

532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 21 ACS Paragon Plus Environment

Energy & Fuels

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552 553 554

Page 22 of 27

Table 3. Metal oxide content (% dry wt., w/w) in biomassa Metal oxide Al2O3

RS

RH

0.135

CS

0.263 A

0.037 A

0.194 B

0.359

0.177 B

0.207

A

CRS

SS

0.181

0.184 0.585

JP

0.195 C

0.016 D

0.235A

0.075B

0.017C

0.094A

0.081B

0.16D

0.099A

0.123E

0.011C

0.261F

K 2O

1.323A

0.09B

0.826C

0.113B

0.097B

1.027D

1.205D

0.904C

1.653E

0.314F

MgO

0.483A

0.177B

0.188B

0.226B

0.144B

1.144B

0.486A

0.471A

0.555A

0.182B

Na2O

0.159A

0.054B

0.578C

0.351D

0.127A

0.121A

0.136A

0.132A

0.534C

0.257D

P 2O 5

0.123

0.183

0.079

0.075

0.103

0.739

0.448

0.110

0.283

0.153

TiO2

0.004

0.010

0.002

0.008

0.008

0.008

0.005

0.007

0.001

0.029

0.002

0.005

0.000

0.001

0.001

0.003

0.004

0.001

0.001

0.003

Oxides

2.604

1.069

2.141

1.421

0.945

3.953

3.152

3.781

3.885

2.173

SiO2c

11.086

16.301

1.349

9.049

3.465

7.167

5.628

4.209

1.185

3.477

c

0.831

0.739 D

0.099A

b

1.838

CC

Fe2O3

a

0.57

C

MS

0.276

b

0.414

SCB

CaO

ZnO

0.212

WS

All experiments were done in triplicate and the mean is reported. Sum of all oxides. Silica content is calculated by the difference of ash and total oxide. Statistical significance has been determined only for those metals which are present in higher amount. Values in the same row with different superscripts letters indicate significant difference at P≤0.05.

555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 22 ACS Paragon Plus Environment

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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

581 582 583 584

Energy & Fuels

Table 4. Crystallinity index and thermogravimetric analysis (weight loss%) a Analysis

RS

RH

CS

WS

SCB

CRS

SS

MS

JP

CrI (%) 61.9 58.8 56.0 58.0 52.6 56.0 55.0 59.6 57.8 Tmax. (cellulose,°C) 323.6 343.3 333.5 338.8 353.0 338.4 333.5 338.4 323.6 Tmax. 282.6 287.9 282.2 287.9 225.4 277.5 277.4 287.6 266.2 (Hemicellulose,°C) TGA stages (°C) Weight loss% Stage 1 6 7 6 5 4 6 6 7 8 Stage 2 46 43 54 47 42 53 44 49 50 Stage 3 38 37 46 35 34 33 53 31 32 Stage 4 21 20 10 13 34 25 16 14 12 a Ash content is included in Table 2 and all experiments were done in dublicate and the mean is reported.

585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 23 ACS Paragon Plus Environment

CC 43.0 332.3 287.3

9 51 38 29

Energy & Fuels

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619 620 621 622 623 624 625

Page 24 of 27

Table 5. Chemical composition (% dry wt.,w/w) of various biomassa (% dry wt., w/w) Water extractives Ethanol extractives Cellulose Hemicellulose Lignin (AIL) Lignin (ASL) Protein content

RS

RH

CS

WS

SCB

CRS

SS

MS

JP

CC

13.3A 3.4A 38.1A 19.9A 11.9A 2.2A 1.3A

6.2B 2.9A 32.4B 13.9B 26.4B 1.4B 3.3B

6.2B 1.4C 39.4A 19.2A 23.2C 1.6B 2.0C

10.9C 2.1D 36.6A 18.9A 20.3D 1.9A 2.5D

17.4D 2.2D 36.6A 18.7A 19.8D 2.7C 2.7E

17.6D 2.9A 28.3C 16.4C 21.5E 2.3A 2.6D

17.4D 0.8C 35.4A 17.4C 18.8F 2.5A 3.2B

8.9E 0.9C 39.5A 18.7A 22.5C 2.2A 2.1C

10.1C 2.8A 38.8A 16.4C 25.4G 2.5A 5.4F

12.6A 2.5A 32.2A 29.0D 15.8H 2.6A 2.6D

a

Ash content is part of chemical composition and is included in Table 2 and all experiments were done in triplicate and the mean is reported. Values in the same row with different superscripts letters indicate significant difference at P≤0.05.

626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 24 ACS Paragon Plus Environment

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658 659 660 661 662 663

Energy & Fuels

Figure captions

Figure 1. Schematic diagram of physicochemical characterization of biomass

664

Figure 2. Differential thermogravimetric analysis of biomass samples

665

Figure 3. X-ray diffraction pattern of biomass samples

666 667

Biomass

Physical analysis Higher heating value

Proximate analysis

Chemical analysis

TGA/DTA

XRD

FTIR

Moisture

Compositional analysis

Extractives

Volatile matter

ICP AES

Acid hydrolysis

Filtrate (Cellulose and Hemicellulose)

Fixed carbon ASL

Neutralisation

Ash Sugars by HPLC

668 669 670 671 672 673 674 675 676 677 678 679 680 681 682

Figure 1.


Ulitmate analysis CHNS/O

Residue (AIL)

Energy & Fuels

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683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703

Figure 2.


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704 705 706 707 708

709 710 711 712 713 714 715 716 717 718

Figure 3.


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