CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study

Accepted Manuscript CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study Ankita Gupta, Sonal K. Thengane, Sanjay Mahajani PII: DOI: Reference:

S0960-8524(18)30620-5 https://doi.org/10.1016/j.biortech.2018.04.097 BITE 19877

To appear in:

Bioresource Technology

Received Date: Revised Date: Accepted Date:

21 March 2018 22 April 2018 23 April 2018

Please cite this article as: Gupta, A., Thengane, S.K., Mahajani, S., CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study, Bioresource Technology (2018), doi: https://doi.org/10.1016/ j.biortech.2018.04.097

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CO2 gasification of char from lignocellulosic garden waste: Experimental and kinetic study Ankita Gupta, Sonal K. Thengane, Sanjay Mahajani* Department of Chemical Engineering Indian Institute of Technology Bombay, Mumbai - 400076, India E-mail: [email protected] Abstract

In this study, the dry leaves litter from jackfruit, raintree, mango and eucalyptus trees, lignin, and cellulose were characterized, pyrolysed, and evaluated for their char reactivity towards CO2 gasification using TGA. The differences in char reactivity were attributed to the difference in char morphology and the varying inorganic contents. The mineral analysis of biomass ash showed the presence of alkali minerals some of which could act as catalysts. The adverse effect of high silica content was also evident through the experimental results. The kinetic parameters for gasification reaction were determined using three different reaction models. A modified random pore model was investigated to account for the influence of inorganic content. The effect of external catalyst on CO2 gasification was also studied by adding potassium carbonate to biomass char and pellets. The results obtained from this study can be conveniently used in the design of a gasifier for lignocellulosic garden waste.

Keywords: gasification; characterisation; kinetics; garden waste; inorganic content Highlights: 

Lignocellulosic dried leaves, lignin and cellulose were characterized and pyrolysed



Different biomass chars evaluated for their reactivity towards CO 2 gasification in TGA



Catalytic effect of alkali & alkaline earth metals, and inhibition effect of silica studied



Kinetic parameters determined using three different gas-solid reaction models



Modified RPM accounting the influence of inorganic content successfully applied

1

Graphical Abstract

Nomenclature

w/w

weight by weight

r

rate of reaction (min-1)

R

reactivity (min-1)

x

conversion

w

weight of sample

t

time shape factor

L

Length of pores

ε

Porosity

S

Surface area

Ea

Activation energy

ko

Pre-exponential factor

n

order of reaction

c

parameter of Modified RPM

2

Subscripts and Superscripts

i

initial (weight), at any time t (reaction)

o

initial

p

parameter of Modified RPM

3

1. Introduction

Generation of municipal solid waste has increased tremendously in Indian cities on account of industrialisation, increased migration to urban areas, and rapidly rising population (Gupta et al., 2015). A metro city like Mumbai generates about 9000 metric tonnes of municipal solid waste (MSW) per day (Joshi et al., 2013). The MSW is comprised of both biodegradable and non-biodegradable wastes coming from residential, municipal, and commercial areas. The biodegradable portion of waste dumped in the landfills of metro cities consists of about 20 % garden waste (Pradhan et al., 2018). The garden waste considered in this study accounts for waste collected from road sweeping, community parks and gardens. Garden waste comprising mainly of leaves, grass and small twigs, has a low density (50-75 kg/m3) and hence occupies a lot of space in landfills. Mismanagement of such biodegradable organic waste not only results in loss of energy contained in it but also causes adverse environmental impacts, public health risk and other socio-economic problems. Hence, there is a need to manage garden waste through different methods such as composting, bio-methanation, combustion and gasification. There are several pros and cons associated with each of these methods. The relatively higher lignin and ash contents in dry leaves make them less preferable for composting and bio-methanation, and more preferable for thermochemical conversion option such as gasification.

The gasification of biomass using gasifying agent such as CO2 has the potential to reduce the greenhouse gas emissions substantially compared to the use of fossil fuels (Lahijani et al., 2015). There have been several studies on gasification of various types of biomass species like waste generated from wood (e.g. sawdust), agricultural farm residue, residues from vegetable oil industries, forests waste, etc. Gasification of biomass is usually carried out in a fixed bed downdraft gasifier for thermal applications owing to minimum tar formation and better process control and higher efficiency

(Sheth and Babu, 2009). A given gasifier

operates at different efficiencies for different feedstocks even at optimised process conditions. Hence, it is important to systematically characterise the types of biomass and biomass components to be used as a feedstock. Akhtar et al. (2016) characterized dry leaf litter from different Indian trees to investigate their performance as feedstock for gasification, bio-oil and bio-alcohol production. They studied the physical and chemical structures, surface area, crystallinity, degree of polymerization and particle size to investigate the performance of each feedstock as bio-fuel. The higher heating value (HHV) observed was in the range of 18-19 4

MJ/kg, and these leaves being good source of holo-cellulose (cellulose + hemicellulose) and lignin could be used for bio-energy production.

For a fixed feedstock, the performance of gasifier depends mainly on the rates of reactions occurring in combustion, pyrolysis, and gasification zones inside a gasifier. Hence, it is important to study the kinetics of reactions occurring in these zones, and propose a model validated with experimental data. The studies on intrinsic kinetics have been carried out in the past, for the char obtained after pyrolysis of the various types of coal and biomass (Huo et al., 2014; Wang et al., 2015). Gao et al. (2016) studied the effects of temperature and reactant partial pressure on rice husk char-CO2 gasification and used the random pore model to determine the intrinsic kinetic parameters based on the experimental data. Nilsson et al. (2014) performed gasification experiments on olive pruning and proposed kinetic equations to study the inhibition effect of H2 in case of steam and that of CO in case of CO2 gasification. Researchers have carried out experiments by varying temperature and partial pressure of the reacting gas using thermo-gravimetric analysis (Mandapati et al., 2012 ; Kirtania and Bhattacharya, 2015). It has been reported that the rate of gasification reaction is governed by the combined effect of its chemical composition and physical structure. The inorganic fraction of the chemical composition is observed to be a critical parameter affecting the gasification performance (Wang et al., 2015). These kinetic studies mainly based on either the volumetric reaction model, random pore model, or shrinking core model, do not highlight the effect of inorganic content. However, there are some studies that have formulated semiempirical models to account for the effect of inorganic content on the rates of gasification reactions (Dupont et al., 2011; Lin and Strand, 2013; Zhang et al., 2008). To further investigate the effect of minerals, studies have also been carried out either by washing the biomass (Fahmi et al., 2007; Duman et al., 2014) or by adding an external alkali mineral catalyst to it (Encinar et al., 2001; Bouraoui et al., 2016). Some experimental studies on char gasification have been extended to laboratory scale fluidized or fixed bed reactors (Veca and Adrover, 2014)

A gasifier could be run on different types of biomass either for thermal applications such as cooking or for power generation. In either application, producer gas is the main desired product that is obtained from gasification of char formed due to pyrolysis. It is char that gets converted in gasification zone of the gasifier. Hence, it is advisable to convert raw biomass species into char and study its gasification. The discussed literature above has primarily 5

focussed on biomass characterization studies and gasification kinetics with the objective of efficient utilization of available biomass as alternative energy resource. Some of these studies have reported gasification of high ash agro residues such as rice husk, wheat straw, and others, but a study on dry leaves litter or garden waste as feedstock is not evident in literature. The dry leaves litter collected mainly from road sweepings, parks and gardens have ash content varying from 5 to 20 % after being cleaned depending on the source trees. Though the separation and collection of any particular kind of leaves do not sound practically feasible, the fact that the leaves of different trees have different chemical composition makes it interesting to study these species individually as feedstock for processes like gasification. Furthermore, efforts are still on to propose a universal kinetic model that would explain the gasification kinetics for the char obtained from any kind of biomass.

The objective of the present study is to examine the reactivity of char prepared from different leaves litter towards CO2 gasification behaviour. The jackfruit, mango, eucalyptus, and raintree (i.e. Artocarpus Heterophyllus, Mangifera Indica, Eucalyptus Globulus, and Samanea Saman) tree leaves, were selected as representative examples, as they are commonly found in many parts of the subcontinent. Pyrolysis was carried out in an inert medium to obtain char for selected biomass species and main biomass components (lignin and cellulose). This was followed by gasification in CO2 medium at 800 oC, to understand the difference in the behaviour of these leaves during the reaction. The influence of char morphology and inorganic content on the rate of gasification reaction was also studied. Three different standard kinetic models namely, volumetric reaction model (VRM), shrinking core model (SCM), and random pore model (RPM) were applied to validate the experimentally obtained kinetic data. In addition, the semi-empirical model was also validated to explain the effect of ash catalytic behaviour during the reaction.

2. Material and Methods

The dry leaves litter of jackfruit, mango, raintree, and eucalyptus trees from surrounding premises of IIT Bombay campus in Mumbai which constitute major portion of garden waste, were selected for this study. It may be noted that there exists a significant variation in the composition of leaves depending on the location and the age of the tree (Orfao et al., 1999). The sorting of specific leaves from dry leaves litter is not feasible on a regular basis as it 6

would consume huge amount of labour work and time. In addition, the gravity of the problem would vary depending on the location and the season. Sometimes, it becomes difficult even to collect dry leaves litter as a whole if it is mixed with other components of MSW and inerts such as stones and soil. In the present study, analyses and measurements were done for the same batch of leaves throughout the experiments. These leaves were collected in the spring season from nearly 10 trees of each species, 1 kg each from a tree, within 100 metres of distance. The dry leaves were mixed, washed to remove the layer of soil and further dried. These dried leaves were powdered and screened to a size range of 90-150 microns using a vibratory sieve tray column containing sieves with appropriate mesh sizes. The laboratory grade versions of the major components of biomass, i.e. cellulose (microcrystalline cellulose with 50 µm average particle size), lignin (alkali lignin in brown powder form with 50 µm average particle size), were purchased from Sigma Aldrich, to compare the rates of pyrolysis and gasification with that of the dried leaves litter. The proximate analysis (dry basis), ultimate analysis (dry basis), higher heating value (HHV) (dry basis), and biomass composition (dry and ash free basis) of the species of interest and the procured lignin and cellulose are given in Table 1.

Table 1 Characterisation of lignocellulose biomass Jackfruit Mango Eucalyptus Proximate analysis (dry basis) (wt. %) Volatile 63±2 67±2 76±1 Fixed Carbon 18 23 20 Ash 19 10 4 Ultimate analysis (dry basis) (wt.%) C 37.5 44.7 51.6 H 4.9 5.7 7.0 N 0.7 0.6 1.3 S <.5 <.5 <.5 O 37.8 38.8 35.1 Biomass composition (ash free dry basis) (wt. %) Cellulose 34±3 40±3 37±3 Lignin 19±3 28±3 23±3 Hemicellulose 4±1 11±1 14±1 Extractives 22±5 24±5 28±5 16 18 20 HHV (MJ/kg) Ash mineral content (ppm) Al NA NA 1983 7

Raintree

Cellulose

Lignin

75±2 20 5

90±1 9.4 0.06

60±2 37 3

51.2 7.8 2.6 <.5 29.5

41.4 6.7 <.5 <.5 51.8

60.8 5.5 <.5 <.5 30.7

25±3 36±3 19±1 30±5 22

99 0 0 NA 16

0 99 0 NA 26

8725

8863

184

Ca Fe K Mg Mn Na Zn Si Ni P

120223 112 13625 4975 540 5938 21 263042 5 1850

160547 706 33427 10829 1157 4393 91 214303 3 3814

212693 2519 94351 24424 2308 17157 785 9555 70 8581

245380 12914 54172 23621 573 15118 218 47233 26 9640

109304 12628 23075 72624 199 28502 179 135543 34 NA

206 101 7160 418 81 197561 8 10000 NA NA

The extractives were analysed using hexane, ethanol, and water as per the procedure described in Akhtar et al. (2016), and cellulose, lignin, and hemicellulose were analysed from the extractive free biomass samples. The hemicellulose and cellulose contents of biomass was determined by Van Soest method, and the acid insoluble lignin content was determined by following the NREL protocol as given in Ayeni et al. (2015). The higher heating value of biomass was determined using Bomb Calorimeter (IKA C-200). All the species were found to have good energy potential with calorific values higher than 15 MJ/kg. The variation in the heating values was due to the differences in the O/C and H/C ratios of the biomass, extents of ash, extractives and their lignin contents (Demirbas, 2002; Singh et al., 2013). In order to determine the mineral content in the biomass, ICP-AES (Inductively Coupled Plasma Atomic Emission Spectroscopy) analysis was performed for the ash prepared by ASTM D1102-84 method and the results are given in Table 1.

2.1. Kinetic studies in TGA

In order to determine the intrinsic kinetic data for the biomass, the char sample was prepared by reducing leaves to 2-3 mm particle size using sieves, and heating in an inert medium at 10 oC/min to 800 oC in a fixed bed reactor. The pyrolysis process was continued till the pyrolysis gases stopped coming out from the reactor. The gases leaving the reactor were continuously analysed using Gas Chromatography equipped with a Thermal Conductivity Detector (NUCON 5765, India) containing carbosieve column for separating H2, N2, CH4, CO and CO2. An external standard with known gas composition, falling in the range of expected product gas composition, was used for quantification. The obtained char sample was then reduced to particles of sizes in the range of 50 to 90 µm. The experiments were carried out in Thermo Gravimetric Analyser (TGA) (NETZSCH STA 409) in the range of 7008

950 oC by spreading the evenly distributed thin layer of char on an alumina crucible with a thickness of less than 0.5 mm (Tanner and Bhattacharya, 2016). The sample was heated in inert atmosphere of nitrogen flowing at the rate of 100 ml/min. Once the desired temperature (700-950 oC) was attained the gas was switched from nitrogen to CO2, and the flow rate of CO2 was set to be 150 ml/min (Mandapati et al., 2012). The experiments were performed thrice to check reproducibility, and an error of less than 3 % was realized. Experiments were also performed to check the effect of the concentration of carbon dioxide. In case of char gasification process, the reactivity (Ri), in min-1 is defined as

Ri 

1 dw 1 dxi .  . w dt 1  xi dt

and, the gasification rate (

ri 

(1)

is defined as

dxi dt

(2)

where, xi and w are conversion and weight of char at any instant of time (Lahijani et al., 2013). The conversion is further defined as

xi 

w0  wt w0  w f

(3)

where, w0 is the weight at time t=0, wt is the weight at specific time t, and wf is the weight of ash left at the end of reaction (Huo et al., 2014). The reactivity of char is a function of reaction conditions such as temperature and gasifying agent, surface area, active sites and mineral content (Lahijani et al., 2015). In the present study, the curve for conversion versus time was plotted to understand the reactivity of the different biomass species.

2.2. Addition of catalyst

It is reported in literature that potassium shows higher catalytic activity on CO2 gasification rate as compared to other alkali minerals (Huang et al., 2009; Perander et al., 2015). From the 9

study of Zhang et al. (2014), it was observed that potassium carbonate (K2CO3) performs better as a catalyst for gasification reaction than any other salt of potassium. In present work, for the experiments on TGA, potassium carbonate was added to the biomass char over a range 5-40 % (w/w) with continuous stirring in deionised water till the sample was uniformly stabilised. This sample was then dried overnight in an oven at 80 oC. CO2 gasification of the catalytic biomass was then carried out in a thermo-gravimetric analyser at 800 oC in isothermal conditions.

For experiments on the fixed bed reactor, the catalyst was blended with biomass to form pellets and subjected to gasification in a fixed bed downdraft type gasifier of 20 mm diameter, equipped with a refractory lining as shown in Fig. 1. The biomass used was a mixture containing equal amounts of raintree and mango leaves. The amount of pellets used in the reactor was 15 g and the external catalyst concentration in the pellets was in the range 0 - 7 % (w/w). The pellets placed in the reactor were heated to 150 oC for 30 minutes, and then to 850 oC and maintained for 20 min, to ensure complete pyrolysis in a nitrogen atmosphere. The reactor was then cooled down to gasification temperature i.e. at 800 oC, and the gas was switched from N2 to CO2. The gas produced was passed through a series of ice-cooled water impingers to get rid of tar and particulates. The cooled gas was then collected in bladder to analyse composition using Gas Chromatography equipped with a Thermal Conductivity Detector (GC-TCD).

10

Fig. 1. Schematic for fixed bed downdraft gasifier system

3. Results and Discussion

3.1. Pyrolysis of garden waste and biomass components

In a fixed bed downdraft gasifier, there are four major processes, namely, drying, pyrolysis, combustion, and gasification, occurring simultaneously in different zones along the length of the gasifier. Since, the main purpose of the combustion zone is to supply heat to other zones, the performance of gasifier would be governed by the rates of reactions mainly in pyrolysis and gasification zones. Detailed investigations of the reaction kinetics in each of these zones would contribute towards better design of the gasifier. The reactions and operating conditions are different in pyrolysis and gasification zones and hence they need to be studied separately for their kinetics and product distribution. Pyrolysis of leafy biomass was carried out in a thermo-gravimetric analyser (TGA) to obtain char which then undergoes gasification. The volatiles produced are either combusted or reformed in the gasifier. Studies were also performed separately for thermal conversion of biomass components - cellulose, and lignin, procured from laboratory grade suppliers. Fig. 2a shows the thermal decomposition of all biomass species and the main components during pyrolysis. Fig. 2b & c represents DTG (derivative thermo gravimetric) curves for thermal degradation of biomass species and main components respectively. From Fig. 2a, the maximum weight loss of about 90 % was observed in case of cellulose, while lignin showed the least loss of around 60 %. As expected, the char obtained in the case of lignin was higher than that in the case of cellulose. The rate of thermal degradation of all the dry leaves litter lies between that of cellulose and lignin.

11

(a) TG curve for all species

(b) DTG curve for biomass

(c) DTG curve for Lignin and Cellulose

Fig. 2. TG and DTG curves for thermal degradation of different species

Since biomass is a complex mixture of several compounds, numerous reactions occur simultaneously during pyrolysis, leading to breakdown and formation of intermediate compounds. In Fig. 2b, a shoulder was observed around 270-280 oC for eucalyptus, mango and jackfruit. Eucalyptus had a sharper shoulder, representing more hemicellulose as compared to others. The maximum weight loss for the samples occurred at around 300350 oC, representing the decomposition of cellulose. Raintree showed a maximum weight loss rate of about 15 %, while jackfruit showed 11 % in the temperature range of 300-350 oC indicating the difference in their holo-cellulose (cellulose + hemicellulose) contents. Further weight loss during pyrolysis over 240-700 oC showed lignin decomposition. It was observed that lignin produces a large amount of char (40 %) as compared to cellulose (10 %), however, 12

in the present case, raintree which has a larger amount of lignin than jackfruit, yields less char (24 %) as compared to jackfruit (38 %). This is due to the lower ash content and higher volatile content in raintree (5 %) as compared to that in jackfruit (19 %). The next section reports the gasification results for char obtained for different biomass species, cellulose and lignin.

3.2. Gasification of biomass char

Fig. 3a, b show the conversion versus time curve for CO2 gasification of char obtained from different biomass species, lignin and cellulose. The char produced from cellulose was ~10 % and took more than 400 min to react fully whereas, lignin produced 40 % char and reacted completely within 60 minutes (Fig. 3b). If this difference in reactivity is attributed to the char morphology, then lignin should show larger char surface area than cellulose char. However, the surface area observed in case of lignin was only 0.5 m2/g similar to that reported in literature (Klapiszewski et al., 2013), while that of cellulose was 200 m2/g (Bridgwater, 2001). This indicates a possibility of another potentially important parameter related to inorganic content governing the reactivity of char. Similarly, the difference in the char reactivity for other biomass species can be attributed to several factors which are discussed in further sections. Some experiments were also performed to study gasification of mixed biomass in different proportions. Fig. 3c shows the conversion versus time curve of CO2-char gasification at 800 oC, for different mixtures in various proportions. The gasification conversion curve for a mixture was always found to be placed between the curves of individual components of corresponding mixture. For example, if the proportion of eucalyptus was increased, the conversion profile moved towards that of eucalyptus. Thus the mixture of biomass species did not show any significant synergism on the reaction rate. Hence, the further characterization and kinetic study was conducted on individual biomass species.

In the case of leaf litter, unlike pyrolysis, gasification behaviour cannot be explained only on the basis of the chemical composition in terms of lignin, cellulose, etc. For example, eucalyptus and mango with high holo-cellulose content exhibited different reaction rates. Eucalyptus char took only 40 min for complete conversion, while mango char took nearly 200 min. In case of raintree and jackfruit, though holo-cellulose amounts were comparable, they 13

showed difference in the gasification rates of corresponding chars. Raintree char conversion rate (80 % conversion) was two to three times faster than that of jackfruit. The difference might be due to the higher lignin content in raintree as compared to that in jackfruit. However, on comparing the behaviours of mango and eucalyptus, the rates cannot be directly correlated with relative lignin contents as mango had more lignin than eucalyptus but still the reactivity of its char was less. This showed that the reactivity of char produced from lignocellulosic biomass is difficult to explain based only on the composition in terms of holocellulose and lignin.

(a) Biomass char

(b) Char of Lignin and Cellulose

(c) Biomass mixtures in various proportions Fig. 3. Conversion versus time curve for CO2 gasification at 800 oC 14

The differences in the rates of reaction between char and CO 2 for various garden waste components are mainly due to the difference in inherent char characteristics, which include, char morphology or char matrix and the presence of inorganic contents (López-González et al., 2014). Di Blasi (2009) found that the reaction rate depends mainly on the surface area, surface accessibility, carbon active sites, and catalytic sites created by inorganic minerals inherited to biomass or added, and on the gasifying medium. The inorganics are present in the form of ion-pair bond with various functional groups of the biomass matrix (Benson and Holm, 1985). It was therefore necessary to understand the influence of char morphology, alkali and alkaline earth metals, which are inherited to the leaves from roots via stem.

3.2.1. Char morphology

The morphology of char is characterized by its surface area (available for gasification reaction), carbon active sites and catalytically active sites (López-González et al., 2014). The surface area of different biomass species was measured using BET (Brunauer–Emmett– Teller) (Model: Micrometics ASAP 2020) sorptometer using nitrogen adsorption at 77 K. The BET surface area of jackfruit, mango, raintree, and eucalyptus chars were determined experimentally and were found to be 80.5, 37.4, 2.8 and 1.9 m2/g respectively. This indicates the possibility of jackfruit being more porous and hence have higher reactivity. However, as observed from Fig. 3a, jackfruit char had lowest gasification reaction rate which implies that there are other parameters having more influence on the reaction rate that need to be identified and studied.

The extent of adsorption of CO2 depends on the number and the nature of active sites present on the char surface. The two types of active sites are carbon active sites associated with organic matter and catalytic active sites associated with inorganic matter in char (Xu et al., 2013). The amount of CO2 adsorbed and desorbed on each biomass char was determined by Temperature Programmed Desorption (TPD) process in a Temperature Programmed Desorption/ Reduction/ Oxidation (TPD/R/O) (Thermo Scientific, TPDRO 1100 series) reactor. The sample was heated in an inert medium of nitrogen at 20 oC/min to 800 oC, kept at 800 oC for about 20 minutes, and then cooled down to 25 oC. At 25 oC, a CO2 pulse was passed every 5 minutes to study CO2 adsorption during one hour of experiment. The same sample was then heated to 800 oC to study desorption. The second and third column of Table 15

2 represents the CO2 adsorbed and the produced CO determined by TPDRO. During the desorption process, the adsorbed CO2 reacts with char, and results in CO formation. The amount of CO produced during desorption was observed to be nearly double the amount of CO2 adsorbed at the room temperature. This stoichiometry observed during CO2 adsorption on and desorption from biomass char supports the occurrence of Boudouard reaction (C+ CO2  2CO). The extent of adsorption and desorption assists in calculating the total number of active sites. However, the nature of active sites could not be determined by this method based on TPDRO. Hence, the method proposed by Xu et al. (2013) was used to determine the proportion of carbon active sites and catalytic active sites. In this method, the char sample was heated to 800 oC in nitrogen, then cooled down to 300 oC in TGA and CO2 was passed over it for 40 minutes. This was followed by passing nitrogen over the sample at 300 oC for sufficient time to ensure complete desorption of CO2. The obtained weight loss corresponded to the organic or carbon active sites. Table 2 represents the organic active sites which are found to be less than the catalytic active sites in case of all the biomass chars.

Table 2 Active sites determined by TPDRO and TGA system Biomass

CO2 Adsorbed (µmol/g)

CO produced (µmol/g)

Carbon active sites (mg)

Catalytic active sites (mg)

Jackfruit

62

149

0.075

0.092

Mango

94

240

0.051

0.26

Eucalyptus

197

422

0.047

0.42

Raintree

157

314

0.065

0.31

Among the four biomass species, the CO2 adsorption is highest in case of eucalyptus and lowest in case of jackfruit. Jackfruit has more carbon active sites but less catalytic active sites than eucalyptus, which is probably one of the reasons for the lower reactivity of jackfruit char. Similar observation was made in the case of mango and raintree chars. Thus, the inorganic active sites govern the char reactivity to a greater extent than carbon active sites and surface area.

3.2.2. Inorganic Content

From the conversion versus time curves for different biomass chars reported in Fig. 3, the order followed in terms of the char reactivity calculated using Eq. 1 would be eucalyptus > 16

raintree > mango > jackfruit. The char reactivity for all the species is found to increase gradually up to 80 % conversion. However, there is a sharp rise in reactivity after 80 % conversion. This can be attributed to the increase in the ratio of inorganic content to the carbon at higher conversions. The inorganic content is primarily comprised of alkali and alkaline earth metals, and species such as silica, phosphorus, sulphur, and a few other trace elements. Most of these constituents are expected to have an effect on gasification rate of different biomass. However, the major influence is reported to be exerted by alkali and alkaline earth metals, and silica on the reaction rate of gasification (Dupont et al., 2011).

The alkali index has been widely used to evaluate gasification reactivity with high alkali index indicating high reactivity (Duman et al., 2014). The alkali index is the parameter that measures the ratio of basic to acidic oxides in the ash which is calculated by the following equation (Wang et al., 2016). The basic oxides generally act as a catalyst while acidic oxides act as an inhibitor.

Alkali Index ( A.I )  Ash%  (

CaO  K 2 O  Na 2 O  MgO  Fe 2 O3 ) SiO2  Al 2 O3

(4)

The alkali indexes for jackfruit, mango, eucalyptus, and raintree were found to be 10.4, 9.8, 121.7, and 31 respectively whereas that of lignin and cellulose were found to be 60.5 and 0.102 respectively. Mango and jackfruit chars took longer time for conversion as observed in Fig. 3a with similar conversion curves and have an alkali index value near 10. The relatively large proportion of silica in mango and jackfruit reduced their alkali index and hence the reactivity. Eucalytpus char with highest alkali index of 121.7 showed highest reactivity whereas jackfruit char with alkali index of 10.4 showed lowest reactivity. The presence of silica is expected to inhibit the reaction as it has tendency to form inactive silicate complexes with K, Ca, Mg, Na and Fe, which reduces the reactivity of char (Lahijani et al., 2015) or it has a tendency to encapsulate alkali and alkaline earth metals which reduces their catalytic activity (Bouraoui et al., 2015). These complexes or encapsulation could block the pores and hinder conversion. The formation of different complexes is addressed later in this section based on Fourier-Transform Infrared Spectroscopy (FTIR) results.

When the biomass chars were analysed using Scanning Electron Microscopy (SEM) before and after reaction, no significant changes were observed in their morphology. The ash of 17

different chars was analysed using Energy Dispersive X-Ray Spectroscopy (EDX) to confirm the percentage of inorganic elements and the results obtained were in agreement with those obtained by ICP-AES in Table 1. The ash residues obtained for eucalyptus and raintree were greyish in colour whereas the ash colour for jackfruit and mango was blackish, indicating the presence of some carbon trapped in ash. The fixed carbon content in ash residue was determined using TGA, and was found to be 8 % and 9 % on dry basis, in the case of jackfruit and mango, respectively. On the other hand, the ash of eucalyptus and raintree showed negligible (~0.5 %) fixed carbon content. FTIR analysis of all four biomass char samples was performed at different conversions (before reaction, after 50% conversion, after 80% conversion, at the end of the reaction) to confirm the presence of different possible complexes of inorganics. In case of jackfruit, species such as calcium carbonate (710, 875, ~1470, 2500 3000 cm-1), potassium meta-silicate (~1625 cm-1), silica salt of calcium/aluminium/iron (SiO-Fe/Ca/Mg) (~950 - 980 cm-1), disiloxanes, cylictrimers of silica and siloxanes (~1000 1150 cm-1), were observed which confirm the presence of organo-silicate and meta-silicate compounds. Similar compounds were observed in mango char but with relatively lower intensity peaks as compared to jackfruit char. However, most of these complexes were either negligible or in much smaller amount in case of eucalyptus and raintree chars. For the case of jackfruit char, these complexes increased with increase in conversion till 80 % but later started decreasing due to the reduced amount of carbon that could form bond with silica. The higher percentage of silica is expected to hinder the rate of gasification reactions irrespective of its form. The inhibition effect of silica has been reported by Bouraoui et al. (2016) over the K/Si mass ratio of 0.2-0.38 where they observed that potassium is well dispersed while silica tends to agglomerate forming complexes in the char structure affecting the reaction rates.

3.3. Gasification Kinetics

The rate of reaction is a function of temperature, concentration of gasifying medium, and other physical and chemical properties of solid. The overall gasification rate can be expressed as,

r

dx  k (T , P). f ( x) dt

(5)

18

where, k (T,P), considers the effect of temperature and partial pressure of CO2 on the reaction rate, while f(x) describes the physical and chemical changes that occur in the char as the reaction proceeds (López-González et al., 2014). In order to determine the kinetic parameters based on a known function f(x), several well-known models are available for heterogeneous gas-solid reactions. The three kinetic models used in this study are volumetric reaction model, shrinking core model and random pore model as shown in Table 3.

The experimentally measured rates in TGA, were used to obtain the kinetic parameters i.e., rate constant k and the order of the reaction n. The rate constant values at different temperatures were then used to determine the activation energy. Table 4 reports the intrinsic kinetic parameters obtained from different solid gas models for CO 2 gasification reaction of different chars. The activation energy values obtained in the present study are in the range of 208 - 250 kJ/mol, which are in accordance with the reported large number of activation energy values for CO2 gasification of biomass (Di Blasi, 2009). The order of the reaction (n) was found by varying the CO2 concentration from 10 to 50 percent of CO2-N2 mixtures. The order of reaction for all the biomass char species was found to vary in between 0.2 to 0.7. The positive value of n shows that the rate of reaction increases with increase in CO 2 concentration, especially while moving towards lower concentrations. Similar observation was made by Tanner and Bhattacharya (2016) in case of coal gasification.

Table 3 Summary of gas-solid reaction models Model name

Reaction rate

Model assumptions

Volumetric

First order reaction is controlling

Reaction Model

step, and it includes no structural

(VRM) (Mandapati

change as the reaction is occurring

et al., 2012)

throughout the char particle.

Shrinking Core

Reaction proceeds to the core

Model (SCM)/

from external surface by leaving

Grain model (GM)

the ash layer behind. The surface

(Mandapati et al.,

area decreases non-linearly as the

2012)

reaction proceeds.

19

Random Pore

There is a change in structure as

Model (RPM)

pores grow in the initial stages of

(Bhatia and

conversion and then destruct due

Perlmutter, 1980)

Lo, length of pore, So pore surface and to carbon burn-off. The rate εo solid porosity, respectively.

passes

through

maximum,

reaction proceeds.

Fig. 4 a, b, c & d shows the comparison between experimental results for the four biomass char species and the model predictions for the selected three reaction models. All the three models gave a good fit with the experimental data for the case of mango and jackfruit chars. However, in case of eucalyptus and raintree chars, the RPM model gave relatively better fit than VRM and SCM models. When the results were compared for their mean square errors, RPM model was the one with minimum deviation. In addition, the RPM model gave a better fit for all the biomass chars up to 80 % conversion. According to RPM theory, for the values of shape factor (Ψ) greater than 2, a maximum in the plot of reaction rate versus conversion is expected at conversions less than 40 % (Bhatia and Perlmutter, 1980). Jackfruit and mango chars have Ψ values less than 2, and raintree and eucalyptus chars have Ψ values greater than 2. The BET analysis confirmed that jackfruit and mango chars have much higher surface areas compared to that of raintree and eucalyptus. However, for the case of eucalyptus and raintree chars, with shape factor greater than 2, the maxima are not observed before conversions of 40 %. This is not in agreement with the criteria of RPM model. Hence, the satisfactory fit obtained by RPM for eucalyptus and raintree chars does not necessarily prove that the gasification proceeds according to the underlying theory of the RPM model, wherein, pores are assumed to evolve as the reaction proceeds. Hence, further improvement in the model is necessary. It may be noted that the basic RPM model does not explicitly consider the effect of inorganic species in the biomass. The presence of inorganic content was seen in intrinsic kinetic parameters in Kirtania and Bhattacharya (2015) in a way that it either reduces the activation energy or increases the pre-exponential factor or causes both. Therefore, to explain the effect of inorganic content, a modified RPM model is investigated in the next section.

Table 4 Intrinsic kinetic parameters from different gas-solid models for CO2 gasification reaction (particle size 50-90 µm)

20

as

Sample

VRM

SCM

RPM

n Ψ

Ea

ko

Ea

ko

Ea

ko

(kJ/mol)

(min-1)

(kJ/mol)

(min-1)

(kJ/mol)

(min-1)

Jackfruit

210±2

2.7E+08

210±2

2.5 E+08

210±6

4.3 E+08

0.5

0.65

Mango

217±2

7.9E+08

218±2

6.6 E+08

216±5

7.6+E08

0.6

0.51

Eucalyptus

243±8

5E+10

236±7

1.2E+10

240±7

2.5 E+10

20

0.20

Raintree

236±8

1E+10

233±7

8E+09

229±6

2.6 E+09

12

0.29

(a) Jackfruit char

(b) Mango char

(c) Eucalyptus char

(d) Raintree char

21

(e) Jackfruit char

(f) Mango char

(g) Eucalyptus char

(h) Raintree char

Fig. 4. Comparison of conversion profiles predicted by the various reaction models with the experimental conversions for CO2–char gasification at 800 oC 3.3.1. Modified random pore model

The standard kinetic models incorporate the effect of surface area and structural parameter changes during the reaction. However, these models need to be modified with additional terms to include the effect of inorganic content. López-González et al. (2014) used modified SCM by adding an additional term, which is dependent on the concentration of calcium. Similarly, Dupont et al. (2011) added a term relating to the concentration of potassium and silica to their kinetic equation. The in-depth effect of potassium and calcium was studied by Kramb et al. (2016) through a modified random pore model (RPM) that includes the effect of potassium and calcium on spruce wood gasification. Zhang et al. (2008) studied the effect of inorganic content in steam gasification of different biomass and modified the random pore 22

model to describe the experimental data over the entire conversion range. In the present work, the modified random pore model proposed by Zhang et al. (2008) was used to validate the experimental data and the constants were determined by fitting the equations in MATLAB. The model includes three parameters, the rate constant defined by RPM model, shape factor and other constants. The parameters determined from model for char of each biomass are shown in Table 5. The generalized equation is as follows:

dx  k (1  x) 1   ln(1  x) .(1  (cx ) p ) dt

(6)

where, c and p are constants which can be related to inorganic content (Zhang et al., 2008). Equation 6 is applicable to all biomass chars, with different values of constants c and p for different biomass. The modified RPM model appropriately fits the experimental data obtained for char gasification of all the biomass species as shown in Fig. 4 e, f, g & h. The values of c and p were observed to be close to zero in case of jackfruit and mango chars thereby reducing the modified RPM back to RPM. In case of eucalyptus and raintree chars, the values of c were nearly constant and the values of p followed an inverse relation with potassium content as reported in Zhang et al. (2008).

Table 5 Model parameters for the Modified Random Pore Model for biomass char-CO2 reaction at 800 oC Biomass

k (min-1).10-2

c

p

Jackfruit

0.66

~0

0.01

Mango

0.78

~0

0.01

Eucalyptus

1.96

1.06

2.61

Raintree

1.62

1.10

8.94

3.4. Catalyst Addition

The addition of the external catalyst such as K2CO3 is expected to increase the reaction rate of gasification. Fig. 5 shows the effect of catalyst addition on conversion rate, ash content and volatile content for four different biomass species. The effect on conversion rate is evaluated 23

in terms of a parameter named enhancement factor. The enhancement factor is calculated by measuring the shift in the slope of conversion versus time curves up to 50 % conversion point with respect to zero catalyst loading. From Fig. 5, it is observed that the addition of K2CO3 as catalyst enhanced the gasification rate for all biomass species significantly up to 20 % loading. The enhancement is very small beyond 20 % loading. Hence, the addition of catalyst to a certain amount is beneficial but the further slight increase in the rate of reaction does not commensurate with the added cost of the catalyst. The residual ash left after gasification, and the volatile content released in pyrolysis of char were found to increase with the increase in catalyst concentration. For example, in case of eucalyptus, the volatile content increased from 11 % (char with no catalyst) to 34 % (char with 40 % catalyst). This may be attributed to vaporization of potassium species (e.g. K2O) possibly arising out of the added catalyst at 800 oC (Skodras, 2013). The ash content of eucalyptus char increased from 20 % to 35 % on increasing catalyst loading from 0 to 40 %. However, the ash obtained in the case of catalyst addition was harder than normal ash generally obtained without catalyst addition. This difference in hardness of ash can be related to the process of agglomeration of softened ash resulting in formation of clinker, which is discussed elsewhere (Sharma and Mahajani, 2016). The entire inorganic content in biomass may not be present in free form and might be bonded to organic carbon of char as observed in FTIR results. These intermediate complexes and compounds may form an eutectic salt that could lower down the melting point of inorganic constituents (Arvelakis and Koukios, 2002). The inorganic constituents of the catalyst would further add to net ash content of char, and formation of such salts leading to hardening of ash. Perander et al. (2015) also reported the formation of intermediate compounds of potassium and carbon in case of catalyst addition. The addition of K2CO3 to the char of biomass species increases the overall potassium concentration in the char. The modified RPM model was applied to investigate the effect of potassium concentration on parameter p. The observed trend confirms that the parameter p varies inversely with potassium concentration as in the previous section.

The quantity of gas produced has not been studied after addition of catalyst to biomass char due to the limitations of the TGA set up. Hence, the series of experiments were performed in a fixed bed reactor linked with gas chromatograph to study the effect of catalyst on the gases produced. The set-up used for these experiments is shown in Fig.1 which needed minimum 10-15 g feedstock. The catalyst was added in different proportions to the biomass during the pelletisation step. Similar to the case of catalyst addition to char, it was observed that catalyst 24

addition to biomass pellets also enhances the reaction rate. However, in the case of pellets experiment, it was possible to analyse the gases produced. From the gas analysis it was observed that the cumulative amount of CO produced in the case of non-catalytic and catalytic pellets did not show significant difference as shown in Table 6. Though the addition of external catalyst did not change the product distribution, it reduced the time taken for complete conversion.

(a) Jackfruit char

(b) Mango char

(c) Eucalyptus char

(d) Raintree char

Fig. 5. Effect of catalyst addition on conversion rate, ash content and volatile content on the gasification 25

Table 6 Effect of adding catalyst to biomass pellets Concentration of catalyst

Amount of gases produced(ml)

Time taken for 100%

in pellets (wt. %)

CO

CO2

H2

conversion (min)

No catalyst

5450

58896

236

225

3%

5350

54785

250

140

7%

5300

59083

226

110

4. Conclusion

Gasification of char of different lignocellulosic species is influenced by char morphology and inorganic content, later being the major factor. Leaves litter with higher silica contents were least reactive due to formation of intermediate complexes. A modified RPM model accounting for inorganic content was applied successfully to explain the experimental data. The catalyst effect was studied by adding potassium carbonate to the biomass, which enhanced the rate of gasification reaction. Addition of catalyst beyond a limit was not preferred as it did not have significant impact on reaction rate and would increase the cost and problems such as clinker formation.

Acknowledgements

This research has been funded by the Tata Centre for Technology and Design, Indian Institute of Technology Bombay, India, through project DGDON 422 and Ministry of Human Resource Development, Government of India for providing fellowship to Ph.D. student.

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30

Supporting Information

Chemisorption curve for biomass char in TGA using Xu et al. (2013) method

31

(a) Jackfruit Char

(b) Mango Char

(c) Eucalyptus Char

(d) Raintree Char

(e) Jackfruit Ash

(f) Mango Ash

(g) Eucalyptus Ash

(h) Raintree Ash

SEM and EDX image of different biomass char and biomass char ash

32

a)

b)

c)

d) FTIR analysis of biomass char reaction with CO2 at 800 oC at various conversions a.) Jackfruit, b) Mango, c) Eucalyptus, d) Raintree 33

Highlights: 

Lignocellulosic dried leaves, pure lignin and cellulose were characterized and pyrolysed



Different biomass chars evaluated for their reactivity towards CO 2 gasification in TGA



Catalytic effect of alkali & alkaline earth metals, and inhibition effect of silica studied



Kinetic parameters determined using three different gas-solid reaction models



Modified RPM accounting the influence of inorganic content successfully applied

34

35