Study on kinetics and bio-oil production from rice husk, rice straw, bamboo, sugarcane bagasse and neem bark in a fixed-bed pyrolysis process

Journal Pre-proof Study on kinetics and bio-oil production from rice husk, rice straw, bamboo, sugarcane bagasse and neem bark in a fixed-bed pyrolysis process Neha Gautam, Ashish Chaurasia PII:

S0360-5442(19)32129-2

DOI:

https://doi.org/10.1016/j.energy.2019.116434

Reference:

EGY 116434

To appear in:

Energy

Received Date: 31 August 2019 Revised Date:

8 October 2019

Accepted Date: 25 October 2019

Please cite this article as: Gautam N, Chaurasia A, Study on kinetics and bio-oil production from rice husk, rice straw, bamboo, sugarcane bagasse and neem bark in a fixed-bed pyrolysis process, Energy, https://doi.org/10.1016/j.energy.2019.116434. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Study on kinetics and bio-oil production from rice husk, rice straw, bamboo, sugarcane bagasse and neem bark in a fixedbed pyrolysis process Neha Gautam, Ashish Chaurasia* Department of Chemical Engineering, Visvesvaraya National Institute of Technology, Nagpur - 440010, Maharashtra State, India ABSTRACT In this study, rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark were pyrolyzed in a fixed-bed pyrolyzer to examine the influence of operating conditions, such as the temperature of the pyrolysis process, residence time of volatiles, and reactor length, on the yield of bio-oil and individual gas components. The temperature of pyrolysis was varied from 350 to 650 °C at increments of 50 °C, and the length of the reactor was varied from 45 to 60 cm at intervals of 5 cm. The maximum bio-oil production of 46.93 wt% and the pyrolysis char of 26.2 wt% was obtained for bamboo at 450 °C. The highest amount of clean syngas (carbon monoxide and hydrogen) was produced for neem bark (52.61 vol%). The gaussian distributed activation energy model data exhibited a superior fit with the experimental data compared with the singlereaction model for bio-oil and all other individual component gases. The presence of C– H, C=C, alcohols and phenolic compounds indicated that the bio-oil obtained from all the biomass species could potentially be used as fuel. The steady-state mass and energy balances for the entire pyrolysis plant were obtained using the Aspen Plus simulation. Keywords: biomass species; pyrolysis; bio-oil; char; syngas; kinetics.

*

Corresponding Author E-mail: [email protected] Phone: 0712-2801784; Fax: 0712-2223969 1

Introduction In Central India, there exists a large forest area and a large amount of agribiomass is generated as waste. The straws and husks obtained from rice are the agricultural raw materials of rice processing industry. These materials are available in rice producing countries like India, Brazil, China, and South East Asia [1]. The production of rice in India reached 110.15 million metric tonnes in 2017–18 [2]. Furthermore, a considerable amount of sugarcane bagasse from the sugar processing industry is available readily in the vicinity as waste. A considerable amount of bamboo, bamboo leaves, and neem barks is available in forest areas. Because the region is far from the coast, motor fuels transported from afar are expensive. Thus, converting this large amount of forest and agri-waste biomass into a locally required transportation fuel is economically beneficial. Energy alternative India [3] estimates that these biomasses exhibit a favorable potential for the production of bio-oil and power. Moreover, the disposal of these biomasses in landfills causes ground water pollution and affects aquatic and terrestrial life [4]. Among various thermo-chemical processes, the fast pyrolysis process is the most suitable for the production of bio-oil and its further upgradation into transport fuels [5]. This process is generally conducted in the temperature range of 450–650 °C with a high heating rate and short residence time. In the fast pyrolysis process, char is formed as a solid product, bio-oil is formed as a liquid product, and noncondensable gases are formed as gaseous products [6]. Depending on the type of reactor, feedstock, and reactor operating conditions, the yield in percent by weight of feedstock varies as 30%–70% for bio-oil, 15–50% for solid char, and 15–20% for gas [7, 8]. Elliott et al. [9] conducted the pyrolysis of five different feedstocks in a fixed-bed reactor system

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and reported the production of bio-oil ranging from 31.6–60.7 wt% for different feedstocks and operating conditions. Chaurasia [10] developed the kinetic scheme of pyrolysis process and recently carried out studies [11, 12] to analyze the effect of particle geometries and shrinkage on pyrolysis product yields. The bio-oil produced from the pyrolysis process can be used for the generation of heat and power and as a transport fuel. This technology is industrially achievable and has a low cost [13, 14]. However, the bio-oil produced is inferior due to its lower heating value compared with that of fossil fuel, low thermal instability, highly corrosive and viscous nature, and high moisture content. Therefore, the characteristics of the produced bio-oil must be enhanced [15, 16]. The objectives of the present study are as follows: 1 To predict the yield of bio-oil and individual component gases at different operating conditions in a fixed-bed pyrolysis reactor for different biomass species, namely rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark. 2 To characterize the bio-oil and primary gaseous products obtained under various operating conditions for the different biomass species. 3

To estimate the chemical kinetics of bio-oil and all other individual component gases, such as carbon monoxide (CO), hydrogen (H2), methane (CH4), and carbon dioxide (CO2), for the different biomass species.

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To estimate the mass and energy balances for the entire process plant by using the Aspen Plus simulation software.

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Materials and methods The biomass species used in the present study were rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark. These biomass species were procured locally from a rice mill, sugar mill, and saw mill. The biomass species were sieved and ground to a particle size of less than 1000 µm. The moisture content in the biomass species was removed by placing them in an oven at 105 °C for more than 1 hour. The species were then stored in containers. In this research, a single-stage fixed-bed reactor was used for the production of bio-oil, syngas, and char. The reactor was operated in the temperature range of 350–650 °C to optimize the production of bio-oil. The effects of different operating conditions on the biomass were also analyzed to estimate the kinetic parameters of bio-oil and char for the different biomass species. Fourier transform infrared (FTIR) spectroscopy was conducted to analyze the chemical functional groups of bio-oil, and gas chromatography mass spectrometry (GC-MS) was performed to analyze the chemical composition of bio-oil. Furthermore, the steady-state mass and energy balances for the entire pyrolysis plant were obtained using the Aspen Plus simulation software. Experiments Bio-oil, char, and gases were produced from the different biomass species by using the pyrolysis reactor displayed in Figure 1. The biomass feed was sprayed into the reactor chamber through the feed hopper after it attained the desired pyrolysis temperature. A biomass feed of 50 g was introduced into the pyrolysis reactor during each experiment. The holding time of biomass in the reactor was approximately 15 min. The temperature of the pyrolysis reactor was controlled from 350 to 650 °C by using a proportional integral derivative controller. Residence times of 27, 30, 33, and 36 s were

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obtained by varying the length of the reactor to 45, 50, 55, and 60 cm, respectively. The produced gas and volatiles were carried downward in the condenser section by using a nitrogen flow of 2 L/min. The solid char generated in the reactor was removed after each experiment to measure its weight. The produced gas was collected in a Tedlar bag at the outlet of the bio-oil trap and analyzed through gas chromatography (GC). The bio-oil was collected from a U-shaped tube. The bio-oil stuck to the wall of the reactor was removed through washing with a mixture of chloroform and methanol (4:1) solvent, as described in our earlier studies [10, 17]. The reactor consisted of single stage that had an internal diameter of 44 mm and a thickness of 4 mm. The U-shaped bio-oil trap was connected to the lower end of the reactor. The details of the reactor design are available in our previous research [18]. The results for the proximate and ultimate analyses of rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark are presented in Table 1. The proximate analysis results indicated that rice husk had the highest ash content (22.5 wt%), followed by rice straw (12.61 wt%), sugarcane bagasse (7.43 wt%), bamboo (6.28 wt%), and neem bark (4.84 wt%). Sugarcane bagasse had the highest fixed carbon content (16.90 wt%), whereas rice husk had the lowest (5.50 wt%). Rice straw exhibited the highest amount of volatile matter (81.10 wt%), whereas rice husk exhibited the lowest amount (72.00 wt%). Rice husk and rice straw contained a considerable amount of ash, which included 70%–90% silica. Thus, rice husk and rice straw are useful if silica must be extracted from the solid char after pyrolysis. Neem bark, bamboo, and sugarcane bagasse contain very low amounts of ash. Therefore, neem, bamboo, and sugarcane are preferred energy crops for optimizing the yield of bio-oil and syngas in the pyrolysis and gasification processes, respectively.

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The ultimate analysis results for the biomass species are also presented in Table 1. Neem bark exhibited the highest hydrogen content (6.10 wt%), whereas rice husk exhibited the lowest hydrogen content (5.05 wt%). Neem bark exhibited the highest carbon content (43.43 wt%), whereas rice husk exhibited the lowest carbon content (35.92 wt%). Carbon and hydrogen positively contribute to the gross calorific value of the fuel by converting to carbon monoxide (CO), methane (CH4), and hydrogen (H2) during the pyrolysis and gasification processes. As seen from proximate and ultimate analysis of Table 1, the biomass considered in the present study are broadly classified on the basis of ash and carbon content present. The rice husk and rice straw has high ash with low carbon content whereas bamboo, sugarcane bagasse and neem bark has low ash with high carbon content. Pyrolysis products Rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark were pyrolyzed individually in the reactor. At the end of each experiment, the produced solid char was removed from the reactor to measure its weight. The bio-oil in the U-tube trap was collected, and the bio-oil stuck to the reactor wall was removed through washing with chloroform and methanol solution. During the experimental run, the produced gas was collected in a Tedlar bag. The gas was analyzed through GC to detect the composition of carbon monoxide, methane, carbon dioxide, and hydrogen for each biomass species considered. The functional groups and chemical composition of the bio-oil were analyzed using FTIR spectroscopy and GC-MS, respectively. Mathematical modelling and kinetic study Very few attempts have been made to model the pyrolysis kinetics of biomass species such as rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark. The

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most discussed model for the pyrolysis of biomass is the Koufopanos model [19], which has the following reaction mechanism: 2




Gases+Volatiles

Virgin Material→Intermediate

3 Char The aforementioned scheme has been extended to include the secondary

products of pyrolysis. The produced char reacts with the primary gaseous products to form a derivative of them [20]. This scheme was also referred to in our previous research [21]. The extended scheme is represented as follows: VB 1

2 3 +

(VG)1

(C)1

(VG)2 + (C)2

The aforementioned scheme considers the pyrolysis of virgin biomass (represented by VB) to provide the combined kinetic parameters of volatiles and gases. It indicates that volatiles plus gases (VG)1 and char (C)1 are generated through the primary pyrolysis reaction. The primary pyrolysis products further react to give secondary products, namely volatiles plus gases (VG)2 and char (C)2. However, the aforementioned scheme does not differentiate between the volatiles and gases produced. Considering this need of differentiation, others models of biomass pyrolysis were examined. Casajus et al. [22] proposed a kinetic model for the sewage sludge pyrolysis process. Tar (T) 1

3



Gas (G)

Sewage Sludge (S)→ Intermediate (I) 5 4 Gas (G)

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Char (C)

The aforementioned model does not fit the biomass species mentioned in this study and has complex kinetics. Therefore, this model was not considered. Chan et al. [23] proposed a model that explains the pyrolysis of biomass. The model of Chan et al. was developed for biomass with high cellulose content, such as newspapers (90%) and wood (85%–90%).

News print (Ws)

kg

Gas (Wg)

kit

Tar (Wt)

kic

Char (Wc)

The aforementioned model can be extended to the pyrolysis of low-cellulose biomass, such as rice husk, which contains 45%–50% cellulose; rice straw, which contains 32%–35% cellulose; sugarcane bagasse, which contains 25%–35% cellulose; and bamboo, which contains 40%–55% cellulose. The model can also be extended to the pyrolysis of high-cellulose biomass, such as neem bark, which contains 60%–68% cellulose. The experimental data obtained from the pyrolysis of biomass species in the present study were used to estimate the kinetic parameters. The kinetic parameters for the different biomass species were obtained by fitting the experimental data with a single-reaction model and distributed activation energy model (DAEM). The details of both these models are available in our previous studies [17, 24]. In the single-reaction model, the overall kinetics are considered. The experimental data were fitted to the firstorder kinetic equation [equation (1)]. In equation (1),  represents the rate of the reaction,  is the product yield at any time t, and ∗ is the product yield at a very high temperature and long residence time. 



=  ∗ −  

(1)

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Due to the limitation of the single-reaction model in fitting the experimental data for the entire range, the DAEM proposed by Xu and Kandiyoti [25] was also used. However, this model was also unable to suitably fit the experimental data. The DAEM has been modified in our previous studies [24, 26]. The product yields were fitted to the modified DAEM presented in equation (2). 

 = ∗   −       !

(2)

A continuous distribution function f ( E ) was used to describe the activation energies. The total individual components of the reactions are given by f ( E )dE , with the activation energies ranging from E to E + dE . The mean activation energy is denoted by E0i , and σ i indicates the standard deviation. The Gaussian distribution curve was approximated by f ( E ) . Integration was conducted over the residence time– temperature history in the reactor over all possible activation energies to predict the yields of bio-oil and individual gas components, such as methane, hydrogen, carbon monoxide, and carbon dioxide. Results and Discussion Numerous experiments were conducted to estimate the yield of the bio-oil and product gas from different biomass species in the pyrolysis reactor by varying the operating conditions. Influence of the pyrolysis temperature and reactor length on the product yield The influence of the reactor temperatures and reactor lengths on the yield of products obtained from the pyrolysis process was examined. Pyrolysis was conducted in the fixed-bed pyrolysis reactor in the temperature range of 350–650 °C at intervals of 50 °C by varying the length of the reactor from 45 to 60 cm at intervals of 5 cm. The temperature selected represents the actual temperature in the commercial pyrolysis unit 9

for the production of bio-oil. The changes in the product yield for rice husk pyrolysis with changes in the reactor temperatures and reactor lengths are illustrated in Figures 2– 4. Figure 2 indicates that the yield of char decreased as the temperature of pyrolysis increased because biomass combustion was enhanced at high temperatures. The bio-oil yield increased from 35 to 42 wt% when the temperature increased from 450 to 550 °C due to the increasing conversion of char into other forms. However, at temperatures higher than 550 °C, the yield of bio-oil decreased due to its secondary cracking to form increased gaseous components. The production of bio-oil was low at low temperatures possibly because the pyrolysis process was partially completed at low temperatures, which resulted in a low yield of bio-oil and high yield of char. Initially, when the temperature increased from 450 to 550 °C, the cracking reaction of highmolecular-weight hydrocarbons intensified, which led to an increase in the production of liquid and gaseous products and a decrease in the production of solid char. However, when the temperature increased further from 550 to 650 °C, secondary cracking of the liquid product occurred, which reduced the yield of bio-oil and increased the yield of gaseous products. This result is in agreement with the results presented by other researchers [27, 28, 29]. The yield of the gas produced during pyrolysis increased with the temperature of pyrolysis. The gas yield increased from 18.28 to 27.63 wt% when the temperature increased from 550 to 650 °C. Figure 3 illustrates the volume composition of individual gas components at different temperatures on a nitrogen-free basis for rice husk. The composition of each component was predicted as a function of the temperature for a reactor length of 55 cm, residence time of 33 s, and nitrogen flow rate of 2 L/min. Figure 3 indicates that the

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production of carbon monoxide, methane, and hydrogen was favored as the temperature increased. Carbon monoxide was the major component of the syngas obtained during the pyrolysis process. The proportion of carbon monoxide increased from 36.39 vol% at 450 °C to 41.39 vol% at 650 °C. The amount of carbon monoxide increased with the pyrolysis temperature due to the endothermic reaction in which carbon dioxide reacted with hot carbon. The methane gas yield increased from 11.96% at 450 °C to 15.45% at 650 °C due to secondary cracking reactions. The hydrogen yield was increased from 5.53% at 450 °C to 15.55% at 650 °C. A reduction reaction occurred between the water vapor formed during pyrolysis and hot carbon, which resulted in the formation of carbon monoxide and hydrogen. Therefore, the amount of hydrogen and carbon monoxide increased as the pyrolysis temperature increased. The yield of carbon dioxide decreased from 43.48% at 450 °C to 26.89% at 650 °C. Figure 4 illustrates the experiments conducted in the temperature range of 450– 650 °C at intervals of 50 ˚C for reactor lengths of 45, 50, 55, and 60 cm, which correspond to the residence times of 27, 30, 33, and 36 s, respectively. The yield of the bio-oil increased as the temperature increased from 450 to 550 °C. The maximum yield of the bio-oil was 42 wt% at 550 °C for a reactor length of 55 cm. As the reactor length was increased from 55 to 60 cm, the yield of the bio-oil decreased to 39.22% at 550 °C due to its secondary cracking to form increased gaseous components. The low bio-oil yield for temperatures less than 550 °C suggested that pyrolysis was not completed and that a high yield of solid char was obtained. An increase in the pyrolysis temperature beyond 550 °C promoted further thermal cracking of the feed, thereby improving the gas yield and decreasing the yield of solid residues and liquid. The decreasing yield of

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liquid products and increasing yield of gaseous compounds beyond 550 ºC could be explained by the secondary cracking reactions of volatile components. Figure 5 displays the optimum temperature conditions for achieving maximum bio-oil production from rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark. The pyrolysis of rice husk was conducted for reactor lengths of 45, 50, 55, and 60 cm, which corresponded to the residence times of 27, 30, 33, and 36 s, respectively. The optimum bio-oil production for rice husk was obtained at the reactor length of 55 cm which corresponded to the residence times of 33 while for other biomass species the optimum bio-oil production was observed for the reactor length of 60 cm, which corresponded to the residence times of 36 s. Figure 5 indicates the optimum temperature for maximizing the bio-oil yield from different biomass species. The maximum bio-oil production of 46.93 wt% was obtained for bamboo at 450 °C, whereas the lowest biooil production of 38.23 wt% was obtained for neem bark at 450 °C. The highest amount of char was observed for rice husk (39.75 wt%), followed by rice straw (34.95 wt%). The lowest amount of char was observed for neem bark (30.28 wt%). However, the char obtained from the biomass species also contained ash, as indicated by proximate analysis. As presented in Table 1, rice husk contained the maximum amount of ash (22.50 wt%), followed by rice straw (12.61 wt%). Neem bark contained the least amount of ash (4.84 wt%) among the biomass species considered. Thus, the net amounts of char present in the biomass species were as follows: rice husk, 17.25 wt%; rice straw, 22.34 wt%; bamboo, 26.2 wt%; sugarcane bagasse, 25.83 wt%; and neem bark, 25.44 wt%. Thus, the net amount of pyrolysis char was the highest for bamboo (26.2 wt%) and the least for rice husk (17.25 wt%).

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Figure 6 depicts the GC analysis results for carbon monoxide, hydrogen, methane, and carbon dioxide present in the different biomass species on oxygen and nitrogen free basis. The GC analysis was performed for the gases obtained at the optimum temperature condition of maximum bio-oil production for the different biomass species. Neem bark and sugarcane bagasse exhibited high hydrogen production at 450 °C. The highest hydrogen production of 20.72 vol% was exhibited by neem bark, whereas the lowest hydrogen production of 9.14 vol% was exhibited by bamboo. The highest carbon monoxide production of 39.65 vol% was observed for rice husk, whereas the lowest carbon monoxide production of 26.19 vol% was observed for sugarcane bagasse. Rice husk exhibited the highest methane production (14.6 vol%), whereas sugarcane bagasse exhibited the least methane production (7.83 vol%). High carbon dioxide production was observed for bamboo and sugarcane bagasse. Sugarcane bagasse exhibited the highest carbon dioxide production of 48.15 vol%, whereas rice husk exhibited the lowest carbon dioxide production of 33.16 vol%. The highest amount of clean syngas (carbon monoxide and hydrogen) was produced for neem bark (52.61 vol%), followed by rice husk (52.23 vol%) and rice straw (51.98 vol%). The lowest amount of syngas (44.03 vol%) was produced for sugarcane bagasse, followed by bamboo (44.37 vol%) . The gross calorific value of neem bark is high because it contains a high amount of hydrogen, which has a high gross calorific value.

Kinetic study Experiments and simulations were performed to estimate the kinetic parameters for the rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark biomass species. The frequency factor and activation energy were varied in a wide range during the simulation study to estimate the best-fit kinetic parameters. The best-fit kinetic

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parameters are the ones that provide minimum error between the experimental and simulated values. The kinetic parameters were estimated using the single-reaction model and Gaussian DAEM as mentioned in our previous studies [17, 24]. The “C” program was written to estimate the kinetic parameters of the bio-oil, carbon monoxide, hydrogen, methane, and carbon dioxide obtained during the primary pyrolysis process at different temperatures ranging from 350 to 650 °C for reactor lengths of 45, 50, 55, and 60 cm, which corresponded to the residence times of 27, 30, 33, and 36 s, respectively. Figures 7 (a) and 7 (b) illustrate the plots of the standard error versus the number of iterations. The plots in Figures 7 (a) and 7 (b) were employed to determine the kinetic parameters of the methane and bio-oil obtained from the pyrolysis of rice husk, respectively, by using the single-reaction model. The values of the frequency factor (A) and activation energy (E) were varied in the range of 105–108 s−1 and 80,000–120,000 J/mol, respectively, as the initial guess to obtain the best-fit kinetic parameters that provided minimum error (δ). As depicted in Figure 7 (a), the standard error for methane decreased from 7.93% to 2.55% in 18 iterations and then became constant. The optimum kinetic parameters for the methane obtained from rice husk pyrolysis were A = 106.41 s−1 and E = 110 kJ/mol. As displayed Figure 7 (b), the standard error for bio-oil decreased from 8.08% to 5.91% in six iterations and then increased to 8.21% with additional iterations. The optimum kinetic parameters attained in six iterations for the bio-oil obtained from the pyrolysis of rice husk were A = 105.49 s−1 and E = 90 kJ/mol. Figure 8 depicts the comparison between the experimental yield data for methane obtained at different temperatures by varying the residence time and the simulated yield data for methane obtained using the kinetic parameters estimated with the singlereaction model. The methane production increased with the residence time and

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temperature. A large deviation between the experimental and simulated values was observed at 450 °C. However, good fitting was observed between the model and simulated values as the temperature was further increased. As displayed in Figure 8, the single-reaction model deviated from the experimental data for low values of temperature. This result may be due to the fact that the single-reaction model only considers a single value of activation energy. Moreover, as indicated by Xu and Kandiyoti [25], a low value of activation energy was obtained when the single-reaction model was used to estimate the kinetic parameters. Therefore, the kinetic parameters should be calculated using the Gaussian distribution model, which considers the number of first-order reactions. The experimental data were further correlated using the Gaussian distribution model to obtain the best fit and to avoid the low values of activation energy obtained with the single-reaction model. Figure 9 (a) displays the plot of the standard error versus the iterations in the DAEM for the bio-oil obtained from rice husk pyrolysis. The activation energy (E) and frequency factor (A) were varied in a wide range to obtain the best-fit kinetic parameters that provided minimum error (δ). As displayed in Figure 9 (a), the standard error decreased from 6.079 to 0.098 in 24 iterations and then became constant. The optimum kinetic parameters attained for the bio-oil obtained from rice husk pyrolysis were A = 6.51×1016 s-1, E = 230 kJ/mol, and σ = 9.2 kJ/mol. Figure 9 (b) displays the plot of the standard error versus the iterations in the DAEM for the hydrogen obtained from the pyrolysis of rice husk. The standard error decreased from 1.190 to 0.094 in 19 iterations and then became constant. The optimum kinetic parameters for the hydrogen obtained from the pyrolysis of rice husk were A = 2.06×1016 s-1, E = 230 kJ/mol, and σ = 9.2 kJ/mol. Figures 10 (a) and 10 (b) display a comparison of the experimental data for the

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bio-oil and hydrogen yield, respectively, with the DAEM simulated values at different pyrolysis temperatures for a residence time of 36 s. Compared with the single-reaction model, the DAEM simulated data had a superior fit with the experimental data. Therefore, further simulations were performed using the DAEM to estimate the best-fit kinetic parameters for the methane, carbon monoxide, and carbon dioxide produced from rice husk. The DAEM simulated values and experimental data were compared for the bio-oil and methane yield of rice straw (Figure S1), bio-oil and hydrogen yield of bamboo (Figure S2), bio-oil and hydrogen yield of sugarcane bagasse (Figure S3), and bio-oil and carbon monoxide yield of neem bark (Figure S4) to predict the kinetic parameters by using the DAEM model. The yields were plotted as a function of the pyrolysis temperature for a residence time of 36 s. The DAEM simulated data fitted very well with the experimental data for all the biomass species. Similar simulations were performed using the DAEM model to predict the kinetic parameters of the remaining individual gas components for all the biomass species. Table 2 presents a comparison of the DAEM-predicted kinetic parameters obtained in this study for bio-oil with the corresponding results obtained in five previous studies [27, 30, 31, 32, 33]. A direct comparison was not possible due to the different biomass species used in different studies. However, the order of magnitude of the pre-exponential factor and activation energies for the biomass species used in the present study matched well with the data of other researchers. The percentage error (δ) between the experimental and DAEM simulated values in the present study was found to be lower than that in other studies. Table 3 presents a comparison of the DAEM-predicted kinetic parameters for carbon monoxide, methane, carbon dioxide, and hydrogen with the corresponding results in two previous studies [27, 33]. The order of magnitude of the pre-exponential factor and

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activation energies of the individual gas components matched well with the corresponding results in the two previous studies. Moreover, a minimum percentage error (δ) was observed between the experimental and simulated DAEM values for the individual gas components in the present study. Characterization of bio-oil The chemical functional groups of the bio-oil obtained at optimum operating conditions from the pyrolysis of rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark were accurately analyzed through FTIR spectroscopy. FTIR spectroscopy is a chemical analysis technique that involves the use of infrared rays for detecting the various functional groups in a sample. Bio-oil was collected from the bio-oil trap after the completion of the experiment at the optimum temperature condition which maximizes its production. The collected bio-oil was irradiated using infrared radiations over a wave number range of 500–4000 cm−1. The radiations are transmitted through the sample to varying degrees depending on the bonds present in the sample and their motions. These differences were obtained as peaks in the FTIR spectra of the samples, as displayed in Figure 11. Figure 11 depicts the number of peaks in the FTIR spectra for the bio-oil produced from rice husk at 550 °C, rice straw at 500 °C, bamboo at 450 °C, sugarcane bagasse at 450 °C and neem bark at 450 °C. The FTIR spectrum of bio-oil mainly consists of aromatic, aliphatic (alkanes, alkenes and alkynes) and oxygenated compounds. The spectra of different bio-oil samples indicated relatively similar chemical structure but there were differences in bond intensity of each sample. The differences in the bond intensity of various chemical functional groups of the bio-oil obtained from different biomass species and comparison with the literature [34, 35] is given in Table 4. The stretching band between 3600–3050 cm−1 for the O–H bond

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indicated the presence of water impurities, hydroxyl compounds and alcohols. The presence of alkanes was indicated by the stretching vibration band between 3000–2850 cm−1 for C–H stretching and between 1490–1325 cm-1 for C–H deformation. The C=O stretching with absorbance 1775–1650 cm-1 indicates the presence of fats, ketones, aldehydes, phenols and carboxylic acid. The presence of alkenes (unsaturated hydrocarbons) was indicated by the stretching band between 1680–1575 cm−1 for C=C stretching. The stretching vibration band between 1550–1475 cm−1 for –NO2 indicated the presence of nitrogeneous compounds. The decomposition of high-molecular-weight polymers into phenols, esters, ethers, and alcohols was indicated by the band between 1300–950 cm−1 for C–O stretching and O-H deformation. The presence of hydrocarbon groups C=C, C-H and alcohols indicated that the bio-oil could potentially be used as fuel. The chemical composition of the bio-oil obtained at optimum operating conditions from the pyrolysis of rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark was also examined through GC-MS at the Sophisticated Analytical Instrument Facility of Indian Institute of Technology Bombay. Helium was used as the carrier during GC-MS analysis. The oven temperature for GC-MS was maintained at 250 °C before the injection of 1 µL of 10% bio-oil in methanol. Figure S5 displays the mass spectra of the bio-oil obtained from rice husk, rice straw, bamboo, sugarcane bagasse and neem bark for a retention time of 0–25 minutes. Mass spectra peaks were identified for the retention times of known species by using the NIST98 library. Table 5, Table S1, Table S2, Table S3, and Table S4 presents the detailed list of chemical compounds in rice husk, rice straw, bamboo, sugarcane bagasse and neem bark bio-oil samples respectively and their retention time detected through GC-MS analysis. These

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tables indicate that the bio-oil obtained through these biomass species are mixture of organic compounds in the range of 2 – 22 carbons. As indicated these bio-oils consists mainly of alcohols, aliphatic compounds, ketones, aldehydes, phenols, acids, nitrogeneous compounds, pyridine and levoglucosan. The presence of similar compounds in bio-oils is also reported by Akancha et al. [36]. These results are also in good agreement with the results of FTIR study for the biomass species considered. The presence of phenolic compounds and alcohols indicates that these bio-oils have the good potential to be used as a bio-fuel. Aspen Plus modeling of the fast pyrolysis process A conceptual model was developed for the conversion of the rice husk, rice straw, bamboo, sugarcane bagasse, and neem bark biomass species into crude bio-oil, syngas, and char through fast pyrolysis by using the “Aspen Plus” process simulation software. The developed model was based on the experimental results generated in the existing pyrolysis setup. The model was simulated for the pilot-scale data available in USDOE Report No. PNNL-23579 [37]. The liquid phase and vapor phase calculations were performed using the activity coefficient method and ideal gas fugacity coefficient method, respectively. A part of the syngas and char was used internally as a fuel to generate process heat. A model compound list for the fast pyrolysis of bio-oil was adopted from USDOE Report No. PNNL-23579 [37] (Table 6). The selected compounds are present in most real bio-oil samples. Most of these compounds are also found in the bio-oil obtained from biomass species of rice husk, rice straw, bamboo, sugarcane bagasse and neem bark (Table 5, Table S1, Table S2, Table S3, and Table S4). Table 7 presents the list of compounds obtained after the hydroprocessing of bio-oil [37]. The general

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processing steps include biomass preprocessing, fast pyrolysis, bio-oil collection, char removal, and bio-oil upgrading. Figure 12 displays the steady-state Aspen Plus simulation flowsheet for the production of a bio-oil through fast pyrolysis. The biomass species used in this study were pretreated before being fed to the fast pyrolysis reactor. The biomass samples were ground to less than 2 mm and then dried to less than 10% moisture content. This pretreated biomass was sent to the fast pyrolysis reactor operated at 550 °C. In the reactor, the biomass was thermally degraded in the absence of oxygen to char, noncondensable gas (NCG), and condensable hot pyrolysis vapors. The output was fed to the cyclone separator, in which the char was separated from the NCG and hot pyrolysis vapors. The NCG and hot pyrolysis vapors were passed to the quenching process unit. The hot pyrolysis vapors were condensed to bio-oil in the quenching process unit, and a part of the NCG was used for fluidization in the pyrolysis reactor. The separated char was combusted in the char boiler to provide the process heat required for the pretreatment and fast pyrolysis process. Tables S5 and S6 present the mass and energy balances, respectively, for the fast pyrolysis process at a dried biomass feed rate of 20835 kg/hr. The information in Tables S5 and S6 can be used to perform techno-economic analysis (TEA) for the entire plant. Conclusions The bio-oil and syngas yields from the primary pyrolysis reaction were measured under different combinations of operating conditions for the rice husk, rice straw, sugarcane bagasse, bamboo, and neem bark biomass species. The amounts of individual components, such as carbon monoxide, hydrogen, methane, carbon monoxide, bio-oil, and char, in the different biomass species were measured at different

20

operating conditions to study their kinetics. The yield of the gas produced during pyrolysis increased with the pyrolysis temperature. The maximum bio-oil production of 46.93 wt% was obtained for bamboo at 450 °C, whereas the lowest bio-oil production of 38.23 wt% was obtained for neem bark at 450 °C. The highest net amount of pyrolysis char was observed for bamboo (26.2 wt%), whereas the lowest net amount of char was observed for rice husk (17.25 wt%). The highest amount of clean syngas (carbon monoxide and hydrogen) was produced for neem bark (52.61 vol%), followed by rice husk (52.23 vol%) and rice straw (51.98 vol%). Sugarcane bagasse exhibited the lowest production of clean syngas (44.03 vol%). Compared with the single-reaction model, the simulated DAEM data had a superior fit with the experimental data for biooil, hydrogen, methane, carbon monoxide, and carbon dioxide. FTIR spectroscopy and GC-MS were conducted to study the chemical composition of the bio-oil obtained from the pyrolysis process. The presence of C–H, C=C, alcohols and phenolic compounds indicated that the bio-oil obtained from all the biomass species could potentially be used as fuel. A steady-state Aspen Plus simulation model was developed to estimate the mass and energy balances for the entire pyrolysis process plant. The obtained values can be used to perform TEA for the entire plant.

Acknowledgments The authors wish to acknowledge the contribution of Visvesvaraya National Institute of Technology, Nagpur, India for providing experimental and other necessary facilities to carry out this research work.

21

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28. Nunes SM, Paterson N, Dugwell DR, Kandiyoti R. Tar formation and destruction in a simulated downdraft, fixedbed gasifier: reactor design and initial results. Energy Fuels 2007;21:3028-35. 29. Nunes SM, Paterson N, Herod AA, Dugwell DR, Kandiyoti R. Tar formation and destruction in a fixed bed reactor simulating downdraft gasification: optimization of conditions. Energy Fuels 2008;22:1955-64. 30. Várhegyi G, Bobály B, Jakab E, Chen H. Thermogravimetric study of biomass pyrolysis kinetics. A distributed activation energy model with prediction tests. Energy Fuels 2011;25:24-32. 31. Várhegyi G, Chen, H, Godoy S. Thermal decomposition of wheat, oat, barley, and brassica carinata straws. A kinetic study. Energy Fuels 2009;23:646-52. 32. Becidan M, Várhegyi G, Hustad JE, Skreiberg O. Thermal decomposition of biomass wastes. A kinetic study. Ind Eng Chem Res 2007;46:2428-37. 33. Jong WD, Pironea A, Wojtowicz MA. Pyrolysis of miscanthus giganteus and wood pellets: TG-FTIR analysis and reaction kinetics. Fuel 2003;82:1139–47. 34. Islam MR, Parveen M, Haniu H. Properties of sugarcane waste-derived bio-oils obtained

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37. Tews IJ, Zhu Y, Drenann CV, Elliott DC, Snowden-Swan LJ, Onarheim K, Solantausta Y, Beckman D. Biomass direct liquefaction options. Technoeconomic and life cycle assessment. USDOE Report No. PNNL-23579, 2014.

26

List of Tables: Table 1. Analysis of different biomass species. Table 2. Comparison of kinetic parameters of bio-oil in present study with other researchers that employed distributed activation energy model (DAEM). Table 3. Comparison of kinetic parameters of individual gas components in present study with other researchers that employed distributed activation energy model. Table 4. FTIR analysis of different biomass species – comparison with literature.

Table 5. Main chemical compounds of rice husk pyrolyzed bio-oil detected through GC-MS analysis. Table 6. Compounds used to model fast pyrolysis bio-oil. Table 7. Compounds used to model hydroprocessed products.

27

Table 1. Analysis of different biomass species. Biomass Proximate, wt% (Dry basis) species VM FC ASH Rice husk 72.00 5.50 22.50 Rice straw 81.10 6.30 12.61 Bamboo 77.73 15.99 6.28 Sugarcane 75.67 16.90 7.43 bagasse Neem bark 79.84 15.32 4.84

28

Ultimate, wt% C H 35.92 5.05 38.02 5.75 42.16 5.74 40.59 5.89

N 0.26 0.65 0.37 0.42

O 58.77 55.58 51.73 53.11

43.43

0.26

50.21

6.10

Table 2. Comparison of kinetic parameters of bio-oil in present study with other researchers that employed distributed activation energy model (DAEM). Researchers Varhegyi et al., 2011 Varhegyi et al., 2009 Becidan et al., 2007

Biomass species

A (s-1) Cornstalk, rice husk, 3.39×1013 sorghum straw, wheat 4.79×1014 straw Straw of cereals, 1.62×1012 ethiopian mustard 5.13×1018 Brewer spent grains, 4.07×1012 coffee waste, 7.94×1020 fiberboard Wood pellets 5.20×1011 Miscanthus sinensis 5.20×1011 Sweet gum hardwood 1×1013

De Jong et al., 2003 Boroson et al., 1989 This study Rice husk Rice straw Bamboo Sugarcane bagasse Neem bark

6.51×1016 6.76×1016 7.41×1019 6.76×1019 6.76×1019

29

E (kJ/mol) – 176–195

σ (kJ/mol) 0–37

δ (%) 0.09–0.38

– 167–232

2–35

0.21–0.47

– 175–236

0–33

0.13–0.40

170 164 234

4.16 4.16 21

38 21.34 1.6

230 230 230 230 230

9.2 9.2 9.2 9.2 9.2

0.0983 0.0988 0.0918 0.0836 0.0821

Table 3. Comparison of kinetic parameters of individual gas components in present study with other researchers that employed distributed activation energy model. Researchers

Biomass

De Jong et Wood pellets al., 2003 Miscanthus sinensis Boroson et Sweet gum al., 1989 hardwood This study Rice husk Rice straw Bamboo Sugarcane bagasse Neem bark De Jong et Wood pellets al., 2003 Miscanthus sinensis Boroson et Sweet gum al., 1989 hardwood This study Rice husk Rice straw Bamboo Sugarcane bagasse Neem bark De Jong et Wood pellets al., 2003 Miscanthus sinensis Boroson et Sweet gum al., 1989 hardwood This study Rice husk Rice straw Bamboo Sugarcane bagasse Neem bark Boroson et Sweet gum al., 1989 hardwood This study Rice husk Rice straw Bamboo Sugarcane bagasse Neem bark

Individual gas A (s-1) components CO 5.20×1011 6.50×1012 CO 5.20×1011 – 6.50×1012 CO 1×1013

E (kJ/mol) σ (kJ/mol)

δ (%)

168 – 298

4.16 – 27.44 1.80 – 3.05

162 – 298

3.33 – 27.44 1.40 – 3.90

236

23

1.2 0.0812 0.0801 0.0886 0.0948

CO CO CO CO

6.81×1016 7.76×1016 7.24×1019 5.25×1019

230 230 230 230

9.2 9.2 9.2 9.2

CO CH4

230 160 – 230

9.2 0.0949 6.65 – 17.46 0.05 – 0.60

162 – 298

4.99 – 13.30 0.15 – 0.76

CH4

5.50×1019 6.50×1011 – 3.10×1012 6.50×1011 – 3.10×1012 1×1013

242

23

0.10

CH4 CH4 CH4 CH4

3.97×1016 3.31×1016 3.31×1019 2.40×1019

230 230 230 230

9.2 9.2 9.2 9.2

0.0815 0.0974 0.0933 0.0858

CH4 CO2

230 152 – 206

9.2 4.16 – 7.48

0.0865 0.90 – 3.10

135 – 206

4.16 – 7.48

0.93 – 7.50

CO2

3.09×1019 5.20×1011 – 2.80×1012 5.20×1011 – 2.80×1012 1×1013

222

40

0.75

CO2 CO2 CO2 CO2

7.92×1016 7.76×1016 7.24×1019 9.12×1019

230 230 230 230

9.2 9.2 9.2 9.2

0.0848 0.0803 0.0929 0.0866

CO2 H2

7.24×1019 1×1013

230 249

9.2 17

0.0968 0.08

H2 H2 H2 H2

2.06×1016 2.51×1016 1.48×1019 1.26×1019

230 230 230 230

9.2 9.2 9.2 9.2

0.0942 0.0860 0.0954 0.0893

H2

3.63×1019

230

9.2

0.0886

CH4

CO2

30

Table 4. FTIR analysis of different biomass species – comparison with literature. Frequency Sugacane Rice husk Rice straw Bamboo Neem bark -1 Cause of peak bagasse range (cm ) (cm-1) (cm-1) (cm-1) (cm-1) [34, 35] (cm-1)

3600–3050

3000-2850

1775–1650

1680-1575

O–H stretching vibration

C–H stretching vibration

C=O stretching vibration

C=C stretching vibration

3284.18

3322.27

3332.39

3323.71

3314.07

2900.41

2903.31

2895.59

2897.04

2898.49

1693.68

1751.05

1710.07

1685.96

1708.62

1634.38

1633.89

1634.86

1635.34

1637.27

1516.74

1540.36

1524.45

1538.92

-

1395.73

1395.25

1394.28

1369.21

1362.94

1270.86

1271.82

1268.93

and

and

and

1050.53

1015.82

1051.01

–NO2 1550-1475

stretching vibration

1490-1325

C–H bending C–O stretching

1300–950

and O–H bending vibration

1271.34 and

1272.79

1016.78

31

Table 5. Main chemical compounds of rice husk pyrolyzed bio-oil detected through GC-MS analysis. Sr. Retention Compound Name Formula Molecular No. time Weight (min) 1 4.34 2,5-Dimethylfuran C6H8O 96 2 4.86 2-Butanone C4H8O 72 3 4.99 1-Acetoxy-2- propanone C5H8O3 116 4 6.06 Butyrolactone C4H6O2 86 5 6.18 1-Methyltetrazole C2H4N4 84 6 6.69 2-Hydroxy-2-cyclopenten-1-one C5H6O2 98 7 7.23 1,1-Divinylsiletane C7H12Si 124 8 8.06 1-Hydroxy-3-methyl-2-butanone C5H10O2 102 9 9.08 3-Methyl-1,2-cyclopentanedione C6H8O2 112 10 10.47 1-Acetyl-2-methylcyclopentene C8H12O 124 11 11.01 Cyclopropyl carbinol C4H8O 72 12 12.99 5-Isopropyl-5-methyl-3-heptyne-2,6-dione C11H16O2 180 13 14.09 2,3-Anhydro-d-galactosan C6H8O4 144 14 15.51 2-Isopropyl phenol C9H12O2 152 15 16.58 3-Methyl-3-cyclohexene-1-carbaldehyde C8H12O 124 16 16.78 2,6-Dimethoxyphenol C8H10O3 154 17 17.75 2,3-Dimethylcyclohexanol C8H16O 128 18 18.24 Vanillin lactoside C20H28O13 476 19 19.28 2-Decenoic acid C10H18O2 170 20 20.01 tert-Butylhydroquinone C10H14O2 166 21 20.65 4-Hydroxy-3-methoxyphenyl acetone C10H12O3 180 22 21.56 2-Propentyl diacetate C7H10O4 158 23 22.03 Hexose C6H12O6 180 24 22.91 3,4-Altrosan C6H10O5 162 25 25.12 Desaspidinol C11H14O4 210

32

Table 6. Compounds used to model fast pyrolysis bio-oil. Compounds Formula Wt% C H Acetic Acid C2H4O2-1 2.7 2 4 Ethylene glycol C2H6O2 0.1 2 6 Acetol C3H6O2-D1 2.9 3 6 Glycolaldehyde C2H4O2-D1 8.7 2 4 Guaiacol C7H8O2-E1 11.4 7 8 Furfural C5H4O2 2.7 5 4 Levoglucosan C6H10O5-N1 27.5 6 10 Water H2O 28.7 0 2 Oleic Acid C18H34O2 8.5 18 34 Ethylthioethanol C4H10OS 0.1 4 10 2-Pyrrolidone C4H7NO-D2 2.0 4 7 Pyrolignin 4.7 24 32 Total 100

33

O 2 2 2 2 2 2 5 1 2 1 1 4

N

S

1 1

Table 7. Compounds used to model hydroprocessed products. Compounds Formula Wt% C H Hexane C6H14 3.7 6 14 Dodecane C12H26 4.4 12 26 4-methylnonane C10H22 4.1 10 22 Ethylcyclopentane C7H14 2.9 7 14 1-methyl-1C8H16 3.7 8 16 ethylcyclopentane Cyclohexane C6H12 3.2 6 12 Propylcyclohexane C9H18 3.2 9 18 1,3-dimethyladamantane C12H20 4.7 12 20 1-ethyl-4-ethyl-Benzene C10H12 7.0 10 12 4-methylphenanthrene C15H12 24.4 15 12 Indeno[1,2,3-cd]pyrene C22H12 6.8 22 12 1,2-Diphenylethane C14H14 8.3 14 14 Indane C9H10 3.4 9 10 1-n-hexyl-1,2,3,4C16H24 17.1 16 24 tetrahydronapthalene 1-phenyl-Napthalene C16H12 2.9 16 12 5-Methyl-2-(1C10H14O 3.7 10 14 methylethyl) phenol 2-4-6-Trimethyl-pyridine C8H10N 4.4 8 10 Debenzothiophene C12H5S 4.1 12 5 Total 100

34

O

N

S

1 1 1

Figure Captions: Fig. 1. Experimental setup for pyrolysis reactor. Fig. 2. Product yields of pyrolysis process at different temperatures for rice husk. Fig. 3. The yield of methane, hydrogen, carbon monoxide and carbon dioxide produced at different temperatures from pyrolysis of rice husk. Fig. 4. The yield of bio-oil produced at different temperatures and lengths from pyrolysis of rice husk. Fig. 5. Optimum temperature condition for maximum bio-oil production for different biomass species on weight basis in a pyrolysis process. Fig. 6. Production of individual component gases at optimum temperature condition for maximum bio-oil production for different biomass species on volume basis in a pyrolysis process. Fig. 7. Simulation of standard error versus number of iterations for rice husk pyrolysis using a single reaction model (a) Methane (b) Bio-oil. Fig. 8. Fitting of single-reaction model data to the experimental methane yield obtained from rice husk as function of temperature and residence time. Fig. 9. Simulation of standard error versus number of iterations for rice husk pyrolysis using distributed activation energy model (DAEM) (a) Bio-oil (b) Hydrogen. Fig. 10. Distributed activation energy model (DAEM) prediction as a function of temperature for residence time of 36 s for rice husk (a) Bio-oil (b) Hydrogen. Fig. 11. FTIR spectrum of bio-oil produced at optimum temperature condition for different biomass species of rice husk, rice straw, bamboo, sugarcane bagasse and neem bark. Fig. 12. Bio-oil production via fast pyrolysis using Aspen Plus.

35

Fig. 1. Experimental setup for pyrolysis reactor.

36

45

Product yield (wt%)

40

35

30

25

20

Char Bio-oil Gas

15

450

500

550

600

650

0

Temperature ( C)

Fig. 2. Product yields of pyrolysis process at different temperatures for rice husk.

CO CO2

50 45

CH4 H2

Gas yield (vol%)

40 35 30 25 20 15 10 5 450

500

550

600

650

0

Temperature ( C)

Fig. 3. The yield of methane, hydrogen, carbon monoxide and carbon dioxide produced at different temperatures from pyrolysis of rice husk..

37

45

45 cm 50 cm 55 cm 60 cm

Bio-oil yield (vol%)

42

39

36

33

30 450

500

550

600

650

0

Temperature ( C)

Fig. 4. The yield of bio-oil produced at different temperatures and lengths from pyrolysis of rice husk.

38

Fig. 5. Optimum temperature condition for maximum bio-oil production for different biomass species on weight basis in a pyrolysis process.

39

Fig. 6. Production of individual component gases at optimum temperature condition for maximum bio-oil production for different biomass species on volume basis in a pyrolysis process.

40

9.0 8 8.5 8.0 Standard error (%)

6 5 4

7.5 7.0 6.5 6.0

3

5.5 2 5.0 0

3

6

9

12

15

18

21

24

27

30

0

2

4

6

Number of Iterations

8

10

12

14

16

18

Number of Iterations

(a) Methane (b) Bio-oil Fig. 7. Simulation of standard error versus number of iterations for rice husk pyrolysis using a single reaction model (a) Methane (b) Bio-oil.

30 0

1 Sim 450 C

0

2 Sim 500 C

0

3 Sim 550 C

Expt 450 C Expt 500 C

27

Expt 550 C

0 0 0

0

4 Sim 600 C

0

5 Sim 650 C

Expt 600 C

24

Expt 650 C

0 0

21

CH4 yield (vol%)

Standard error (%)

7

18

5

4

15

3

12 2

9 6

1

3 0 27

28

29

30

31

32

33

34

35

36

Residence time (s)

Fig. 8. Fitting of single-reaction model data to the experimental methane yield obtained from rice husk as function of temperature and residence time.

41

20

1.2

5

1.0

4

0.8

Standard error (%)

Standard error (%)

6

3 2

0.6 0.4

1

0.2

0

0.0 0

2

4

6

8

10

12

14

16

18

20

22

24

0

2

4

6

Number of Iterations

8

10

12

14

16

Number of Iterations

(a) Bio-oil

(b) Hydrogen

Fig. 9. Simulation of standard error versus number of iterations for rice husk pyrolysis using distributed activation energy model (DAEM) (a) Bio-oil (b) Hydrogen.

0.45

0.20 Experimental (Rice husk) Simulated (Rice husk)

Experimental (Rice husk) Simulated (Rice husk)

0.15

H2 yield (vol%)

Bio-oil yield (wt%)

0.40

0.35

0.30

0.10

0.05

0.25 450

500

550

600

0.00

650

450

500

550

600

0

Temperature ( C)

0

Temperature ( C)

(a) Bio-oil

(b) Hydrogen

Fig. 10. Distributed activation energy model (DAEM) prediction as a function of temperature for residence time of 36 s for rice husk (a) Bio-oil (b) Hydrogen.

42

650

18

20

Rice husk

Transmittance (%)

Rice straw

Sugarcane bagasse

Bamboo

Neem bark

4000

3500

3000

2500

2000

1500

1000

500

-1

Wavenumber (cm ) Fig. 11. FTIR spectrum of bio-oil produced at optimum temperature condition for different biomass species of rice husk, rice straw, bamboo, sugarcane bagasse and neem bark.

Fig. 12. Bio-oil production via fast pyrolysis using Aspen Plus.

43

Highlights •

Bamboo exhibited maximum bio-oil and net char production of 46.93 and 26.2 wt%.

•

Neem bark, rice husk and straw have syngas production of 52.6, 52.2 and 51.9 vol%.

•

Phenolic compounds and alcohols in these bio-oils show its potential as a biofuel.

•

The gaussian DAEM data exhibited a superior fit to the experimental data.

•

Mass and energy balances for the entire pyrolysis plant were obtained using Aspen.

VISVESVARAYA NATIONAL INSTITUTE OF TECHNOLOGY, NAGPUR - 440010 (INDIA) CHEMICAL ENGINEERING DEPARTMENT Dr. A S CHAURASIA M.E., Ph.D. (BITS, PILANI), AMIIChE, LMISTE Post Doctorate (N.C.L., PUNE) Post Doctorate (IMPERIAL COLLEGE, LONDON) Associate Professor of Chemical Engineering

Grams Phone Mobile Fax E-mail URL

: VNIT, NAGPUR : +91-712-2801784 : +91-9552385659 : +91-712-2223969 : [email protected] [email protected] : www.vnit.ac.in

October 08, 2019

Declaration of Interest Statement Title of Manuscript: Study on kinetics and bio-oil production from rice husk, rice straw, bamboo, sugarcane bagasse and neem bark in a fixed-bed pyrolysis process Name of Authors: Neha Gautam and Ashish Chaurasia I have read the guidelines of ‘Energy Journal’ applicable to reviewers/authors and agree to abide by provisions thereof. I hereby declare that: (i) I have no conflict of interest of any form pertaining to the proposed manuscript. (ii) No funding was received for this work. (iii) This manuscript includes the original research work done at Visvesvaraya National Institute of Technology (VNIT), Nagpur, India. (iv) All authors have seen the manuscript and approved to submit it to the journal (v) This work is not under consideration for publication elsewhere. (vi) The manuscript has been prepared as per the instruction Journal guidelines.

Thanking you With best regards A. S. Chaurasia