Modeling and Experimental Validation of Cashew Nut Shell Char

2070

Energy & Fuels 2008, 22, 2070–2078

Modeling and Experimental Validation of Cashew Nut Shell Char Gasification Adopting Chemical Equilibrium Approach M. Venkata Ramanan,* E. Lakshmanan, R. Sethumadhavan, and S. Renganarayanan Institute for Energy Studies, College of Engineering, Anna UniVersity, Chennai 600025, India ReceiVed August 4, 2007. ReVised Manuscript ReceiVed January 5, 2008

Cashew nut shell, a waste product obtained during deshelling of cashew kernels, by past field experience, had been deemed as a fuel unfit for gasification, owing to its high occluded oil content. The oil, a source of natural phenol, oozes upon gasification, thereby making the gasifier throat, downstream equipment, and associated utilities clogged with oil, leading to ineffective gasification and premature failure of utilities because of its inherent corrosive characteristics. To overcome this drawback, the cashew shells were deoiled, by charring them in closed chambers, and were subsequently gasified in an autothermal down-draft gasifier. Stoichiometric and nonstoichiometric (adopting Gibbs free-energy minimization concept) equilibrium modelings were carried out to predict the behavior of the system under varying performance-influencing parameters, viz., equivalence ratio and moisture content. The outcome of the modeling was compared to that of experimental results. The trends of both the models predicting gas composition is observed to be similar; however, the magnitude of composition predicted by them varies, albeit marginally. The developed model satisfies well with the experimental outcome at the equivalence ratio (ER) as applicable to gasification systems, i.e., 0.3-0.4. The sensitivity analyses revealed that (i) the mole fraction of H2, CO, and CH4 decreases while CO2, N2, and H2O increases with ER and (ii) H2, CH4, CO2, N2, and H2O increases, while CO decreases with the moisture content. The deviation among stoichiometric and nonstoichiometric models, with respect to the experimental outcome, was observed to be at a minimum for H2 while at a maximum for CO2. The higher heating value (HHV) of the gas predicted by stoichiometric and nonstoichiometric models was observed to deviate from the experimental results by +17.89 and +1.32%, respectively.

1. Introduction Biomass, the general term used for referring to organic matters, such as wood, plants, vegetable oils, and materials such as manure and sewage sludge, is often inhomogeneous feedstock. Owing to the rise in conventional fuel costs and its difficult sourcing, attention has now been focused toward the use of biomass. Cashew nut shell (Anacardium occidentale Linn.), one of the gristly biomass, is observed to possess enormous potential as a promising renewable energy source for India.1 India is the largest producer, processor, and exporter of cashew kernels in the world.2 Cashew cultivation in India encompasses an area of about 0.84 million hectares, producing over 573 000 metric tons of raw cashew nuts per year. The world production of cashew nut kernel was pegged at 907 000 metric tons in 1998. Hence, India accounts for about 65% of the cashew kernel production in the world. Cashews, upon processing for recovery of cashew kernels, generate cashew nut shells as byproducts. The cashew shell, formed as a protective layer for the kernel, is leathery in nature and has a thickness of about 1/8 in., constituting about 65% by weight of the raw nut. These shells possess a soft honeycomb structure embracing a dark reddish brown viscous liquid termed cashew nut shell liquid (CNSL). The CNSL is reported to be * To whom correspondence should be addressed. Telephone: +91-4422203269. Fax: +91-44-22353637. E-mail: [email protected] and/or [email protected]. (1) Das, P.; Sreelatha, T.; Anuradda, G. Biomass Bioenergy 2004, 27, 265–275. (2) Patel, R. N.; Bandyopadhyay, S.; Ganesh, A. Bioresour. Technol. 2006, 97, 847–853.

15–20% by weight of the unshelled nut in Africa and 25–30% by weight in India.3 Gasification, a thermochemical process involving substoichiometric combustion, is one of the effective and efficient technologies for conversion of biomass to energy. The major advantages of gasification systems include better turn down ratio, modularity, and fuel versatility. Attempts had been made toward conversion of raw cashew shells into a burnable low British thermal unit (Btu) gas-adopting gasification route. However, the inherent CNSL content of these shells made the gasification technology a nonfeasible one for the long run.4 To overcome the drawback posed by CNSL, the shells were deoiled by charring them. A rectangular chamber made of stainless steel (SS) was loaded with raw cashew shells. The bottom plate of the chamber was inclined at 10° to the horizontal, and provisions were made along the side plate for recovery of CNSL. The shells were heated with hot flue gas obtained from combustion of fire wood. The flue gas was passed along the sides and bottom of the chamber. Prolonged heating of cashew shells led to oozing of CNSL. Extracted CNSL finds abundant applications as a cheaper alternative to phenol. The characteristics of raw and charred cashew nut shell are presented in Tables 1 and 2. The main objectives of this study are (a) gasification of cashew nut shell char (CNSC) in a down-draft gasifier, (b) to model the CNSC gasification process using the chemical equilibrium (3) Das, P.; Ganesh, A. Biomass Bioenergy 2003, 25, 113–117. (4) Research and Development report. Decentralized Power Generation through Carbonization and Gasification of Cashew Shells; Institute for Energy Studies (IES): Anna University, 2006.

10.1021/ef700467x CCC: $40.75  2008 American Chemical Society Published on Web 02/27/2008

Cashew Nut Shell Char Gasification

Energy & Fuels, Vol. 22, No. 3, 2008 2071

Table 1. Proximate Analysis of Raw and Charred Cashew Shells

moisture volatile matter fixed carbon ash

CNS (wt % on as received basis)

charred CNS (wt % on as received basis)

10.43 69.31 19.26 1.00

7 28 59 6

Table 2. Ultimate Analysis of Raw and Charred Cashew Shells

carbon hydrogen nitrogen oxygen

CNS (wt % on as received basis)

charred CNS (wt % on as received basis)

48.7 6.96 0.36 43.98

63 3.6 6.4 27

concept, and (c) to compare and analyze the model outcome with the experimental results. 2. Modeling of Gasification Systems Modeling of a complex physio-chemical system is largely a process of simplifying the system to mathematical equations. Gasifier modeling is normally carried out by two techniques, viz., mathematical modeling and chemical equilibrium modeling. A mathematical model uses the knowledge of gasifier dimensions and operating conditions to calculate the nature of changes that occur in the gasifier to predict the output. Because mathematical models rely on knowledge of reaction rates, they are also termed as kinetic models. These models are more suitable for small reaction rates, because the equilibrium concepts in these reactions are no longer valid as a result of the difference in the rates of forward and backward reactions.5 Furthermore, the solid-gas reaction rate, accounted in the kinetics of the gasification process, depends upon the type and size of the fuel and the conditions taking place in each zone of the gasifier (pyrolysis, combustion, and gasification zones).6 Although the information on the gasifier design as well as flow rates into the gasifier and prevailing temperatures are required for kinetic modeling, the knowledge on the exit conditions are not required.7–14 Scanty kinetic data available in open resources are applied for the model, while the majority of unavailable kinetics is either generated or assumed.12 Chemical equilibrium modeling is based on a locally stable equilibrium state, which is independent of time, previous history of the system, and position within the system. Besides, the state (5) Smith, W. R.; Missen, R. W. Chemical Reactions Equilibrium Analysis: Theory and Algorithms; John Wiley and Sons: New York, 1982. (6) Govind, R.; Shah, AIChE J. 1984, 30, 79–92. (7) Babu, B. V.; Sheth, P. N. Energy ConVers. Manage. 2006, 47, 2602– 2611. (8) Babu, B. V.; Chaurasia, A. S. Energy ConVers. Manage. 2003, 44, 2135–2158. (9) Esposito, V.; Di Blasi, C. Modeling biomass gasification units. In Biomass Energy EnVironment: Proceedings of the European Bioenergy Conference; Chartier, P., Eds; Elsevier: Oxford, U.K., 1996; Vol. 2, pp 1423–1428. (10) Yang, W.; Ponzio, A.; Lucas, C.; Blasiak, W. Fuel Process. Technol. 2006, 87, 235–245. (11) Gøbel, B.; Henriksen, U.; Jensen, T. K.; Qvale, B.; Houba, N. Bioresour. Technol. 2006, doi: 10.1016/j.biortech.2006.08.019. (12) Corella, J.; Sanz, A. Modeling circulating fluidized bed biomass gasifiers. Fuel Process. Technol. 2005, 86, 1021–1053. (13) Chen, C.; Horio, M.; Kojima, T. Fuel 2001, 80 (10), 1513–1523. (14) Hamel, S.; Krumm, W. Powder Technol. 2001, 120 (1–2), 105– 112.

of the system is assumed to be resistant to fluctuations in the composition. 3. Chemical Equilibrium Modeling Two unique options are adopted for chemical equilibrium modeling: one is based on equilibrium constants (also referred as stoichiometric modeling),15–19 while the other one is minimization of the free energy20–25 (also referred as nonstoichiometric modeling). The assumptions made in equilibrium modeling are listed below: • Ideal gas laws are valid. • All reactions are at thermodynamic equilibrium. • Fuel is made of only carbon, hydrogen, oxygen, and nitrogen. • Producer gas comprises CO, CO2, H2, CH4, N2, and H2O. • Gases formed are in equilibrium during the flow through the char bed. • The pressure in the char bed is atmospheric and constant. • Reactions proceed adiabatically. • Nitrogen present in both fuel and air is inert. • Ash is inert and not involved in any of the reactions as either a chemical species or a catalyst. • No radial temperature gradients or concentrations exist. • There is no accumulation of gas in the char bed. • There is no resistance to conduction of heat and diffusion of mass inside the char particles. • Tar formation is neglected, because the volatile matter in CNSC is comparatively minimal. • Carbon conversion efficiency is 100%. • Standard state fugacity is 1. 4. Stoichiometric Equilibrium Modeling Prediction of gas composition using stoichiometric equilibrium modeling requires independent stoichiometric equations of formation for each product species and its associated equilibrium constant. Smith and Van Ness26 detailed the complexity of solving the equations simultaneously when the equilibrium constant method is applied to multireaction equilibria. The typical chemical formula of CNSC based on a single atom of carbon is observed to be CH0.686O0.32N0.09. The global gasification reaction of CNSC with air could be written as CH0.686O0.32N0.09 + wH2O + mO2 + 3.76mN2 ) x1H2 + x2CO + x3CO2 + x4H2O + x5CH4 + 3.76mN2 (1) (15) Zainal, Z. A.; Ali, R.; Lean, C. H.; Seetharamu, K. N. Energy ConVers. Manage. 2001, 42, 1499–1515. (16) Mathieu, P.; Dubuisson, R. Energy ConVers. Manage. 2002, 43, 1291–1299. (17) Khadse A.; Parulekar P.; Aghalayam P.; Ganesh A. Equilibrium model for biomass gasification. In AdVances in Energy Research; IIT: Mumbai, India, 2006; pp 106–112. (18) Schuster, G.; Loffler, G.; Weigl, K.; Hofbauer, H. Bioresour. Technol. 2001, 77, 71–79. (19) Ni, Q.; Williams, A. Fuel 1995, 74 (1), 102–111. (20) Melgar, A.; Pe´rez, J. F.; Laget, H.; Horillo, A. Energy ConVers. Manage. 2007, 48, 59–67. (21) Pellegrini, L. F.; de Oliveira, S., Jr. Energy 2007, 32, 314–327. (22) Chan, S. H.; Wang, H. M. Int. J. Hydrogen Energy 2000, 25, 441– 449. (23) Li, X. T.; Grace, J. R.; Lim, C. J.; Watkinson, A. P.; Chen, H. P.; Kim, J. R. Biomass Bioenergy. 2004, 26, 171–193. (24) Li, X.; Grace, J. R.; Watkinson, A. P.; Lim, C. J.; Ergu Èdenler, A. Fuel 2001, 80, 195–207. (25) Altafini, C. R.; Wander, P. R.; Barreto, R. M. Energy ConVers. Manage. 2003, 44, 2763–2777.

2072 Energy & Fuels, Vol. 22, No. 3, 2008

Ramanan et al. Table 3. Heat of Formation at 298.15 K28

The above reaction represents an overall reaction but a number of competing intermediate reactions take place during the process, predominant of which are C + O2 ) CO2

oxidation steam gasification

(-393.8 kJ mol )

C + H2O ) CO + H2

Boudouard reaction

methanation reaction water gas shift reaction

-1

C + CO2 ) 2CO C + 2H2 ) CH4

(2)

(+131.4 kJ mol-1) (3) (+172.6 kJ mol-1) (4) (-74.9 kJ mol-1) (5)

CO + H2O ) CO2 + H2

(-41.2 kJ mol-1) (6) Among these, only four reactions are independent reactions, which are oxidation, steam gasification, Boudouard reaction, and methanation reaction. Von Fredersdorff27 stated that the oxidation reaction is typically assumed to be very fast and goes to completion quickly, while the other three reactions, namely, steam gasification, Boudouard reaction, and methanation reaction are in equilibrium. The water gas shift reaction can be regarded as the subtraction of the steam gasification and Boudouard reactions. Hence the water gas shift reaction can also be considered to be in equilibrium. In the global reaction (eq 1), there are six unknowns x1, x2, x3, x4, x5, and m, representing the molar composition of five unknown species in the producer gas and oxygen content of the reaction. Hence, to predict the constituents of producer gas, a set of six equations are required, which are formulated by balancing the different constituents involved in the global reaction. carbon balancing 1 ) x2 + x3 + x5

(7)

2w ) 0.686 ) 2x1 + 2x4 + 4x5

(8)

hydrogen balancing

oxygen balancing

(x1)2

∆H°f 298 (kJ kmol-1)

g l g g g g g g

-241 818 -285 830 -393 509 -110 525 -74 520 0 0 0

Table 4. Heat Capacities (Constants A, B, C, and D)29 10-5D

chemical species

Tmax

A

103B

106C

methane hydrogen carbon monoxide carbon dioxide nitrogen water carbon

1500 3000 2500 2000 2000 2000 2000

1.702 3.249 3.376 5.457 3.280 3.470 1.771

9.081 0.422 0.557 1.047 0.593 1.450 0.771

-2.164 0.083 -0.031 -1.157 0.040 0.121 -0.867

If moisture content is known, then the value of w is a constant. The reaction process is assumed to be adiabatic. Hence, heat balancing of the reactants and products of the global reaction leads to an equation as presented below H°fCNSC + w(H°fH O(l) + H(vap)) + mH°fO + 3.76mH°fN ) 2

2

2

x1H°fH + x2H°fCO + x3H°fCO + x4H°fH O + x5H°fCH + 2

2

2

4

∆T(x1CpH + x2CpCO + x3CpCO + x4CpH O + x5CpCH + 3.76mCpN ) 2

2

2

4

2

(14) The heating value of the fuel (H°fCNSC) was determined experimentally by a bomb calorimeter. The heat of formation of the various gases could be sourced from JANAF thermochemical tables.28 The ∆H° for the gases constituting the present study is presented in Table 3. The dependence of specific heat on the temperature is given by various empirical equations, and the most simplified version29 is

[

]

D C Cpam ) R A + BTam + (4T 2am - T1T2) + 3 T1T2

(10)

The values of the heat capacity constants, applicable to our equations, are shown in Table 4. The heat of formation (∆H°) is basically a function of the temperature and hence could be equated as15

equilibrium constant from the methanation reaction (eq 5) K1 )

phase

(9)

w ) 0.32 + 2m ) x2 + 2x3 + x4 x5

chemical species water water carbon dioxide carbon monoxide methane hydrogen oxygen nitrogen

(15)

equilibrium constant from the water gas shift reaction (eq 6) (x3)(x1) K2 ) (x2)(x4) MC )

mass of water × 100% mass of wet biomass

18w × 100% MC ) 17.80 + 18w

(11) (12)

∆B 2 ∆C 3 ∆D ∆H° J ) + (∆A)T + T + T (16) R R 2 3 T The equilibrium constant K is a function of the temperature and could be calculated as15 ln K ) -

(13)

(26) Smith, J. M.; Van Ness, H. C. Introduction to Chemical Engineering Thermodynamics, 4th ed.; McGraw-Hill International Editions: New York, 1987. (27) Von Fredersdorff, C. G.; Elliot, M. A.; Chemistry of Coal Utilization; Lowry, H. H., Ed.; Wiley Publisher: New York, 1963. (28) Stull, D. R.; Prophet, H. JANAF Thermochemical Tables: National Space Research and Development Agency-National Bureau of Standards (NSRDA-NBS): Washington, D.C., 1971. (29) Robert, H. P.; Don, W. G. Perry’s Chemical Engineer’s Handbook, 6th ed.; McGraw Hill: New York, 1984.

∆B ∆C 2 ∆D J + ∆A ln T + T+ T + 2 + I (17) RT 2 6 2T

The dependence of ∆G° with temperature can be analyzed as

(

∆G ) J - RT ∆A ln T +

)

∆C 2 ∆D ∆B T + T + 2 + I (18) 2 6 2T

Both J and I are calculated from eqs 16 and 18 at a temperature of 298.15 K, respectively. The values of Gibbs free energy of formation, applicable to our equations, are shown in Table 5. Two equilibrium equations are required to determine



the equilibrium constants K1 and K2. K1 is the equilibrium constant for the reaction of eq 5 and is solved as follows: C + 2H2 ) CH4

∆ ) CH4 - C - 2H2

and

∆A, ∆B, ∆C, and ∆D can be obtained from the data of heat capacity. Substituting these values in eq 17 reveals the value of K1. A similar procedure could be adopted for computing K2. Figure 1 depicts the calculation procedure for the stoichiometric equilibrium model in a concise fashion. The molar concentration of individual species could be predicted using K1 and K2 and subsequently by solving the eqs 7-14. 5. Nonstoichiometric Equilibrium Modeling Nonstoichiometric equilibrium modeling is based on the fact that for a specified temperature and pressure, the Gibbs free energy of the system is minimal at equilibrium. This thermodynamic function can be interpreted as the driving force of a reaction.5 In the case of multireaction equilibria, this criterion can be applied to determine the total Gibbs free energy of the system, under the constraint of the atomic balances, to find the composition of the products that minimizes G for specified T and P. This procedure, usually called minimization of Gibbs free energy, is adopted for the nonstoichiometric modeling as per the procedure detailed below: Step 1: Computation of the number of gram of atoms of each atom present in the system. Step 2: Determination of the number of gram of atoms of each element present per gram of mole of each substance. Step 3: Determination of the Gibbs free energy of formation ∆G°f for each compound at operating temperature. Step 4: Adoption of the Gibbs free-energy minimization30 equation.

( )

yiφiP + fi

∆Gf + RT ln

∑ (λ a

k i,k) ) 0

(19)

k

The values of Gibbs energy of formation, as applicable to our equations, are shown in Table 5. Step 5: Material-balance and mole-fraction equations are given by

∑ya i

i,k )

i

Figure 1. Flowchart of the stoichiometric model.

Figure 2. Flowchart of the nonstoichiometric model.

scheme. The approach of the nonstoichiometric equilibrium model is illustrated in Figure 2. 6. Experimental Setup

Ak nT

(20)

Step 6: Resolving the nonlinear equations eqs 19 and 20 simultaneously. The model accounts four elements (C, H, O, and N) in CNSC and six species in the producer gas (CO, CO2, CH4, H2, H2O, and N2). The model upon execution yields the final molar composition of all species (yCO, yH2, yCO2, yN2, yH2O, yCH4, λCO, λCO2, λN2, λH2, λH2O, and λCH4). The Lagrange multiplier has no physical significance and can be eliminated from the solution Table 5. Gibbs Functions of Formation at 298.15 K28 chemical species

phase

∆G°f 298 (kJ kmol-1)

water water carbon dioxide carbon monoxide methane hydrogen oxygen nitrogen

g l g g g g g g

-228 572 -237 129 -394 359 -137 169 -50 460 0 0 0

Figures 3-5 depict the schematic and photograph of the experimental setup. A closed top, 20 kWe, down-draft gasifier was chosen for the study. Provision for air entry to the gasification system had been made through two inclined tuyeres unclosed at the throat. A grate made of SS was used for holding the feedstock. The region between throat and grate works as a reduction zone. A vibrator was attached on the gasifier to tremble the system for ensuring smooth fuel flow. The whole gasifier assembly was mounted on a toughened helical spring to enable it to pulsate during operation of the vibrator. A poking rod was fixed at the bottom and linked to the grate. Pushing and pulling the poking rod creates an impact on the grate, thereby disintegrating and discharging any clogged residual particles to the ash box. The hot producer gas is passed along the annuli of the reduction zone for maintenance of a higher reaction temperature. A cyclone separator was placed at the gasifier outlet, to remove particulates from producer gas, ahead of the draft-inducing blower. An aerated burner had been used (30) Othmer, K. Encyclopedia of Chemical Technology, 4th ed.; WileyInterscience Publisher: New York, 1998.


Ramanan et al.

Figure 3. Schematic of the gasifier.

for flaring the producer gas generated. Provisions were made in the gasifier, at appropriate locations, for measuring the temperature, pressure, and gas composition. A stand with ladder arrangement had been bestowed for feeding the raw material in the fuel feed port. 7. Process Instrumentation and Control The base fuel characteristics were established with a proximate analyzer (muffle furnace plus microweigh balance with associated auxiliaries). The parameters studied include moisture content (ASTM D 3173-73), volatile matter (ASTM D 3175-73), and ash content (ASTM D 3174-73). Fixed carbon was assumed to be the rest. The calorific value of CNSC was established using a standardized (benzoic-acid-based) bomb calorimeter, and the result was observed to match well with the correlations framed for estimation of the calorific value for biofuels.31 Junkers gas calorimeter was used for determining the calorific value of the producer gas. Producer gas composition was analyzed using Siemens make online gas analyzers, viz., Oxymat 61 (estimates O2 using a paramagnetic principle), Ultramat 23 (estimates CO, CO2, and CH4 using nondispersive infrared multilayer technology), and Calomat 61 (estimates H2 using a thermal conductivity principle). Details regarding the producer gas composition were logged using Siprom-GA software for every second. The gas sampling system consisted of a wash bottle, condensation pot,

coalesce filter, suction pump, fine filter, flame arrestor, and diaphragm pump. Chromel-Alumel (K-type) thermocouples were used for measuring the temperature at different zones (T1-T6). Thermocouples were fixed permanently, and the temperature was measured continuously in all zones except at the throat (T4). For temperature measurement at the throat, a flexible K-type thermocouple was inserted along the air port at regular intervals. Temperatures from different zones were logged simultaneously using an Agilent make (34907 A) data acquisition system. The surface temperature of the gasifier was measured with a Kane make infrared thermometer (UEI-INF 200). Airflow make Thermo Anemometer (TA 35) was used for measuring the air flow to the gasifier. Calibrated “S” type Pitot Tube and Comark make digital manometer were employed for establishing the producer gas flow. The air entry in to the system, thus ER, is governed by a control valve placed at the discharge end of the blower. Water-filled U tube manometers were deployed for measuring the pressure buildup across the gasifier bed. 8. Experimental Procedure Preweighed batches of CNSC, each weighing approximately 25 kg, were placed near the system for hassle-free operation during (31) Parikh, J.; Channiwala, S. A.; Ghosal, G. K. Fuel 2005, 84, 487– 494.



Figure 6. Molar composition versus ER (stoichiometric modeling). Figure 4. Photograph of the experimental setup.

Figure 7. Molar composition versus ER (nonstoichiometric modeling).

Figure 5. Gasification of CNSC.

fuel loading. Gasification of CNSC was initiated by keeping the gas valve in open condition, followed by operation of ID blower and holding a flame near air tuyere. The flame was sucked into the system because of the draft created by the blower. Within 3 min, flue gas was observed at the flare port. With time (normally 5–10 min), the onset of gasification commences and producer gas emanates in the flare port. Experimental analysis was started once the system attained stabilization. Generally, it took 60–90 min to attain stabilization, which was ensured by inferring a constant temperature in raw gas and reduction zone. The fuel consumption rate was measured by recharging the gasifier on an hourly basis and filling the gasifier volume to a predetermined level at the top of the gasifier hopper. The ash door was operated at regular intervals to prevent ash accumulation on the grate. The major performance influential parameters for a gasification system are equivalence ratio, moisture content of feed stock, and bed temperature. The equivalence ratio was varied by adjusting the air supplied to the gasifier bed. The performance of the system was also predicted for varying fuel moisture content. Because the gasifier being used is an autothermal one, the possibility of maintaining a constant bed temperature at varied ER or MC is not feasible. Hence, the influence of temperature on gas composition is not worked upon. The experiments were conducted with varying ER, and the outcome is compared to the modeled results.

9. Results and Discussions 9.1. Influence of Equivalence Ratio on Molar Concentration. For an ideal combustion, the value of ER should be 1,

while in practical cases, it is always greater than 1. The value of ER used is predominantly governed by the type of fuel being combusted. For gasifiers, the value of ER is always lower than 1, and it would normally range from 0.15 to 0.4. In autothermal gasification, part of the fuel is burnt to release energy to sustain the endothermic gasification reactions. The lower limit of ER in an autothermal gasifier is fixed by considering a variety of factors, such as the minimum quantity of air required to burn a part of the fuel to release energy for supporting endothermic reactions, required carbon conversion efficiency, fixed loss of heat that need to be accounted for maintaining the reactor temperature, etc. Similarly, the upper limit of ER is fixed by factors, such as tar quantity, gas quality, reactor temperature, ash fusion point, etc. The influence of ER on gasification of CNSC holding 7% moisture (as determined by proximate analysis) and operating at a throat temperature of 1373 K, as predicted by the models, is depicted in Figures 6 and 7. The maximum HHV of the gas, upon experimentation, was observed to be at an ER of 0.35. The throat temperature attained by the gasifier (1373 K) at this ER of 0.35 was applied by predicting the gas composition. It is a common reality that with an increase in ER, the temperature of any oxidation reaction is bound to increase. However, in the formulated models, the temperature had been assumed to be constant at different ERs. Because the outcome of the model had been analyzed for its deviation only at an ER of 0.35, this assumption is presumed to be a convincing one. Figure 8 depicts the comparison of the results of stoichiometric and nonstoichiometric modeling with that of experimental values at an ER of 0.35, RT of 1373 K, and MC of 7%.


Ramanan et al.

Figure 8. Comparison of the model outcome with experimental results at ER ) 0.35, RT ) 1373 K, and MC ) 7%. Table 6. Proximate Analysis of Casuarina Wood

Table 7. Ultimate Analysis of Casuarina Wood

(wt % on as received basis) moisture volatile matter fixed carbon ash

12.5 67.5 18 2

(wt % on as received basis) carbon hydrogen oxygen nitrogen

48.5 6.06 43.3 2.14

9.1.1. Influence of ER on Hydrogen and CO. The yield of hydrogen from both the models is observed to follow a decreasing trend with an increase in ER. Both the models predict unreasonably high H2 at lower ER. Ruggiero et al.32 described the irrelevance of chemical equilibrium models at lower ERs, which assumes perfect gas behavior, because these models could not describe pyrolysis processes as a result of the presence of liquid hydrocarbons as pyrolytic products. However, at an ER of 0.35, the trend lines of both the models collimate and the values are found to be in reasonable agreement with the actual readings (Figure 8). At maximum ER of 1, the percent H2 from both the models is null, indicating complete combustion and conversion of all H2 to water vapor. The prevailing conditions and criteria quoted for H2 are equally applicable for CO, albeit at different magnitudes. The concentration of CO predicted by the nonstoichiometric model is lower than the stoichiometric model, possibly because of kinetic limitations of char-reforming reactions. Moreover, the prediction of CO, from both the models, is moderately higher than that of experimental values, possibly owing to the assumption of 100% carbon conversion efficiency. Accounting for this efficiency factor would minimize the deviation. 9.1.2. Influence of ER on CO2 and CH4. The molar percentage of CO2,as predicted by both of the models, is observed to increase with an increase in ER. The stoichiometric model results for CNSC indicated subzero values of CO2 at lower ER (Figure 6), which is quiet unrealistic. Hence, for analysis as a result of the stoichiometric model, it was decided to check the trend of CO2 for other proven gasification fuels. Accordingly, the devised model was applied to casuarina wood (CW), whose proximate and ultimate analyses are listed in Tables 6 and 7. The trend of CO2 for CW is observed to be increasing and positive for all ERs (Figure 9). With the other constituents of

the global reaction (eq 1) remaining same, the carbon/hydrogen and carbon/oxygen ratios of CNSC were observed to be 2.18 and 2.08 times greater than the corresponding ratios of CW. Hence, it was inferred that the carbon/hydrogen ratio, to some extent, influences the model output at lower ER. This was confirmed by increasing the hydrogen/carbon ratio on the reactants side in the global reaction (eq 1) by increasing the fuel moisture content and analyzing the molar percentage of CO2 at different ERs. At a moisture level of 40%, the yield of CO2 is observed to be all positive (Figure 10), indicating the influence of the carbon/hydrogen ratio on stoichiometric modeling at lower ERs. Although the values depicted by the model at lower ERs, per se, would not be relevant to gasification, the cause for it had been reported. It was found that the stoichiometric model predicts well the CO2 concentration at higher ERs and that pertaining to gasification. The nonstoichiometric model, on the other hand, predicted all positive but meager values of

(32) Ruggiero, M.; Manfrida, G. Renewable Energy 1999, 16, 1106– 1109.

Figure 9. Molar percentage of CO2 versus the ER (model, stoichiometric; fuel, casuarina wood).


Figure 10. Molar percentage of CO2 with varied moisture versus the ER (model, stoichiometric; fuel, CNSC).

CO2 at lower ER. Nevertheless, it was found that both the models predict the CO2 concentration reasonably well at ER g 0.3. Mansaray et al.33 inferred that increasing the ER lead to a decrease in the concentrations of methane and other light hydrocarbons, which have relatively large heating values. The modeling results validate the statement of a decreasing CH4 concentration for increasing ER. Conversely, the prediction on CH4 made by the model is lower than the actual ones by a large margin (Figure 8). Luiz et al.21 experienced similar differences in CH4 predicted by the model as against experimental results and referred the cause as a result of the sudden cease of gasification reactions at the bottom of the reactor. This cease could probably be attributed to the consequence of the temperatures at the bottom, which are too low to start the reactions. In chemical equilibrium modeling, it is assumed that all reactions achieve a steady-state condition; thus, no kinetic effects (such as sudden cease) are considered. To overcome the differences in the mole fractions, a fixed CH4 molar correction factor as reported by Fock and Thomsen34 may be adopted. 9.1.3. HHV of Gas. The calorific value of gas is influenced by the presence of combustibles, viz., CO, CH4, and H2, in the gas. The heat contribution by both CO (12.71 MJ m-3) and H2 (12.78 MJ m-3) is almost equal, while that from CH4 is almost 3.5 times (39.76 MJ m-3). The HHV of gas predicted by the nonstoichiometric model is observed to be lower than stoichiometric modeling. This could be due to higher concentrations of CO and CH4 predicted by stoichiometric modeling as compared to nonstoichiometric modeling. Although the H2 prediction by nonstoichiometric modeling is higher than stoichiometric modeling, the magnitude of the difference is observed to be subtle. 9.2. Influence of Moisture on the Molar Concentration. Moisture content is one among the most significant properties of any biomass that are known to influence the gasification process. CNSC, being a carbonized one, possesses a near constant inherent moisture content. The surface moisture is influenced by the ambient or handling conditions. The devised model was applied to predict the influence of the moisture content on gas composition. The influence of moisture was analyzed by assuming CNSC to possess nil moisture, existing moisture (7%), and twice the present moisture (14%). (33) Mansaray, K. G.; Ghaly, A. E.; Al-taweel, A. M.; Hamdullahpur, F.; Ugursal, V. I. Biomass Bioenergy 1999, 17, 315–332. (34) Fock, F.; Thomsen, K. P. B. Modelling a Biomass Gasification System by Means of EES; The Scandinavian Simulation Society, Technical University of Denmark: Lyngby, Denmark, 2000.


Figure 11. Influence of moisture on producer gas composition.

The moisture in the producer gas is an amalgamation of the moisture sourced from substoichiometric air (specific humidity), water vapor formed because of the oxidation of hydrogen in fuel, and the moisture associated with fuel. Among these contributing factors, the first two factors absorb only sensible heat for superheating the moisture, while the last factor absorbs sensible, latent, and superheat for converting the moisture to a superheated vapor form. The higher the fuel moisture content, the higher is the heat absorbed by the moisture, paving the way for a reduced reaction temperature and associated incomplete cracking of the hydrocarbons released from the pyrolysis zone. Reed et al.35 specified that the moisture content of feedstock should be below 33% for generating a burnable, good-quality gas, while a moisture content higher than 67% makes the producer gas too lean for ignition. McKendry36 inferred that fuel with a moisture content above 30% makes ignition difficult and reduces the HHV of the producer gas. Bridgewater et al.37 cited that moisture constraints for any gasifier fuel are dependent upon the type of gasifier used. Higher values are possible in up-draft systems, but the upper limit acceptable for a downdraft reactor is generally considered to be around 40% dry basis. Both the models revealed a decrease in CO and an increase in H2, CO2, and CH4 with an increase in moisture (Figure 11). Increased levels of moisture and the presence of CO at lower ERs produce more H2 and CO2 by the water gas shift reaction. Hence, the decrease in CO and increase in hydrogen content with an enhanced moisture content of fuel. The increased H2 content of the gas produces more CH4 by direct hydrogenation, leading to an increase in methane content with an increase in fuel moisture. 10. Conclusion Cashew nut shells could be successfully gasified, without any major oil-related problems, after charring them. The concept of equilibrium modeling, carried out by adopting both stoichiometric and nonstoichiometric methods, applies well for predicting the effects of ER and MC on gasification of CNSC. Among all constituents, the deviation among the models is observed to be minimal in the prediction of H2. The experimental outcome of CH4 is observed to be fairly higher than the modeled results, (35) Reed, T. B.; Das, A. Handbook of Biomass Downdraft Gasifier Engine System; SERI: Golden, CO, 1988. (36) McKendry, P. Bioresour. Technol. 2002, 83, 55–63. (37) Bridgewater, A. V.; Double, J. M.; Earp, D. M. Technical and Market Assessment of Biomass Gasification in the United Kingdom; ETSU Report, UKAEA: Harwell, U.K., 1986.


requiring fixed methane molar constants to curtail the deviation (a reality that is in line with the observations made by other researchers). The models predict unlikely gas composition at lower ERs possibly because of pyrolysis. In addition, the hydrogen/carbon ratio of fuel in stoichiometric modeling, at lower ERs, also influences the prediction of CO2 composition significantly. Both the models predict the influence of moisture content in a relevant manner. On a whole, it is observed that nonstoichiometric model is comparatively better suited for predicting the gasification of CNSC, owing to minimal deviations in terms of both gas composition and gas HHV. Acknowledgment. The financial support provided by the Ministry of New and Renewable Energy, Government of India, New Delhi, India (203/1/10/2000 B M) is thankfully acknowledged. The support rendered by Mr. M. Sundaresan, Fichtner Consulting Engineers, Chennai, and Mr. S. Kasiraman, GE-Bangalore, is appreciated.

Nomenclature ∆A, ∆B, ∆C, and ∆D ) coefficients for determining specific heat ∆H° ) heat of formation (kJ kmol-1) A, B, C, and D ) constants for the properties of the gases ai,k: number of kilograms of atom/kilograms of mole Ak ) number of kilograms of atom Cp ) specific heat (kJ kmol-1 K-1) Cp(am) ) specific heat at the arithmetic mean temperature (kJ kmol-1 K-1) f°i ) standard state fugacity G ) Gibbs free energy I and J ) constants K1 ) equilibrium constant for the methanation reaction K2 ) equilibrium constant for the water gas shift reaction

Ramanan et al. m ) amount of oxygen per kilomole of wood MC ) moisture content per mole of wood nT ) total number of moles P ) pressure (bar) R ) universal gas constant (J mol-1 k-1) T ) temperature (K) T1 ) reference temperature (K) T2 ) reaction temperature (K) Tam ) arithmetic mean temperature (K) w ) amount of water per kilomole of wood x1, x2, x3, x4, and x5 ) coefficients of constituents of the producer gas yi ) mole fraction ∆G°f ) Gibbs energy of formation (kJ kmol-1) λk ) Lagrange multiplier φi ) fugacity coefficient Subscripts f ) formation i ) substance k ) element T ) total AbbreViations CNSC ) cashew nut shell char CNSL ) cashew nut shell liquid CW ) casuarina wood ER ) equivalence ratio HHV ) higher heating value (kJ kg-1 or MJ kg-1) ID ) induced draft RT ) reaction temperature (K) SS ) stainless steel EF700467X

Modeling and Experimental Validation of Cashew Nut Shell Char

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