Reac Kinet Mech Cat (2015) 114:63–74 DOI 10.1007/s11144-014-0780-5
Determination of methanolysis rate constants for low and high fatty acid oils using heterogeneous surface reaction kinetic models Yousuf Jamal • Ahmed Rabie • Bryan O. Boulanger
Received: 9 April 2014 / Accepted: 10 September 2014 / Published online: 24 September 2014 Ó Akade´miai Kiado´, Budapest, Hungary 2014
Abstract Catalytic methanolysis of soybean oil with Amberlyst A26-OH basic ion-exchange resin was studied in the presence and absence of free fatty acids. Catalytic methanolysis of soybean oil is a key step in the biodiesel production process. The use of the heterogeneous Amberlyst A26-OH basic resin to catalyze this conversion is of interest due to the resin’s ability to be recovered, regenerated, and reused. This current research modeled methanolysis on the surface of the heterogeneous catalyst using both the Eley–Rideal and Langmuir–Hinshelwood–Hougen– Watson reaction kinetic models. For all experiments, soybean oil (with and without 5 % oleic acid incorporated) was reacted with methanol at a molar ratio of 1:10 soybean oil to methanol in the presence of Amberlyst A26-OH (added at 20 % by weight of oil). The reaction was carried out over multiple investigations in a batch reactor where the reactants were mixed at 550 rpm and held at 50 °C and atmospheric pressure. The resulting data demonstrated that the Eley–Rideal reaction mechanism offered the best description of the surface interactions occurring on the resin. Based upon Eley–Rideal model fitting, the mean (± standard deviation) methanolysis rate constant (the rate at which methanol was consumed during the reaction) was determined to be 7.48 9 10-4 (±4.05 9 10-5) h-1 with free fatty acid absent and 1.94 (± 1.25 9 10-1) h-1 with free fatty acid present. Electronic supplementary material The online version of this article (doi:10.1007/s11144-014-07805) contains supplementary material, which is available to authorized users. Y. Jamal (&) School of Civil and Environmental Engineering, Institute of Environmental Sciences and Engineering, National University of Sciences and Technology, H-12, Islamabad 44000, Pakistan e-mail:
[email protected] A. Rabie Department of Petroleum Engineering, Texas A&M University, College Station, TX 77843, USA B. O. Boulanger Department of Civil Engineering, Ohio Northern University, Ada, OH 45810, USA
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Keywords Methanolysis Heterogeneous catalysis Eley–Rideal Langmuir– Hinshelwood–Hougen–Watson Kinetic modeling
Introduction Biodiesel is a popular alternative fuel used within many sectors, including transportation, heating, and electricity generation. Compared to conventional diesel, biodiesel emits lower levels of carbon, sulfur, and nitrogen oxide emissions [1] and is also biodegradable [2]. With the cost to produce fossil diesel on the increase [3], biodiesel production from lipid-based biomass feed stocks is becoming more economically favorable. Biodiesel is produced by the alcoholysis (transesterification or esterification) of chemically or mechanically extracted lipids originating from various feed stocks including whole plants, plant seeds, algal biomass, animal fats, wastewater sludge and waste cooking oils [4–8]. Conversion of feed stock extracted lipids to biodiesel occurs in the presence of a short chain alcohol (alcoholysis) and a basic or acidic catalyst at reaction temperatures ranging from 50–120 °C. Selection of the catalyst used in the reaction depends largely upon the free fatty acid (FFA) and triglyceride contents of the extracted lipids [9]. Conventionally, the use of homogeneous catalysts to facilitate biodiesel production is most common. Homogeneous catalysts are inexpensive and well characterized for their ability to carry out transesterification (base catalyzed conversion) and esterification (acid catalyzed conversion). The use of heterogeneous catalysts during production is less common due to their higher initial costs, but heterogeneous catalysts offer the benefit of their potential for recovery, regeneration, and reuse [10–12]. Research characterizing the kinetics of homogeneously catalyzed alcoholysis reactions of many different types of pure lipid based feed stocks [13]; lipid feed stocks with variable contents of FFA [14]; and the two-step reaction for biodiesel production (esterification followed by transesterification) is well documented [15, 16]. However, the same level of understanding is currently lacking for heterogeneously catalyzed alcoholysis. The purpose of this study is to explore the catalytic methanolysis of soybean oil facilitated by the heterogeneous ion-exchange resin, Amberlyst A26-OH. Amberlyst A26-OH is a macroporous ion-exchange resin with quaternary ammonium functional groups that impart a strongly basic and reactive surface. The quaternary ammonium functional group in the resin is known to facilitate transesterification and to adsorb FFA [17–20]. Two surface reaction kinetic models, Eley–Rideal (ER) and Langmuir–Hinshelwood–Hougen–Watson (LHHW), are used to elucidate the interactions occurring on the resin surface and to determine the methanolysis rate constant. The presented research has implications for alternative methods for processing oil feed stocks and biodiesel conversion.
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Experimental Materials Amberlyst A26-OH (basic macroporous resin), 90 % commercial grade oleic acid, and degummed soybean oil were purchased from Sigma-Aldrich (St. Louis, MO). HPLC grade methanol was purchased from VWR International (Sugarland, TX). Nitrogen gas (99 % purity) was purchased from Botco (Bryan, TX). Experimental setup Methanolysis was investigated in a batch reaction system. The reaction systems consisted of a glass flat bottom 250 mL round flask equipped with vapor recovery traps sitting in a temperature controlled water bath. The feed for the reaction system was either degummed soybean oil or 5 % oleic acid in degummed soybean oil (mass: mass). The feed was prepared fresh daily. Feedstock oil used in experiments was preheated under controlled conditions at 100 °C for 5 min to remove background moisture. Prior to the reaction, 4 grams Amberlyst A26-OH (basic resin) was pre-soaked in methanol (MeOH) for 4 h in the reaction flask. 20 g of the preheated feedstock, maintained at 50 °C, was then poured in the flask containing the soaked resin. Separate reactors were used to investigate methanolysis of soybean oil and 5 % oleic acid in soybean oil at a 1:10 molar ratio of soybean oil to methanol using Amberlyst A26-OH basic resin as a catalyst. All experiments were maintained at 50 °C, while the reaction mixture was stirred at 550 rpm using a magnet bar stirrer. The reaction systems were evaluated in duplicate for 0, 1, 2, 6, 12, and 18 h reaction durations using sacrificial reactors. At each evaluated time step, the appropriate reactors were pulled and centrifuged in order to separate the top fraction of reaction solution. The top fraction was then decanted and reduced to dryness under a gentle flow of nitrogen. The resulting dried mass of the ester product from the top layer was recorded as the ester content produced in the reaction [21–23]. ER and LHHW surface reaction modeling Time series data from methanolysis of feedstock oils with and without the presence of free fatty acids using Amberlyst A26-OH were evaluated against the ER and LHHW surface reaction kinetic models. The evaluation was conducted to determine the kinetic model and fitted parameters that describe the data and to help clarify the reaction process occurring on the surface of the resin catalyst. The models were also used to determine the impact of FFA on reaction kinetics. Figures S1 and S2 (in the electronic supplementary materials) show the mechanisms for both the ER and LHHW models. Both models involve adsorption of methanol to the surface of the catalyst followed by a surface reaction. The primary difference between the two models is that in the ER model the surface bound methanol reacts with triglycerides in bulk solution, whereas in the LHHW model, the triglyceride molecule first adsorbs to the surface of the catalyst and then the two surface bound reactants
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combine to form products. The ER and LHHW surface reaction models are written as a series of individual reactions steps according to Table 1. While both the ER and LHHW model were previously used to model alcoholysis on the surface of basic catalysts [21, 24, 25], the models have not been developed for reaction on strong anion exchange resin Amberlyst A26-OH. This presented research also applied a modified version of the models to account for the presence of FFA in feedstocks. The hypothesis was defined based upon the supposition that when FFA is in reaction mixture, the FFA competes for binding sites on the catalyst surface and decreases the methanolysis reaction rate constant. The resulting modified ER and LHHW models were evaluated against the experimental data of methanolysis of a 5 % oleic acid in soybean oil feedstock with Amberlyst A26-OH. All model fitting of experimental data was based upon the minimization of the normalized root mean squared deviation (Dq) between measured versus predicted values and the resulting linear correlation coefficient (r2). For the purpose of this modeling effort, Dq is defined by: Dq ¼
RMSD ð½Et;experimental;max ½Et;experimental;min Þ
RMSD ¼
sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Rð½Et;experimental ½Et;modeled Þ2 N
where Dq = normalized root mean squared deviationRMSD = root mean squared deviation[E]t = concentration of the esters at any time t; andN = number of replicates (data points).
Results and discussion Methanolysis of soybean oil with Amberlyst A26-OH Fig. 1 shows the concentration of esters and glycerol generated over 18 h of reaction of methanol and Amberlyst A26-OH with soybean oil alone and with a 5 % oleic acid in soybean oil. The triglyceride concentration in the reactor was calculated based upon the difference between the known initial molar concentration of triglyceride in the feed and the weight of the evolved dried ester product. The methanol and glycerol concentrations within the reactor were calculated according to reaction stoichiometry. The reaction rate of methanol consumption was calculated with and without the presence of FFA in the soybean oil. One of two possible mechanisms was proposed to explain the findings. Either methanol reacts with FFA over the basic resin or FFA facilitates the approach of triglyceride, through methanol, to the basic resin surface through decreasing the hydrophilic nature of the resin surface. A review of the literature did not yield indications that FFA would react with methanol without the presence of an acidic catalyst. Therefore, the difference in observed reaction rate constants for methanol consumption is most likely caused by
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Table 1 Stepwise reactions considered in ER and LHHW kinetic reaction models Reaction step
ER model
LHHW model
Methanol adsorption
MeOH þ MeOH
MeOH þ MeOH
n/a
T þ T
k1
k1
k1
Triglyceride adsorption
k1
k2
k2
Surface reactions
k2
k3
T þ MeOH E þ D
T þ MeOH E þ D k2
k3
k3
k4
D þ MeOH E þ M
D þ MeOH E þ M k3
k4
k4
k5
M þ MeOH E þ G
M þ MeOH E þ G k4
Desorption
k5
k5
k5
D þ D
D þ D
k5
k5
k6
k6
M þ M .
M þ M
k6
k6
k7
k7
G þ G
G þ G
k7
k7 k8
E þ E k8
MeOH Methanol, * Surface site, MeOH* Methanol adsorbed on surface, T Triglyceride, D Diglyceride, M Monoglycerides, E Methyl ester, G Glycerol, T* T adsorbed on surface, D* D adsorbed on surface; M* M adsorbed on surface, G* G adsorbed on surface, E* E adsorbed on surface; k1 through 8 are the forward reaction rate constants; and k -1 through -8 are the reverse reaction rate constants
1 E-02
Concentration (mol/L)
Fig. 1 Concentration of observed esters (filled triangle) and estimated glycerol (filled circle) in the reactor over the course of the reaction of Amberlyst A26-OH with soybean oil alone (open markers) and with a 5 % oleic acid mixture in soybean oil (filled markers). Reaction performed at 50 °C, 550 rpm and 1:10 molar ratio of soybean oil to methanol
1 E-02 1 E-02 8 E-03 6 E-03 4 E-03 2 E-03 0 E+00 0
5
10
15
20
reaction duration (h)
the ability of FFA to foster triglyceride migration to the resin surface by lowering the hydrophilicity of the surface. The mechanism for methanolysis of triglycerides with or without FFA over Amberlyst A26-OH is explained in Fig. 2. Heterogeneous transesterification starts with the formation of methoxide over the basic resin surface when the resin is
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soaked in methanol. Specifically, methoxide forms at the quaternary ammonium functional group (QN?OH-) as methanol adsorbs to the resin and then reacts. For every mole of methanol adsorbed and then reacted, equimolar amounts of methoxide and water are produced [26]. The formed methoxide then reacts with triglyceride in the oil to form esters. The role of water formation within the adsorption step is of interest because of the effect moisture may have on the remainder of the reaction process. Cren et al. has reported no impact of moisture presence on the adsorption of oleic acid on Amberlyst A26-OH [27]. Similar results are reported in our earlier studies on the adsorption of oleic acid from soybean oil on a pre-conditioned, pre-soaked mixed bed ion exchange resin in methanol when water is present to up to 5 % of the reaction solution [17]. However, Diaz et al. proposes that moisture should be removed in advance to avoid any triglyceride hydrolysis [28]. However, there is evidence that the presence of produced moisture in the reaction solution around the basic resin quaternary ammonium functional groups (QN?OH-) results in the hydrolysis of the triglycerides (pure soybean oil) into glycerides (di, mono and glycerol) and reduces the mass transfer resistance of triglycerides from bulk oil solution to the methanol catalyst interface [29]. While several studies explore the impact of water presence in the reaction process, there is a lack of information concerning the important role that FFA within the oil has in the reaction process. Because adsorption of FFA with the basic quaternary ammonium site on the resin was reported earlier [17, 18, 20, 27, 30], this adsorption was forecasted to block the reaction of methanol with the basic site. Therefore, a decrease in the reaction rate was expected to account for lower consumption rates of methanol during the reaction. However, the opposite was observed (and confirmed several times) and an increase in the reaction rate of methanol consumption was observed. This is most likely due to the change in hydrophilicity of the surface. For every FFA adsorbed on the quaternary ammonium (QN?OH-) functional site on the resin, there is dissociation of a hydroxyl from the resin surface. A non-polar molecule now covers the surface instead and this coverage lowers the hydrophilicity of the surface. The new surface properties reduce the mass transfer resistance of triglycerides from the oil phase to the methanol resin interface, where glycerides react with resin surface bound methoxide(QN?CH3O-) to produce esters. The presence of oleic acid in triglyceride oil mixture is known to have miscibility towards triglycerides because of oleic acid’s unsaturation and high melting point [20]. This results in more migration of triglycerides over the basic resin surface giving a higher methanolysis reaction rate in the presence of FFA. One other study available in the literature, completed by Marchetti et al., also reported an increasing trend of reaction rate with an increase of oleic acid content [31]. Their study was conducted by reacting refined sunflower oil with anhydrous ethanol and the basic Dowex Monosphere 550A gelular resin. While they did not determine the reaction rate constant and explain mechanism for alcohol consumption they did still observe the increasing reaction rate trend. Taken together, the Marchetti et al. results and our reaction rate data demonstrate the effect of FFA on
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Fig. 2 Methanolysis reaction mechanism of soybean oil (triglycerides) with and without oleic acid (free fatty acids) over Amberlyst A26-OH basic ion-exchange resin. (R = alkyl group)
the reaction system when quaternary ammonium functionality containing resins are used as catalyst for biodiesel conversion in the presence of FFA. Fig. 3 shows a graphical depiction of the mechanism. Fig. 3 illustrates that when FFA approaches the basic surface, glycerides (tri, di, and mono) interact with FFA through hydrophobic interactions due to reduced hydrophilic nature of the anionic
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Fig. 3 Graphical depiction of triglyceride approach to the basic resin surface with FFA within alcohol layer due to reduced hydrophilicity at the resin surface. (TRIG = Triglyceride, FFA = Free Fatty Acids, MeOH = Methanol, -OH = Anionic surface)
resin. This interaction allows glycerides to approach the basic surface more readily resulting in transesterification and higher methanolysis rates. ER and LHHW reaction modeling Through the evaluation of the initial methanolysis reaction rates on basic catalysts found in the literature, the ER and LHHW models were derived with methanol adsorption as the rate limiting step [21, 24, 32–34]. Methanol adsorption as the rate limiting step is further supported within the literature, because the reaction will not proceed without the formation of methoxide (a surface facilitated reaction with methanol) [26, 32–37]. The derived ER and LHHW models also assume that all surface reaction sites demonstrate equal reactivity towards methanol and adsorption is isothermal. Full derivations of both the ER and LHHW models are provided in the electronic supplementary material for this article (ESM Appendices A and B, respectively). The final reduced versions of the model used in this work are presented in Table 2. Table 2 also provides the rate constants for the best fit of the data along with linear correlation coefficient (r2) and the error associated with the best fit observed and model data (Dq). Figure 4 provides the model fit showing the consumption of methanol as a function of time for each derived model. For ER model fitting, the methanol consumption rate constant was determined to be 7.48 9 10-4 h-1 with FFA absent and 1.94 h-1 with FFA present. The LHHW model results in a determined rate constant of methanol consumption of 6.20 9 10-2 h-1 and 1.71 h-1 in the absence and presence of additional FFA, respectively. Therefore, the presence of FFA in solution increased the rate constant for methanol consumption. Similar methanolysis rate constants for heterogeneous catalysts are noted by Kapil et al. in their research investigating transesterification of triglyceride lipids on anionic hydrotalcite catalysts [24]. They report a rate constant for methanol consumption in the range of 1 9 10-6–7 9 10-6 s-1 based upon the ER model and 1 9 10-2–9 9 10-3 s-1 for the LHHW model. Their reactions were run in the absence of FFA within a system with glyceryl tributyrate and methanol as the reactants with hydrotalcite catalysts [38]. Dossin et al. reported on the reaction rate constant for methanol consumption during transesterification of triglycerides in
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k½MeOH
k½MeOH ð1þK2 ½T þ K6 ½E þ K9 ½G þ K10 ½FFAÞ
LHHW kinetic model without FFA
LHHW kinetic model with FFA
ð1þK7 ½G þ K8 ½FFAÞ
k½MeOH
ð½1 þ K7 ½GÞ
k½MeOH ð1þK2 ½T þ K6 ½E þ K9 ½GÞ
ER kinetic model with FFA
ER kinetic model without FFA
1/h L/mol 1/h L/mol L/mol 1/h L/mol L/mol L/mol 1/h L/mol L/mol L/mol L/mol
k = 7.48 9 10-4 ± 4.05 9 10-5 K7 = 1.10 9 10-4 ± 1.12 9 10-6 k = 1.94 ± 1.25 9 10-1 K7 = 2.15 9 107 ± 8.28 9 101 K8 = 5.29 9 104 ± 9.72 9 102 k = 6.20 9 10-2 ± 2.85 9 10-3 K2 = 2.75 9 102 ± 1.38 9 101 K6 = 2.99 9 105 ± 7.76 9 10-2 K9 = 9.11 9 101 ± 7.49 9 10-2 k = 1.71 ± 1.10 9 10-1 K2 = 1.34 9 104 ± 3.93 9 10-2 K6 = 1.29 9 102 ± 2.12 9 102 K9 = 1.75 9 107 ± 7.31 9 101 K10 = 3.42 9 103 ± 3.69 9 103
0.66
0.98
0.67
0.98
Table 2 ER and LHHW kinetic models used to fit the experimental data presented with the best fit parameter values and resulting model statistical evaluation Parameter values Units R2 Model Rate equation, d½MeOH dt ¼
0.27
0.06
0.26
0.06
Dq
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Time (h)
Time (h) 0
4
8
12
16
20
0
-2E-3
-2E-3
-4E-3
dCMeOH/dt
-1E-17
dCMeOH/dt
-1E-17
ER (without FFA)
4
-4E-3
12
16
20
LHHW (without FFA)
-6E-3
-6E-3
Time (h) 0
4
8
12
Time (h) 16
20
0
-1E-17
-2E-3
-4E-3
-6E-3
4
8
12
16
20
-1E-17
ER (with FFA)
dCMeOH/dt
dCMeOH/dt
8
-2E-3
-4E-3
LHHW (with FFA)
-6E-3
Fig. 4 ER and LHHW kinetic models fit to the mean experimental methanol consumption data (mol/L h) for methanolysis of soybean oil and a 5 % oleic acid in soybean oil mixture with Amberlyst A26-OH used as a catalyst in the presence of methanol. The label within each box details the model (ER or LHHW) and whether or not FFA was present in the soybean oil (with or without FFA)
using basic metal oxide catalysts (without FFA present) [34]. Their reported rate constant was 0.148 m3/Kg cat/s; compared to 1.6 9 10-6 m3/Kg cat/s for our basic resin catalyst. This comparison indicates that basic metal oxide catalysts have a much higher reaction rate during transesterification compared to the basic quaternary ammonium anion resins used in this study.
Conclusions Methanolysis of soybean oil with and without FFA present was investigated in the presence of methanol using Amberlyst A26-OH as a basic resin catalyst. In order to gain a better understanding of reaction mechanism occurring on the surface, both the ER and LHHW surface reaction kinetic models were used to evaluate the data. The models were used to predict the change in methanol consumption over time in the reactor. Both the ER and LHHW model were able to simulate the observed data. The addition of the FFA term into the model considerably improved the model prediction when FFA was present (compared to when the model was used without a
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term for FFA). However, even with a term accounting for FFA, when FFA was present both models resulted in over predicting the beginning phases of the reaction. Additional evaluation of the models demonstrated that the reaction mechanism tends towards an ER hypothesized mechanism due to the presence of methanol in excess within the reactor. At excess levels of methanol the triglyceride component of the LHHW model plays a reduced role in the denominator of model equation. However, as the molar ratio of methanol to triglyceride decreases, the importance of triglyceride sorption on the surface of the resin increases. A similar phenomenon is observed as the ester yield increases. Therefore, given the reaction conditions, the models indicate that methanol adsorption is the key step in reactions where methanol is present in excess. The proposed mechanism of methanolysis on ion exchange resins when methanol is present in excess also supports the theory of transesterification on other basic catalysts present in the literature [19, 32, 34, 39]. Based on ER model fitting the methanol consumption rate constant was determined to be 7.48 9 10-04 h-1 with FFA absent and 1.94 h-1 with FFA present. This finding highlights that the presence of FFA in the oil allows triglycerides to approach the basic resin surface more readily resulting in transesterification and higher methanolysis rates. Acknowledgments The authors are thankful to the US Department of State Bureau of Educational and Cultural Affairs Fulbright Program and the Texas Engineering Experiment Station (Project Number 32296-19386) for the financial support. The authors would also like to thank Mr. Guofan Luo and Mr. Charlie Kuo for their laboratory support.
References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21.
Russbueldt BME, Hoelderich WF (2009) Appl Catal A 362:47–57 Pasqualino JC, Montane´ D, Salvado´ J (2006) Biomass Bioenergy 30:874–879 Owen NA, Inderwildi OR, King DA (2010) Energy Policy 38:4743–4749 Pastore C, Lopez A, Lotito V, Mascolo G (2013) Chemosphere 92:667–673 Revellame E, Hernandez R, French W, Holmes W, Alley E (2010) J Chem Technol Biotechnol 85:614–620 Dong T, Wang J, Miao C, Zheng Y, Chen S (2013) Bioresour Technol 136:8–15 Chen L, Liu T, Zhang W, Chen X, Wang J (2012) Bioresour Technol 111:208–214 Saydut A, Kafadar AB, Tonbul Y, Kaya C, Aydin F, Hamamci C (2010) Energy Explor Exploit 28:499–512 Georgogianni KG, Katsoulidis AK, Pomonis PJ, Manos G, Kontominas MG (2009) Fuel Process Technol 90:1016–1022 Shibasaki-Kitakawa N, Honda H, Kuribayashi H, Toda T, Fukumura T, Yonemoto T (2007) Bioresour Technol 98:416–421 Feng Y, He B, Cao Y, Li J, Liu M, Yan F et al (2010) Bioresour Technol 101:1518–1521 Niu X, Xing C, Jiang W, Dong Y, Yuan F, Zhu Y (2013) React Kinet Mech Catal 109:167–179 Darnoko D, Cheryan M (2000) J Am Oil Chem Soc 77:1263–1267 Berchmans HJ, Morishita K, Takarada T (2013) Fuel 104:46–52 Jain S, Sharma MP (2010) Bioresour Technol 101:7701–7706 Wang Y, Ou S, Liu P, Zhang Z (2007) Energy Conversat Manag 48:184–188 Jamal Y, Boulanger BO (2010) J Chem Eng Data 55:2405–2409 Jamal Y, Luo G, Kuo CH, Rabie A, Boulanger BO (2014) J Food Process Eng 37:27–36 Liu Y, Lotero E, Goodwin JG Jr, Lu C (2007) J Catal 246:428–433 Maddikeri GL, Pandit AB, Gogate PR (2012) Ind Eng Chem Res 51:6869–6876 Agarwal M, Singh K, Chaurasia SP (2012) J Renew Sustain Energy 4:701–709
123
74 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36.
Reac Kinet Mech Cat (2015) 114:63–74
AlKabbashi AN, Alam MZ, Mirgani MES, Al-Fusaiel AMA (2009) J Appl Sci 9:3166–3170 Leung DYC, Guo Y (2006) Fuel Process Technol 87:883–890 Kapil A, Wilson K, Lee AF, Sadhukhan J (2011) Ind Eng Chem Res 50:4818–4830 Dossin TF, Reyniers M-F, Marin GB (2006) Appl Catal B 62:35–45 Di Serio M, Tesser R, Pengmei L, Santacesaria E (2008) Energy Fuels 22:207–217 Cren E´C, Meirelles AJA (2005) J Chem Eng Data 50:1529–1534 Dı´az L, Brito A (2014) J Adv Chem Eng 4:1–6 Lukic´ I, Kesic´ Zˇ, Maksimovic´ S, Zdujic´ M, Liu H, Krstic´ J et al (2013) Fuel 113:367–378 Ilgen O (2014) Fuel Process Technol 118:69–74 Marchetti JM, Miguel VU, Errazu AF (2007) Fuel 86:906–910 Ilgen O (2012) Fuel Process Technol 95:62–66 Veljkovic´ VB, Stamenkovic´ OS, Todorovic´ ZB, Lazic´ ML, Skala DU (2009) Fuel 88:1554–1562 Dossin TF, Reyniers M-F, Berger RJ, Marin GB (2006) Appl Catal B 67:136–148 De Filippis P, Borgianni C, Paolucci M (2005) Energy Fuels 19:2225–2228 Arzamendi G, Campo I, Arguin˜arena E, Sa´nchez M, Montes M, Gandı´a LM (2008) J Chem Technol Biotechnol 83:862–870 37. Chantrasa A, Phlernjai N, Goodwin JG Jr (2011) Chem Eng J 168:333–340 38. Cantrell DG, Gillie LJ, Lee AF, Wilson K (2005) Appl Catal A 287:183–190 39. Marchetti JM, Errazu AF (2010) Biomass Bioenergy 34:272–277
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