Molecular-Level Kinetic Modeling of Methyl Laurate: The Intrinsic

Article Cite This: Energy Fuels 2018, 32, 5264−5270

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Molecular-Level Kinetic Modeling of Methyl Laurate: The Intrinsic Kinetics of Triglyceride Hydroprocessing Pratyush Agarwal,† Nicholas Evenepoel,† Sulaiman S. Al-Khattaf,‡ and Michael T. Klein*,†,‡ †

Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States Center for Refining and Petrochemicals, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

Energy Fuels 2018.32:5264-5270. Downloaded from pubs.acs.org by UNIV OF NEW ENGLAND on 10/04/18. For personal use only.

‡

ABSTRACT: A molecular-level kinetic model for the hydroprocessing of methyl laurate was constructed. The reaction network was deduced using experimental observations in the context of the delplot method for the discernment of product rank. The resulting 45 species and 83 reactions were used to construct the set of material balances in the kinetic model. Kinetic parameters of the model were determined by minimizing the difference between model outputs and experimental data for methyl laurate hydroprocessing. Differences in reactivity as a result of catalyst metal composition were modeled via the catalyst family concept. The model results show good agreement with the experimental results for a range of process conditions.

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INTRODUCTION The utilization of biomass as a raw material for the production of fuels and chemicals is an active area of research and commercial practice aimed at reducing the world’s reliance on fossil resources.1−3 A promising pathway is the production of engine fuels via upgradation of triglycerides, fatty acids, and fatty acid esters from algal, plant, and animal sources. This is because these feedstocks have a relatively high energy density coupled with a low oxygen content. While triglycerides can be directly used as engine fuels, the high viscosity and cloud point of vegetable oils lead to poor fuel atomization and incomplete combustion in a diesel engine. This results in a high engine wear and a significant increase in particulate and CO emissions. Additionally, oxygenated compounds, such as triglycerides and fatty acid methyl esters (FAMEs), have poor oxidation stability and are incompatible with petroleum fuels.4−6 Hydroprocessing of these long-chain triglyceride molecules can produce fuels, such as green diesel, which has been shown to be a viable renewable diesel fuel because of its high cetane number, high oxidation stability, low impurity content, and feed flexibility.6−9 These biomass feeds usually exist as a complex mixture of different types of triglycerides and fatty acids. Additionally, as a result of the limited supply, economics, food co-production, and seasonal dependence of biomass, there can be a wide selection of molecules and mixtures.3,10 Studying model compounds that represent typical feed moieties can alleviate the burden of determining the behavior of every feed component in every possible feed. These model compounds should undergo the same intrinsic reactions as the whole feeds, but their use allows for an isolated kinetic study of the products for varying reactor conditions. Extensive experimental work has been performed to study the kinetics of fatty acids and FAMEs as model compounds.11−16 However, few studies have modeled the process at the molecular level. Azizan et al. developed a simulation for triolein hydrodeoxygenation (HDO) based on thermodynamic equilibrium that provided qualitative insights in the model with no comparison to experimental data.17 Forghani et al.18 and Anand et al.19 considered lumped kinetic models for triglyceride hydroprocessing, where the products were © 2018 American Chemical Society

described in terms of four carbon-number-based lumps. Kumar and Froment described the hydrocracking activity in detail in a mechanistic kinetic model but did not focus on the deoxygenation activity.20 This present work explored recently published experimental data to develop a molecular-level kinetic model that will serve as a starting point for a more complex feed, such as coconut oil. In the experimental work, NiMo supported over alumina was tested to hydrogenate the carboxyl group in methyl laurate (ML). The reaction conditions were 300 °C and 0.1−0.8 MPa of H2 pressure to produce the green diesel.12,13 From the data, delplot analysis allowed for elucidation of the reaction pathways. The resulting network was used in a kinetic model to extract the rate constants of the prevalent reactions in the hydroprocessing of biomass-derived feeds.

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REACTION NETWORK GENERATION Reaction pathway analysis to determine the product rank was performed via the method of delplots. The delplot method is a set of plots that allow for the rank-based separation of products. A first-rank delplot is a plot of molar yield/conversion (Y/X) versus conversion (X) for the yields of the various products. The intercepts as X → 0 in the first-rank delplot “rank” the product as follows: primary products have finite intercepts, and higher rank products have zero intercept. The method can be extended to higher rank delplots by plotting Y/Xn versus X, where n is the delplot rank. In each case, the current rank (n) products have finite intercepts: higher rank products have zero intercept, and lower rank products diverge.21 The data of Imai et al.12 are shown in the delplot context in Figure 1. The data represent the conversion of ML over catalysts with different Ni/Mo ratios under similar reaction conditions. Inspection of Figure 1a shows that methane, C11, and C2−C10 cracking products were primary products that formed directly from the ML reactant. Figure 1b, the secondReceived: February 23, 2018 Revised: March 27, 2018 Published: March 29, 2018 5264

DOI: 10.1021/acs.energyfuels.8b00647 Energy Fuels 2018, 32, 5264−5270

Article

Energy & Fuels

Figure 1. (a) First-rank, (b) second-rank, (c) third-rank, and (d) fourth-rank delplots for ML conversion from experimental data by Imai et al.,12 where Y is the yield and X is the conversion of ML.

rank delplot, shows methane, C11, and C2−C10 diverging and C12 with a zero intercept. In Figure 1c, C12 seems to have a finite intercept, suggesting that it is a third-rank product, while methane, C11, and C2−C10 still diverge. The fourth-rank delplot in Figure 1d shows all of methane, C11, C12, and C2−C10 diverging. For all delplots, the carbon monoxide formation was difficult to interpret as a result of the methanation activity of the nickel catalyst, resulting in a non-detectable concentration of CO in some cases. Along with experimentally observed products,1,2,9,16 this information was used to generate the candidate reaction families for ML hydroprocessing shown in Table 1. To make nundecane as a primary product, a decarbonylation reaction was used, with CO and methanol as co-products. Methane as a primary product could be produced by either a CO bond cleavage reaction on the ML ester group, resulting in carboxylic acid and methane, or a CC hydrogenolysis at the end of the carbon chain in ML. Hydrogenolysis reactions are typically much slower than CO bond cleavage and, therefore, were ignored on ML. As a result of it being a co-product in the methane product, carboxylic acid formation then coincides with the formation of n-dodecane as a third-rank product via a carboxylic acid intermediate, followed by two successive HDOs to form the alcohol intermediate and the dodecane product. The other possible pathway for third-rank n-dodecane production is a CO bond cleavage reaction on the ML ester group, resulting in an aldehyde that can undergo hydrogenation and HDO to form the alcohol intermediate and the dodecane product, respectively. However, it should be noted that aldehydes are not experimentally observed in significant quantities as a result of their high reactivities; therefore, it is difficult to discern their rank.9 Additionally, although C2−C10 formation can be directly from cracking ML (first rank) or any

other intermediate, the cracking function of the reactor is low. Therefore, cracking was limited to the paraffins to reduce the overall network complexity. A methanation reaction22,23 was used to model the conversion of the CO group to CH4 on the nickel sites as experimentally observed by the lack of CO formation on nickel catalysts.12 The reactions identified using the delplot method represent the likely reactions during hydroprocessing of ML. The reaction network was systematically generated starting from ML using the Interactive Network Generator (INGen).24,25 INGen uses bond-electron matrices to computationally represent molecule structures. A reactive site can be identified as a subgroup of the overall molecule that is universal for a homologous series of reactions (reaction family). The reaction itself is characterized as a matrix addition operation representing bond-making and -breaking behavior of the reactive subgroup. The reactive subgroup and reaction matrices are presented in Table 1. Table 2 shows the final network statistics. There was a total of 45 species: the ML FAME, dodecanoic acid, dodecanal, dodecanol, methanol, CO, water, hydrogen, and the remainder being ndodecane and n-undecane with their cracking and isomerization products. Some additional reaction rules were used for the reaction families in Table 2. CO hydrogenolysis was only allowed to break a CO bond in an ester group with the use of hydrogen. HDO was for the specific CO hydrogenolysis, where an alcohol group was cleaved with water as a product. Decarbonylation searched for CO groups in any configuration: FAME, aldehyde, or carboxylic acid. Aldehyde hydrogenation was limited to search for aldehyde groups and reducing them to alcohol groups with hydrogen. Paraffin isomerization was allowed to create methyl branches only, and paraffin cracking could only occur at a branch site, which would not require a 5265


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Energy & Fuels Table 1. Reaction Families for ML Hydroprocessinga

a

Atom superscripts represent atom labels.

methanation reaction could only convert carbon monoxide and hydrogen to methane and water. This resulted in a total of 83 reactions for the ML feed. The final network is depicted in Figure 2.

Table 2. Network Statistics Generated Using INGen species type

number

reaction type

count

FAME carboxylic acid aldehyde alcohol n-paraffin isoparaffin CO, H2O, and H2

1 1 1 2 12 25 3

total species

45

CO hydrogenolysis HDO decarbonylation aldehyde hydrogenation paraffin isomerization paraffin cracking CC hydrogenolysis methanation total reactions

2 3 3 1 25 28 19 1 83

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MODEL EQUATIONS AND KINETICS The reaction network was used to generate a set of material balances, one for each species. The material balances, along with the initial conditions of the feed and the reactor, defined the initial value problem used to solve the kinetic model. The model development was performed in an in-house software, the Kinetic Model Editor (KME).24 In this study, a fixed-bed hydrotreating unit with a bifunctional metal/acid catalyst under plug-flow conditions was modeled. The Langmuir−Hinshelwood−Hougen−Watson (LHHW) rate law formalism assuming surface reaction rate control was used to model the reactions on catalyst surfaces, as shown in eq 1.26,27 An explicit

primary carbenium ion formation in its underlying mechanism on an acid site. CC hydrogenolysis was used in the case where C−C bond breaking occurred on a metal site and allowed for methane and ethane cracking from the paraffins. The 5266


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Figure 2. Parallel reaction pathways for paraffin production from a ML reactant.

corresponding state functions. Marrero and Gani32 and Benson et al.33 group contribution methods were used to calculate the gas-phase properties.

H2 partial pressure dependence was assumed on the adsorption denominator. reactants

ksr ∏i r=

⎛ reactants K ad, i ycat ⎜∏i Pi − ⎝ species

PHα ,2k(1 + ∑l

products

∏j

K eq

Pj ⎞

⎟ ⎠

ln K ad, k = aad, k +

n

K ad, kPl)

(1)

Surface rate constants ksr were modeled using the Arrhenius equation modified with the Bell−Evans−Polyani28,29 linear free energy relationship (LFER) to describe the activation energies for each reaction, as shown in eq 2. The LFER concept exploits the systematic differences in rates of reaction between member reactions i of a reaction family j, where a reaction family is a homologous set of reactants subject to the same type of reaction. This significantly reduced the number of tunable parameters in the model, with each reaction family requiring only three tunable parameters, Aj, E0j, and αj, rather than a preexponential factor and activation energy for each reaction. A further extension of the LFER concept to catalyst families was also made to account for the differences in activity of different catalysts. This was accomplished using a departure term on the ln A factor, as shown in eq 3.30 The catalyst family extension models the change in reactivity between two different catalysts as a constant for all reactions in a reaction family. This further reduces the number of model parameters of the same reaction system on different catalysts, so that only one additional parameter is needed per reaction family for each new catalyst. ln ksr, i = ln Aj −

ln Keq = −

■

ln Aj , c = ln Aj , cref + Δln Aj , c → cref

RT

ΔGrxn RT

(4)

(5)

KINETIC MODEL EVALUATION The data of Kimura et al.13 and Imai et al.12 were used to optimize the parameters of the kinetic model. They include values for the conversion of ML and the selectivity and yields of the measured products on the basis of the carbon content of the fatty acid chain in ML. The ML hydroprocessing was performed at three different hydrogen pressures and four different catalyst Ni/Mo ratios. Measured products included undecane, dodecane, methane, carbon monoxide, and the cracked alkanes. The model simulation time is ∼0.03 s for a once-thru simulation on a standard desktop running Windows 10 with an Intel i7-4770K CPU and 16GB RAM. ⎛ yobs − ypred ⎞2 ⎟⎟ obj = ∑ ∑ ⎜⎜ σ ⎠ ⎝ y set exp

(6)

An objective function of the form given in eq 6 was minimized to reduce the difference between each observed and predicted experiment for all sets of data. A simulated annealing algorithm was used for the optimization, where the E0 and α values were fixed to 20 and 0.1, respectively. The E0 and α values represent approximate values that can be adjusted with experimental data at different reaction temperatures. The ln A values were varied between 0 and 20 until a suitable solution was found, where each different Ni/Mo ratio had a different set of ln A values by the catalyst family concept. Because only the metal sites were changed by changing the Ni/Mo ratio for the catalysts,12 the acid site cracking and isomerization family ln A values were kept constant. Figure 3 shows the result of the optimization as a parity plot with good agreement between experimental and simulated products. The vertical intercepts of the plots in the delplot method can be used to determine the ratio of the apparent first-order rate constants for parallel reaction routes in ML hydroprocessing.

E 0j + αjΔHi RT

bad, k NC + cad, kNO

(2) (3)

A quantitative structure/reactivity relationship (QSRR) developed by Korre and Klein31 was modified and used to calculate the adsorption constants Kad, described in eq 4. The QSRR defines the adsorption constant of a particular molecule as a function of its structure and the type of site to which the species is adsorbing. In this case, the carbon and oxygen numbers were considered. Two sets of parameters were used for each type of site k: one for metal site adsorption and one for acid site adsorption. The equilibrium constants were calculated from the standard thermodynamic formulation, as given in eq 5. Thermodynamic properties for the species were calculated at the reaction temperature from group contribution methods and 5267


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increasing Mo content of the catalyst. This supports the higher conversion and paraffin yields observed experimentally with an increasing Mo content. A high methanation activity was observed for the Ni catalyst (0% Mo), with the methanation activity being severely reduced by the Mo content of the catalyst. Hydrogenolysis, which represented metal-site cracking of the paraffin products, decreased with an increasing Mo content. Paraffin isomerization and cracking on the acid sites of the catalyst were kept constant over different Ni/Mo ratios. However, the cracking families have overall minimal reactivity as a result of the small selectivity to the cracking products seen experimentally and, therefore, do not greatly impact the simulation. By application of the concept of catalyst families to the values in Table 3, relations can be made between the parameters by curve fitting the parameters as a function of the Mo content of the catalyst. An example of this is provided in eq 7, where the log A value for CO hydrogenolysis of ML to dodecanoic acid and methane is represented. Equation 7 can then be used to approximate the CO hydrogenolysis activity of the catalyst at different Ni/Mo ratios without further experimental studies. Extending this to all of the reaction families subsequently defines all parameters needed to study the hydroprocessing activity of catalysts with different Ni/Mo ratios. The concept should also apply to other catalysts with different metals and different acid supports given some information about the reactivity on those catalysts with relations based on a fundamental property of the metal or acid support.

Figure 3. Parity plot between experimental12,13 and model results for ML hydroprocessing, describing ML conversion and the product yield and selectivity with r2 = 0.995.

The tuned kinetic model was used to simulate hydroprocessing of both ML and dodecanoic acid. From the first-rank delplot in Figure 4a, the ratio of k1/k2/k3 of 0:83:17 for dodecanal, undecane, and dodecanoic acid formation from ML, respectively, can be extrapolated. This indicates that most ML directly decarbonylates to the undecane product. For dodecane formation, ML proceeds through carboxylic acid exclusively. An approximate ratio of k4/k5/k6 of 0:72:13 for dodecanal, dodecanol, and undecane formation from dodecanoic acid, respectively, can be extrapolated from the vertical axis in Figure 4b. Therefore, dodecanoic acid mostly hydrodeoxygenates to alcohol with a slight decarbonylation activity. Dodecanal in both cases is trivial and can be ignored in the network. This coincides well with the earlier delplot analysis from Figure 1 and the high selectivity toward undecane seen in the experimental results. The important kinetic parameters were extracted from the model. The acid and metal adsorption pressure dependence exponents were determined to be 1.26 and 0.472, respectively. Table 3 represents the tuned log A values of the reaction families in the hydroprocessing model at different Ni/Mo ratios. In general, the decarbonylation, HDO, and CO hydrogenolysis activities of the catalyst increased with an

log A CO hydrogenolysis

■

= 6.48 − 0.0216 (wt % Ni) + 0.114 (wt % Mo)

(7)

CONCLUSION A molecular-level model was created to describe the kinetics of ML hydroprocessing that agrees well with experimental data. Reaction pathways were identified to produce the product nC11 and n-C12 paraffins via decarbonylation and HDO. The model contained 45 species and 7 types of reaction families that well represent the hydroprocessing of ML. ML contains essentially the same reactive moieties as triglycerides that are commonly part of plant and vegetable oils. Therefore, the reaction pathways and matrices developed in this work should

Figure 4. First-rank delplots of the kinetic model simulation of hydroprocessing (a) ML and (b) dodecanoic acid with 0.25 mL of 8 wt % Mo and 20 wt % Ni on an alumina catalyst at 300 °C and 0.4 MPa hydrogen pressure. 5268


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Energy & Fuels Table 3. Kinetic Parameters for the Reaction Families in the Hydroprocessing of ML log A, catalyst wt % Ni/wt % Mo reaction family

20:0

20:2

20:6

20:8

10:0

10:1

10:3

10:4

CO hydrogenolysis (ester → aldehyde) CO hydrogenolysis (ester → carboxylic acid) decarbonylation (aldehyde) decarbonylation (carboxylic acid) decarbonylation (ester) HDO (alcohol → paraffin) HDO (carboxylic acid → alcohol) HDO (carboxylic acid → aldehyde) hydrogenolysis (paraffin) methanation paraffin isomerization (paraffin) paraffin cracking (paraffin) hydrogenation (aldehyde)

5.97 6.11 4.73 10.04 8.80 9.80 8.67 7.85 8.12 10.76 6.71 7.75 9.15

5.97 6.38 4.73 10.05 8.69 9.99 8.97 7.85 7.61 7.91 6.71 7.75 9.46

5.90 6.61 4.81 10.05 9.09 10.43 9.64 7.85 7.28 7.48 6.71 7.75 9.61

5.80 6.93 4.87 10.07 9.35 11.08 10.00 7.85 6.66 0.00 6.71 7.75 9.86

5.77 6.04 4.73 9.68 8.20 8.81 7.78 7.85 7.37 10.09 6.71 7.75 9.05

5.71 6.36 4.73 9.68 8.74 9.89 8.52 7.85 7.24 10.09 6.71 7.75 9.15

5.71 6.79 4.73 9.68 9.29 10.30 9.02 7.85 6.97 7.55 6.71 7.75 9.23

5.71 6.80 4.73 9.68 9.15 10.55 9.16 7.85 6.61 6.83 6.71 7.75 9.44

be applicable to any mixture of different triglycerides, fatty acid esters, and fatty acids. Additionally, the reactivity with different metal ratios of the catalyst was studied and can suggest catalyst design specifications based on desired activity. The kinetic parameters in this work define the catalyst family parameters that can be used to interpolate or extrapolate to the hydroprocessing activity of different Ni and Mo metal ratios. Because the molecules are similar, the kinetic parameters should provide a suitable estimate for hydroprocessing triglycerides with similar reaction conditions. These extensions will allow for faster construction and parameter optimization for triglyceride hydroprocessing kinetic models. This will be explored in future work based on this model.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

Aj,c = Arrhenius constant for reaction family j on catalyst c Δln Aj,c→cref = difference between the Arrhenius constant of reaction family j for catalyst c and a reference catalyst cref Kad,k = adsorption constant on site type k (metal or acid) aad,k, bad,k, and cad,k = site-dependent constants relating the structural elements to adsorption constants NC = number of carbon atoms NO = number of oxygen atoms R = universal gas constant T = temperature ΔGrxn = Gibbs free energy of reaction yobs = experimental property value from the experiment ypred = experimental property value from the simulation σy = experimental property weight

REFERENCES

(1) Choudhary, T. V.; Phillips, C. B. Renewable fuels via catalytic hydrodeoxygenation. Appl. Catal., A 2011, 397 (1−2), 1−12. (2) Mikulec, J.; Cvengroš, J.; Joríková, L.; Banič, M.; Kleinová, A. Second generation diesel fuel from renewable sources. J. Cleaner Prod. 2010, 18 (9), 917−926. (3) Huber, G. W.; Iborra, S.; Corma, A. Synthesis of transportation fuels from biomass: Chemistry, catalysts, and engineering. Chem. Rev. 2006, 106 (9), 4044−4098. (4) Altın, R.; Ç etinkaya, S.; Yücesu, H. S. The potential of using vegetable oil fuels as fuel for diesel engines. Energy Convers. Manage. 2001, 42 (5), 529−538. (5) Srivastava, A.; Prasad, R. Triglycerides-based diesel fuels. Renewable Sustainable Energy Rev. 2000, 4 (2), 111−133. (6) Kalnes, T.; Marker, T.; Shonnard, D. R. Green Diesel: A Second Generation Biofuel. Int. J. Chem. React. Eng. 2007, 5 (1), 1−15. (7) Sotelo-Boyas, R.; Liu, Y.; Minowa, T. Renewable Diesel Production from the Hydrotreating of Rapeseed Oil with Pt/Zeolite and NiMo/Al2O3 Catalysts. Ind. Eng. Chem. Res. 2011, 50, 2791−2799. (8) Gong, S.; Shinozaki, A.; Shi, M.; Qian, E. W. Hydrotreating of Jatropha Oil over Alumina Based Catalysts. Energy Fuels 2012, 26 (4), 2394−2399. (9) Kubička, D.; Kaluža, L. Deoxygenation of vegetable oils over sulfided Ni, Mo and NiMo catalysts. Appl. Catal., A 2010, 372 (2), 199−208. (10) Goering, C. E.; Schwab, A. W.; Daugherty, M. J.; Pryde, E. H.; Heakin, A. J. Fuel Properties of Eleven Vegetable Oils. Trans ASAE 1982, 25 (6), 1472−1477. (11) Deliy, I. V.; Vlasova, E. N.; Nuzhdin, A. L.; Gerasimov, E. Y.; Bukhtiyarova, G. A. Hydrodeoxygenation of methyl palmitate over sulfided Mo/Al2O3, CoMo/Al2O3 and NiMo/Al2O3 catalysts. RSC Adv. 2014, 4 (5), 2242−2250.

ORCID

Pratyush Agarwal: 0000-0002-3592-3564 Sulaiman S. Al-Khattaf: 0000-0001-5071-4034 Michael T. Klein: 0000-0001-5444-1512 Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Michael T. Klein acknowledges collaborations with and support of colleagues via the Saudi Aramco Chair Program at King Fahd University of Petroleum and Minerals (KFUMP) and Saudi Aramco.

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NOMENCLATURE ksr = surface rate reaction constant Kad,i = adsorption constant for species i ycat = catalyst site distribution Pi = partial pressure of species i Keq = reaction equilibrium constant Pα,k H2 = hydrogen partial pressure adsorption dependence on site type k Aj = Arrhenius constant for reaction family j E0j = activation energy factor in the Bell−Evans−Polyani linear free energy relationship for reaction family j αj = reaction index factor in the Bell−Evans−Polyani linear free energy relationship for reaction family j ΔHi = enthalpy of reaction for reaction i 5269


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for the estimation of thermochemical properties. Chem. Rev. 1969, 69 (3), 279−324.

(12) Imai, H.; Kimura, T.; Terasaka, K.; Li, X.; Sakashita, K.; Asaoka, S.; Al-Khattaf, S. S. Hydroconversion of fatty acid derivative over supported Ni-Mo catalysts under low hydrogen pressure. Catal. Today 2018, 303, 185−190. (13) Kimura, T.; Imai, H.; Li, X.; Sakashita, K.; Asaoka, S.; Akhtar, M. N.; Al-Khattaf, S. S. X-ray Photoelectron Spectroscopy Study of MoNi/γ-Al2O3 Catalysts for Hydroconversion of Fatty Oil Derivatives. Arab J. Sci. Eng. 2014, 39 (9), 6617−6625. (14) Gusmão, J.; Brodzki, D.; Djéga-Mariadassou, G.; Frety, R. Utilization of vegetable oils as an alternative source for diesel-type fuel: Hydrocracking on reduced Ni/SiO2 and sulphided Ni-Mo/γ-Al2O3. Catal. Today 1989, 5 (4), 533−544. (15) Hermida, L.; Abdullah, A. Z.; Mohamed, A. R. Deoxygenation of fatty acid to produce diesel-like hydrocarbons: A review of process conditions, reaction kinetics and mechanism. Renewable Sustainable Energy Rev. 2015, 42, 1223−1233. (16) Chen, J.; Yang, Y.; Shi, H.; Li, M.; Chu, Y.; Pan, Z.; Yu, X. Regulating product distribution in deoxygenation of methyl laurate on silica-supported Ni-Mo phosphides: Effect of Ni/Mo ratio. Fuel 2014, 129, 1−10. (17) Azizan, M. T.; Jais, K. A.; Sa’aid, M. H.; Ameen, M.; Shahudin, A. F.; Yasir, M.; Yusup, S.; Ramli, A. Thermodynamic Equilibrium Analysis of Triolein Hydrodeoxygenation for Green Diesel Production. Procedia Eng. 2016, 148, 1369−1376. (18) Forghani, A. A.; Jafarian, M.; Pendleton, P.; Lewis, D. M. Mathematical modelling of a hydrocracking reactor for triglyceride conversion to biofuel: Model establishment and validation. Int. J. Energy Res. 2014, 38 (12), 1624−1634. (19) Anand, M.; Sinha, A. K. Temperature-dependent reaction pathways for the anomalous hydrocracking of triglycerides in the presence of sulfided Co−Mo-catalyst. Bioresour. Technol. 2012, 126, 148−155. (20) Kumar, H.; Froment, G. F. Mechanistic Kinetic Modeling of the Hydrocracking of Complex Feedstocks, such as Vacuum Gas Oils. Ind. Eng. Chem. Res. 2007, 46 (18), 5881−5897. (21) Bhore, N. A.; Klein, M. T.; Bischoff, K. B. The delplot technique: A new method for reaction pathway analysis. Ind. Eng. Chem. Res. 1990, 29 (2), 313−316. (22) Mills, G. A.; Steffgen, F. W. Catalytic Methanation. Catal. Rev.: Sci. Eng. 1974, 8 (1), 159−210. (23) Araki, M. Methanation of carbon monoxide on nickel and nickel-copper alloys. J. Catal. 1976, 44 (3), 439−448. (24) Wei, W.; Bennett, C. A.; Tanaka, R.; Hou, G.; Klein, M. T., Jr.; Klein, M. T. Computer aided kinetic modeling with KMT and KME. Fuel Process. Technol. 2008, 89 (4), 350−363. (25) Bennett, C. A. User-Controlled Kinetic Network Generation with INGen. Ph.D. Thesis, Rutgers, The State University of New Jersey, New Brunswick, NJ, 2009. (26) Froment, G. F.; Bischoff, K. B.; De Wilde, J. Chemical Reactor Analysis and Design. 3rd ed.; Wiley: Hoboken, NJ, 2010. (27) Klein, M. T.; Hou, G; Bertolacini, R. J.; Broadbelt, L. J.; Kumar, A. Molecular Modeling in Heavy Hydrocarbon Conversions; CRC Press: Boca Raton, FL, 2006; DOI: 10.1201/9781420030617. (28) Bell, R. P. The Theory of Reactions Involving Proton Transfers. Proc. R. Soc. London 1962, 268, 527. (29) Evans, M. G.; Polanyi, M. Some applications of the transition state method to the calculation of reaction velocities, especially in solution. Trans. Faraday Soc. 1935, 31, 875. (30) Horton, S. R.; Klein, M. T. Reaction and catalyst families in the modeling of coal and biomass hydroprocessing kinetics. Energy Fuels 2014, 28 (1), 37−40. (31) Korre, S. C.; Klein, M. T. Development of temperatureindependent quantitative structure/reactivity relationships for metaland acid-catalyzed reactions. Catal. Today 1996, 31 (1), 79−91. (32) Marrero, J.; Gani, R. Group-contribution based estimation of pure component properties. Fluid Phase Equilib. 2001, 183−184, 183− 208. (33) Benson, S. W.; Cruickshank, F. R.; Golden, D. M.; Haugen, G. R.; O’Neal, H. E.; Rodgers, A. S.; Shaw, R.; Walsh, R. Additivity rules 5270


Molecular-Level Kinetic Modeling of Methyl Laurate: The Intrinsic

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