Simulation of Hydrodesulfurization UsingArtificial Neural NetworkWeizhi Wang,1 Qikai Zhang,1 Lianhui Ding2 and Ying Zheng1*

1. Department of Chemical Engineering, University of New Brunswick, 15 Dineen Drive, P.O. Box 4400, Fredericton, NB,Canada E3B 5A3

2. National Centre for Upgrading Technology, 1 Oil Patch Drive, Suite A202, Devon, AB, Canada T9G 1A8

Artificial neural network (ANN) is applied to investigate the hydrodesulfurization (HDS) process with light-cycle oil as feed and NiMo/Al2O3 ascatalyst. ANN models frequently work as a “black box” which makes the model invisible to users and always need significant data for training.In this work, a new ANN is proposed. The Langmuir–Hinshelwood kinetic mechanism is incorporated into the model so that the proposed ANNmodel is forced to follow the given reaction mechanisms. Both advantages of self-learning ability of ANN and the existing knowledge of HDSwere taken into account. Lengthy training process is minimised. Effects of operating temperature, pressure, and LHSV on the sulfur removal rateare studied. The inhibition of nitrogen compounds is also investigated. It is shown that the presence of nitrogen can significantly reduce theconversion rate of sulfur components, in particularly, hard sulfur such as 4,6-DMDBT.

Le modele de reseau neuronal artificiel (RNA) est applique pour etudier le procede d’hydrodesulfuration (HDS). Les modeles RNA fonctionnentfrequemment comme une « boıte noire », ce qui rend le modele opaque aux utilisateurs et necessite toujours un volume eleve de donnees pourla formation. Dans cette etude, un nouveau modele de RNA est propose. Le mecanisme cinetique de Langmuir–Hinshelwood est incorpore aumodele de sorte que le modele de RNA propose est contraint de suivre les mecanismes de reaction fournis. Ceci permet de tirer profit aussi biende la capacite d’auto-apprentissage du RNA que de la connaissance existante du procede HDS. Le long processus de formation est reduit auminimum. Les effets de la temperature de fonctionnement, de la pression, et de la vitesse spatiale horaire du liquide sur le taux d’elimination dusoufre sont etudies. L’inhibition des composes azotes est egalement etudiee. Il est demontre que la presence de l’azote peut reduire de maniereconsequente le taux de conversion des composants sulfureux, en particulier, le soufre dur tel que le 4.6-DMDBT.

Keywords: oil, gas and coal, energy, pollutants and toxic agents, environment

INTRODUCTION

Diesel is one of important transportation fuels. The new reg-ulations require refineries in North America to reduce thesulfur content of diesel at pump of 15 ppm, down from the

previous 500 ppm, and it will be further reduced to 10 ppm in 2011.Meeting and exceeding the fuel specifications presents a signifi-cant challenge since this should be achieved at the lowest possiblecost (Song, 2003; Brunet et al., 2005). Hydrodesulfurization(HDS) is a catalytic chemical process that is widely used to removesulfur (S) from refined petroleum products. HDS is a complicatedprocess and the rate of sulfur removal is subject to numer-ous factors, including catalyst activity, operation conditions,feed compositions, etc. There have been extensive experimentalstudies reported regarding the HDS process. Due to the com-plex physical/chemical transformations and catalyst deactivationmechanisms in the reaction system, individual experiments can-not fully demonstrate the HDS process. Modelling and simulation

provide a tool for prediction and optimization of the HDS pro-cess. Extensive studies have been carried out to simulate plugflow catalytic reactors with either pseudo-homogeneous modelsor heterogeneous models (Derkx and Dixon, 1997; Szukiewicz etal., 1998). Chen et al. (2001) studied the dynamic behaviour of thereactor during the start-up, during shut-down, or during an upset(Chen et al., 2001). Liu et al. (2008a) introduced a new systemdynamics model to simulate the influence of operation conditionson the efficiency of HDS, hydrodenitrogenation and aromaticshydrogenation, and the consumption of hydrogen gas (Liu et al.,

∗Author to whom correspondence may be addressed.E-mail address: yzheng@unb.caCan. J. Chem. Eng. 88:801–807, 2010© 2010 Canadian Society for Chemical EngineeringDOI 10.1002/cjce.20342Published online 17 June 2010 in Wiley Online Library(wileyonlinelibrary.com).

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2008a,b). These process models are developed from fundamen-tal principles, involving various assumptions and pre-determinedparameters. This challenges its application to the complex HDSprocess.

Artificial Neural Network (ANN) is an effective computationalmodelling tool that can give good predictions without detailedknowledge of a process. ANNs are based on the collaboration ofhigh interconnection simple functional units that express a com-plicated cross-correlation (Basheer and Hajmeer, 2000). A numberof studies were documented to apply the ANNs for simulating HDSprocess. For instance, Berger et al. (1996) applied ANN to simu-late the deep HDS process of atmospheric gas oil (Berger et al.,1996). Arce-Medina and Paz-Paredes (2009) employed ANN forprediction of the sulfur removal from naphtha. Bellos et al. (2005)applied ANN for studying catalyst deactivation and the correla-tion of kinetic parameters with feed quality. The architectures ofANNs reported in these studies are based on pure mathematicalcalculations and there is no need of knowledge of the mecha-nism of a process (Linares et al., 2008). Given this empiricalnature, ANN models need large sets of data to train the mod-els for accuracy. Application of the established ANN models ishighly associated with the specific source of the data used for thetraining and extrapolation is impossible. To eliminate the disad-vantages, a new ANN model is proposed in this work. The kineticmodels of HDS are introduced to the ANN architecture so as tostrengthen the self-learning ability of ANN models in the HDSprocess.

ARTIFICIAL NEURAL NETWORKS ARCHITECTURE

Mathematical FormulationAn ANN is a computational-based, nonlinear empirical model,inspired by the biological neural networks. ANN can self-learncomplex dynamic behaviours of chemical engineering processes.This capability highly relies on a large quantity of train-ing data because it is based on pure mathematical algorithmwith no involvement of the mechanism of studied processes.Thus, ANN might not work well when data source is lim-ited. Although the HDS process has been extensively studied,the experimental conditions are diverse and systematic dataare surprising limited. Langmuir–Hinshelwood (L-H) type ofkinetics models has been widely applied in the kinetics studyof HDS reactions (Laredo et al., 2001; Ishihara et al., 2004;Sanchez-Minero et al., 2008). In this study, the L-H kineticequations are introduced to ANN architecture. Thus, the ANNmodels are forced to follow the HDS mechanisms. Three sul-fur components, DBT, 4-MDBT, and 4,6-DMDBT are studied inthis work.Langmuir–Hinshelwood kinetics model is shown asfollows:

rHDS,i = kHDS,iKiC˛i

1 + ∑KiCi + KH2SCH2S

× KH2CH2

1 + KH2CH2

(1)

where rHDS,i is the reaction rate of sulfur compound i and ˛ isreaction order, kHDS,i is the rate constant of sulfur compound i,and Ki, KH3S, KH2 denotes the adsorption equilibrium constants ofsulfur compound i (i = DBT, 4-MDBT, or 4,6-DMDBT), hydrogensulfide, and hydrogen; and Ci, CH2S, CH2 denotes the concentra-tion of component i (i = DBT, 4-MDBT, or 4,6-DMDBT), hydrogensulfide, and hydrogen. In HDS reactions, hydrogen is significantlyexcessive and hydrogen sulfide presents low concentration. Thus,KH2SCH2S is negligible and KH2CH2 � 1. �KiCi represents the col-

lective contribution of all sulfur compounds to the HDS reactionrate. Due to the low concentration of sulfur compounds, �KiCi

is significantly <1. Assuming a pseudo-first-order behaviour, therate equation (Equation 1) can be simplified to (Kabe et al., 1993;Laredo et al., 2001):

rHDS,i = dCi

d�= kiC

˛i (2)

where:

ki = kHDS,iKi (3)

� = 1LHSV

(4)

� is the space time and LHSV is the liquid hourly space velocity.Integrate Equation (2),

1C˛−1

i

= 1C˛−1

i0

+ (˛ − 1)ki

LHSV(5)

Cio is the initial concentration of component i (i = DBT, 4-MDBT, or4,6-DMDBT), ki varying with temperature follows the Arrhenius’law (Farag, 2007).

ki = Ae−E/RT (6)

where A is pre-exponential factor, R is a gas constant, and E isactivation energy. Inhibiting effects of nitrogen compounds havealso been reported using a L-H type equation (Laredo et al.,2001).

r′HDS,i = kiCi

1 + (KNCN)n(7)

where CN is the total nitrogen concentration and KN is the adsorp-tion constant of nitrogen compounds, n is a constant to correctthe underestimation of the adsorption of nitrogen compounds atlower partial pressure.

Structure of ANNMultilayer perception neural network (MLP) is used in this work.MLP is a widely used multilayered feed forward neural network.The structure of an MLP is presented in Figure 1. As shown, anMLP consists of an input layer, an output layer and one or morehidden layers. Each layer contains a number of neurons. The con-nections between neurons are calculated values called weights.The weights denote the “strength” of connection between neu-rons. The initial weights are pre-set as 1 in this work rather thanrandomly assigned by the computer. There are nine variablesshown in the input layer and they are independent of temperature,total pressure, LHSV, rate constants, and initial concentrations ofDBT, 4-MDBT, and 4,6-DMDBT. Three variables are shown in theoutput layer and they are the concentrations of DBT, 4-MDBT, and4,6-DMDBT of products. The original experimental data for bothinput layer and output layer are pre-processed using followingequations:

C′j0 =

(1

Cj0

)˛−1/2

(8)

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Figure 1. Architecture of a neural network with three layers: an inputlayer, a hidden layer, and an output layer.

C′j =

(1Cj

)˛−1

(9)

LHSV′ = 1LHSV

(10)

where Cj0 and C′j0 are original and modified data for initial

concentrations of sulfur components (j = DBT, 4-MDBT, and 4,6-DMDBT). Cj and C

′j are original and modified data for instant

concentrations of sulfur components.Figure 2a shows the overall structure of the ANN, which con-

tains of nine neurons in the input layer, nine neurons in the hiddenlayer and three neurons in the output layer. Figure 2b shows thedetailed algorithm. The three neurons in the input layer are therate constant, LHSV, and initial sulfur concentration. The neu-ron in the output represents the instant concentrations of sulfurcompounds (DBT, 4-MDBT, and 4,6-DMDBT). To integrate the ratelaw with the ANN structure, a y = x2 equation is applied as theactive function in the hidden layer. The weights between the hid-den layer and the output layer are varied with ˛. In another word,the ANN structure is flexible with the reaction order. The weightscan be optimised through data training and then optimal reactionorder can be determined. Reaction temperature and total pressureare normalised as follows:

Yi = Xi − Xi min

Xi max − Xi min(11)

where Yi denotes the normalised array from Xi array. Xi is originaldata of temperature and total pressure; Ximax and Ximin stand forthe maximum and minimum value in the Xi array.

Figure 3 shows the loop of the program. The calculated resultsof the output layer are compared to the experimental results. Thedifference is evaluated by mean squared error (MSE). If the MSEis larger than the given value (10−4 is used in this work), theweights in each layer will be adjusted by back propagation (BP)from the last layer to the first layer. BP is the most commonlyused algorithm in testing ANN. If the MSE equals to or is smallerthan the given value, the program will be terminated and the finalresults will be produced.

Figure 2. Architecture of ANN.

Figure 3. Mechanism of a neural network.

RESULTS AND DISCUSSION

Training of ANNNineteen sets of data are selected from the literature for ANN train-ing (Song, 2003; Turaga and Song, 2003; Choi et al., 2004; Yang et

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Table 1. ANN operation conditions

Active functions y = x; y = x2

Training function Levenberg–Marquardt backpropagationtraining function

Learning function Gradient descent w/momentumweight/bias learning function

Performance function MSELearning speed 0.4Hidden layer # 1Neuron # in hidden layer 9Operating platform Matlab 6.5.1 with ANN toolbox,

Windows XP, Intel Pentium 4

al., 2004; Liu et al., 2008a). The training conditions are listed inTable 1. The selected data were used to train the structure of ANNand identify rate constants. The active functions chosen for threelayers are y = x, y = x2, and y = x. Levenberg–Marquardt BP train-ing function and Gradient descent w/momentum weight/biaslearning function, which are the default functions in Matlab tool-box, are used for training and learning, respectively. The functionapplied to the performance test is MSE, which calculates the dif-ference of output data and experimental data:

MSE =(

1p

× 1s

) s∑q=1

p∑k=1

(tq,k − Qq,k)2 (12)

where MSE is the MSE, p and s are the length and width of theoutput and target matrices, tq,k and Qq,k are the matrices of theoutput data and experimental data, respectively. q and k are thevariables for counting. The learning rate is set as 0.4. A singlehidden layer with nine neurons is used as discussed above. Theentire program runs using the Matlab 6.5.1 platform.

During training, the ANN program will automatically adjust theweights of each neuron so as to make the MSE approaching thepre-set value, 10−4. When the MSE is equal to or lower than 10−4,the training is terminated. As shown in Figure 4, the MSE for thisANN program reaches the 10−4 after 19 epochs. This suggests thatthe output data well match the target data.

Figure 5 shows the overall reaction orders of HDS with andwithout the inhibition of nitrogen containing compounds. As a

Figure 4. The performance of ANN.

Figure 5. The optimal reaction order of HDS.

parameter in the model, the reaction order is pre-set before thetraining begins and is refined during the training. The accuracy ofthe pre-set value is reflected by epoch of the training process. Forinstance, smaller epoch indicates a better pre-set value. The trendsof epoch against overall reaction order is demonstrated in Figure5, which illustrated the two minimum points 1.5 and 1.23 for theoverall reaction order of HDS with and without the inhibition ofnitrogen containing compounds, respectively.

ValidationExperimental data published in the open literature are alsoselected for validating the simulation. Given that the publishedexperimental data cannot cover all the conditions, the accuracy ofthe ANN is tested in the range of 573–613 K, 4.5–7 Mpa, 0–4 h−1,as shown in Figure 6. Some of the simulated data are convertedto conversion in order to do the comparison (Turaga and Song,2003; Choi et al., 2004; Yang et al., 2005):

Ei = Xi − Yi

Yi

(13)

where E is the error, Xi and Yi denote the simulation value andexperiment value, respectively.

Figure 6. The accuracy of the ANN, 573–613 K, 4.5–7 Mpa, 0–4 h−1.

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Table 2. The properties of the LCO feedstock

Components Concentration

Total sulfur 1.1 wt.%Total nitrogen 774 wppma

Aromatics 77.3 wt.%Paraffins 8.5 wt.%Cycloparaffins 8.0 wt.%

Marcel Dekker INC.a Liu et al. (2008a).

The accuracy of the predicted data was assessed by T-test (Equa-tion 14):

t = XP − XE√VarP/nP + VarE/nE

(14)

where, t is t-value, XP, VarP, nP, XE, VarE, and nE are the means,variances, and numbers of predicted data and experiment data,respectively. P = 0.05 was chosen as probability level which indi-cates 95% probability of making a correct statement. With 95%probability, t should equal to or less than 2.06 according to thestandard table. In this calculation, t is 0.0023, much less than 2.06.The difference between the predicted data and the experiment datais minimal. This suggests that the ANN model is workable.

Simulation and DiscussionDBT, 4-MDBT, and 4,6-DMDBT are typical hard sulfur componentsof diesel feedstocks. In this work, the ANN is applied to study theHDS process of the above-mentioned sulfur components of light-cycled oil. The properties of the feedstock are listed in Table 2. It isalso assumed that the easy sulfur compounds such as substitutedBT can be completely removed during desulfurization process.A common catalyst, NiMo/Al2O3 is selected in this simulation.The catalyst properties are summarised in Table 3. The activationenergy and pre-exponential factor for the rate constants of HDSare obtained from the literatures (Chen et al., 2004; Liu et al.,2008a, Table 4). The effect of nitrogen compounds on the rateconstants is also considered. The impacts of nitrogen compoundsare calculated using Equation (7). The reaction order of nitrogencomponents was determined using the reaction orders of quino-line (0.25), indole (0.5), and carbazole, (0.5). The calculationmethod follows Yang et al. (2005).

The effects of temperature, pressure, and LHSV are investigatedwith/without considering the presence of nitrogen components.The simulation is operated by varying one variable while othervariables are fixed. The range of the operation conditions areshown in Table 5. Figure 7 shows the effect of temperature on HDSreaction in a range of 513–653 K without the presence of nitrogencomponents in the feedstock. The conversions of the substitutedDBTs increase with increasing temperature. The substituted DBTs,in particular, 4,6-DMDBT favours high temperature. Comparedto DBT and 4-MDBT, 4,6-DMDBT needs higher temperature fora similar conversion. For instance, conversion of 4,6-DMDBT

Table 3. The properties of catalysts

Parameters Values

Equivalent diameter 1.3 mmPorosity 0.6 mf3/mp3

Density 1 420 kgcat/mp3

Table 4. The activation energy and pre-exponential factors ofreactions

Reactions Pre-exponential factor, Activation energy,k0 (h−1) Ea (J/mol)

HDS of DBTHydrogenolysis 3.7 × 109 1.06 × 105

Hydrogenation 1.4 × 1010 1.25 × 105

HDS of 4 or 6-MDBTa

Hydrogenolysis 2.67 × 109 1.16 × 105

Hydrogenation 4.17 × 1010 1.20 × 105

HDS of 4,6-DMDBTa

Hydrogenolysis 1.52 × 109 1.29 × 105

Hydrogenation 2.68 × 108 9.55 × 104

HDN of quinolineb 1.1 × 1010 1.15 × 105

HDN of indoleb 1.3 × 1010 1.17 × 105

HDN of carbazoleb 1.6 × 1010 1.23 × 105

Activation energy from: aChen et al. (2004); bGirgis and Gates (1991).

Table 5. Summary of operation conditions

Variable Range

Temperature (K) 513–653Total pressure (Mpa) 3–71/LHSV (h) 0–1.1

approaches 100% conversion at as high as 653 K while a com-plete removal of DBT can be reached at 620 K. This may be dueto the fact that 4,6-DMDBT has steric hindrance, which makes itmore difficult to react on a certain temperature. Additionally, Fig-ure 8 illustrates the effect of LHSV when no nitrogen presents inthe feedstock. Higher LHSV results in lower conversion rate of sul-fur. This can be attributed to the fact that insufficient contact timebetween sulfur containing compounds and catalysts. 4,6-DMDBTis quite sensitive to residence time, indicating it is a relativelyslow reaction. DBT and 4-MDBT approach 100% conversion whenthe LHSVs are below 4 and 1 h−1, while the conversion for 4,6-DMDBT is as low as 57% at 1 h−1. Comparing Figure 7 to Figure8, it seems that the effect of temperature is more significant thanthat of LHSV. The conversion for 4-MDBT increases 70% whentemperature increases from 510 to 630◦C, while only 10% wasincreased with the decrease of LHSV from 6 to 1 h−1. Additionally,the conversion rates of 4-MDBT and 4,6-DMDBT increased from97% to 99% and from 56% to 82%, respectively, when the totalpressure increased from 3 to 7 MPa for the same LHSV (1.6 h−1).It seems that the total pressure takes more effect for sulfur com-pounds having low reactivity, such as 4,6-DMDBT. This is maybebecause high pressure is helpful in reducing steric hindrance ofsulfur containing compounds, which leads to more chances for4,6-DMDBT to get contacting with the activated sites of catalysts.This suggests that total pressure plays an important role in theHDS reactions and an increase in the total pressure can enhancethe conversion rate of sulfur components.

The effect of temperature on HDS with consideration of theinhibition of nitrogen compounds is investigated, as it shown inFigure 9. Compare Figure 9 and Figure 7, it is seen that the conver-sions of sulfur components are reduced in the studied temperaturerange when nitrogen inhibition is considered. Nitrogen inhibitionappears more significant for 4,6-DMDBT. The sulfur conversion

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Figure 7. Effect of temperature on the conversion of sulfur components.No nitrogen in feedstock, 7 Mpa, 1.6 h−1.

Figure 8. Effect of LHSV without nitrogen, 3 Mpa, 613 K.

Figure 9. Effect of temperature with the inhibition of total nitrogencompounds at 774 wppm.

rate reduces from 70% to 35% at 613 K, 7 MPa, and 1.6 h−1 whennitrogen components (774 wppm) present. 4,6-DMDBT is a typi-cal difficult sulfur and has very low reactivity over catalyst. Whennitrogen presents, 4,6-DMDBT is hard to compete with nitrogencomponents over the active sites of catalyst. This results in a sig-nificant drop in the conversion rate when there is nitrogen in thefeedstock.

CONCLUSIONThe ANN has been successfully applied to simulate the HDS pro-cess of DBT, 4-MDBT, and 4,6-DMDBT with light-cycle oil as feedand NiMo/Al2O3 as catalyst. L-H kinetics is introduced into theANN network. The proposed ANN model maintains self-learningability and involves HDS reaction mechanism. Deviation fromthe reaction mechanism is avoided so that the training processis shortened. The models are validated using published experi-mental data. The effects of temperature, pressure, and LHSV onHDS process are studied and the inhibition of nitrogen compoundsis taken into account. The activity of the studied three sulfurcomponents is decreased in order: DBT > 4-MDBT > 4,6-DMDBT.Clearly, nitrogen components have negative impact on the activ-ity of sulfur components, in particular, difficult sulfur such as4,6-DMDBT.

ACKNOWLEDGEMENTSThe authors gratefully acknowledge the financial assistance fromthe Atlantic Innovation Fund, Canada Foundation for Innovationand NSERC.

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Manuscript received June 7, 2009; revised manuscriptreceived November 20, 2009; accepted for publication November23, 2009.

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