Sequence-based Process Modeling of Fluidized Bed Biomass

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Sequence based process modeling of fluidized bed biomass gasification Hamid Asadi-Saghandi, Amir Sheikhi, and Rahmat Sotudeh-Gharebagh ACS Sustainable Chem. Eng., Just Accepted Manuscript • Publication Date (Web): 09 Sep 2015 Downloaded from http://pubs.acs.org on September 9, 2015

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ACS Sustainable Chemistry & Engineering

Sequence based process modeling of fluidized bed biomass gasification Hamid Asadi-Saghandi1, Amir Sheikhi1,2*, Rahmat Sotudeh-Gharebagh1* 1

Process Design and Simulation Research Centre, Oil and Gas Processing Centre of Excellence, School of Chemical Engineering, College of Engineering, University of Tehran, P.O. Box 11155-4563, Tehran, Iran. 2

*

Department of Chemistry, McGill University, Montreal, Quebec H3A 0B8, Canada.

Corresponding authors: Amir Sheikhi; Tel.: +1 514 632-8878. E-mail: [email protected], and Rahmat Sotudeh-Gharebagh; Tel.: +98 21 6697-6863; fax: +98 21 6646-1024. E-mail: [email protected]..

Abstract Despite tremendous effort to model fluidized bed biomass gasifiers, as major sustainable waste-to-energy devices, current equation-oriented approaches suffer from implementation difficulties. In this research, a comprehensive cocurrent sequence based process model is introduced to simulate bottom-fed bubbling fluidized bed biomass gasifiers (BFBGs). The gasifiers include two operating regions, namely dense bed and freeboard. The dense bed is divided into several sections of logically ordered ideal reactors to describe the behavior of interacting phases, i.e., bubble and emulsion. The bubble phase is well characterized by an ideal plug flow reactor (PFR), and the emulsion phase is simulated as a continuous stirredtank reactor (CSTR). The freeboard is successfully mimicked with a PFR. Hydrodynamic and kinetic sub-models describe physical and chemical phenomena taking place in the gasifiers, respectively. Dynamic two phase model is adopted as the hydrodynamic sub1 ACS Paragon Plus Environment


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model, and the kinetic sub-model is derived from the literature. Several sets of experimental data from biomass gasifiers with various biomass feedstocks are analyzed to evaluate the reliability of proposed model. Close agreement between the experimental data and the model shows that the proposed simple and in-hand method is able to predict the behavior of complex BFBGs. Finally, the modeling package is used to optimize the hydrogen production in BFBGs. The proposed model can be integrated into the industrial process simulators such as AspenOne© modules to represent highly non-ideal reactors.

Keywords: biomass gasification; sequential modular simulation (SMS); bubbling fluidized beds; industrial process simulators; waste-to-energy modeling; hydrogen production

Introduction Two major environmental concerns, namely fossil fuel combustion issues, such as soil and air pollution, as well as global warming combined with the soaring rate of bio-waste production1,2 have drawn a lot of attention toward waste-to-energy management. Among the natural sources, biomass, as a renewable energy source, provides significant advantages over the fossil fuels. Hydrogen, as a clean and environmentally friendly fuel can be directly produced from biomass via gasification processes2 reducing the production of heat-trapping gases, such as carbon dioxide.3-5 Such processes demand a proper unit operation to optimally exploit the biomass energy.

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Gasification, as compared to incineration, is one of the most suitable environmentally friendly and economically feasible waste-to-energy methods.6 While the strict regulations on the hazardous gases

(e.g., NOx and SOx) emission level places incineration among expensive

technologies,7 gasification provides sustainable energy from biomass through cost-efficient thermochemical processes by converting solid wastes to high calorific clean gases such as hydrogen.8

Three general types of gasifiers are used in the biomass gasification processes: fixed bed, fluidized bed, and entrained flow,5 among which fluidized bed gasifiers are preferred due to several advantages, such as improved mixing and enhanced contact between gas and solid phases. This provides high conversion and excellent heat and mass transfer.5 Safe control of temperature and a high volumetric capacity compared to fixed bed gasifiers are among other advantages of fluidized bed gasifiers.2,5

Gasification of biomass in bubbling fluidized beds has been extensively investigated and developed for industrial applications. Many researchers9-15 have modelled the biomass gasification in fluidized bed reactors. These models can be classified in three main groups: computational fluid dynamics models (CFDM), fluidization models (FM), and black-box models (BBM)5 using an equation oriented system (EOS) as a common modeling approach. In such approach, nonlinear differential equations are solved simultaneously. As compared to EOS, sequential modular approach is able to simulate a process in a unit-by-unit basis, bypassing the complications raised from the coupled reactions and heat and mass transfer equations, which consequently reduces the computational costs significantly.



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Sequential modular approach, as effective mathematical-conceptual method, has been applied to simulate several processes in fluidized bed reactors. Steam reforming in fluidized beds

16

and

fluidized bed membrane reformers (FBMR),17 drying,18 fermentation of glucose in three-phase fluidized bed reactors,19 natural gas20,21 and coal volatile22 combustion, and photo-catalytic oxidation23 are some examples of processes successfully simulated by the sequential modular approach. Recently, the flow direction effect on the performance of a fluidized bed gasifier is well described by the SMS method.24 Yet, there has been no effort to comprehensively model fluidized bed gasifiers operated at different conditions within a generalized sequential framework to provide a process optimization roadmap. Therefore, it is essential to adopt a reliable userfriendly method, which has the capability of being integrated into the industrial process simulators to simulate, optimize, and scale-up fluidized bed biomass gasifiers.

In the present work, a sequence-based modular simulation has been developed to evaluate the performance of bottom-fed BFBGs with various geometries. The objective is to simulate nonideal bubbling fluidized bed biomass gasifiers to accommodate complex feed composition through a combination of ideal process units based on a simple and practical approach. The aim is to develop a model in which the fluidized bed is divided into several sections each of which consisting of bubble and emulsion phases. According to our approach, the calculations are initiated by a known feed composition and flow rate, which continue on a unit-by-unit basis to furnish all the unknowns in the flowsheet. Thus, this work contributes in the modeling and optimization of non-ideal biomass gasifiers using fundamental concepts of chemical engineering


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with minimal knowledge of mathematical modeling and without any further requirement of solving coupled differential equations.

Model development Sequential modular approach was developed to simulate a non-ideal fluidized bed gasifier by applying ideal reactors. The proposed model is based on dividing the fluidized bed reactor into several sections with equal volumes each of which consisting of two phases behaving as two ideal reactors. In each simulation stage, the bubble phase is directed through a plug flow reactor (PFR) and the emulsion phase is perfectly mixed through a continuous stirred-tank reactor (CSTR). Chemical reactions take place in both reactors and mass transfer occurs at the exit of reactors between effluent streams.19,20,25 The schematic diagram of the proposed method is shown in Figure 1.

To describe the physical and chemical phenomena occurring in the reactor, two sub-models were adopted; hydrodynamic and reaction sub-models were coupled together in developing the model. Dynamic two-phase model was used as the hydrodynamic model for describing the hydrodynamic behavior of the gasifier, and the chemical evolution of species was explained by the reaction kinetics derived from literatures. The following assumptions were made in developing the governing equations and hydrodynamic parameters:22 •

Radial concentration variation is neligible as a result of decent mixing.

•

Both bubble and emulsion phases follow steady-state mole balance equations. 5 ACS Paragon Plus Environment


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•

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The fluidized bed reactor is operated at an isotermal condition and, as a result of uniform temperature, the physical properties of components (density, viscosity, etc.), hydrodynamic parameters (such as bubble and emulsion phase fractions), and the reaction rate constants are considered to be constant along the reactor bed.

•

Bubble growth along the bed is negligible due to adopting immediate equilibrium bubble size upon gas entrance in the reaction region.

Governing equations Based on the aforementioned assumptions, the mole balance in the ith sequence of the model for bubble and emulsion phases is as follows, respectively:25 bubble phase, zi

C Ab ( i −1)U b Ab − Abε b

∫r

A(i )

dz − K be (C Ab ( i ) − C Ae ( i ) )Vb ( i ) − C Ab ( i )U b Ab = 0,

(1)

zi−1

and emulsion phase,

δ

CAe(i −1)Ue Ae − rA(i )VCSTR(i ) + Kbe (CAb(i ) − CAe(i ) )Ve(i ) ( ) − CAe(i )Ue Ae = 0. 1− δ

(2)

The mass transfer (third term in Eqns. 1 and 2) are calculated according to the fluidization and mass transfer expressions listed in Table 1.26-28 The calculations are performed for both phases gradually in all simulation stages until the top of the bed is reached. The volume of ith stage [V(i)], the volume of bubble [Vb(i)] and emulsion [Ve(i)] phases and the volume of PFR [VPFR(i)] and CSTR [VCSTR(i)] in each stage are calculated based on the following equations:25


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V(i ) =

(3)

Vt n

V b ( i ) = V ( i )δ

(4)

V e( i ) = V ( i ) (1 − δ )

(5)

V P F R ( i ) = Vb ( i )ε b

(6)

V C S T R ( i ) = V e ( i )ε e

(7)

Hydrodynamic sub-model Dynamic two-phase model (DTP) is chosen to calculate hydrodynamic parameters and characterize properties of bubble and emulsion phases. Compared to conventional two-phase models, this model is suitable, because it considers the solid particle existence in the bubble phase and permits higher-than-minimum fluidization emulsion velocity. Required hydrodynamic parameters are presented in Table 2.25,28,29

Reaction kinetic sub-model A comprehensive list of homogeneous and heterogeneous reactions occuring in a biomass gasifier is presented in Table 3 . All reactions take place in the reactor bed and freeboard zone; however, tar cracking and water gas shift are the most important reactions in the freeboard.30 In gasification processes, the product gas is mainly a mixture of hydrogen, carbon monoxide, carbon dioxide, methane, trace amounts of higher hydrocarbons, water, and contaminants such as tars and oils. High tar content in the product gas is one of the main barriers in the biomass gasification commercialization besides other technical challenges such as pollution control and



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equipment reliability.31,32 Thus, knowledge of optimum operating condition to maximize the tar cracking is of utmost importance, which can securely be followed by process simulation. To simulate the behavior of a bubbling fluidized bed gasifier, the hydrodynamic and reaction sub-models are integrated together. In each simulation section, reactions take place in both bubble and emulsion phases in the dense bed, and the mass is transferred at the end of each section. The schematic of mass transfer between the bubble and emulsion phases is illustrated in Figure 2.

Results and discussion Several sets of experimental data3,6,33-36 are considered to validate the performance of our proposed model in predicting the behavior of bubbling fluidized bed gasifiers. The geometrical details and operating conditions of each experimental case are presented in Table 4.3,33-36 Several biomass compositions are taken into account as the feedstock. Proximate and ultimate analyses of biomass feeds are listed in Table 5.3,33,35,36 Among the most important operating conditions influencing the gasification efficiency are temperature, equivalence ratio (ER, the ratio of actual air-to-fuel ratio to the stoichiometric air-to-fuel ratio), steam to biomass ratio (S/B), and biomass average particle size.3,4,33,37 The effect of these parameters on the composition of four main gaseous products (H2, CO, CO2, and CH4) as well as carbon conversion efficiency are studied in this study to evaluate the performance of our proposed model. Note that, the carbon conversion efficiency ηc, as a cumulative representative of reactor performance and ER, are determined according to the following equations, respectively:33


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ηc =1 −

ER =

Total carbon flow rate in the outlet stream Total carbon flow rate in the feed stream

(8)

(9)

mO2 (air ) / mdry biomass Stoichiometric O2 (air ) / Biomass ratio

Effect of reactor temperature Carbon conversion efficiency The effect of temperature on the carbon conversion efficiency using different simulation stage number is presented in Figure 3. As shown in this figure, increasing the gasifier temperature increases the carbon conversion. This behavior can be attributed to the fact that thermal cracking and steam reforming reactions proceed more rapidly at higher temperatures, leading to a higher carbon conversion.33 According to Figure 3, increasing simulation section number results in a higher carbon conversion. In fact, increasing the number of CSTRs in a tank-in-series model20 leads to plug-like flow behavior resulting in a higher conversion degree than a single CSTR. Thus, the carbon conversion is improved by increasing the number of sections in series. Furthermore, the model corresponding to 4 sections provides the best performance in predicting the experimental conversions (Figure 3, dotted blue line). Using the acquired optimum stage number (n = 4, also refer to Supporting Information), the behavior of 7 experimental reactors3,6,33,35,38-40 is simulated, and the parity plot of the calculated against experimental carbon conversion efficiency is depicted in Figure 4. As seen in this figure, the simulation results are in a good agreement with the experimental data for a wide range of operating variables reported in Table 4.



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Gas composition Figures 5-8 show the effect of the reactor temperature on the main product gas (H2, CO, CO2, and CH4) composition for 4 sets of experimental data. Taking the optimum simulation stage number, as furnished by the carbon conversion curves (see Figure 3). The effect of temperature on hydrogen production is shown in Figure 5. Hydrogen production increases by increasing the reactor temperature in all cases for the whole range of experimental temperature (650 °C < T < 900 °C). At low temperatures, more unburnt hydrocarbon and tar are formed, and since tar cracking is delayed at low temperatures, less hydrogen is produced in the reactions.9 It can be seen in Figure 5 that the suggested simulation method is able to predict the hydrogen production well. The effect of simulation stage number (n = 2-5) on the hydrogen concentration predication for all case studies is detailed in Supporting Information. Carbon monoxide production versus reactor temperature is presented in Figure 6. According to Lv et al.33 and Herguido et al.3, by increasing the gasifier temperature, the production of carbon monoxide decreases, while an opposite trend is observed in other studies.34,36 The char combustion reaction determines the CO content, and since the reaction is exothermic, Le Chatelier’s principle suggests that with the increase in temperature, the production of carbon monoxide is degraded.33 Based on this principle, a high temperature is unfavorable for the products (CO) in exothermic reactions.33 Increased rate of exothermic water-gas shift reaction ( + ⇄ + ) has been sourced as a possible reason for this behavior in another study.4 It has been mentioned that, if the shift reaction equiblirium is not reached, its rate increases by increasing temperature, which decreases CO concentration.4 Schuster34 and Narvaez et al.36 reported an increasing trend of CO production by increasing temperature, which is 10 ACS Paragon Plus Environment

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consistent with our simulation results. Similar trend was reported by Kaushal et al.12 Formation of CO from CO2 via endothermic Boudouard reaction ( + → 2)41 is probably the reason for such behavior of CO profile: while the CO2 production is always exothermic, the reaction enthalpy has a global minimum at T ~ 600 K.41

Figures 7 and 8 present the influence of temperature on carbon dioxide and methane concentrations, respectively. A decreasing trend is observed for both species by increasing temperature. Similar trends were reported in Van der Aarsen42 and Bilodeau et al.13 studies, where the production of CO2 and CH4 were decreased by increasing the gasifier temperature. This behavior can be attributed to the consumption of CO2 in endothermic Boudouard reaction41 as well as CH4 in endothermic methane decomposition reaction.33 These figures also show satisfactory model predictions for CO2 and CH4 profiles.

Effect of equivalence ratio Equivalence ratio is one of the most important factors in biomass gasification, which can significantly affect both carbon conversion and gasification efficiency. According to literature, the equivalence ratio can have a contradictory effect on the quality of product gases and process efficiency.33 Lv et al.33 suggested that by increasing the equivalence ratio, the oxidization reaction proceeds due to a high oxygen content introduced to the reactor, which has a negative effect on the gas composition leading to a low gasification efficiency. As a tradeoff, a high equivalence ratio is able to increase the temperature of gasifier (through exothermic oxidation reactions) and improve the gasification by increasing syngas production rate.9,33



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Effect of equivalence ratio on the product gas composition is presented in Figures 9-12. Narvaez et al.36 showed that by increasing the equivalence ratio, the produced fuel gases (H2, CO and CH4) decrease, while CO2 increases. More CO2 is produced in complete oxidation of carbon reactions by increasing the equivalence ratio. This leads to a decrease in CO production, and as a consequence, less H2 is produced in the water gas shift reaction.41 Similar trends are obtained by Lv et al.33 reporting an optimum value for the equivalence ratio ~ 0.23 as a result of the tradeoff discussed earlier. Our simulation method is able to successfully predict the overall trend of experimental data in both cases, as shown in Figures 9-12.

Effect of steam to biomass ratio The comparison of simulation results with the experimental data in terms of the steam to biomass ratio (S/B) effect on the concentration of product gases is shown in Figure 13 (a-d). As seen in these figures, increasing S/B enhances the production of hydrogen and carbon dioxide while carbon monoxide and methane concentrations decrease. Higher steam to biomass ratio favors the production of CO2 and H2 from CO through the water-gas shift reaction. Moreover, the higher the S/B, the more favored is the operating condition for methane steam reforming resulting in a decrease in methane concentration. The model predictions are in good agreement with the experimental

data in terms of H2, CO2, and CH4; however, carbon monoxide profile diverges from the model prediction at low temperatures. The assumption of isothermal condition, which disregards the effect of temperature variation resulted from the entering gasifying agent may be a possible reason.9


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Effect of average biomass particle size The composition and yield (g/gbiomass) of gaseous products versus biomass particle size are presented in Figures 14 and 15, respectively. As shown in Figure 14, over the range of average particle size (0.25-0.75 mm) used for pine sawdust gasification, the composition of product gases are not affected significantly by the particle size, which is compatible with the literature.9 The influence of initial biomass particle size on the gas yield (H2, CO, CO2, and CH4) for the gasification of white oak spheres43 (dp = 6- 25 mm) (dp,i = 6- 25 mm) is reported in Figure 15. As can be seen in this figure, the smaller the particle sizes the greater is the yield for all light gases the main product gas (H2, CO, CO2, and CH4). Similar gas yield profiles were observed by Rapagnà and di Celso44 for gasification of spherical wood particles at similar particle sizes (5-20 mm). The gas yield reduction by increasing the particle size is attributed to the mass transfer limitations inside the solid particles.9,44 For small particles, the pyrolysis process is mainly controlled by the reaction kinetic, whereas for larger particles, the gas diffusion is the determining step.33 Therefore, biomass particle size does not have a significant effect on the composition of product gases.

Tar content in the product gas As mentioned earlier, tar formation and removal is one of the main problems of biomass gasification process. Several tar species have been detected in the exhaust gas from the gasifier such as benzene, toluene, phenol, o-xylene, naphthalene, etc.45 The thermal tar cracking of main compounds i.e. phenol, benzene and naphthalene have been considered in the present work and the corresponding reactions were extracted from literature (the last seven reactions in Table 3).4613 ACS Paragon Plus Environment


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The influence of gasifier temperature, equivalence ratio, and steam to biomass ratio on the

amount of produced tar was investigated by the SMS model. Figure 16 shows the effect of equivalence ratio and temperature on the tar content,36,51 and the influence of steam to biomass ratio and temperature on the tar yield3,38 is presented in Figure 17. The amount of tar decreases by increasing the gasifier temperature, equivalence or steam to biomass ratio. As mentioned earlier, the temperature of reactor increases by increasing the ER resulting in the production of less tar.9 On the other hand, higher ER favors the combustion reactions of phenol, benzene and naphthalene (reactions 12-14 in Table 3).9,36 Based on reactions 9 and 11, steam converts heavy components of tar to light gas products including H2, CH4 and CO.52 Also tar cracking reactions proceed better by high temperature; therefor, the amount of tar decreases by increasing gasifier temperature and/or steam to biomass ratio.53 These figures attest that the proposed model can predict the decreasing trend of tar profiles accurately.

Optimization of independent variables So far, it has been shown that the proposed sequential model is able to predict the behavior of BFBGs. As discussed earlier, the independent process variables (reactor temperature, equivalence ratio, steam to biomass ratio, and average biomass particle size) influence the composition of produced gas. Here, a sequential-based optimization was performed to maximize the efficiency of biomass gasification. Biomass particle size was neglected in this section and three other parameters (ER, S/B, T) were chosen as the independent variables. Since syngas production (particularly hydrogen) is one of the most important indices of biomass gasification, the optimization was performed to maximize the hydrogen yield and H2/CO ratio in pine sawdust gasification.33 Moreover, hazardous gas (CO + CO2) emmision was also optimized. 14 ACS Paragon Plus Environment

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A multiple response method is adopted to optimize the hydrogen production, H2/CO, and CO+CO2 production (taken as the responses). Equation (10) represents the objective function (D) that reflects the desirable ranges for each response (di), where m is the number of responses:54

D = ( d 1 × d 2 × ..... × d n )

1 m

1

 m m =  ∏ d i  .  i =1 

(10)

The objective function is optimized to furnish the operating condition (in terms of independent variables) in which the optimum hydrogen, H2/CO, and CO+CO2 production are resulted.

The first step is to obtain the effect of the independent variables on H2 production through the sequential simulation. The binary effect of the variables on the H2 production are graphically shown by three-dimensional surface plots and two-dimensional contour plots in Figure 18. The binary effect of temperature and equivalence ratio on hydrogen production is presented in Figure 18a. It is evident that the H2 production increased by increasing the reactor temperature and decreases by increasing in the equivalence ratio. The maximum H2 production was found in the temperature range of 880-900 °C and ER range of 0.19-0.255, as evidenced from the contour plot (Figure 18a.2).

Figure 18b shows the dual effect of S/B and ER on H2 production. Also, the effect of temperature and S/B on H2 concentration is shown in Figure 18c. Similarly, an increase in H2 production is achieved by increasing S/B. The maximum H2, as obtained from the corresponding contour plots (Figure 18b.2), is achieved when S/B ~ 3.5-4 and ER ~ 0.19-0.21. The corresponding optimum region of temperature and S/B are T ~ 875-900 °C and S/B ~ 2.5-4, as



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shown in Figure 18c.2. Finally, the multiple response method furnishes the optimum condition for the maximum H2 production as T ~ 888.5 °C, ER ~ 0.205, and S/B ~ 3.85.

A similar procedure was performed to find the influence of independent variables on CO and CO2 production. Obviously, the H2/CO and CO + CO2 production depend on the produced H2, CO and CO2. For pine sawdust, Figures 19a-c present the binary effect of independent parameters on H2/CO resulted from the SMS. Furthermore, CO+CO2 production is shown in Figures 19d-f. The goal is to obtain the operating conditions in which H2/CO is maximized and hazardous gas emission is minimized. The corresponding contour plot (not shown in Figure 19) shows that the maximum H2/CO is located in T ~ 875-900 °C, S/B ~ 3.5-4, and ER ~ 0.19-0.225. Furthermore, T = 880 - 900 °C, S/B = 0 - 0.5, and ER = 0.24 - 0.265 yield minimum CO + CO2 production. Accordingly, T ~ 895.8 °C, S/B ~ 3.75, and ER ~ 0.207 are the optimum conditions for H2/CO ratio, and T ~ 892.2 °C, S/B ~ 0.2, and ER ~ 0.256 are the optimum conditions to minimze hazardous gas emission in the gasification of pine sawdust.

Conclusions Obtaining energy from bio-resources in the form of sustainable fuels, such as hydrogen, has drawn tremendous attention recently. Among the most common industrial waste-to-energy processes, fluidized bed biomass gasification offers several advantages, such as ease of operation and control, and high rates of heat and mass transfer. Despite the industrial interest in such processes, there has been no user-friendly sequential modeling methodology to predict the behavior of BFBGs to avoid complicated equation-oriented mathematical models. In this paper, 16 ACS Paragon Plus Environment

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we develop the first comprehensive biomass gasification sequential model, which can be easily implemented by industries with basic engineering mathematics knowledge. A series of interrelated standard reactors are logically ordered to simulate the bottom fed biomass gasifiers accommodating hydrodynamic and kinetic sub models. Sequential modular simulation (SMS) results successfully suggests that the hydrogen production and carbon conversion efficiency is increased by increasing the reactor temperature. Also, temperature increases carbon monoxide production and decreases the carbon dioxide and methane. The syngas (H2 and carbon monoxide) production is decreased by increasing the equivalence ratio, and the increase in gasifying agent (steam) to biomass ratio (S/B) has a contrary effect on syngas production. Moreover, the composition of product gases is not significantly influenced by the average particle size of biomass. Finally, BFBGs are optimized using the SMS output. This work paves the way toward developing user-friendly packages for complex multiphase simulations and optimization.

Acknowledgments Financial support from Iran National Science Foundation (INSF) (Grant Number 90007670) is gratefully acknowledged. Authors would like to thank Ms. H. Hasanzadeh Shahrivar for the discussion at the beginning of this work.

Supporting Information Hydrogen concentration as a function of temperature using different simulation stage numbers (n=2-5). This information is available free of charge via the Internet at http://pubs.acs.org/.



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Nomenclature A

cross-sectional area (m2)

Ar

Archimedes number [dp3ρg(ρs − ρg)g/µg2]

CA

concentration of component A (mol/m3)

D

objective function in optimization section

DAB

diffusion coefficient (m2/s)

Db

bubble mean diameter (m)

di

response (H2 production) in optimization section

dp

particle diameter (m)

G

acceleration of gravity (m/s2)

H

bed height (m)

K

reaction rate constant

Kbc

bubble to cloud mass transfer coefficient (s-1)

Kbe

bubble to emulsion mass transfer coefficient (s-1)

Kce

cloud to emulsion mass transfer coefficient (s-1)

N

number of stages

M

number of responses in the measure

P

gas component partial pressure, Pa

R

gas constant, 8.3145 J/(mol K)

ri

reaction rate based on component i (mol/m3s)

Re

particle Reynolds number

T

reactor operating temperature (K)


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U

superficial gas velocity (m/s)

Ub

bubble velocity (m/s)

Ue

emulsion velocity (m/s)

Umf

minimum fluidization velocity (m/s)

Vb

bubble phase volume (m3)

Ve

emulsion-phase volume (m3)

X

char conversion

Z

distance from distributor (m)

Greek letters

δ

bubble phase fraction

ρg

gas density (kg/m3)

ρp

solid density (kg/m3)

µg

gas viscosity (Pa s)

ε

average bed porosity

εb

bubble phase porosity

εe

emulsion phase porosity

Subscripts b

bubble

e

emulsion

i

initial

mf

evaluated at minimum fluidizing velocity



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

References: (1) González, J. F.; Román, S.; Bragado, D.; Calderón, M. Investigation on the reactions influencing biomass air and air/steam gasification for hydrogen production. Fuel Process. Technol. 2008, 89, 764-772. (2) Kalinci, Y.; Hepbasli, A.; Dincer, I. Biomass-based hydrogen production: a review and analysis. Int. J. Hydrogen Energy. 2009, 34, 8799-8817. (3) Herguido, J.; Corella, J.; Gonzalez-Saiz, J. Steam gasification of lignocellulosic residues in a fluidized bed at a small pilot scale. Effect of the type of feedstock. Ind. Eng. Chem. Res. 1992, 31, 1274-1282. (4) Gil, J.; Aznar, M. P.; Caballero, M. A.; Francés, E.; Corella, J. Biomass gasification in fluidized bed at pilot scale with steam-oxygen mixtures. Product distribution for very different operating conditions. Energy Fuels. 1997, 11, 1109-1118. (5) Gómez-Barea, A.; Leckner, B. Modeling of biomass gasification in fluidized bed. Prog. Energy Combust. Sci. 2010, 36, 444-509. (6) Campoy, M.; Gómez-Barea, A.; Vidal, F. B.; Ollero, P. Air–steam gasification of biomass in a fluidised bed: Process optimisation by enriched air. Fuel Process. Technol. 2009, 90, 677-685. (7) Eriksson, O.; Carlsson Reich, M.; Frostell, B.; Björklund, A.; Assefa, G.; Sundqvist, J.-O.; Granath, J.; Baky, A.; Thyselius, L. Municipal solid waste management from a systems perspective. J. Cleaner Prod. 2005, 13, 241-252. (8) McKendry, P. Energy production from biomass (part 2): conversion technologies. Bioresour. Technol. 2002, 83, 47-54. (9) Nikoo, M. B.; Mahinpey, N. Simulation of biomass gasification in fluidized bed reactor using ASPEN PLUS. Biomass Bioenergy. 2008, 32, 1245-1254. (10) Puig-Arnavat, M.; Hernández, J. A.; Bruno, J. C.; Coronas, A. Artificial neural network models for biomass gasification in fluidized bed gasifiers. Biomass Bioenergy. 2013, 49, 279-289. (11) Sreejith, C.; Muraleedharan, C.; Arun, P. Performance prediction of fluidised bed gasification of biomass using experimental data-based simulation models. Biomass Convers. Biorefin. 2013, 3, 283-304. (12) Kaushal, P.; Abedi, J.; Mahinpey, N. A comprehensive mathematical model for biomass gasification in a bubbling fluidized bed reactor. Fuel. 2010, 89, 3650-3661. (13) Bilodeau, J. F.; Therien, N.; Proulx, P.; Czernik, S.; Chornet, E. A mathematical model of fluidized bed biomass gasification. Can. J. Chem. Eng. 1993, 71, 549-557. (14) de Souza-Santos, M. Comprehensive modelling and simulation of fluidized bed boilers and gasifiers. Fuel. 1989, 68, 1507-1521. (15) Jiang, H.; Morey, R. V. A numerical model of a fluidized bed biomass gasifier. Biomass Bioenergy. 1992, 3, 431-447. (16) Habibi, R.; Hajizadeh, S.; Sotudeh‐Gharebagh, R.; Mostoufi, N. Two‐Phase Sequential Simulation of a Fluidized Bed Reformer. Chem. Eng. Technol. 2008, 31, 984-989.


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(17) Sarvar-Amini, A.; Sotudeh-Gharebagh, R.; Bashiri, H.; Mostoufi, N.; Haghtalab, A. Sequential simulation of a fluidized bed membrane reactor for the steam methane reforming using ASPEN PLUS. Energy Fuels. 2007, 21, 3593-3598. (18) Bizmark, N.; Mostoufi, N.; Sotudeh-Gharebagh, R.; Ehsani, H. Sequential modeling of fluidized bed paddy dryer. J. Food Eng. 2010, 101, 303-308. (19) Sheikhi, A.; Sotudeh-Gharebagh, R.; Eslami, A.; Sohi, A. H. Sequential modular simulation of ethanol production in a three-phase fluidized bed bioreactor. Biochem. Eng. J. 2012, 63, 95-103. (20) Hashemi Sohi, A.; Eslami, A.; Sheikhi, A.; Sotudeh-Gharebagh, R. Sequential-Based Process Modeling of Natural Gas Combustion in a Fluidized Bed Reactor. Energy Fuels. 2012, 26, 2058-2067. (21) Sotudeh-Gharebagh, R.; Chaouki, J.; Sheikhi, A. Environmentally Feasible Natural Gas Combustion in Fluidized Beds. (22) Eslami, A.; Hashemi Sohi, A.; Sheikhi, A.; Sotudeh-Gharebagh, R. Sequential Modeling of Coal Volatile Combustion in Fluidized Bed Reactors. Energy Fuels. 2012, 26, 51995209. (23) Asadi-Saghandi, H.; Sotudeh-Gharebagh, R.; Dashliborun, A. M.; Kakooei, H.; Hajaghazadeh, M. Sequential-based process modelling of VOCs photodegradation in fluidized beds. Can. J. Chem. Eng. 2014, 92, 1865-1874. (24) Hasanzadeh Shahrivar, H.; Sheikhi, A.; Sotudeh-Gharebagh, R. On the flow direction effect in sequential modular simulations: A case study on fluidized bed biomass gasifiers. Int. J. Hydrogen Energy. 2015, 40, 2552-2567. (25) Jafari, R.; Sotudeh‐Gharebagh, R.; Mostoufi, N. Modular simulation of fluidized bed reactors. Chem. Eng. Technol. 2004, 27, 123-129. (26) Cai, P.; Schiavetti, M.; De Michele, G.; Grazzini, G.; Miccio, M. Quantitative estimation of bubble size in PFBC. Powder Technol. 1994, 80, 99-109. (27) Kunii, D.; Levenspiel, O. Fluidization Engineering: Butterworth-Heinemann, Boston; 1991. (28) Mostoufi, N.; Cui, H.; Chaouki, J. A comparison of two-and single-phase models for fluidized-bed reactors. Ind. Eng. Chem. Res. 2001, 40, 5526-5532. (29) Chaouki, J.; Gonzalez, A.; Guy, C.; Klvana, D. Two-phase model for a catalytic turbulent fluidized-bed reactor: Application to ethylene synthesis. Chem. Eng. Sci. 1999, 54, 20392045. (30) Radmanesh, R.; Chaouki, J.; Guy, C. Biomass gasification in a bubbling fluidized bed reactor: experiments and modeling. AlChE J. 2006, 52, 4258-4272. (31) Leung, D. Y. C.; Yin, X. L.; Wu, C. Z. A review on the development and commercialization of biomass gasification technologies in China. Renewable Sustainable Energy Rev. 2004, 8, 565-580. (32) Dayton, D. A review of the literature on catalytic biomass tar destruction. US DOE NREL Report Golden, CO. 2002, 510-32815. (33) Lv, P. M.; Xiong, Z. H.; Chang, J.; Wu, C. Z.; Chen, Y.; Zhu, J. X. An experimental study on biomass air-steam gasification in a fluidized bed. Bioresour Technol. 2004, 95, 95-101. (34) Schuster, G.; Löffler, G.; Weigl, K.; Hofbauer, H. Biomass steam gasification–an extensive parametric modeling study. Bioresour. Technol. 2001, 77, 71-79.



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(35) Campoy, M.; Gómez-Barea, A.; Villanueva, A. L.; Ollero, P. Air− Steam Gasification of Biomass in a Fluidized Bed under Simulated Autothermal and Adiabatic Conditions. Ind. Eng. Chem. Res. 2008, 47, 5957-5965. (36) Narvaez, I.; Orio, A.; Aznar, M. P.; Corella, J. Biomass gasification with air in an atmospheric bubbling fluidized bed. Effect of six operational variables on the quality of the produced raw gas. Ind. Eng. Chem. Res. 1996, 35, 2110-2120. (37) Fercher, E.; Hofbauer, H.; Fleck, T.; Rauch, R.; Veronik, G. Two years experience with the FICFB-gasification process: na; 1998. (38) Tomishige, K.; Asadullah, M.; Kunimori, K. Syngas production by biomass gasification using Rh/CeO 2/SiO 2 catalysts and fluidized bed reactor. Catal. Today. 2004, 89, 389403. (39) Huang, Z.; He, F.; Zhao, K.; Feng, Y.; Zheng, A.; Chang, S.; Zhao, Z.; Li, H. Natural iron ore as an oxygen carrier for biomass chemical looping gasification in a fluidized bed reactor. J. Therm. Anal. Calorim. 2014, 116, 1315-1324. (40) Xiao, X.; Le, D. D.; Li, L.; Meng, X.; Cao, J.; Morishita, K.; Takarada, T. Catalytic steam gasification of biomass in fluidized bed at low temperature: conversion from livestock manure compost to hydrogen-rich syngas. Biomass Bioenergy. 2010, 34, 15051512. (41) Panda, C. Aspen plus simulation and experimental studies on biomass gasification, NATIONAL INSTITUTE OF TECHNOLOGY, ROURKELA; 2012. (42) Van den Aarssen, F. Performance of rice husk fuelled fluidized bed pilot plant gasifier. Paper presented at: First International Producer Gas Conference, Colombo, Sri Lanka1983. (43) Gaston, K. R.; Jarvis, M. W.; Pepiot, P.; Smith, K. M.; Frederick, W. J.; Nimlos, M. R. Biomass Pyrolysis and Gasification of Varying Particle Sizes in a Fluidized-Bed Reactor. Energy Fuels. 2011, 25, 3747-3757. (44) Rapagnà, S.; Mazziotti di Celso, G. Devolatilization of wood particles in a hot fluidized bed: Product yields and conversion rates. Biomass Bioenergy. 2008, 32, 1123-1129. (45) Abdelouahed, L.; Authier, O.; Mauviel, G.; Corriou, J. P.; Verdier, G.; Dufour, A. Detailed Modeling of Biomass Gasification in Dual Fluidized Bed Reactors under Aspen Plus. Energy Fuels. 2012, 26, 3840-3855. (46) Gungor, A. Modeling the effects of the operational parameters on H2 composition in a biomass fluidized bed gasifier. Int. J. Hydrogen Energy. 2011, 36, 6592-6600. (47) Gerun, L.; Paraschiv, M.; Vîjeu, R.; Bellettre, J.; Tazerout, M.; Gøbel, B.; Henriksen, U. Numerical investigation of the partial oxidation in a two-stage downdraft gasifier. Fuel. 2008, 87, 1383-1393. (48) Morf, P.; Hasler, P.; Nussbaumer, T. Mechanisms and kinetics of homogeneous secondary reactions of tar from continuous pyrolysis of wood chips. Fuel. 2002, 81, 843-853. (49) Jess, A. Catalytic upgrading of tarry fuel gases: A kinetic study with model components. Chem. Eng. Process. 1996, 35, 487-494. (50) Smoot, L. D.; Smith, P. J. Coal combustion and gasification. 1985. (51) Kurkela, E.; Ståhlberg, P. Air gasification of peat, wood and brown coal in a pressurized fluidized-bed reactor. I. Carbon conversion, gas yields and tar formation. Fuel Process. Technol. 1992, 31, 1-21.


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(52) Coll, R.; Salvado, J.; Farriol, X.; Montane, D. Steam reforming model compounds of biomass gasification tars: conversion at different operating conditions and tendency towards coke formation. Fuel Process. Technol. 2001, 74, 19-31. (53) Wan Ab Karim Ghani, W.; Moghadam, R. A.; Salleh, M.; Alias, A. Air gasification of agricultural waste in a fluidized bed gasifier: hydrogen production performance. Energies. 2009, 2, 258-268. (54) Myers, R. H.; Montgomery, D. C. Response surface methodology: Taylor & Francis; 1988. (55) Wurzenberger, J. C.; Wallner, S.; Raupenstrauch, H.; Khinast, J. G. Thermal conversion of biomass: Comprehensive reactor and particle modeling. AlChE J. 2002, 48, 2398-2411. (56) Westbrook, C. K.; Dryer, F. L. Chemical kinetic modeling of hydrocarbon combustion. Prog. Energy Combust. Sci. 1984, 10, 1-57. (57) Gungor, A.; Eskin, N. Two-dimensional coal combustion modeling of CFB. Int. J. Therm. Sci. 2008, 47, 157-174. (58) Groppi, G.; Tronconi, E.; Forzatti, P.; Berg, M. Mathematical modelling of catalytic combustors fuelled by gasified biomasses. Catal. Today. 2000, 59, 151-162.



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Page 24 of 50

Table 1- Fluidization and mass transfer correlations.26-28 Bubble diameter

Db = 0.21H 0.8 (U −Umf )0.42 exp[−0.25(U −Um f )2 − 0.1(U −Um f )]

Bubble velocity

U b = U − U e + u br

Bubble rise velocity

u br = 0 .711 gD b

Emulsion velocity

Ue =

U − δU b 1−δ

Bubble-to-emulsion

mass transfer coefficient

1 1 1 = + K be K bc K ce U   D1/ 2 g 1/ 4  K bc = 4.5 e  + 5.85 AB 5 / 4   Db   Db  D ε u K ce = 6.77  AB 3e br  Db

  

1/ 2


Page 25 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 2- Hydrodynamic parameters adopted in the SMS.25,28,29

ρ g d 3p ( ρ p − ρ g ) g Ar = µ g2

Archimedes number

U mf =

Minimum fluidization velocity

µg ρg d p

(

27.22 + 0.0408 Ar − 27.2

)

 − (U − U mf )   4.439  

Bubble phase voidage

ε b = 1 − 0.146 exp 

Emulsion phase voidage

ε e = ε mf + 0.2 − 0.059 exp 

Bubble phase fraction

δ = 0.534 − 0.534 exp 

 − (U − U mf )    0.429 

 − (U − U mf )    0.413 



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 50

Table 3- Gasification reaction kinetics.

Chemical Reaction

Kinetics

= & + → 2(& − 1) + (2 − &)

−13.078 = 1.5 × 10 exp

!" (1

)

1 + )

55

*" !

*" !

+ )+

*"

−18522 ) ) = 4.93 × 10+ exp ( ) = 1.11 × 10exp (

−3548 )

)+ = 1.53 × 10./ exp (

−29844 )$!"

+ = 4364exp (

+ 142 →

−20119

1 = 3.98 × 10 exp ( )$! !2.3 "

+ 142 →

−13127

3 = 2.19 × 10/ exp ( )*" !"

1 + 342 → + 2

= 1.585 × 105 exp (

5 = 2.7 × 10+ exp

−24157 2.5 2.' )$*6 !"

−1510 ($! *"! − $!" *" ⁄7' )

75 = 0.0265exp (39684)


56

25161 )

+ → 2

+ → +

− $ ).

−30178 & = 3 × 10' exp ( )

=

+ → +

Ref.

56

55

55

57

58

Page 27 of 50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


−103 )$: *:! 9

47

→ + 0.42 ' + 0.15 + 0.11 + 0.75

' = 105 exp (

+ 3 → 4 + 21 + 2

/ = 105 (

2 ' → 7.38 + 0.275 + 0.971 + 1.235

−3.5 × 103 .

2 = 1.7 × 101 exp ( )$;?@( )$;

Sequence-based Process Modeling of Fluidized Bed Biomass

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