Modeling Tar Recirculation in Biomass Fluidized Bed Gasification

Article pubs.acs.org/EF

Modeling Tar Recirculation in Biomass Fluidized Bed Gasification Wolfram Heineken,*,† Daniel De La Cuesta,†,‡ and Nico Zobel† †

Fraunhofer-Institut für Fabrikbetrieb und -automatisierung IFF, Sandtorstrasse 22, 39106 Magdeburg, Germany Escola de Enxeñaría Industrial, Universidade de Vigo, Lagoas-Marcosende s/n, 36310 Vigo, Spain

‡

S Supporting Information *

ABSTRACT: A biomass gasification model is proposed and applied to investigate the benefits of tar recirculation within a gasification plant. In the model, tar is represented by the four species phenol, toluene, naphthalene, and benzene. The model is spatially one-dimensional, assuming plug flow for the gaseous compounds and perfect mixing of the fuel particles in the bed; it includes fuel particle heating and drying, bed fluidization, pyrolysis, a kinetic reaction mechanism, entrainment of char particles from the fluidized bed into the freeboard, the motion of char particles in the freeboard and in pipes, and the particle and gas flow within a cyclone. An uncertainty analysis identifies the most critical parameters of the model that severely affect the results of simulation. The model is validated against experimental data obtained at a pilot gasifier plant. A detailed parameter study suggests that an efficiency rise can be expected, the tar and soot content in the produced gas will be lowered, the heating value of the gas can be increased, and the bed temperature will decrease when recirculation is applied. Critical operational conditions leading to an unlimited tar accumulation in the gasifier are identified.

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INTRODUCTION Recently, biomass gasification has received a lot of attention due to its high efficiency,1 despite the fact that direct combustion2−4 is still the main technology used to generate heat and power from biomass fuels. Gasification converts a solid fuel into a combustible gas which can be stored or used in an additional burning unit. In fluidized bed gasifiers,5 fuels with relatively low ash-melting points can be gasified without loss of efficiency by keeping the bed temperature low enough to prevent ash agglomeration.6 The main drawbacks of gasification are unconverted char5 and contaminant emissions,7 especially tars.1 These emissions need to be removed in order to avoid tar condensation, polymerization, and aerosol formation1 in downstream process units. There is not just one formal definition of tar in the literature; however, many authors agree that tars can be defined as those organic substances produced under partial oxidation or combustion of any organic material. They are largely aromatic,8,9 condensable1,10 and usually heavier than benzene.5,11 Since tar formation cannot be prevented inside the gasifier,1 methods to remove the tars from the combustible gas are required.12 Three different categories of tar removal techniques exist: mechanical/physical, thermochemical, and catalytic methods.13 Catalytic conversion is very efficient but, on the other hand, requires a high economic investment.14 Thermochemical cracking requires an additional heat source to reach at least 1000 °C and needs a longer residence time of the outflow gas in the chamber.12 Sometimes even a partial combustion of the product gas is carried out to achieve a higly efficient conversion of tars.9 Filters, scrubbers, electrostatic precipitators, or granular beds are typical examples of mechanical tar removal methods, being especially effective in the capture of particle contaminants.9 The tar adsorption capacity of several solids has been analyzed,15,16 and it was found that light tars are more susceptible to be condensed in granular beds than heavier © XXXX American Chemical Society

aromatic tars. Waste material (usually water) is continuously generated and should receive additional treatment.17 To overcome the drawbacks of tar removal, a recirculation of tar into the combustion chamber has been suggested by some authors.9,12,18 Tar recycling generates neither solid nor liquid wastes, it reduces the effort of gas purification, and it is expected that the overall efficiency of the gasification process is increased due to the regasification of the condensed tars.19 In the literature, three different cases of commercial application of tar recirculation have been found. The BIOSYN technology, commercialized by Enerkem Technologies, Montreal (Canada), recycles water-insoluble tars and carbon-rich ashes into the gasifier.20 The OLGA gasifier, developed and patented by the Energy Research Center of The Netherlands,21 includes a multiple stage scrubber where heavy tar particles can condense and be taken out of the main stream of combustible gas to be recirculated into the gasification chamber. The third case is a sewage sludge gasification plant developed and patented by the company Kopf AG22 at Balingen (Germany). The combustible gas flows through a granular bed formed by the fuel itself, where the tar condenses over the fuel particles. Afterward, the mixture is added to the gasifier, while an additional filter removes the rest of the contaminants remaining in the combustible gas. During the gasification of biomass fuels, a great number of physical and chemical processes interact simultaneously. The understanding, description, and, necessarily, simplification of such a complex field has been a challenge for researchers for many years, and a large number of models of different complexity have been proposed to describe the processes involved. For a deeper insight on modeling the main relevant Received: January 21, 2016 Revised: March 22, 2016

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DOI: 10.1021/acs.energyfuels.6b00150 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels

Figure 1. Schematic of the gasifier with tar recirculation.

model is validated against experimental data. Finally, the gasification model is used to study several effects of tar recirculation within the process: It is investigated (a) if the efficiency can be increased by tar recirculation, (b) how the heating value of the product gas will be affected by tar recirculation, (c) if the tar load of the gas stream can be lowered by tar recirculation, and (d) how the recirculation will affect the temperature of the fluidized bed. Applying the model, conditions are identified that ensure a steady state operation and avoid unlimited tar accumulation caused by the recirculation.

phenomena in biomass gasification plants, the comprehensive review by Gómez-Barea and Leckner5 is recommended. With respect to the spatial dimension that is covered, the models usually applied fall into three categories. On the one hand, black box models are the simplest ones; they solve the overall energy, mass, and momentum balances without detailed description of the processes inside the gasifier. Black box models have been proposed by many authors, including Ergüdenler et al.,23 Schuster et al.,24 and Konttinen et al.25 Many more references can be found in ref 5. On the other hand, more complex alternatives are Computational Fluid Dynamics (CFD) models26−30 with chemical reaction mechanisms, which provide detailed two- or three-dimensional information on many parameters inside the reactor, demanding at the same time advanced computational resources and long calculation times. One-dimensional fluidization models are a trade-off solution between the simplicity of black box models and the detailed description of fluid dynamics from CFD models. One-dimensional fluidization models have been proposed, e.g., by van den Aarsen,31 Jiang and Morey,32 Sadaka et al.,33 Corella and Sanz,34 and Radmanesh et al.35 Again, more references are given by Gómez-Barea and Leckner.5 In this article, a one-dimensional model of a gasification plant will be presented. The model was developed by the authors to investigate some of the main open questions regarding the operation of a gasification plant with tar recirculation, including the effect of the recirculation on the overall efficiency of the process, and its ability to reduce the tar concentration of the gas. In addition, it was studied if tar recirculation could, under certain conditions, lead to a critical unlimited accumulation of tar inside the reactor. The model covers processes such as fuel particle heating and drying, bed fluidization, pyrolysis, homogeneous and heterogeneous reactions, entrainment of char particles from the fluidized bed into the freeboard, motion of char particles in the freeboard and in pipes, the flow of gas and particles through a cyclone, and heat loss through the walls. In the next section of this article, the most relevant parts of the mathematical model will be presented, including a pyrolysis model, the reaction mechanism, and balances of substance and enthalpy. An uncertainty analysis investigates the influence of key parameters−as the water content, composition and heating value of the fuel as well as the fuel and air supply rates and reaction kinetics−on the behavior of a fluidized bed gasification system. For the case without tar recirculation, the gasification

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MATHEMATICAL MODELING Among the numerous processes involved in gasification, the formation and conversion of tars is a particularly complex subject. Under the designation of “tar”, a vast number of hydrocarbons are summarized, and the extremely complex reaction network of those species is far from being completely understood. Any kind of modeling of this matter will, therefore, introduce severe simplifications. In the literature on tar modeling, two general approaches can be identified. In the first approach, instead of single chemical compounds, tar classes are considered. Any tar class is characterized by a single chemical formula, and chemical reactions (with reaction rates) are defined on classes rather than compounds. In these models, physical properties like boiling point, specific heat etc. of the tar classes are generally not known. Models dealing with tar classes have been introduced by many authors, from the most simple one class models36−39 to two,35,40−42 three43 and even six class models.44 In a second approach, a limited number of chemical compounds is chosen to represent the tar species involved. Such a strategy has been followed by several authors,45−49 who all apply a similar reaction scheme with tar components phenol, benzene, and naphtalene. In Su et al.,50 toluene is added as a fourth tar representative. In the model introduced in this study, the reaction mechanism of Su et al.50 is adopted, with some minor modifications. The advantage of dealing with species rather than abstract tar classes is that physical properties are now known, including the boiling point which is needed to decide which one of the tar components will be condensed and recirculated. A schematic of the gasification plant under consideration is drawn in Figure 1. The gasification reactor is divided into two different regions: the fluidized bed in the lower part and the B


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Energy & Fuels freeboard. In the fluidized bed, a mixture of sand and wood particles is fluidized and rapidly mixed by the gas flow, which is mainly directed upward. The gas flow is formed by primary air, injected at the bottom of the bed, and by gases generated from drying, pyrolysis and gasification of the fuel. A secondary inlet of air is situated in the freeboard, where the homogeneus reactions among gas and tar species and the burning of the remaining char particles occur. The gas outflow is placed in the top of the gasifier leading to a cyclone filter that eliminates a part of the unconverted char. Having passed the cyclone, the tar laden gas flows through a condenser filled with biomass fuel particles where the gas is cooled down and a certain fraction of condensable tar species, as well as soot and remaining unburnt char is absorbed by the biomass fuel. The tar laden fuel particles are then introduced into the gasifier forming a closed recirculation loop that increases the residence time of tar contaminants. The gas leaving the tar condenser is cleaned at a gas purification unit where most of the remaining tars, soot, unburnt char and water is removed. The concept of tar recirculation has two objectives: first, the chemical energy of the tarry species is kept to the process, leading to increased efficiency of the gasifier; and second, the gas stream gets precleaned already before the purification unit. The points P1 to P8 in Figure 1 indicate measurement locations and particular reference points referred to later in the article. The one-dimensional gasification model covers the following processes: • fuel particle heating, drying, and pyrolysis, • bed fluidization, • a kinetic reaction mechanism, • entrainment of char particles from the fluidized bed into the freeboard, • motion of char particles in the freeboard. In this section we will focus on the issues directly related to gasification, namely pyrolysis and the reaction mechanism. The remaining processes are described in the Supporting Information. The mechanism of tar formation and decomposition used in the model is essentially based on a reaction scheme introduced by Su et al.50 It is shown schematically in Figure 2, where the strength of the arrows indicates the reaction rates.

commonly given by its ultimate analysis, i.e. the elementary mass fractions of C, H, O, and possibly further elements like N and S. In the experimental and numerical investigations in this article, the fuel has always been wood, where the elementary content of elements other than C, H and O is small and can safely be neglected. The ultimate analysis mass fractions of C, H, and O are denoted by wC,ult, wH,ult, and wO,ult, respectively. The ultimate analysis and the lower heating value of eight wood samples have been measured at Magdeburg University. For the ultimate analysis, LECO CHN 628 and LECO CS 230 determinators have been used. The heating value was obtained with the IKA Calorimeter C 4000. The results are given in Table 1, together with their mean and standard deviation Table 1. Wood Analysis Data Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 mean stnd dev est

wC,ult

wH,ult

wO,ult

Hi,fuel(dry) [MJ/kg]

0.5037 0.4938 0.4995 0.5172 0.5091 0.5237 0.5313 0.5216 0.5125 0.0131

0.0528 0.0569 0.0554 0.0554 0.0641 0.0642 0.0637 0.0626 0.0594 0.0047

0.4435 0.4493 0.4451 0.4274 0.4268 0.4121 0.4050 0.4158 0.4281 0.0165

18.783 18.163 18.272 18.616 18.731 18.653 19.403 18.356 18.622 0.388

estimate. (For n measurements x1, ..., xn, the mean is (∑in= 1 xi)/ n, and the standard deviation estimate is n

(∑i = 1 (xi − x ̅ )2 )/(n − 1) .) In this study the mean of these data will be used, as given in Table 1. With this mean ultimate analysis, the chemical formula of wood is C1 H1.3810 O0.6271, if the subscript of C is normalized to 1. There exists a great variety of pyrolysis models in the literature, showing large differences in the kind and amount of pyrolysis products created. In this article, we follow the pyrolysis model of Ragland et al.51 for two reasons: the ultimate analysis of wood therein closely agrees with the data given in Table 1, and the ultimate analysis of tar resulting from Ragland′s model is compatible to the tar model of Su et al.50 that is going to be used. In the model of Su et al.,50 the tar created by pyrolysis is represented by the two species phenol and toluene. It is assumed by Su et al. that the mass fractions of phenol and toluene as pyrolysis products are equal. The mass fractions of pyrolysis products from Ragland et al., together with Su′s assumption, are given in Table 2. Since those values do not exactly agree with the given ultimate analysis of wood, they are fitted to yield exact agreement. The fitted values are also given in Table 2; they only slightly deviate from the original values. With these mass fractions of pyrolysis products, the chemical equation of pyrolysis reads

C1 H1.3810 O0.6271(wood)

Figure 2. Mechanism of tar formation and decomposition.

→ 0.3910 C(char) + 0.0247 C6H6O + 0.0251 C7H8

Pyrolysis. Pyrolysis is the thermal decomposition of biomass into char and volatiles, where volatiles include a number of light gases such as water vapor, carbon monoxide, carbon dioxide, methane, and hydrogen as well as heavier hydrocarbons referred to as tars. In our model, the tars created in pyrolysis are represented by only two species, phenol and toluene. The chemical composition of the biomass fuel is

+ 0.3233 H 2O + 0.0619 CO2 + 0.1554 CO + 0.0675 CH4 + 0.0576 H 2

Reaction Mechanism. In this work, the following reaction mechanism is applied 2 CO + O2 → 2 CO2 C

(R1) DOI: 10.1021/acs.energyfuels.6b00150 Energy Fuels XXXX, XXX, XXX−XXX

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Energy & Fuels Table 2. Mass Percentage of Pyrolysis Products model

char

tar

Ragland et al. Ragland et al. with assumption of Su et al. this study (fitted values)

20 20 20.04

20

C6H6O 10 9.93

C7H8

H2O

CO2

CO

CH4

H2

10 9.88

25 25 24.85

11.5 11.5 11.62

18.3 18.3 18.57

4.7 4.7 4.62

0.5 0.5 0.50

Table 3. Kinetic Data Equation

A [mol/(m3 s) ]

α [−]

βi [−]

E [J/mol]

Reference

(R1)

5 × 10

0

1.256 × 10

βH2 O = βO2 = 0.5

Hannes54

(R2)

1011

0

4.2 × 104

βCO = 1 βH2 = βO2 = 1

Di Blasi38

0

1.256 × 10

βH2 O = βCO = 1

Bı ́ba et al.55

(R3a) inside bed

6

2.78 −10

5

5

(R3a) outside bed

2.978 × 10

0

3.691 × 10

βH2 O = βCO = 1

Karim & Mohindra56

(R3b) inside bed

104.9

0

4.547 × 104

βH2 = 1

Bı ́ba et al.55

βCO2 = 1

Yoon et al.57

0

4.020 × 10

βH2 = 1

Karim & Mohindra56

βCO2 = 1

Yoon et al.57

βCH4 = βO2 = 1

De Souza-Santos58

βCH4 = 1.7

Jess59

(R3b) outside bed

−8

1.124 × 10

(R4)

1.776 × 1011

(R5)

3.3 × 10

(R6) (R7)

5

5

−1

130536

0

3.29 × 10

7.589 × 106

0

1.256 × 105

1.7 × 1016

0

4.43 × 105

11

5

βH2 = −0.8 βC6H6 = −0.1

Westbrook & Dryer60

βO2 = 1.85 βC6H6 = 1.3

Jess59

βH2 = −0.4 βH2 O = 0.2 (R8) (R9)

107 655

0

105

1

8.023 × 10

4

βC6H6O = 1

Morf et al.43

βC6H6O = 0.5

Gerun et al.45 Smoot & Smith61

βO2 = 1

Gerun et al.45

(R10)

107

0

105

βC6H6O = 1

Morf et al.43

(R11)

6 × 106

0

1.256 × 105

βC7H8 = −0.1

Ji et al.46 Westbrook & Dryer60

(R12)

2.3 × 1015

0

3.56 × 105

βC7H8 = 1

Taralas et al.62

(R13)

3.3 × 10

10

0

2.5 × 10

βC7H8 = 1

Taralas et al.62

(R14)

1.6 × 1014

0

3.5 × 105

(R15)

655

1

8.023 × 104

(R16)

3.6 × 10

0

3.1 × 10

βO2 = 1.85 5

βH2 = 0.5 βC10H8 = 1.6

Jess59

βH2 = −0.5

9

2 H 2 + O2 → 2 H 2O

5

(R2)

CO + H 2O → CO2 + H 2

(R3a)

CO2 + H 2 → CO + H 2O

(R3b)

2 CH4 + O2 → 2 CO + 4 H 2

(R4)

CH4 + H 2O → CO + 3 H 2

(R5)

C6H6 + 4.5 O2 → 3 H 2O + 6 CO C6H6 + 2H 2O → 2.5 CH4 + 2 CO + 1.5 C (soot)

βC10H8 = 0.5

Smoot & Smith61

βO2 = 1

Gerun et al.45

βH2O = βC(soot) = 1

Jess63

C6H6O → 0.4 C10H8 + 0.15 C6H6 + 0.1 CH4 + CO + 0.75 H 2

(R8)

C6H6O + 4 O2 → 3 H 2O + 6 CO

(R9)

C6H6O + 3 H 2O → 2.95 CH4 + CO2 + 2 CO + 0.1 H 2 + 0.05 C (soot)

(R10)

C7H8 + 3.5 O2 → 7 CO + 4 H 2

(R11)

(R6)

2 C7H8 + 21 H 2O → 7 CO2 + 7 CO + 29 H 2

(R12)

(R7)

C7H8 + H 2 → C6H6 + CH4

(R13)

D


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Energy & Fuels

• the Sherwood number Sh = 2 + (Sh2lam + Sh2turb)1/2 with Shlam = 0.644 Re1/2 Sc1/3 and Shturb = 0.037 Re0.8 Sc/(1 + 2.443 Re−1(Sc2/3 − 1)). The chemical limitation is expressed by (Rumpel65)

C10H8 → 0.275 C6H6 + 0.97 CH4 + 1.235 H 2 + 7.38 C (soot)

(R14)

C10H8 + 7 O2 → 4 H 2O + 10 CO

(R15)

C (soot) + H 2O → CO + H 2

(R16)

C (char) +

⎛ 139100 J/mol ⎞ ⎟ kchem = 4.8 × 109 exp⎜ − ⎝ ⎠ RT

⎛ ⎛2 ⎞ 1 2⎞ O2 → ⎜2 − ⎟ CO + ⎜ − 1⎟ CO2 ϕ ϕ⎠ ⎝ ⎝ϕ ⎠

and the reaction rate is r(R17) =

(R17)

C (char) + H 2O → CO + H 2 C (char) + CO2 → 2 CO

(R18)

where mchar is the mass of char in a control volume of size V. The reaction order α = 0.59 is taken from Rumpel.65 The kinetics of reaction (R18),

(R19)

In reaction (R17), ϕ is set according to Field et al. and Arthur:53 Let dchar be the char particle diameter, and ψ = 2500 exp (−6240 K/T). Then ϕ is given by 52

Achar /m 2 T · V /m 3 K ⎛ 129700 J/mol ⎞ C H2O mol ⎟ × exp⎜ − · ⎝ ⎠ mol/m 3 m 3 s RT

r(R18) = 3.42

⎧ 2ψ + 2 if dchar ≤ 5 × 10−5 m ⎪ + ψ 2 ⎪ ⎪ −5 ⎨ 2ψ + 2 − ψ (dchar − 5 × 10 m) if 5 × 10−5 m −4 ⎪ψ+2 9.5 × 10 (ψ + 2) < dchar ≤ 10−3 m ⎪ ⎪ if dchar > 10−3 m ⎩1

has been suggested by Hobbs et al.66 Here, Achar is the total surface area of char particles in a control volume of size V. The kinetics of reaction (R19) is modeled in a similar form as the one of reaction (R17). With the binary diffusion coefficient DCO2,N2 = 1.35 × 10−5 (T/273 K)1.71 m2/s, the Schmidt number Sc = νG/DCO2,N2, and the Sherwood number as in reaction (R17), the diffusion limitation is

The rates of reactions (R1)−(R16) are given in Arrhenius form ⎛ Ci ⎞ βi ⎛ T ⎞α ⎛ E ⎞ ⎟ ∏ ⎜ r = ϵA⎜ ⎟ exp⎜ − ⎟ ⎝K ⎠ ⎝ RT ⎠ ⎝ mol/m 3 ⎠ i

kdiff

The parameter ϵ is the voidage of the fluidized bed if the reaction takes place there. The voidage ϵ is assumed to be constant in the fluidized bed, and it is obtained from calculating the fluidization as given in the Supporting Information to this article. Outside the fluidized bed, ϵ is set to 1. This means that the reaction rates r are based on the total volume, occupied by both gas and particles. The concentrations Ci of gases and soot, however, are always meant to be the amount of substance per gas volume, i.e., excluding the volume of particles therein. The volume occupied by soot can be neglected. Reactions (R3a) and (R3b) are the two directions of the reversible water−gas shift reaction, which is written here using two equations to fit into the Arrhenius form. For each homogeneous reaction (R1)−(R16), the parameters A, α, E, and βi can be found in Table 3. As in Tepper,42 inside the fluidized bed, where ash and char catalyze the water−gas reaction, a faster reaction rate is used than in the freeboard. The kinetics of char oxidation (reaction (R17)) includes both chemical and diffusion limitation of the reaction rate. It is given in a form suggested by Hedden.64 The diffusion limitation is expressed by kdiff =

1/kdiff

α mchar /kg ⎛ CO2 ⎞ mol 1 · ·⎜ ⎟ + 1/kchem V /m 3 ⎝ mol/m 3 ⎠ m 3 s

6DCO2,N2Sh ⎛ CCO2 ⎞0.3 kg s = ·⎜ ⎟ · 2 ⎝ mol/m 3 ⎠ m 3 ρchar dchar

the chemical limitation is (Groeneveld and Swaaij67) kchem =

⎛ 217100 J/mol ⎞ 107 ⎟ exp⎜ − ⎝ ⎠ MC/(kg/mol) RT

and the reaction rate reads r(R19) =

1/kdiff

0.7 m /kg ⎛ CCO2 ⎞ mol 1 · char 3 ·⎜ ⎟ + 1/kchem V /m ⎝ mol/m 3 ⎠ m 3 s

Heat Loss Through the Wall. The heat loss through the reactor wall is particularly significant near the fluidized bed, where it depends strongly upon the geometry of the reactor, including the nozzles for primary air supply, as well as of the characteristics and positioning of insulating material. Instead of applying a model that covers the complicated geometric details, we describe the heat loss through a wall section of area Awall by the ansatz Q̇ wall = h Awall(T − Tenv), where T is the temperature inside, and Tenv is the temperature outside the reactor. In all simulations presented in this article, Tenv is set to 10 °C. The heat transfer coefficient h is estimated using temperature measurements during a test run of the gasifier, resulting in

6DO2,N2Sh ⎛ CO2 ⎞1 − α kg s ·⎜ ⎟ · 3 2 ⎝ mol/m 3 ⎠ m ρchar dchar

h= ⎧ h = 25 W/(m 2 K) at the fluidized bed ⎪ bed ⎪ ⎪ hfrb = 9 W/(m 2 K) at the freeboard ⎨ ⎪ h = 1.5 W/(m 2 K) at the pipe after freeboard, ⎪ pc ⎪ and at the cyclone ⎩

In order to evaluate this formula, we need to define −4

• the binary diffusion coefficient DO2,N2 = 3.13 × 10 (T/ 1500 K)1.75 m2/s (Field et al.52), • the Schmidt number Sc = νG/DO2,N2, where νG denotes the kinematic viscosity of the gas, E

(20)


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Energy & Fuels

Following a model of Scala et al.,68 the Sauter mean diameter of char particles in the fluidized bed can be estimated by dchar = 0.34dfuel, and the particle density of char is given by ρchar = 2.3wchar,fuel(dry)ρfuel(dry). In our simplified model, no char particle size distribution is considered, and all char particles in the bed are assumed to have the Sauter mean diameter given above. In the above equations, dfuel is the fuel particle diameter, wchar,fuel(dry) is the mass fraction of char in dry fuel, and ρfuel(dry) is the particle density of dry fuel. These are known input parameters which are assumed to be constant for a given fuel. The amount of char in the freeboard is calculated using a model that includes the drag force on the particles. It is given in detail in the Supporting Information of this article. If, in the above notation, ṅNg+2,j−1 is the molar flow of char into a cell Cj of the freeboard, and ṅNg+2,j is the molar flow of char out of this cell, then the diameter of a char particle in cell Cj is estimated by

Species Balances. The computational domain is divided into subsequent cells C1, ..., CNcb, ..., CNc, starting at the bottom of the fluidized bed. Ncb is the number of cells in the bed, and Nc is the total number of cells. The cell Cj is characterized by its volume Vj, its void fraction ϵj, its temperature Tj, and the molar amount of species Si inside, which is denoted by ni,j. Since throughout this article we only consider stationary flow and processes, also the balances for species and enthalpy are given here for the stationary case. This means that ϵj, Tj, and ni,j do not depend upon time. The void fraction inside the bed is calculated using a fluidization model which is given in the Supporting Information. Outside the bed, ϵj = 1 holds. Let S1, ..., SNg be the gases involved, SNg+1 be soot, and SNg+2 be char. The k-th chemical reaction of the mechanism given above can be N +2 formally written as ∑i = g 1 νi,kSi = 0, where negative stoichiometric coefficients νi,k correspond to the reactants and positive ones to the products. Let rj,k denote the reaction rate r(Rk) evaluated at cell Cj. In cell Cj, the source term of species Si reads

dchar, j

Nr

Enthalpy Balance. The enthalpy flow into the fluidized bed

qi , j = Vj ∑ νi , krj , k + qi′, j

is

(1)

k=1

where Nr is the number of chemical reactions. The first term of qi,j accounts for sources of species due to chemical reactions. The term qi,j′ stands for additional sources, being water from wood drying and pyrolysis products, which are distributed equally into the cells of the fluidized bed, as well as primary and secondary air. The conservation of substance is given by the equations

Ḣ bed,in =

Ng + 2

Ḣ bed,out =

holding for all i = 1, ..., Ng + 2, where ṅi,j is the molar flow of species Si from cell Cj to cell Cj+1. In eq 1, the reaction rates rj,k depend on the concentrations of gases and soot, and on the amount and diameter of char particles in the cell. Since gas and soot are assumed to move with equal velocity, and the volume occupied by soot is neglected, the concentration of gas species and soot in cell Cj can be estimated by Ci,j = ṅi,j/V̇ G,j for i = 1, ..., Ng + 1 and j = 1, ..., Nc, where V̇ G,j is the total gas flow leaving cell Cj. Volume and molar gas flow are connected via the ideal gas law

p

T0

∑

Mini̇ , Ncb(

∫T

Tbed,out

cp , i dT + Hi, i) + Q̇ wall,bed

0

i=1

where Mi is the molar mass of species Si, and the heat loss Q̇ wall,bed through the wall of the fluidized bed is defined as given above. In our model, the enthalpy is balanced over the entire bed, and temperature inside the bed is held constant. This means that we impose the conditions Ḣ bed,in = Ḣ bed,out and Tj = Tbed,out for all cells Cj lying inside the bed. Outside the fluidized bed, the enthalpy is balanced over each cell. Let Cj be a cell outside the bed, and off the secondary air inlet. The enthalpy flow into Cj is

Ng

∑ ni̇ ,j

Ng + 2

Ḣ in, j =

i=1

∑ i=1

The amount of char in the fluidized bed is obtained from the balance

Mini̇ , j − 1(

∫T

Tin, j

cp , i dT + Hi, i)

0

The enthalpy balance over cell Cj reads Ḣ in, j = Ḣ in, j + 1 + Q̇ wall, j

Ncb

wchar,fuel(ar)ṁ fuel + MC ∑ q N + 2, j = ṁchar,entr j=1

cp , i dT + Hi, i)

where T0 = 25 °C is the standard temperature. In this formula, S is an index set representing the species flowing into the bed, i.e. biomass fuel, primary air, and recirculated tar and soot. Furthermore, ṁ bed, in, i are the mass flows, Tbed, in, i the temperatures, cp,i the specific heats, and Hi,i the lower heating values of those inflowing species. Likewise, the enthalpy flow leaving the fluidized bed is given by

ni̇ , j = ni̇ , j − 1 + qi , j , j = 2 ,..., Nc

RTj

Tbed,in, i

∑ ṁ bed,in, i(∫ i∈S

ni̇ ,1 = qi ,1 ,

VG,̇ j =

⎛ n ̇N + 2, j ⎞1/3 g ⎟ d = ⎜⎜ ⎟ char, j − 1 n ̇ N 2, j 1 + − ⎝ g ⎠

where Q̇ wall,j is the heat loss through the wall of cell Cj. The temperature inside the cell, Tj, is set to be the mean of inflow and outflow temperature: Tj = (Tin,j + Tin,j+1)/2. For the cell with secondary air inlet, an additional enthalpy flow accounting for secondary air needs to be regarded likewise. In the enthalpy balance given here the contribution of ash has been neglected. This is justified if the fuel is wood with its very low ash content. If a fuel with a significant amount of ash is

g

where wchar,fuel(ar) is the mass fraction of char in the fuel (as received), known from proximate analysis, ṁ fuel is the fuel mass flow into the gasifier, MC is the molar mass of carbon, and ṁ char,entr is the mass flow of char particles entrained from the bed into the freeboard, which will be defined in the Supporting Information of this article. F


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Energy & Fuels considered instead, models for the motion of ash particles need to be included, and the contribution of ash should be taken into account in the enthalpy balance.

Table 4. Input Parameters in the Reference Case input parameter

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UNCERTAINTY ANALYSIS A numerical uncertainty analysis has been carried out for the model without tar recirculation. The analysis shows to which extent the output parameters of the model are influenced by changes in the input parameters. Let there exist m input parameters pin,i and n output parameters pout,j. In the gasification model every output parameter is a function of all input parameters, which can be formally expressed by pout, j = Fj(pin,1 , ..., pin, m ), j = 1, ..., n

We consider a reference case pout,ref, j = Fj(pin,ref,1, ..., pin,ref,m), and for each input parameter pin,ref,i an uncertainty interval [pin,min,i, pin,max,i] ∋ pin,ref,i is estimated. The reference case and the estimated uncertainties are given in Table 4. In order to account for the uncertainty of reaction kinetics, we replace any reaction rate r(Ri) by air(Ri). For the water−gas shift reaction (reaction (R3)), which has different kinetics inside and outside the fluidized bed, the corresponding factors are denoted by a3,ins.bed and a3,outs.bed, respectively. The uncertainty intervals for the factors ai are also given in Table 4. The reference case values in Table 4 are approximately referring to operating conditions during a gasification experiment on a pilot plant. For the ultimate analysis and the heating value of the fuel, the reference value and uncertainty in Table 4 are based on the laboratory investigations of eight wood samples carried out at Magdeburg University; see Table 1. Given n measurements xi with i = 1, ..., n, an uncertainty interval can be defined by69 Iα = [x ̅ − z(1 + α)/2s ,

x ̅ + z(1 + α)/2s]

(∑i n= 1

where x̅ = xi)/n is the mean of the measurements, n s = (∑i = 1 (xi − x ̅ )2 )/(n − 1) is the standard deviation estimate, and z is the quantile function of the standard normal distribution, i.e. zp = √2 erf−1(2p − 1). This means that, if the measured expression were in fact normally distributed with expected value x̅ and standard deviation s, then the probability of a measurement to be in the interval Iα would be α. In this way, taking α = 0.8, we construct the uncertainty intervals for ultimate analysis and heating value. Since α = 0.8, roughly 80% of the measurement values lie within this interval. For the heating value Hi,fuel(dry), the uncertainty interval is plotted together with the measurement values in Figure 3. In the case of the ultimate analysis fractions, the uncertainty has to be considered simultaneously, since the mass fractions sum up to one. Thus, if one of the mass fractions is changed within its uncertainty interval, at least one of the other two mass fractions has to change as well. The uncertainty intervals for wC,ult, wH,ult, and wO,ult correspond to uncertainty strips on the plane that is defined by the condition wC,ult + wH,ult + wO,ult = 1. We define an uncertainty region on this plane to be the intersection of the three uncertainty strips. This procedure is illustrated in Figure 4. The perturbed ultimate analysis parameters that will be used in the uncertainty analysis are the edge midpoints of the uncertainty region. They are indicated by the red points in Figure 4. The mass fractions of the pyrolysis products can also not be changed independently, since they need to match the ultimate

symbol

reference case

uncertainty

ṁ fuel Tfuel

180 kg/h 25 °C

±15% ±1% of abs. temp.

wwater,fuel

0.1

±20%

wC,ult

0.5125

[0.4958, 0.5292]

wH,ult

0.0594

[0.0534, 0.0654]

wO,ult

0.4281

[0.4069, 0.4493]

Hi,fuel(dry)

18.622 MJ/kg

[18.125, 19.119]

dfuel

1 mm

±50%

ρfuel(dry)

470 kg/m3

±10%

V̇ prim

210 m3/h STP

±15%

Tprim

25 °C

±1% of abs. temp.

V̇ sec

25 m3/h STP

±15%

Tsec

25 °C

±1% of abs. temp.

wchar,pyr wC6H6O,pyr

20.04 wt % 9.93 wt %

±2 wt % −9.93 wt %, +10 wt %

C7H8

wC7H8,pyr

9.88 wt %

−9.88 wt %, +10 wt %

H2O

wH2O,pyr

24.85 wt %

±10 wt %

CO2

wCO2,pyr

11.62 wt %

±10 wt %

CO CH4

wCO,pyr wCH4,pyr

18.57 wt % 4.62 wt %

±10 wt % −4.62 wt %, +10 wt %

H2

wH2,pyr

0.50 wt %

−0.50 wt %, +10 wt %

reaction kinetics factors mass of sand in fluidized bed sand particle diameter sand particle density wall heat loss fluidized bed freeboard pipe and cyclone

a1, ..., a19

1

[0.1, 10]

msand

150 kg

±10%

dsand

0.5 mm

±30%

ρsand

2600 kg/m3

±5%

cbed cfrb cpc

25 W/(m2 K) 9 W/(m2 K) 1.5 W/(m2 K)

±20% ±20% ±20%

fuel mass flow fuel temperature fuel water content ultimate analysis of fuel (daf) mass fraction of C mass fraction of H mass fraction of O lower heating value of dry fuel fuel particle diameter density of dry fuel particle primary air flow primary air temperature secondary air flow secondary air temperature mass fraction of pyrolysis products char C6H6O

analysis of the fuel. A special algorithm has been designed to meet the following requirements: • One selected mass fraction is perturbed toward the maximum or minimum of its uncertainty interval. • Then, the other mass fractions have to change, but they are required to always lie within their uncertainty intervals, and their changes are minimized. G


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⎛ |p − pout,ref, j | |pout, j , i ,max − pout,ref, j | ⎞ out, j , i ,min ⎟ ⎜ uj , i = max⎜ , ⎟ |pout,ref, j | |pout,ref, j | ⎠ ⎝

The output parameters studied in the uncertainty analysis are • the gas temperatures at positions P1, ..., P6 shown in Figure 1, which are named T1, ..., T6, the volume fractions of carbon monoxide, methane, and hydrogen in the product gas, i.e. at position P8 in Figure 1, which are denoted by xCO, xCH4, and xH2, respectively, • the total tar concentration Ctar (which is the sum of the concentrations of the four tar species), and the soot concentration Csoot, both at position P7, i.e. before gas purification, • the lower heating value Hi,PG of the product gas, i.e. at P8. The relative uncertainties are shown graphically in the Supporting Information of this article. We call pin,i a “critical input parameter” if there exists at least one output parameter pout,j with relative uncertainty uj,i > 0.1. By application of this criterion, the following critical input parameters have been detected: • the fuel ultimate analysis fractions wC,ult, wH,ult, and wO,ult, • the mass fractions of pyrolysis products wCO,pyr, wCO2,pyr,

Figure 3. Measurement values, mean, and uncertainty interval of Hi,fuel(dry).

wCH4,pyr, wC6H6O,pyr, wC7H8,pyr, wH2,pyr, wH2O,pyr, • the lower heating value of the dry fuel, Hi,fuel(dry), • the fuel and primary air feed, i.e. ṁ fuel and V̇ prim, • the reaction kinetics of (R1), (R3) in the bed, (R8), (R10), (R12), and (R14), and • the wall heat loss coefficient cbed. The critical input parameters will be used to calculate uncertainties when the model is validated against experimental data in the next section.

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VALIDATION OF THE MODEL Experimental data has been obtained at a 72 h run of a pilot gasification plant, fuelled with sawdust. The pilot plant consists of the fluidized bed gasifier, a cyclone, and a gas purification unit as shown in the schematic in Figure 1. The height of the gasifier is 4.13 m, its diameter is 0.55 m in the lower part and 0.87 m in the upper part. At 11 time instants, • the input parameters ṁ fuel, Tfuel, V̇ prim, Tprim, V̇ sec, Tsec, • and the output parameters T1, ..., T6, xCO, xCH4, xH2

Figure 4. Measurement values, mean, and uncertainty region of the ultimate analysis mass fractions.

have been measured. All other parameters of the experiments conducted are according to the reference case as given in Table 4. The 11 measurement samples will be used to validate the simulation model in this section. The samples are ordered such that the total air−fuel equivalence ratio λtot = λprim + λsec is increasing from sample 1 to sample 11. As the formula indicates, the total air−fuel equivalence ratio is the sum of contributions from primary and secondary air. It is shown in Figure 5. In the first five samples, no secondary air was supplied. For each of the 11 measurement samples, an uncertainty analysis as described in the previous section has been carried out. In the uncertainty analyses, the “critical input parameters” defined in the previous section are varied within their estimated uncertainty intervals given in Table 4. Measurements, simulation results, and uncertainties of the output parameters resulting from the uncertainty analysis are shown in Figures

The values of the mass fractions of pyrolysis products calculated by this algorithm are given in the Supporting Information. If the input parameter pin,i is perturbed to the minimum of the uncertainty intervall, we get the output parameters pout, j , i ,min ≔ Fj(pin,ref,1 , ..., pin,min, i , ..., pin,ref, m )

and if it is perturbed to the maximum of the uncertainty interval, we get pout, j , i ,max ≔ Fj(pin,ref,1 , ..., pin,max, i , ..., pin,ref, m )

The relative uncertainty of the output parameter pout,j obtained by perturbing pin,i is defined by H


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Energy & Fuels

Figure 5. Air−fuel equivalence ratio for measurement samples 1 to 11.

6−14. Numbers in the figures indicate which input parameter variation caused a maximum deviation of the particular output

Figure 7. Temperature at position P2. Measurements, simulation values, and uncertainty intervals.


parameter. In Figure 6, the measurement samples of the bed temperature T1, which is mainly affected by the primary air− fuel equivalence ratio, are arranged such that λprim increases from left to right. Result. The average deviation between simulated and measured temperature is 47 K. The least agreement occurs for T2 at measurement sample 11, where the simulated temperature is 155 K above the measured one. This is also the only case when a measurement lies outside the uncertainty interval of the simulated value. In 58 of the total of 66 temperature measurements, the deviation between measurement and simulation is less than 80 K. As expected, there is a general tendency that temperatures rise when the air−fuel equivalence ratio increases. This trend is more pronounced with the simulation values than with the measurements. This effect could be due to the fact that the heat


conductivity of the wall insulation material was held constant in the model, while it might in reality increase with temperature. The uncertainty intervals of temperature cover a range of up to 400 K. They were in all cases spanned by the uncertainty of the oxygen content of the fuel. The carbon monoxide content shows the trend to decrease with increasing air−fuel equivalence ratio. It lies between 16 and 20 vol % for both measurement and simulation. In most cases experimental and simulation values are in good agreement, with a deviation of less than 1 vol %. The least agreement occures for measurement sample 4 where the I


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simulated CO content is 2.8 vol % lower than the measured one. Figure 12 shows also that the CO content would lower substantially if the kinetics of the water−gas shift (WGS) reaction were sped up by a factor of 10. This reveals that the WGS reaction is in fact far from being in equilibrium. Outside the fluidized bed, the WGS reaction rate is extremely low, and the short residence time of the gas inside the bed is obviously not sufficient to attain equilibrium. The measured hydrogen content lies in the range from 8 to 10 vol %, the simulated one between 6 and 10 vol %. The

Figure 12. Volume fraction of CO in the product gas. Measurements, simulation values, and uncertainty intervals.

uncertainty intervals of the hydrogen fraction are rather large, covering usually more than 10 vol %. In most cases the agreement of simulation and experiment is satisfactory, however, for measurement samples 6 and 7 the deviation is around 3 vol %. The methane content lowers with increasing air−fuel equivalence ratio, both experimentally and in the simulation. J


Energy & Fuels

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Article

EFFECTS OF TAR RECIRCULATION: SIMULATION RESULTS There are two objectives of tar recirculation in a gasification process: (1) the efficiency of the gasification process should be increased, and (2) the contamination of the gas with tar and soot should be lowered already before the gas purification unit. In this section we investigate whether these objectives are met by simulation results with the model presented in this article. In addition, we show how fuel water content and air supply influence the composition of the product gas. The following parameters are varied in order to study their influence: • the fuel water content, where two cases are considered: fuel with 10 and 30 wt % water content, • the air supply: total air−fuel equivalence ratio is varied λtot = λprim + λsec, and set λprim = 0.85λtot and λsec = 0.15λtot, • the recirculation efficiency, i.e. the percentage of tar and soot to be recirculated to the fluidized bed: four cases are considered 0% (no recirculation), 50%, 80%, and 100% recirculation efficiency. All other parameters are set according to the reference case given in Table 4 throughout this section. For any simulation case, the following output parameters have been evaluated: • the chemical enthalpy flow Ḣ chem,P8 of the product gas, i.e., at P8 in the schematic shown in Figure 1, • the lower heating value Hi,P8 of the product gas at P8, • the temperature of the fluidized bed T1, • the total tar concentration Ctar,P7 before gas purification, i.e., at P7, • the total soot concentration C soot,P7 before gas purification, i.e., at P7, • the volume fractions of carbon monoxide, methane, and hydrogen in the product gas at P8, denoted by xCO,P8, xCH4,P8, and xH2 P8, respectively. The simulation results are illustrated in Figures 15−22. In these figures, the triangles mark the optimal operation point of the gasifier, meaning that air is supplied such that the chemical enthalpy flow Ḣ chem, P8 is maximized. The simulation results at the optimal operation point are given numerically in Tables 5 and 6. In the case of 100% recirculation, any air supply below the optimal operation point led to a nonconverging numerical solution. It may be possible that under these conditions the tar accumulates in the gasifier, which is why no steady-state solution can be found. For the cases studied with less than 100% recirculation, the solution always converged. Result. Increasing the air−fuel equivalence ratio enhances the combustion of burnable gas components, leading to a higher temperature of the fluidized bed. Tar recirculation results in a ceretain temperature drop, since the higher tar concentration in this case promotes endothermic tar reactions. At the optimal operation point, the bed temperature is always in the range of 850 to 950 °C. Result. The curves of the chemical enthalpy output Ḣ chem, P8 always show a local maximum, which we call the optimal operation point. This maximum occures at an air−fuel equivalence ratio λtot ≈ 0.39 for fuel with 10 wt % water content, and at λtot ≈ 0.49 for fuel with 30 wt % water content. For small λtot, the chemical enthalpy output Ḣ chem, P8 becomes small since the low temperature in this case reduces the reactions of tars to CO, CH4, and, most significantly, to H2, which is, to a large extent, produced from the reaction of

Figure 13. Volume fraction of H2 in the product gas. Measurements, simulation values, and uncertainty intervals.

Figure 14. Volume fraction of CH4 in the product gas. Measurements, simulation values, and uncertainty intervals.

The simulated values are always slightly less than the measured ones, but in most cases, the deviation does not exceed 1 vol %. K


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Energy & Fuels

Figure 17. Lower heating value of the product gas (legend as in Figure 15).

Figure 15. Fluidized bed temperature.

Figure 18. Total tar concentration before gas purification (legend as in Figure 15).

Result. The air−fuel equivalence ratio has a strong effect on both tar and soot concentration after the recirculation, i.e. at P7. With small λtot, the temperature gets too low to allow for significant tar destruction, which results in very high tar concentration in this case. Soot, that is mainly produced from these tar destructing reactions, has a low concentration in this case. The contrary holds for high λtot, when tar destructing reactions are sped up by the high temperature and large amounts of soot are produced. The recirculation of tar and soot is a way of enlarging their residence time in the reactor, which in most cases leads to lower tar and soot concentrations after the recirculation, i.e., at point P7. A reason for this behavior is that, if tar and soot stay longer in the reactor at high temperature, they are for a longer period of time consumed by destructing reactions. This means that the recirculation has indeed a cleaning effect to the produced gas, which was one of the reasons this method had been introduced.

Figure 16. Chemical enthalpy output Ḣ chem,P8 of the gasifier (legend as in Figure 15).

toluene with water. For high λtot, on the other hand, the chemical enthalpy output Ḣ chem, P8 and the heating value get small since in this case a larger amount of the burnable gas components is consumed by combustion. If λtot is high enough, tar recirculation increases both the chemical enthalpy output Ḣ chem, P8 and the heating value since it enhances the production of CO, CH4, and H2 from tar by increasing tar concentration. However, if λtot is small, tar recirculation lowers the chemical enthalpy output and the heating value, since in this case it strongly decreases temperature, and especially the production of hydrogen from toluene and water is highly suppressed if temperature is low. L


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Energy & Fuels

Figure 19. Soot concentration before gas purification (legend as in Figure 15).

Figure 22. H2 content in the product gas (legend as in Figure 15).

Table 5. Simulation Results for Fuel with 10 wt % Water Content at the Optimal Operation Point recirculation efficiency 0%

50%

80%

100%

λtot Ḣ chem,P8 [kW] Hi,P8 [MJ/m3 STP] Ctar,P7 [g/m3 STP] Csoot,P7 [g/m3 STP] xCO,P8 [vol %] xCH4,P8 [vol %]

0.388 497 4.30 13.74 0.213 15.77 3.61

0.388 502 4.34 12.96 0.170 15.92 3.62

0.390 509 4.37 11.40 0.118 16.12 3.64

0.400 525 4.41 6.56 − 16.65 3.63

xH2,P8 [vol %]

9.46

9.54

9.60

9.33

T1 [°C]

930

923

911

877

Table 6. Simulation Results for Fuel with 30 wt % Water Content at the Optimal Operation Point recirculation efficiency 0%

50%

80%

100%

λtot Ḣ chem,P8 [kW] Hi,P8 [MJ/m3 STP] Ctar,P7 [g/m3 STP] Csoot,P7 [g/m3 STP] xCO,P8 [vol %] xCH4,P8 [vol %]

0.493 314 3.07 9.77 0.124 10.73 2.72

0.488 322 3.16 9.10 0.075 11.09 2.78

0.484 333 3.26 7.74 0.035 11.59 2.85

0.490 349 3.36 4.31 − 12.11 2.88

xH2,P8 [vol %]

6.85

7.09

7.22

7.43

T1 [°C]

903

889

871

856

Figure 20. CO content in the product gas (legend as in Figure 15).

An exception is the tar concentration at low λtot, which is actually slightly increased by increasing the recirculation efficiency. In this case, the recirculation decreases temperature so much that tar consuming reactions are suppressed. Results. A higher fuel water content leads to a lower CO content in the product gas. The CO content has a local maximum with respect to λtot. The CO rise for low λtot is mainly due to char oxidation enhanced by increasing air flow. At higher λtot, increasing air supply lowers CO by oxidation to CO2. At the optimum operation point, tar recirculation slightly increases the CO content.

Figure 21. CH4 content in the product gas (legend as in Figure 15).

M


Energy & Fuels

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Methane in the product gas is little influenced by both fuel water content and tar recirculation. On the other hand, CH4 strongly drops with increasing air supply. This has two main reasons. First, increasing air consumes CH4 by oxidation, and second, the higher temperature at high air supply favors phenol decomposition (reaction (R8)) at the expense of phenol reforming (reaction (R10)), the latter reaction being a methane source. A higher fuel water content results in less hydrogen in the product gas. Hydrogen shows an interesting dependency upon increasing air supply. It first lowers a little to a minimum, then strongly rises to a maximum, and then drops again. The drop at low λtot is a result of hydrogen oxidation, the rise at medium λtot is mainly due to toluene reforming (reaction (R12)) enhanced by increasing temperature, and the drop at high λtot is supported by both hydrogen oxidation and the water−gas shift reaction. The following conclusions can be drawn from the results given in Tables 5 and 6. They are valid under the assumption that the process is operated at the optimal operation point. • Recirculation of tar and soot led to a higher chemical enthalpy output. A 100% recirculation increased the chemical enthalpy output by 5.6% for fuel with 10 wt % water content, and by 11.2% for fuel with 30 wt % water content. • Recirculation increased the lower heating value of the product gas. • Recirculation significantly decreased both the total tar and the soot concentration before the purification unit, i.e., at point P7 in the schematic shown in Figure 1. • Recirculation increased the CO content and had little effect on the CH4 content of the product gas. For 10% fuel water content, recirculation had little effect on the H2 content. For 30% fuel water content, recirculation increased the H2 fraction in the product gas. • Recirculation lowered the temperature in the fluidized bed. A 100% recirculation led to a temperature drop of about 50 K.

Article

ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.energyfuels.6b00150. Modeling details of bed fluidization, particle heating, particle drying, entrainment of char, and the motion of char particles in the freeboard; a definition of the perturbed mass fractions of pyrolysis products which are used in the section Uncertainty Analysis; a detailed graphical representation of the uncertainty analysis results; an investigation of unlimited tar accumulation due to recirculation. (PDF)

■

AUTHOR INFORMATION

Corresponding Author

*E-mail: wolfram.heineken@iff.fraunhofer.de. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS The authors would like to express their gratitude to the Investitionsbank Sachsen-Anhalt for financial support of this research project. Daniel de la Cuesta wishes to acknowledge financial support from Project EN2012-36545 of the Ministry of Economy of Spain.

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NOMENCLATURE A pre-exponential factor in the Arrhenius reaction rate, [mol/(m3 s)] Achar total surface area of char particles in a control volume, [m2] Awall area of wall section, [m2] a3,ins.bed uncertainty factor of the rate of reaction (R3) inside the fluidized bed, [1] a3,outs.bed uncertainty factor of the rate of reaction (R3) outside the fluidized bed, [1] ai uncertainty factor of the rate of reaction (Ri), [1] Ci molar concentration of species i, [mol/m3] Csoot,P7 mass concentration of soot at position P7, [kg/m3] Ctar,P7 mass concentration of tar at position P7, [kg/m3] Cj cell number j in the computational domain cp,i specific heat of species Si, [J/(kg K)] DCO2,N2 binary diffusion coefficient (mixture of CO2 and N2), [m2/s] DO2,N2 binary diffusion coefficient (mixture of O2 and N2), [m2/s] dchar diameter of char particle at the fluidized bed, [m] dchar,j diameter of char particle at cell Cj outside the fluidized bed, [m] dfuel diameter of fuel particle, [m] dsand diameter of sand particle, [m] E activation energy in the Arrhenius reaction rate, [J/ mol] Fj parameter transfer function enthalpy flow into fluidized bed, [W] Ḣ bed,in Ḣ bed,out enthalpy flow from the fluidized bed into the freeboard, [W] Ḣ chem,P8 chemical enthalpy flow at position P8, [W] Hi,fuel(dry) lower heating value of dry fuel, [J/kg] Hi,i lower heating value of species Si, [J/kg]

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CONCLUSIONS A one-dimensional gasification model has been proposed to simulate the operation of a biomass gasifier with tar recirculation. In an uncertainty analysis, several parameters have been identified that strongly affect the product gas composition, the soot and tar content, and the temperature inside the reactor. In particular, the ultimate analysis of the fuel, the mass fractions of pyrolysis products, the kinetics of the water−gas shift reaction, and the air−fuel ratio were shown to have a major impact on the model results. A comparison with experimental data showed that the temperature at several positions inside the reactor as well as the product gas composition agreed with the simulation results within acceptable error bounds. A parameter study with the model resulted in a significant efficiency rise if the condensable tars were recirculated. The study suggested further that tar recirculation could substantially reduce the tar and soot contamination as well as increase the heating value of the gas produced. Finally, the study indicates that the bed temperature can be lowered by tar recirculation. N


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Energy & Fuels Ḣ in,j Hi,P8 h hbed hfrb hpc Iα Mi mchar ṁ bed,in,i ṁ char,entr ṁ fuel msand Nc Ncb Ng ni,j ṅi,j P1, ..., P6 p pin,i pin,max,i pin,min,i pin,ref,i pout,j pout,j,i,max pout,j,i,min pout,ref,j Q̇ wall Q̇ wall,bed Q̇ wall,j qi,j q′i,j R Re (Rk) r rj,k Sc Sh Si s T T1, ..., T6 Tbed,in,i Tbed,out Tenv Tfuel Tin,j Tj Tprim

enthalpy flow from cell Cj−1 into cell Cj, [W] lower heating value of the gas at P8, [J/m3] heat transfer coefficient, [W/(m2 K)] heat transfer coefficient of the wall adjacent to the fluidized bed, [W/(m2 K)] heat transfer coefficient of the wall adjacent to the freeboard, [W/(m2 K)] heat transfer coefficient of pipe and cyclone walls, [W/(m2 K)] uncertainty interval to the probability α molar mass of species i, [kg/mol] mass of char in a control volume, [kg] mass flow of species Si flowing into the fluidized bed, [kg/s] mass flow of char particles entrained from the bed into the freeboard, [kg/s] fuel mass flow into the gasifier, [kg/s] mass of sand in the fluidized bed, [kg] total number of cells, [1] number of cells in the fluidized bed, [1] number of gases, [1] molar amount of species Si at cell Cj, [mol] molar flow of species Si from cell Cj to cell Cj+1, [mol/s] positions in the gasifier, [] pressure, [Pa] input parameter number i of the model maximum value of the input parameter pin,i minimum value of the input parameter pin,i reference value of the input parameter pin,i output parameter number j of the model value of pout,j if pin,ref,i is perturbed to the maximum value of pout,j if pin,ref,i is perturbed to the minimum reference value of the output parameter pout,j heat flow through a wall section, [W] heat flow through the wall of the fluidized bed, [W] heat flow through the wall of cell Cj outside the fluidized bed, [W] total source term for species Si at cell Cj, [mol/s] source term for species Si at cell Cj (except sources due to reaction), [mol/s] universal gas constant, [J/(K mol)] Reynolds number, [1] chemical reaction number k reaction rate, [mol/(m3 s)] rate of reaction (Rk) at cell Cj, [mol/(m3 s)] Schmidt number, [1] Sherwood number, [1] species number i standard deviation estimate temperature, [K] gas temperatures at positions P1, ..., P6, [K] temperature of species Si flowing into the fluidized bed, [K] temperature of the species flowing from the fluidized bed into the freeboard, [K] temperature outside the reactor, [K] fuel temperature, [K] temperature of the species flowing from cell Cj−1 into cell Cj, [K] temperature at cell Cj, [K] temperature of primary air, [K]

Tsec uj,i V V̇ G,j Vj V̇ prim V̇ sec wC,ult wH,ult wO,ult wchar,fuel(ar) wchar,fuel(dry) wi,pyr wwater,fuel xCO,P8 xCH4,P8, xH2,P8, xi xi x̅ z α βi ϵ ϵj λprim λsec λtot νG νi,k ρchar ρfuel(dry) ρsand

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temperature of secondary air, [K] relative uncertainty control volume, [m3] total gas volume flow from cell Cj to cell Cj+1, [m3/ s] volume of cell Cj, [m3] volume flow of primary air, [m3/s] volume flow of secondary air, [m3/s] mass fraction of carbon in the ultimate analysis of fuel (dry and ash-free), [1] mass fraction of hydrogen in the ultimate analysis of fuel (dry and ash-free), [1] mass fraction of oxygen in the ultimate analysis of fuel (dry and ash-free), [1] mass fraction of char in the fuel (as received), [1] mass fraction of char in the dry fuel, [1] mass fraction of species i within the products of pyrolysis, [1] mass fraction of water in the fuel (as received), [1] volume fraction of CO at position P8, [1] or [vol %] volume fraction of CH4 at position P8, [1] or [vol %] volume fraction of H2 at position P8, [1] or [vol %] volume fraction of gas i, [1] general notation for measurements mean of the xi quantile function of the standard normal distribution temperature exponent in the Arrhenius reaction rate, [1] concentration exponent in the Arrhenius reaction rate, [1] void fraction, [1] void fraction at cell Cj, [1] primary air−fuel equivalence ratio, [1] secondary air−fuel equivalence ratio, [1] total air−fuel equivalence ratio, [1] kinematic viscosity of the gas, [s/m2] stoichiometric coefficient of species Si in reaction (Rk), [1] density of char particle, [kg/m3] density of dry fuel particle, [kg/m3] density of sand particle, [kg/m3]

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Modeling Tar Recirculation in Biomass Fluidized Bed Gasification

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