Berkel, A.I. van, Brem, G. & Valk, M. (1998). A dynamic model of biomass gasification i.n a circulating flu idized bed as a tool for optimising designand operation. In Biomass for energy and industry : l Oth eur ope.cm conference and teclmonology exhibition

A DYNAMIC MODEL OF BIOMASS GASIFICATION IN A CIRCULATING FLUIDIZED BED AS A TOOL FOR OPTIMISING DESIGN AND OPERATION

A.I. van Berkeltl, G. Brem! and M. Yalkt t University of Twente, thermal engineering group

lTNO institute of environmental sciences, energy research and process innovation Department of Thermal Conversion

P.O. box 342, 7300 AH Apeldoorn , The Netherlands phone: + 31-55-5493289, fax: + 31-55-5493287

e-mai l: a.i .vanberkel@mep.tno.nl

ABSTRACT: Biomass gasification, followed by a combined cycle seems to be an attractive option to generate electricity from sustainable sources. However using wood gas in engines is difficult due to tar, variations in calorific va lue of the gas and var iations in volume flux. A dynamic model of an !BGCC installation helps to improve design and operation of these insta ll ations, to the point where they are economically feasible. Such a model should be highly modular. It is proposed to make a model consi sting of four independent modules: preparation , gasifier, gas cleaning and combined cycle. The models of these modules will be based on first principles. As an example, the modelling approach for the gasifi er is shown . In the gasifier, the riser is the most important part. It is shown how a system of mass, momentum and energy balances, togeth er with a popul ation balance equation and a particle model can describe the ri ser. The population bal ance equation is a powerful tool to translate single particle behaviour to full scale reactor behaviour. A simple riser model demonstrates the use of this tool.

1. INTRODUCTION

Biomass gasification in an atmospheric or pressurised circulating fluidi sed bed , followed by a combined cycle, seems to be an attract ive option for large-scale electricity generation from biomass. However, attempts to use wood gas as an engine fu el have encountered many difficulties. T he main so urces of difficu lties are the formation of tar in the gasifier, vari ation s in the calorific value and variations in the volume flux of the gas leaving the gasifi er. Most engines have fa irly strict limits to gas tar content, variations in calorific value and vari ations in fue l vo lume flux. In order to meet these requirements, a good design and control of the installation and a proper training of the operators are essential [ 1]. Dynamic modelling of the Integrated Biomass Gasification Combined Cycle (!BGCC) plant provides insight in the formation of tar and the dynamic response of the system to fluctuations in the feed quality, the air suppl y and the load of the system. Such insight can faci litate the design and implementation of IBGCC pl ants considerabl y, thus promoting the use of biomass as a viable energy source. In this paper, the development of a dynamic !BGCC model will be di scussed. After a short introduction on the desired capabilities of the model and the general modelling approach , the paper wil l elaborate on the modelling of the circu lating fluidi sed bed gasifier as the most crucial part of the installation . For brevity, the di scussion is restricted here to the modelling approach, w ithout showing results.

2. DYNAMIC MODELING OF IBGCC PLANTS

2.1 Desired model capabilities An IBGCC plant wi ll have to be extremely flexib le in terms of the properties of the feed stock, in order to operate in an economicall y feasible way. The variation in properties of the biomass availabl e for gasification is quite large. This should be taken into account in the design and operation of the plant. The dynamic model will be most helpful in thi s respect, provided that it is capab le to: • Predict the effects of changes in the design and scale of

the apparatus and the process • Translate standardised, fundamenta l feed

characterisation tests to full scale plant operation • Predict the effects of various control settings on the

plant operation • Test the start-up, shut-down and emergency procedures

2.2 Modelling approach Generally one can divide the !BGCC plant in four main components : • Feed storage and preparation • Gasifi er • Wood gas cleanup • Energy system To allow for changes in the design of the process or of various apparatus, the model will have to be highly modular. Therefore, every component will be modelled separately and independentl y. These models wil l be based on first principles, to provide fl ex ibility with respect to changing design , scale and feed properties.

Each component model eventually provides a function that relates the output of the component to the input, with the control settings appearing as parameters. Now the overall model consists of these, generally non­linear, functions , combined with some equations that set the output of one apparatus equal to the input of another one. When this is formu lated correctly, it can be solved numerically, yie lding the complete model formulation. What the output and input of the components look like will depend on the complexity of the component models, which can be chosen freely. As an example of one of the component models, the remainder of this paper will describe the modelling approach of the most interesting component of the system: the gasifier.

3. CIRCULATING FLUIDIZED BED GASIFIER MODEL

3.1 Function and relevance The circu lating fluidised bed gasi fier is at the heart of the IBGCC pl ant. It converts the biomass feedstock to fuel gas for the gas cleaning and the energy conversion (i.e. the combined cycle). Thus the gasifi er is the only component in the system in wh ich the biomass properties play a sign ificant role. This model translates the temporal variations of feed properties to temporal variations in the fuel gas. Therefore, the gasifier model will be crucial for the prediction of the dynamics of the system. Moreover it will dictate the way in which the biomass feedstock must be characterised.

3.2 General modelling approach The circulating fluidised bed again can be divided in five components: the riser, the fan (air supply), the particle separator (usually one or more cyclones), the downcomer, which returns the particles to the bottom of the bed and the feed system for the biomass. Only the riser, being the actual reactor, will be modelled in detail. The other components provide boundary conditions for the risermodel.

4. RISERMODEL

Three phases are distinguished in the riser: gas, bed material and biomass (or char). The gas consists of various components, such as CO, CH 4, C02, H2, H20, 0 2, N2 and tar. The movement and interaction of the gas phase and the bed material are treated usi ng the Eulerian-Eulerian approach, as suggested among others by Nieuwland [2]. Usually, the biomass particles occupy only a very small fraction of the riser volume. Their mass is very small compared to the mass of bed material. It can therefore be assumed that they have no influence on the hydrodynamics of the riser, other than by releasing gas. Thus no drag forces of the biomass on the gas and sand phases will appear in the Eulerian momentum balances.

Because the biomass particles are relatively scarce, Eulerian balances are not realistic. Every biomass particle will be treated as a discrete particle. The description of the biomass phase can then best be accomplished by a population balance equation

5. EULERIAN-EULERIAN EQUATIONS

The Eulerian-Eulerian equations for the flow of a gas-solid mixture are well-established (cf. Nieuwland [2]). They will therefore be quoted here without introduction. Mass balances for the various gas species:

8Ep ~+Y'·Ep;u=Y'D errY'P ; +R; (I)

The reaction term R; must be calculated from the gas phase reaction mechanism and the population balance equation. Bed material mass balance:

Y'·v=O (2) Gas phase momentum balance:

Epg(: +u · Y'u) = (3)

-EV'p- ~(u-v) + µg V' 2 u - uIR;

Notice the last term in eq. (3). In cold fluidised beds, or beds used for catalysed reaction, this term is usually absent, because no net mass is added to the gas phase due to reaction. However, in the case of gasification, this term can be quite important. Therefore, one should be careful to apply standard correlations for circulating fluidised beds to a gasifier. The second term on the right hand side accounts for the drag between gas and bed material. The factor J3 usually is an empirical function. Bed material momentum balance:

(1-E)Pb(: +v · Y'v) = (4)

-(1-E)V'p+J3(u-v)+µbV' 2v Notice that the presence of biomass particles has been neglected in sett ing the volume fraction of bed material to 1-E. The last term in eq. (4) contains a viscosity. This accounts for all interactions between bed material particles. Some rigorous attempts have been made to specify this viscosity [3]. However, Nieuwl and suggests using the constant value of 0. 725 Pa s [2]. If thermal equilibrium between the phases is assu med , and viscous dissipation is neg lected, the energy equation is:

((1-E)pbcp• +EpgcpJ ~ +

( (1- E )pbcbg v + Epgcpg u )vT = (5)

V'1'.V'T+: +R;~H; + LDerrY' P;Y'T I

6. POPULATION BALANCE EQUATION

6.1 Concept The behaviour of an individual biomass particle can be described based on various models for pyrolysis of biomass and gasification or combustion of char. These models are already very advanced (cf. Gronli [4]). The challenge is to translate this knowledge about individual particle behaviour to the behaviour of the complete reactor. In order to do thi s, one must know not only how the particles are di stributed over the ri ser at any time, but also how they are distributed over the various properties, that appear in the single particle model. Such properties may be the particle diameter, the temperature di stribution in the particle, the residence time of the particle etc. The population balance equation provides a means to obtain this distribution. A very clear introduction to the concept of a population balance equation was given by Hulburt and Katz [5]. Suppose a single biomass particle can be characterised in

the riser by a set of variables cp. Some components of this vector may e.g. be the position of the particle in the riser, the velocity of the particle and various properties of the particle that are important in the pyrolysis and/or gasification model.

Now we introduce a di stribution function f (cp) , such that f(1.j1)d1.j1 is the number of particles in the riser with cp

between \jl and 1.j1+d1.j1. Thus the distribution function is very similar to a probability density function, although it is not necessarily a stochastic function and it is not normalised.

Now any known function g(cp) can be evaluated over the particles in the riser through:

G = f g(<j>)f(<j>)df (6)

Where the integration is taken over cp or over a subset of cp. If one is interested for instance in the total gas release by biomass particles on one specific location in the gasifier, then g(cp) will be the function (or model) relating ambient conditions and particle properties to the gas release of an individual particle. The integration would be over all

components of cp except the ones indicating the position of

the particle. These components could still appear in g(cp) as parameters, because the ambient conditions of the particle may depend on the position in the riser. Obviously, g itself may also be position-dependent.

6.2 The equation Hulburt and Katz [5] give the following general form for the population balance equation:

3f at +Y'·h(<j>,t)f=s(<j>,t) (7)

Where s is the net production or destruction of particles and the vector h is given by:

h = d<l>;(<!> , t) 1

dt

Models for particle movement and reaction determine these functions.

7. APPLICATION: A SIMPLE RISER MODEL

The general framework of the risermodel that has been set up in the previous section can be made as simple or as complicated as one desires. In applying thi s framework one should aim to make as many simplifications as possible, without losing essential characteristics of the system. In this section a very simple application will be presented.

7.1 Assumptions The following assumptions are made: A. Gas flow is plug flow (i.e. no diffusion) 8. All particles are well mixed C. The temperature is homogeneous D. The distribution of biomass particles over the riser is

the same as the distribution of bed material E. Radial gradients in the riser are not important (i.e. a

ID model will suffice)

7.2 Simplified system of equations The assumptions mentioned above reduce the system of equations to a very simple system, similar to the engineering model for circulating fluidised bed by Nieuwland [2] : v =constant

3p; +u 3p; = R at az I

EPg (~ + u ~~) =

-Eapa -P(u - v) + Fg -uLR; z i

(1-E)Pb(~ +v :) =

- ( 1 - £) ap + p ( u - v) + Fb az

((1-E)pbcpb +EpgcpJ ~~ = R;L'lH;

(8)

(9)

(I 0)

(II)

( 12)

Where Fs and Fb account for the friction of the gas and the bed material with the wall. Before the last equation, the population balance can be specified, a particle model must be developed.

7.3 Particle model A very simple particle model is used to demonstrate the use of the population balance equation clearly. It is assumed that the particles can be represented by flat plates or cylinders. Furthermore it is assumed that the fluidised bed imposes its temperature on the surface of this particle and that the interior of the particle is heated by conduction only. In that case, the position x of a line of constant temperature is related to the residence time of the particle as:

x=c-fr (13)

Where c is a constant and a function of the biomass properties and the particle dimensions and shape. It is now assumed that the pyro lysis reaction takes place at one specified temperature and that the conduction of heat is rate-limiting. The advancement of the reaction is thus proportional to the velocity of the line of constant temperature. Thus:

dm k dt - ~ (14)

Where k is a function of particle dimensions, biomass properties and bed temperature. For this model , the biomass characterisation should therefore be aimed at determining k. Obviously, a more sophisticated model is needed, to simplify the characterisation. It should be noted th at this particle model assumes that particles react in an isothermal environment. This is only justified if the timescale of temperature changes is much larger than the average reaction time of a particle. Thus, this model is only applicab le to medium-sized particles (0.1-10 mm).

7.4 Population balance The vector of particle properties, <p , contains only one element in this example. Position and velocity of the particle in the riser are not important, because the particles are ideall y mi xed and their distribution is already known from the bed material distribution. The particle model only contains one variable: the residence time. Now:

d-r = 1 dt

(15)

And no particles are generated in the riser (i.e. no sudden jumps in residence time or k). Thus eq. (7) becomes:

ar+ar= 0 at ac

The genera l so lution of this equation is:

f = g(t-T)

(16)

(17)

Where g is an arbitrary function. Since the number of particles w ith residence time 0 should always be the number of particles entering the system at that time, g is the number of particles entering the system as a function of time (the feed) . The tota l pyro lysis gas production by the biomass in the bed is now g iven by:

Ts' k(t-T) Rbiomass = ) -r g(t-T)dT (18)

0

Where' 1 is the total pyro lys ing time of the particle.

7 .5 Overall model The model is still far from complete. The parts that are still missing are: I. Constitutive equations

2. A gas phase reaction scheme (including carbon), the reactions proposed by Van den Aarsen are a good option [6]

3. A distribution of biomass pyro lysis products over the gas species

4. Boundary conditions These parts will not be discussed here. The main purpose of this paper is to discuss the general modelling approach. If these parts are properly specified, the system of equations (8)-( 12) and ( 18) can be solved, yielding the complete riser model.

8. CONCLUDING REMARKS

In this paper, an approach to creating a dynamic model for an !BGCC plant has been proposed. Further research is being done to elaborate all components of the model.. With respect to the riser model, the research will be concentrated on formulating a more sophisticated particle model that can be used in combination with the population balance equation. This particle must be ab le to predict the composition of pyrolysis products in order to be able to predict tar production. Furthermore the gas phase reaction mechanism will be refined and attempts will be made to relax the assumption of ideal mixing of the particles.

9. REFERENCES [I] W.P.M. van Swaay, F.G. van den Aarsen, A.V.

Brigdewater and A.B .M. Heesink, A review of biomass gasification, Report to the EC for DGXII Joule, 1994

[2] J.J. Nieuwland, Hydrodynamic modelling of gas­solid two phase flow, Ph.D. thesis University of Twente, 1995

[3] S. Abu-Zaid and G. Ahmadi , A thermodynamically consistent rate-dependent model for turbulent two­phase flows, Jnt . Journal of non-linear mechanics, V30 no 4 pp 509-529, 1995

[4] M.G. Grnnli , A theoretical and experimental study of the thermal degradation of biomass, Ph.D. thesis Norwegian University of Science and Technology, NTNU 1996

[5] H.M. Hulburt and S. Katz, Some problems in particle technology; a statist ical mechanical formulation , Chemical engineering science, V 19 pp 555-574, 1964

[6] F.G. van den Aarsen , Fluidised bed wood gasifier performance and modelling, Ph.D. thesis university ofTwente, 1985

A dynamic model of biomass gasification in a circulating fluidized bed

a tool for optimising design and operation A.I. van Berke11 .2, G. Brem1 and M. Valk2

1 TNO lnaUtute of Envlronmental Sclencea, Energy Re ... rch and Proce .. lnnovaUon, Laan van Weatenenk 501, 7334 DT Apeldoom phone: +31 55 5493289, lax: +31-55-5493287, IHllllll: a.l.vanberkelOmep.tno.nl

2 Unlveralty of Twente, P.O. Box 217, 7500 AE Enachede

Dynamic model of an Integrated biomass gasification combined cycle plant

Dynamic modelling of an IBGCC plant contributes to a smooth implementation of biomass gasification [1 ). It provides a tool for: • Translating fundamental biomass characterisation experiments

to full scale plant performance. • Operator-training • Predicting the effects of plant modifications and upscaling • Designing the control system • Testing start-up, shut-down and safety procedures.

The various components of the plant to be modelled are shown schematically in figure 1. The plant consists of: 1 . A feeding system 2. A gasifier, a circulating fluidized bed gasifier will be modelled. 3. A gas cleaning and conditioning system 4. An energy conversion system (e.g. STEG).

The gasifier model

The riser is modelled in detail, the other components provide boundary conditions for the riser model.

Riser: Detailed model of heterogeneous and homogeneous reactions, heat and mass transfer, hydrodynamics and movement of the

particles. ~ The population balance equation is used to translate individual particle behaviour to full scale riser behaviour [2]:

(Jf

at

Where f is a density in qi-space. The qi-space contains all characteristic variables of the particles, e.g. location, velocity, residence time and conversion . The choice of the qi-space is determined by the formulation of the heterogeneous reaction models and it in turn determines the characterisation method for the biomass. The heterogeneous reactions describe particle drying, pyrolysis, gasification and combustion .

h is a velocity vector in qi-space, defined by the particle model and the biomass characterisation.

Figure 2

Figure 1

Gas cleaning

and condi· tioning

Schematic representation of an IBGCC plant

Recirculation system: The pressure drop over the particle separator provides a relation between the pressure at the top of the riser and the

I pressure in the gas cleaning system. Thus it provides a boundary condition for the riser.

The rate of solids returned to the riser is determined by: • Separation efficiency • Pressure balance between riser and

recirculation system • Systemdesign

Fan: The Q-H curve of the fan determines the gas flow rate through the riser.

Literature

[1] A.I. van Berkel, G. Brem and S. van Loo, Dynamische modellering van een CFB­STEG voor de vergassing van biomassa, TNO report R 97/197

Riser hydrodynamics are taken from literature (3). Homogeneous are reactions based on standard syngas kinetics . The kinetics of tar conversion in the gas phase are measured in a specially designed experimental set-up.

The CFB gasifier model

[2) H.M. Hullburt and S. Katz, some problems in particle technology; a statisctical mechanical formulation, chemical engineering science, V19 (1964) pp 555-574

[3] J .J . Nieuwland, Hydrodynamic modelling of gas-solid two phase flow, Ph.D. thesis University of Twente 1995