simulation, plant optimization, gasifier performance map. âACKNOWLEDGMENTS. ... methanol, synthetic natural gas, etc.). As the thermal ... loop seal additional fuel loop seal. Figure 1: Dual bed steam gasification of solid biomass. 1 ...
Fluidized Bed Steam Gasification of Solid Biomass - Performance Characteristics of an 8 MWth Combined Heat and Power Plant∗ Tobias Pröll, Reinhard Rauch, Christian Aichernig, and Hermann Hofbauer
Abstract The work focuses on a dual fluidized bed gasification technology that is successfully operated for combined heat and power production at a scale of 8 MWth in Guessing/Austria since 2002. The reactor concept consists of a circulating fluidized bed system with a steam-fluidized bubbling bed integrated into the solids return loop. Accompanying the operation of the commercial scale plant, parameter models have been developed and validated by comparison to measured data. As the models naturally fulfill mass and energy balances, the simulation also allows the validation of measurement data. The behaviour of the plant is studied by carrying out variations of selected parameters. Evaluation of different plant operation cases yields correlations between process variables. The solids circulation rate is shown versus riser exit velocity. Fuel water content and gasification temperature significantly influence global plant performance. Simulation predicts the efficiency of the existing power plant in optimized operation. Finally, part load behaviour is investigated and performance maps of the CHP plant are presented. High fuel water content at high gas engine load results in high gas velocities in the riser (erosion limit) and higher heat ratio in the produced energy. It is concluded that CHP-concepts based on fluidized bed steam gasification can reach high electric efficiencies and high overall fuel utilization rates even at small plant capacities of about 10 MWth. KEYWORDS: fluidized bed gasification, steam gasification, gasification modelling, process simulation, plant optimization, gasifier performance map ACKNOWLEDGMENTS. This work has been performed within RENET Austria – Energy from Biomass. The authors gratefully acknowledge the financial support from the Austrian public funds program KIND/KNET. They also thank E. Perz and S. Bergmann (SimTech Simulation Technology, Graz, Austria) for the good cooperation and the technical support regarding the software IPSEpro.
∗
This Article has been published in the International Journal of Chemical Reactor Engineering: Pröll, T., Rauch, R., Aichernig, C., Hofbauer, H., 2007, "Fluidized Bed Steam Gasification of Solid Biomass - Performance Characteristics of an 8 MWth Combined Heat and Power Plant," International Journal of Chemical Reactor Engineering: Vol. 5: A54. Available at: http://www.bepress.com/ijcre/vol5/A54
INTRODUCTION The utilization of biomass as primary energy source contributes to the preservation of natural resources and reduces the need for long-distance transport of energy. Due to the limiting effect of local heat demand and the economy of scale, a high electricity share in the energy output is desired for decentralized combined heat and power (CHP) production. Gasification technologies generally offer possibilities for high electrical efficiencies and heat decoupling at reasonable temperature levels at the same time. Fluidized bed steam gasification of solid biomass produces a high quality synthesis gas, which can be used for efficient CHP production using gas engines, gas turbines, or fuel cells and as an intermediate product for chemical syntheses (Fischer-Tropsch, methanol, synthetic natural gas, etc.). As the thermal decomposition of organic matter requires high temperatures, gasification processes normally provide heat from hot gas streams as a by-product. A dual fluidized bed (DFB) technology has been developed in Austria using steam as the gasification agent and providing the heat for the gasification reactor by circulating bed material (Hofbauer et al., 1997; Hofbauer et al., 2002). As shown in Figure 1, the biomass enters a bubbling fluidized bed gasifier where the steps of drying, devolatilization, and partially heterogeneous char gasification take place at temperatures of 850900 °C. Residual biomass char leaves the gasifier together with the bed material through an inclined, steam fluidized chute towards the combustion reactor. The combustion zone (riser) serves to heat up the bed material and is designed for high solids transport rates controllable by staged air introduction. After particle separation from the flue gas in a cyclone, the hot bed material flows back to the gasifier via a loop seal. Both, the loop seal and the connecting chute are fluidized with steam. That prevents gas leakage between the gasification and the combustion zone and at the same time allows high solids throughput. The temperature difference between combustion and gasification reactor is determined by the necessary energy for gasification and the bed material circulation rate. Further parameters with energetic significance are the amount of residual char that leaves the gasifier with the bed material and the bed temperature in the gasifier. The system is flue gas producer gas inherently auto-stabilizing in the sense that a decrease of the gasification temperature leads loop seal to a higher amount of residual char, which enhances combustion. This, in turn, gasifier transports more energy into the gasification zone and stabilizes the temperature. In riser practise, the gasification temperature can be influenced by addition of fuel (recycled producer gas, dry saw dust, etc.) into the biomass combustion section. The pressure in both additional fuel gasifier and riser is close to atmospheric conditions. The technology produces two connecting separate gas streams, a high quality producer chute gas and a conventional flue gas at high temperatures. The producer gas is generally characterized by a low content of condensable higher hydrocarbons (tar), low steam N2 (< 1 v-%), and a high H2 content of air 35-40 v-% (dry basis). The tar content decreases if catalytically active bed material Figure 1: Dual bed steam gasification of solid biomass. is used (Hofbauer and Rauch, 2000).
1
THE BIOMASS COMBINED HEAT AND POWER PLANT AT GUESSING ,The technology has been successfully demonstrated at a scale of 8 MWth (fuel power based on lower heating value) together with appropriate gas conditioning and electricity generation in a gas engine at Guessing/Austria (Hofbauer et al., 2003). Besides district heat for residential heating, the plant provides heat for industrial drying facilities and is in continuous operation throughout the whole year. By end of 2006, more than 24 000 hours of engine operation have been reached since the generator has been connected to the grid for the first time in April 2002. The configuration of the CHP plant is shown in Figure 2. Wood chips from forestry are used as biomass fuel. The wood trunks are dried naturally by storage of 1-2 years in the forest before they are delivered and chipped on-site. The actual biomass water content is about 25-35 wt-%.
Figure 2: The 8 MWth biomass CHP plant at Guessing/Austria. The raw producer gas is cooled to about 150 °C before the bag filter. The entrained char separated in the filter is introduced into the combustion zone of the gasification system. The tar scrubber uses rapeseed oil methyl ester (RME) as solvent and reaches high tar separation efficiencies of about 98 % for tars detectable with gravimetric methods. For the operation conditions used, condensation of water occurs in the product gas scrubber. This leads to an increase of the clean gas heating value and allows the removal of water-soluble trace components like NH3 and HCl. The condensate is separated from the organic scrubbing liquid and is partially used for generation of fluidizing steam while the rest is fed into the combustion reactor as saturated steam in order to avoid a liquid effluent from the process. The water content in the clean producer gas is limited by water vapour saturation at the scrubber exit. Seasonal variation of the scrubber exit temperature (45…70 °C) results in clean gas water contents between 10 and 30 v-%. About 15-20 kg/h of new RME are continuously supplied to the scrubber system while the corresponding excess of tar-loaded RME/condensate emulsion from the scrubber is withdrawn and combusted in the DFB combustion reactor. A low amount of clean producer gas is also recycled into the combustion reactor in order to control the gasification temperature. A GE Jenbacher J620 gas engine is used for power generation. An oxidation catalyst minimizes the emissions from the engine. The only streams exiting the plant are the clean stack gas and the ash from the flue gas filter. Heat for the local district heating grid is transferred from producer
2
gas cooling, flue gas cooling, and engine exhaust cooling. Alternatively to the gas engine, a conventional gas boiler for heat generation is available. The district heating grid is operated at relatively high temperatures of about 120 °C, which is not a problem for the gasification based CHP concept due to the high-level heat available out of the hot gas streams. The design data of the plant are summarized in Table 1.
Table 1: Design data of the CHP plant at Guessing/Austria. Thermal fuel power (basis LHV) Net power of producer gas (basis LHV) Generator output Electrical consumption of the plant Net electrical output Net heat production
8000 5600 2000 200 1800 4500
kW kW kW kW kW kW
SOFTWARE STRUCTURE In order to perform scientific research accompanying plant operation, process simulation turned out to be a powerful tool for both plant balancing and optimization of plant operation. Already in the phase of basic engineering, the equation-oriented steady state simulation software IPSEpro has been successfully used (Kaiser et al., 2000). The main advantage of IPSEpro is its perfectly transparent structure with respect to the model equations and physical property functions used. Models can be edited by the user and new models can be defined. External functions are defined in dynamic-link-libraries (DLLs) and are called by the solver at runtime. The equation-oriented structure of the solving strategy requires good estimates of the start values for the iteration but allows fast convergence, what is important for parameter variations.
MDK - Model Development Kit Model Development Kit (MDK)
Projects
Projects
PSE Standard Shell
e de sy e Sh mo Eantimfixed P S Ru (
Model Editor
PSE - Process Simulation Environment
Model Libraries Model Libraries
Model Compiler
Kernel
(Analyser, Solver)
ll ) ls
c if i ec Sp er Us
ell Sh
C-Programming IDE C/C++ Property Libraries Libraries (DLLs) Property (DLLs)
Figure 3: Structure of the software package IPSEpro (extended from SimTech, 2001). During the first two years of plant operation, an IPSEpro model library has been developed that is adapted to the description of gasification plants. The basic features of this set of models are: • mass and energy balances are strictly fulfilled for all process units • models can be switched to pure balance models for validation of measured values • four classes of substances can be treated o
ideal gas mixture consisting of a number of species relevant for gasification (Ar, CH4, C2H4, C2H6, C3H8, CO, CO2, H2, H2O, H2S, HCl, N2, NH3, HCN, N2O, NO, O2, SO2)
o
water/steam modeled as real fluids
o
organic substances (consisting of C, H, O, N, S, and Cl) in different states of aggregation (biomass, fuel oil, RME, tar and char in gas streams, etc.)
o
inorganic solids (solid streams in fluidized bed system, ash in organic streams, dust in gas streams, etc.)
3
MODEL OF THE DUAL FLUIDIZED BED GASIFICATION PROCESS Modelling the DFB gasification process as three separate units coupled by substance flows is in accordance with the real interactions between the reactors and represents a suitable basic structure for the description of the core process in Figure 4 (Kaiser et al., 2000). If solids are partly elutriated and leave the system as dust with the gas streams, the balance must be made up by steadily feeding new bed material into the system. The fluidization of the connecting chute and the loop seal is not separately represented by the model. These relatively small amounts of steam can be added to the gasifier fluidization when setting up the simulation flow sheet. The substance classes for description of the streams in Figure 4 are summarized in Table 2. flue gas
producer gas
11
12
cyclone
9
continuous bed material feed
gasifier
combustionchamber (riser)
10
solid fuel 1
3
4
8
5
7
2 fluidization gasifier (steam)
additional fuels
6
gaseous liquid/solid
fluidization riser (air)
Figure 4: Simulation model of the dual fluidized bed gasification process consisting of gasification reactor, combustion reactor, and cyclone.
Gasifier In the case of the gasifier, streams 1-3, 10, and 12 of Figure 4 enter the balance. Because chemical reactions take place, the mass balances are formulated on the basis of chemical elements. Only inorganic solids (bed material, ash) are treated as inert, i.e. the conservation of chemical species is formulated for the solids. The first problem of gasifier modelling is to determine how the fuel input is distributed to the different output streams. A certain structure can be obtained if split conditions are defined. With regard to the organic fractions char to bed, char to producer gas (PG), and tar to PG, the split conditions refer to water and ash free fuel:
ϕCh ,Bed =
ϕCh ,PG =
ϕT ,PG =
m& Bed ,out ⋅ β Ch ,Bed ,out
(
)
(1)
)
(2)
)
(3)
m& Fuel ⋅ 1 − wH O ,Fuel − wAsh ,Fuel
(
2
m& PG ⋅ β Ch ,PG
m& Fuel ⋅ 1 − wH O ,Fuel − wAsh ,Fuel
(
2
m& PG ⋅ βT ,PG
m& Fuel ⋅ 1 − wH O ,Fuel − wAsh ,Fuel 2
4
Table 2: Types of streams and substance classes in the dual fluidized bed gasifier model. no.
main substance class
stream
1
fuel to gasifier
organic
2 3 4 5 6 7
fluidisation gasifier bed material from gasifier bed material new bed material to riser fluidisation riser additional fuel to riser
gas solid solid solid gas organic
8 9 10 11 12
producer gas recycle to riser transport stream from riser bed material hot to gasifier flue gas from cyclone producer gas from gasifier
gas gas solid gas gas
additional substances transported denomination substance class water content ash char char water content ash tar bed material (dust) dust dust char tar
water/steam solid organic organic water/steam solid organic solid solid solid organic organic
For the inorganic solids the fraction of elutriated bed material and the fraction of biomass ash in the producer gas can be defined:
ϕ Bed ,PG =
ϕ Ash ,PG =
m& Bed , Attrition m& Bed ,in
(4)
m& Ash ,PG
(5)
m& Fuel ⋅ wAsh ,Fuel
The amount of dust in the producer gas is given as the sum of bed material attrition and fly ash:
m& PG ⋅ β Dust ,PG = m& Bed , Attrition + m& Ash ,PG
(6)
The split conditions can be determined from measurements and global material balances and they can later be prescribed for predictive simulations. However, the split conditions are defined globally and do not include information about the actual composition of the streams. An exception are by the inert inorganic solids, where the dust composition follows from species balances for given split conditions (4) and (5). The remaining degrees of freedom to determine the output stream compositions and thermodynamic states must be covered by functional equations or by prescription of the respective output composition. For organic char and tar composition, the model relies on prescribed values determined by measurements. For the producer gas composition in the case of steam gasification, the following restrictions can be made:
yi , PG = 0
for i = O2, SO2, N2O, NO
(7)
5
If all input streams to the gasifier are known, the element balances together with Equation (1) determine the concentrations of Ar, H2S and HCl in the producer gas. Seven of the eleven remaining producer gas components (C2H4, C2H6, C3H8, CH4, CO, CO2, H2, H2O, N2, NH3, HCN) must be prescribed or addressed by additional equations in order to fully determine the gas composition. The procedure of producer gas composition prediction is described in the parameter model section later in this work. Further characteristic quantities of the gasifier operation with relevance are the steam to fuel ratio especially, if comparisons to other systems are to be made
ϕ SF =
m& Fluid ⋅ wH 2O ,Fluid + m& Fuel ⋅ wH 2O ,Fuel m& Fuel ⋅ (1 − wH 2O ,Fuel − wAsh ,Fuel )
(8)
and the specific water consumption of the reactions in the gasifier, also in relation to the dry organic fuel introduced:
XH O =
m& Fluid ⋅ wH O ,Fluid + m& Fuel ⋅ wH O ,Fuel − m& PG ⋅ wH O ,PG 2
2
2
m& Fuel ⋅ (1 − wH O ,Fuel − wAsh ,Fuel )
2
(9)
2
The water consumption according to Equation (9) is not dependent on the steam/fuel ratio by definition. It returns the amount of H2O consumed for the conversion of one fuel unit. Another quantity is the specific amount of unconverted carbon related to the carbon supplied with the fuel. In the following definition, the carbon content of the tar is regarded as converted, while the carbon in producer gas char and bed char are regarded as unconverted (residual char):
XC = 1−
m& Bed , a ⋅ β Ch , Bed , out ⋅ wC , Ch , Bed , out + m& PG ⋅ β Ch , PG ⋅ wC ,Ch , PG m& Fuel ⋅ 1 − wH O , Fuel − wAsh , Fuel ⋅ wC , org , Fuel
(
2
)
(10)
The carbon conversion according to Equation (10) can be estimated by a semi-empiric kinetic approach as described later in the parameter model section. With respect to solids circulation, the specific circulation rate in relation to the fuel quantity is defined in the gasifier model:
rcirc =
m& Bed ,in
(11)
m& Fuel ⋅ (1 − wH O ,Fuel − μ Ash ,Fuel ) 2
Practically, the circulating bed material flow is mainly determined by the hydrodynamics of the combustion reactor (riser). Therefore rcirc can be used for design studies but should not be set to a fixed value during parameter variations.
Combustion reactor (riser) and cyclone The structure of the combustion reactor model is simpler compared to the gasifier because no splitting of reactive compounds of different exit streams occurs. While the inorganic solids are only heated and exit the unit as dust load of the flue gas stream, all organic compounds are oxidized to the products CO2, H2O, NO, and SO2. An exception is formulated for CO, where a certain slip is defined as:
sCO =
yCO ,FG
(12)
yCO ,FG 2
6
For the gaseous species in the flue gas the following assumptions are made:
yi ,FG = 0
for i = CxHy, H2, NH3, HCN, N2O, H2S
(13)
No thermal NO formation is considered, whereas fuel nitrogen is converted to NO:
∑ (n&
n& FG ⋅ y N ,FG = 2
g ,in
⋅ y N , g ,in 2
)
(14)
Equations (12)-(14) together with the elemental mass balances allow the determination of the flue gas quantity, composition, and thermodynamic state if all feed streams to the combustion reactor are known. The excess air ratio λ is defined for the combustor according to:
λ ⋅ n& FG ⋅ ⎜ yO ,FG − ⋅ yCO ,FG ⎟ = (λ − 1) ⋅ ∑ (n& g ,in ⋅ yO , g ,in ) ⎛ ⎝
1 2
2
⎞ ⎠
2
(15)
Further, the superficial gas velocity at the riser exit section
U R ,exit =
V&FG ,R ,exit
(16)
AR
and the specific solids transport rate GS
GS =
m& Bed ,circ
(17)
AR
are calculated from the actual volume flow and riser cross sectional area. A rough correlation between Uriser and GS will be presented in the parameter variation section in order to predict the solids circulation rate at different operating conditions. The cyclone separates most of the solids from the flue gas stream. The separation efficiency of the cyclone is defined as:
η sep , Dust = 1 −
β Dust , FG , out β Dust , FG ,in
(18)
All three unit models for gasifier, combustion reactor, and cyclone allow the definition of a certain heat loss to the environment. The heat loss values must be prescribed or may follow from temperature measurements provided to the process model.
Overall process model The simulation model of the CHP plant covers the entire process shown in Figure 2 including the hot water cycle for internal heat transfer and the connection to the district heating grid. The detailed description of all unit models used would go beyond the scope of this paper. However, the most important reference numbers are defined at this point.
7
The aim of gasification is an efficient conversion of solid fuels to a producer gas with a high energy content or synthesis gas of suitable composition. A frequently used key figure is therefore the chemical efficiency of the gas generation ηchem, defined as:
ηchem =
m& PG ,exp ⋅ lhvPG (m& Fuel ⋅ lhvFuel )
(19)
∑
The subscript “exp” in Eq. (19) refers to actually exported producer gas. This means with respect to the process in Figure 2 that the recycled producer gas to the combustion chamber must be treated as internal flow. The brut electrical efficiency of the CHP plant combines the chemical efficiency with the efficiency of the power generation step and is defined as:
ηel ,brut =
∑ (m&
Pel ,Gen
Fuel
⋅ lhvFuel )
=
m& PG ,GE ⋅ η chem ⋅ ηel ,GE &G m& PG ,exp
(20)
The right hand side expression in Eq. (20) takes into account that some of the exported producer gas can be directed to the gas burners instead to the gas engine. The heat efficiency of the CHP plant is defined as follows:
ηQ =
∑
Q& DH ,total (m& Fuel ⋅ lhvFuel )
(21)
Finally, the brut fuel utilization rate of the CHP plant reflects the total energy utilized out of the primary fuel energy:
ηFU ,brut = ηel ,brut + ηQ
(22)
It is important to notice at this point that plant efficiencies generally depend on the fuel properties. For serious comparison of different processes, a standard fuel composition would be required. The effect of fuel water content on the plant performance will be discussed in the results section below.
PLANT ANALYSIS The overall view on the entire process allows the inclusion of a maximum number of measurements and avoids unknown boundary quantities, which would appear if the system were divided into sub-systems. Data for process analysis are taken in different forms at the CHP plant: •
temperatures, pressures, flow rates, and concentrations from the process control system
•
dry producer gas composition from an online gas chromatograph
•
discontinuous sampling at different points of the plant (dust, tar, etc.)
The simulation always refers to steady state operation. Therefore, average values of the measurements are calculated for suitable time intervals of typically 2-5 hours. The complete set of measured data together with the balance model of the process present a system that is over-determined by measurements, i.e. there are more equations than variables. The new feature PSValidate, which the software developer of IPSEpro tested during
8
this project, allows the solution of such systems using the method of Lagrange multipliers. That results in the solution of a least squares problem for the deviation between measured value and the reconciled solution: 2
∑ i
⎛ xi − xi ⎞ ⎜ ⎟ → Min ⎜ tol x ⎟ ⎝ ⎠
(23)
i
The absolute tolerances tolx account for varying units and include information about the quality of the measured value. Comparison between calculated errors and estimated tolerances allows the localization of systematic errors in both simulation and measurements. After exclusion of systematic errors and adjustment of tolerances to reasonable values respectively, the simulation describes the actual plant operation best within the limits of the model structure. Besides the validation of the measured quantities, all process variables that are not directly measured are known then from simulation. This is essential for quantities, which practically cannot be measured (solid circulation rate, amount of char to the combustion section, etc.). If operation states at varied parameters are evaluated, correlations between certain process variables can be determined. Further, the validated flow rates from the equilibrated solution represent the basis for balancing trace elements like N, S, and Cl. Within the present work, a reference case for actual plant operation has been defined using measured data from a representative time interval. The plant performance in this reference case has still been below the design values mainly because of three reasons: •
the fuel water content amounted to 27.5 wt-% instead of the designed 10.0 wt-%
•
the gasification temperature of 900 °C was higher than the designed 850 °C
•
more than 10 % of the producer gas was needed for stand-by operation of the gas boiler and was therefore not available for electricity generation
Nevertheless, the reference case presents a well-defined basis for the optimization of plant operation.
PARAMETER VARIATIONS ON THE CHP PLANT Parameter variations on the large scale plant are restricted because the operator aims at maintaining a high availability and power output. However, some parameter variations have been performed and natural changes of input parameters have been exploited for gaining understanding. Different operation states of the CHP plant have been evaluated using the mass and energy balance models. The reconciled solutions cover important process parameters that can subsequently be correlated to other parameters. One such empirical correlation is the dependency of the solids circulation rate of the system on the gas velocity in the riser. The solids circulation rate can be calculated from the mass and energy balances of the two fluidized bed reactors. Because of the significant energy consumption in the gasifier leading to a temperature difference between gasification and combustion reactor, the solids circulation rate is a key parameter for the dual fluidized bed system and can be calculated from the mass and energy balances. Figure 5 shows the specific solids transport rate in the combustion reactor (riser) versus the superficial gas velocity at riser exit. The total height of the riser is 9 m. The bed material is natural olivine (Fe2SiO4/Mg2SiO4) with a mean particle diameter of 540 µm. The slope of the regression line is mainly determined by the single datum at an exit velocity of about 8 m/s. The rest of the data points scatter between 45 and 70 kg/(m2 s) in a narrow velocity range between 10 and 12 m/s. According to cold flow model results on the DFB behaviour, cross sensitivity on the solids circulation rate can be expected from the total solids inventory and from the air staging in the riser. However, the data on these quantities are not accurately determined at the large plant and, therefore, the model is kept simple taking only the exit velocity into account. It can be observed that the riser velocity is relatively high compared to common circulating fluidized bed applications, where values of 5-7 m/s are typically designed. On the one hand, high solids circulation rates are advantageous with respect to energy efficiency because of lower temperature differences between the reactors and consequently lower flue gas
9
exhaust temperatures. On the other hand, increased erosion in the riser exit zone and cyclone requires shorter maintenance intervals for the refractory lining. Practically, the high velocities are the result of fuel water contents much higher than the plant has been initially designed for. The refractory was reworked for the first time after about 12 500 hours of operation. It can be recommended for future plants to choose a design that combines high solids transport with moderate riser velocities, e.g. by increasing the solids hold up in the riser via the total solids inventory.
70
2.
Specific solids transport GS [kg/(m s)]
80
60 50 40 30 20
Regression: G S [kg/(m2.s)] = 10.4.U R,exit [m/s] - 61.0
10
R 2 = 0.75
0 0
5 10 Superficial velocity riser exit [m/s]
15
Figure 5: Solids transport rate vs. riser exit velocity.
PARAMETER MODEL AND SIMULATION OF PARAMETER VARIATIONS The aim of this section is the optimization of operation parameters for the existing CHP plant without changing the process configuration. The benefit of measures for plant optimization can be estimated using process simulation. There is an essential difference between a simulation model focusing on plant description using measured data (i.e. mass and energy balancing) and a model that is able to predict the behaviour of plant components during variation of operation parameters. In addition to the balance equations, these models contain correlations describing the detail behaviour of the apparatuses. Examples for such correlations used in the present study are compressor isentropic efficiencies, load-dependent performance curves of the gas engine, separation efficiencies of filters or cyclones, conversion rates in the oxidation catalysts, etc. The main task when dealing with DFB-gasification is the prediction of the behaviour of the fluidized bed system including the fuel conversion in the gasification zone. Once the models are validated, the simulation is a reliable tool for both plant optimization and engineering of alternative plant configurations. A detailed description of the models would go beyond the focus of this paper. However, some fundamental principles are summarized in the following: 1. The solid circulation rate is determined by the conditions in the riser, what has already been shown in extensive cold flow model studies (Löffler et al., 2003; Kaiser et al., 2003). Therefore, an empirical correlation between the superficial velocity at the riser exit and the specific solid circulation rate has been determined from several operation states of the Guessing plant. 2. Kinetic modelling showed that the char conversion is dominated by the steam gasification reaction:
C(S) + H 2O ↔ CO + H 2
(24)
10
Therefore, the kinetics of Reaction (24) according to Barrio et al. (2000) are used to describe char conversion as a function of proximate analysis, temperature, steam partial pressure, and solids residence time in the gasifier. 3. Biomass char is not modeled as pure carbon, but as an organic mixture of C, H, and O. This approach is supported by the measured mass and energy balances. As the actual composition of the char in the hot bed material cannot be determined, literature values for biomass char composition after fast pyrolysis (Reed, 1981) are taken for the initial char composition. 4. The producer gas composition is largely dominated by the primary pyrolysis product distribution. Measurements show that the concentrations of hydrocarbons and nitrogen compounds (CH4, C2H4, C2H6, C3H8, NH3, HCN) in the producer gas are largely independent of operating conditions within the range operated at the Güssing plant. In thermodynamic equilibrium practically no hydrocarbons would be present. This indicates that no substantial conversion of these species takes place in the gasifier freeboard. The measured hydrocarbon fractions are therefore kept constant during the simulation of parameter variations. A different behaviour has been found for the species involved in the CO-shift reaction
CO + H 2O ↔ CO 2 + H 2
(25)
The shift reaction is close to equilibrium at gasifier exit, what indicates that this reaction effectively takes place. It has been further observed that the composition is always on the side of CO and H2O, and that the distance from equilibrium is higher if the catalytic activity of the bed material is lower. The catalytic activity can be cross-checked at the CHP plant via the tar concentration in the producer gas. According to Reed (1981), the homogeneous mechanism of the shift reaction (25) is not significantly working below 1070 °C. It can be concluded that only for an appropriate choice of bed material or catalytically active biomass ash shift equilibrium prevails at the exit of the gasifier. The simulation model has been used to study the behaviour of the CHP plant during variation of selected operation parameters. As an example, the dependency of the chemical efficiency on gasification temperature, steam to fuel ratio, fuel water content, and fluidization steam mass flow is shown in Figs. 6 and 7. The chemical efficiency of gas generation is defined as the chemical energy (basis lower heating value – LHV) transported by the clean producer gas to gas engine and gas boiler divided by the total fuel power (basis lower heating value - LHV) of biomass and the ~ 17 kg/h of scrubber solvent (RME). The steam to fuel ratio is the mass flow of total H2O to the gasifier (fluidization steam and fuel water content) divided by the dry biomass flow to the gasifier. The reference case is marked in Figs. 6 and 7 by the broken lines. After the variation of pairs of two parameters the optimization of a larger number of parameters within reasonable ranges leads to an optimized plant operation state. Figure 8 shows the impact of different optimization measures on the electrical plant efficiency. The optimization measures are summarized in Table 3 and briefly discussed in the following. Reduction of the fuel water content significantly increases the heating value of the fuel and reduces the energy requirement of the gasifier related to dry fuel input. This means that also the heat transferred to the gasifier with the circulating bed material is lower in case of dry fuel. Additionally, the steam content in the raw producer gas decreases with decreasing fuel water content and less energy is withdrawn from the process as heat transferred in the producer gas cooler. The same applies for the flue gas stream from the CFB-riser that is lower because of a lower thermal load of the riser that is achieved by a lower amount of clean producer gas recycled to the riser. Summarizing, the use of dry fuel leads to lower thermal loads in the whole gas generation and gas cleaning section and therefore increases efficiency. The reduction of the gasification temperature acts mainly through lowering the energy withdrawn by cooling the hot gas streams. A lower fluidizing steam mass flow also reduces the producer gas flow and the energy withdrawn by producer gas cooling and water condensation in the scrubber. The corresponding measure on the oxidation side is the decrease of the air ratio λ in the riser leading to
11
70
70
ϕ SF [kg/kgwaf]
& steam [kg/h] m
0.60
450 Chemical efficiency [%]
Chemical efficiency [%]
0.70 0.73
65
0.80
Ref. case 60
600 65
750
Ref. case 60
P chem = 5090 kW P chem = 5090 kW 55
850
900
10
950
20
30
Gasification temperature [°C]
Figure 7: Variation of fuel water content and fluidization steam mass flow to the gasifier for constant producer gas output – effect on the chemical efficiency of gas generation.
R ef er Fu en el ce Electrical plant efficiency [%] w at c a .c se on 12 t. (2 /2 7 00 G .5 as 3 -> 15 to G .0 bo as w ile ifi t-% r( ca 1 ) tio 1 .3 n El t > e ec m 5. tri p. 0 ca % (9 ) lo 00 ut -> p Ai 85 ut rr 0 (1 at °C 66 io ) 6in >1 th 96 e ris 0. Fl .. er ui d. (1 st .2 ea 0m >1 (6 .0 5) 00 -> 50 0 kg /h )
Figure 6: Variation of gasification temperature and steam to fuel ratio in the gasifier for constant producer gas output – effect on the chemical efficiency of gas generation.
26
1.78
25 24
21.7
22 21 20
24.0
1.37 23.1
23
40
Fuel water content [wt-%]
24.5
24.9
25.2
0.90
0.52
19.9
19
0.45
0.28
2.0 1.5 1.0 0.5 0.0
El. eff. increase by single measure [%-pts]
55 800
Figure 8: Cumulative and partial increase of electrical plant efficiency by different optimization measures.
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Table 3: Measures for optimization of plant operation. Unit
Reference case
Optimized operation
wt.% °C kg/h
27.5 900 597
15.0 850 500
Air ratio λ
---
1.20
1.05
Gas utilization: Effective generator output Part of producer gas to boiler
kW %
1666 11.2
1960 5.0
Gasifier Fuel water content Gasification temperature Mass flow fluidization steam Riser:
a lower flue gas mass flow. The increase in efficiency through full load operation of the gas engine is because of the engine-specific part load behaviour. The part of producer gas that is fed to the producer gas boiler and therefore lost for electricity generation has a direct influence on the electrical plant efficiency.
PART LOAD BEHAVIOUR AND PERFORMANCE MAPS A key task for process simulation is the description of the part load behaviour of the CHP plant. A type of performance map well known for combustion-based power plants can as well be used for the gasification based CHP plants showing both electrical and heat output for the practical range of operation (Kaiser et al., 2000). The first gasifier performance maps were calculated during the design phase of the plant. Now, gasifier performance maps can be drawn based on models validated with measured data. The typical quantities used to describe the operating range are the total wet fuel mass flow, the effective electrical power output at the generator, and the total heat generation for district heating purposes. Alternatively, the respective plant efficiencies can be plotted against the total thermal fuel power input. This second possibility offers a more general view on the technology while the classical version suites if the focus is on a specific plant. The process parameters used to further describe the different operating states are fuel water content, riser exit velocity, and gas engine load. The baseline for the calculations is a reference plant operation based on measured data (Pröll et al., 2005). The most important of the other operating parameters, which are not subject to variation within the present work, are summarized in Table 4. The Table 4: Important constant process parameters. efficiency of the gas generation step increases as the gasifier temperature Lower heating value (LHV) dry fuel (wf) MJ/kg 17.55 decreases. Therefore, the gasifier Gasifier bed temperature K 1123 temperature is practically set to the lowest value possible with respect to Gasifier bed pressure drop (solids hold up) kPa 10.5 tar formation. The combustion Steam for fluidization kg/h 500 reactor temperature is coupled to the Part of combustion air to bottom nozzles % 20 gasifier temperature by the circulating solids and is typically 40Part of combustion air to primary air level % 55 70 K higher than the gasifier Part of combustion air to secondary air level % 25 temperature depending mainly on the Excess air ratio at combustion reactor exit 1.05 fuel water content. The gas temperature after the tar scrubber Gas temperature after tar scrubber K 313 determines the water content in the Part of producer gas to district heating boiler % 5.0 engine fuel gas and must be kept as
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low as possible. The district heating boiler in bypass to the gas engine (Figure 2) must be kept hot at stand-by, what requires 5 % of the clean producer gas. The performance map for electrical power output is shown in Figure 9. It is assumed that the whole plant is operated in part load if the gas engine is operated in part load. The bounds of the operating range are the maximum engine load on top, the maximum riser exit velocity (erosion limit) to the right, and the minimum engine load or minimum solids circulation rate respectively at the bottom. The bounds to the left (15 wt-% fuel water content) and to the lower right (40 wt-% fuel water content) are not of technological nature but represent the maximum range of available fuel at site. Figure 9 shows that the lines of constant riser velocities are almost vertical and, therefore, practically directly dependent on the wet fuel mass flow. At a given engine load, the fuel mass flow increases with increasing water content or decreasing heating value respectively. Since the gas engine is the only source of electrical power, the load factor of the engine is strictly linked to the electrical plant output. The performance map for district heat output is shown in Figure 10. The tendencies observed are similar to Figure 9 with the main difference that the heat output increases for constant gas engine load with increasing fuel water content. The reason is a higher cooling power from the gasifier producer gas and combustion reactor exhaust gas because of higher gas mass flows for increased water loads in the system. The reason for the slight change in slope between 20 and 15 wt-% of fuel water content is that the amount of condensate available for steam generation is getting less than the required steam and, for fuel water contents lower than about 18 wt-%, additional water must be added for generation of the fluidization steam. Figure 11 shows the correlation between electrical efficiency and fuel power input. The boundaries of the operating range are the same as above. Maximum efficiency is observed at design conditions (low fuel water content and 100 % engine load). The dependency of the electrical plant efficiency on the fuel content (Figure 7) is also obvious from Figure 11. Finally, the efficiency of the plant with respect to utilizable heat (provided at 120 °C for the district heating grid) is plotted in Figure 12. The heat efficiency does not vary significantly. Within the operating range, the values are all between 50 and 54 %. The maximum heat efficiency occurs in the range of 20 wt-% fuel water content. Below this point the quantity of condensate from the producer gas scrubber does not cover the amount needed for generation of fluidizing steam. Additional water must be supplied and evaporated, what slightly lowers the total heat efficiency. It is important to notice that in the range above 20 wt-% fuel water content, where there is excess of condensate, no liquid is disposed but the condensate is evaporated and fed into the post combustion chamber of the DFB system (see Figure 2). Summarizing, the use of wet fuel in the DFB gasifier leads to significantly higher riser velocities and increases the share of heat in the total energy output. It is obvious that also the electrical plant efficiency decreases with increasing fuel water content. The data shown in the performance maps represent the energybased performance of the CHP plant in its current configuration. Further optimization will require changes of the plant equipment. Starting from improved control loops to minimize the amount of producer gas to the stand-by boiler to zero, the integration of fuel drying and the utilization of high level heat in Rankine cycles for higher electrical output are currently discussed on a techno-economic basis. The basic engineering of the next generation plant, a 10 MWth CHP installation, which is currently being erected in Austria, is finished and considers the main results referring to optimization.
CONCLUSIONS A dual fluidized bed technology for steam gasification of solid biomass has been successfully demonstrated at the 8 MWth biomass combined heat and power (CHP) plant at Guessing/Austria. Accompanying the operation, the simulation model of the entire process has been developed in parallel and validated using the equationoriented software IPSEpro.
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2.2
LHV = 14.55 MJ/kg
12.55 MJ/kg
Power output generator [MW]
2.0
13.55 MJ/kg
1.8
1.6
1.4
9.55 MJ/kg
1.2
10.55 MJ/kg
11.55 MJ/kg 1.0 1.2
1.4
1.6
1.8 2.0 2.2 2.4 2.6 2.8 Total mass flow fuel [103kg/h]
3.0
15 20 25 30 35 40 6 7 8 9 10 11 100 90 80 70 60
Fuel water content [wt-%]
Riser exit velocity [m/s]
Load factor gas engine [%]
3.2
Figure 9: CHP plant performance map 1: electrical power output vs. total fuel mass flow.
4.8
15 20 25 30 35 40 6 7 8 9 10 11 100 90 80 70 60
Heat to district heating grid [MW]
4.6 4.4 4.2 4.0 3.8 3.6 3.4 3.2 3.0 2.8 1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
Fuel water content [wt-%]
Riser exit velocity [m/s]
Load factor gas engine [%]
3.2
3
Total mass flow fuel [10 kg/h]
Figure 10: CHP plant performance map 2: district heat output vs. total fuel mass flow.
15
27 Transformed reference case Transformierter Referenzbetrieb: 85 % %Motorlast engine load 85 (1666 kW) 27.5 Brennstoff27.5% wt-% fuel wassergehalt water content
26
Electric plant efficiency [%]
25
15 20 25 30 35 40 6 7 8 9 10 11 100 90 80 70 60
Optimierter Betrieb: Optimised operation Brennstoff-WG 1515%%fuel wat. cont. Brennstoff-WG 2020%%fuel wat. cont.
24 23 22 21 20 19 18 17 5
6
7
8
9
Fuel water content [wt-%]
Riser exit velocity [m/s]
Load factor gas engine [%]
10
Total fuel power input [MW] Figure 11: CHP plant performance map 3: brut electrical efficiency vs. total fuel power input.
Transformed reference case: 85 % engine load (1666 kW) 27.5 % fuel water content
Heat efficiency of CHP plant [%]
54
Optimised operation 20 % fuel wat. cont. 15 % fuel wat. cont.
53
52
51
50 5
6
7 8 Total fuel power input [MW]
9
15 20 25 30 35 40 6 7 8 9 10 11 100 90 80 70 60
Fuel water content [wt-%]
Riser exit velocity [m/s]
Load factor gas engine [%]
10
Figure 12: CHP plant performance map 4: utilizable heat efficiency vs. total fuel power input.
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Plant analysis using process simulation allows the detection of systematic errors in both simulation model and measurements. After elimination of these errors, the simulation describes the actual plant operation best within the limits of the model structure. The reference case of the actual plant operation shows still a high potential for optimization. Specific parameter variations on the real plant are difficult to perform. However, the natural changes of input parameters can be exploited for correlation of process variables. The presented correlation between solids circulation rate and riser exit velocity shows that the quality of the semi-empirical correlations is highly dependent on the available data. Reliable process simulation can be a valuable tool to predict the effect of parameter variations. For this purpose, a comprehensive parameter model of the process is required. The simulation shows the expected high impact of gasification temperature and fuel water content on the chemical efficiency of gas generation. The simulation results for the Güssing plant showed that a reduction of fuel water content, a reduction of producer gas to the gas boiler, and a lowering the gasification temperature have the highest potential for improvement of plant performance. Performance maps are presented for the CHP plant. The practical operating range for the available fuel (15-40 wt-% water content) is limited by min/max engine load and by the maximal gas velocity in the riser (erosion limit). It is shown, that the potential of the CHP plant in its current configuration is about 25 % electrical efficiency for standard biomass fuel (20 wt-% water content) at a high overall fuel utilization (heat and electricity) of approximately 75 %. For the next generation plant, optimization of plant design and process configuration on a technoeconomic basis is considered. This includes improvement of the DFB design in order to maintain high solids circulation at lower gas velocities in the DFB riser of 7-8 m/s. This can be potentially achieved by increasing the amount of bed material in the system with the positive side effect of better gas-solid contact in the gasifier. Important measures with high impact on electrical plant efficiency are low temperature biomass drying and a Rankine cycle (steam or organic working fluid) for additional power generation from the high level heat of the gas streams.
NOTATIONS A GS
inner cross sectional area of a reactor specific solids transport rate in the riser
m2 kg.m-2.s-1
lhv, LHV
lower heating value
J.kg-1
m& n&
mass flow rate
kg.s-1
mole flow rate
mol.s-1
Pchem
chemically combined energy transport rate in producer gas
W
rcirc
fuel-specific bed material circulation rate in the gasifier
kg.kgwaf-1
R2
variance in data fit
div.
sCO
CO slip in combustion reactor related to CO2 content
mol.mol-1
tol x
absolute tolerance for measured value xi
div.
U R , exit
superficial gas velocity at riser exit
m.s-1
V&
volumetric flow rate
m3.s-1
wi
mass fraction of component i
kg.kg-1
i
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xi
measured value
div.
xi
true value after data reconciliation
div.
Xi
conversion rate of component i related to water and ash free fuel feed rate
kg.kg-1
Yi Greek symbols: load of dust, char, or tar on a gas stream β
kg.kggas-1
ϕ
split ratio
kg.kg-1
ϕ SF
total water to dry and ash free fuel ratio
kg.kg-1
ηchem
chemical efficiency of gas generation
---
ηel ,brut
brut electrical plant efficiency
---
η FU ,brut
brut total fuel utilization in the CHP plant (electricity + heat)
---
ηQ
heat utilization efficiency
---
ηsep
separation efficiency of the cyclone
---
λ
air ratio in the combustion reactor (riser)
---
Subscripts: Ash Attrition Bed brut C Ch circ DH,total Dust el exit exp FG Fluid Fuel g G GE GE&G Gen i
biomass ash elutriation of bed material from gasifier bed material without consideration of the own electricity consumption of the CHP plant carbon biomass char circulating bed material flow total district heat export of CHP plant dust in gas stream referring to electrical power exit section of a reactor producer gas exported from gas generation step flue gas from combustion reactor (riser) fluidization gas into reactor biomass fuel to gasifier gas stream in general gasifier (gasification reactor in DFB system) gas engine gas engine and electricity generator electricity generator referring to component or value i
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in org out PG R steam T waf
feed flow into unit operation organic (fuel) stream drain flow out of unit operation producer gas riser fluidizing steam to gasifier biomass tar from gasifier water and ash free organic substance
REFERENCES Barrio, M., Gobel, B., Risnes, H., Henriksen, U., Hustad, J.E., Sorensen, L.H., “Steam gasification of wood char and the effects of hydrogen inhibition on chemical kinetics”, Progress in thermochemical biomass conversion, Innsbruck, Austria, September 2000. Hofbauer, H., Rauch, R., “Stoichiometric water consumption of steam gasification by the FICFB-gasification process”, Progress in Thermochemical Biomass Conversion, Innsbruck, Austria, September 2000 (http://www.ficfb.at). Hofbauer, H., Rauch, R., Bosch, K., Koch, R., Aichernig, C., “Biomass CHP plant Guessing – a success story”, In: Bridgwater, A.V. (Ed.): Pyrolysis and Gasification of Biomass and Waste, CPL Press, Newbury, UK, pp. 527-536 (2003). Hofbauer, H., Rauch, R., Löffler, G., Kaiser, S., Fercher, E., and Tremmel, H., “Six Years Experience with the FICFB-Gasification Process”, In: W. Palz et al. (Eds.): Proc., 12th European Biomass Conference, ETA Florence, Italy, pp. 982-985 (2002). Hofbauer, H., Veronik, G., Fleck, T., Rauch, R., “The FICFB gasification process”, In: Bridgwater, A.V., Boocock, D. (Eds.): Developments in thermochemical biomass conversion, Vol. 2, Blackie Academic & Professional, Glasgow, U.K., pp. 1016-1025 (1997). Kaiser, S., Löffler, G., Bosch, K., Hofbauer, H., “Hydrodynamics of a dual fluidized bed gasifier. Part II: simulation of solid circulation rate, pressure loop and stability”, Chemical Engineering Science, Vol. 58, No. 18, 4215-4223 (2003). Kaiser, S., Weigl, K., Schuster, G., Tremmel, H., Friedl, A., Hofbauer, H., “Simulation and optimization of a biomass gasification process”, In: Kyrisits, S. et al. (Eds.): Proceedings of the First World Conference on Biomass for Energy and Industry, James&James., London, UK, pp. 1922-1925 (2000). Löffler, G., Kaiser, S., Bosch, K., Hofbauer, H., “Hydrodynamics of a dual fluidized-bed gasifier - Part I: simulation of a riser with gas injection and diffuser”, Chemical Engineering Science, Vol. 58, No. 18, 4197-4213 (2003). Pröll, T., Rauch, R., Aichernig, C., Hofbauer, H., “Fluidized bed steam gasification of solid biomass: analysis and optimization of plant operation using process simulation”, ASME Paper FBC2005-78129, Proc. of the 18th Int. Conf. on Fluidized Bed Combustion, May 23-25, 2005, Toronto (2005). Reed, T.B., Biomass gasification, Noyes Data Corporation, New Jersey, USA (1981). SimTech, “IPSEpro user manual”, SimTech Simulation Technology (www.simtechnology.com), Graz, Austria (2001).
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