Design and Characterization of an Entrained Flow Reactor for the

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Design and Characterization of an Entrained Flow Reactor for the Study of Biomass Pyrolysis Chemistry at High Heating Rates Alexander L. Brown,†,‡ David C. Dayton,*,† Mark R. Nimlos,† and John W. Daily‡ National Renewable Energy Laboratory (NREL), 1617 Cole Boulevard, MS 3322, Golden, Colorado 80401, and University of Colorado-Boulder, Department of Mechanical Engineering, Center for Combustion and Environmental Research, Boulder, Colorado 80309 Received April 10, 2001. Revised Manuscript Received June 29, 2001

A laminar entrained flow reactor has been designed for studying the chemistry of fast biomass pyrolysis. This is the first of two papers on the reaction system. Peak heating rates in the reactor are on the order of 104 K/s. The reactor is capable of interfacing with a molecular beam mass spectrometer for rapid analysis of gas phase chemistry. Computational fluid dynamic simulations are used to predict an accurate time-temperature profile for the reactants and to better understand the internal processes in the reactor. Predicted and measured reaction rates compare favorably for a gas phase reaction standard. Particle devolatilization is modeled to help understand the tradeoff between heat transport and kinetic control of the pyrolysis rate. Biomass and cellulose particles below about 50 µm are expected to be sufficiently small to avoid heat transport pyrolysis control, and thus allow study of kinetically controlled pyrolysis in this reactor. This paper is the first of two, and describes the characterization of the entrained flow reactor and methodologies developed for determining quantitative kinetic measurements. The second paper describes the application of these techniques to the study of cellulose pyrolysis at high heating rates.

Introduction Efforts to generate clean, renewable electricity from biomass continue to be of considerable interest in light of increasing concerns regarding global climate change, and limited domestic fossil resources. Biomass conversion technologies range from traditional direct-fired biomass combustion systems designed to raise steam, to advanced integrated biomass gasification combined cycle systems with high projected efficiencies. The first step in all biomass thermal conversion systems is the initial pyrolysis of the solid feedstock. A reliable description of the global kinetics of biomass pyrolysis is the building block for accurately describing the high temperature chemistry that occurs in developing biomass thermochemical conversion systems and potentially can be used to optimize system design and performance. Practical biomass pyrolysis conditions can vary from very slow heating rates (104 °C/ s), as is the case for industrial boilers where typical particle sizes can range from several centimeters in length to e100 µm. Classical pyrolysis experiments involve collecting and measuring the residual materials. Unreacted solid is generally classified as char, or a char-ash combination. The materials that condense at room temperature are experimentally classified as * Corresponding author. Fax: (303) 384-6363. david•[email protected]. † National Renewable Energy Laboratory (NREL). ‡ University of Colorado-Boulder.

E-mail:

“tars”. The remainder generally consists of light gases and water, with a possibility of some solid products depending on the severity of pyrolysis. Fast biomass pyrolysis studies typically involve large particle sizes and analysis of the classical products. Biomass pyrolysis models are quite complex,1-3 and generally involve rates that were determined from low heating rate experiments. Pyrolysis severity is known to have an impact on the ultimate product formation.4 It is difficult to make controlled measurements under fast pyrolysis conditions, which is manifest in the paucity of kinetic data in the literature on this subject compared to slow pyrolysis studies. The fast pyrolysis field is of growing interest as the use of biomass for industrial applications increases.4-10 Reacting solid particles undergo a variety of physical and chemical processes. Heating of solid particles (1) Miller, R. S.; Bellan, J. Combust. Sci. Technol. 1997, 126, 97137. (2) Di Blasi, C.; Russo, G. In Advances in Thermochemical Biomass Conversion; Bridgwater, A. V., Ed.; Blackie Academic and Professional: New York, 1994; Vol. 2, pp 906-921. (3) Niksa, S. Proc. Combust. Inst. 2000, 28, 2727-2733. (4) Graham, R. G.; Bergougnou, M. A.; Overend, R. P. J. Anal. Appl. Pyrolysis 1984, 6, 95-135. (5) Scott, D. S.; Piskorz, J. Can. J. Chem. Eng. 1982, 60, 666-674. (6) Scott, D. S.; Piskorz, J.; Bergougnou, M. A.; Graham, R.; Overend, R. A. Ind. Eng. Chem. Res. 1988, 27, 8-15. (7) Scott, D. S.; Majerski, P.; Piskorz, J.; Radlein, D. J. Anal. Appl. Pyrolysis 1999, 51, 23-37. (8) Di Blasi, C. Ind. Eng. Chem. Res. 1996, 35, 37-46. (9) Di Blasi, C.; Branca, C.; Santoro, A.; Bermudez, R. A. P. J. Anal. Appl. Pyrolysis 2001, 57, 77-90. (10) Di Blasi, C.; Gonzalez Hernandez, E.; Santoro, A. Ind. Eng. Chem. Res. 2000, 39, 873-882.

10.1021/ef010083k CCC: $20.00 © 2001 American Chemical Society Published on Web 08/28/2001

Entrained Flow Reactor for Study of Biomass Pyrolysis

generally occurs at the external surface of the particle. Temperature inside the particle is then a function of the ability of the particle to internally conduct heat. As the temperature of the solid increases, chemical reactions occur. The rates of chemical processes typically increase exponentially with temperature. Generally, as reactions occur, smaller and more volatile products are formed from the breakdown within the solid. Surface products will escape to the gaseous environment. Products formed within a particle must somehow escape or diffuse through the solid matrix before leaving the system. To accurately study the true pyrolysis chemistry, it is important to limit the extent to which heat transfer and diffusion control the reaction rate. Both thermal and diffusive limitations decrease with decreasing particle size. Hence, it is important to maintain sufficiently small particle sizes to avoid alternative control regimes when studying pyrolysis chemistry. A common instrument for studying pyrolysis and rates of pyrolysis is a thermogravimetric analyzer (TGA), which weighs a sample as it is heated at a controlled rate. Kinetic control of TGA experiments is approached at conditions of slow heating and for small samples. Another common apparatus for studying solid fuel conversion chemistry is an entrained flow reactor. These reactors are capable of high heating rates while maintaining precise experimental control of the reaction conditions. Numerous examples of such instruments have been presented in the literature.11-21 Usually, operation involves feeding a solid reactant into an inert or chemically controlled gaseous environment. The solid reactant drops through the tubular reactor, and permanent gases, condensable liquids, and residual solids are sampled at the bottom. Properly designed, these instruments can be used to create product samples and study particle reaction dynamic processes. These instruments are commonly used to study coal,12-15,17 but have been used on a more limited scale for biomass as well.4,18,20-22 To address the need for accurate chemical kinetic measurements of biomass thermal conversion, a laminar entrained flow reactor (LEFR) was designed, fabricated, and characterized for studying biomass pyrolysis and gasification kinetics. The extent of biomass pyrolysis is measured by collecting residual solid and condensable products as a function of particle residence time and temperature. The LEFR also accommodates real time monitoring of gas phase and condensable products using a molecular beam sampling mass spectrometer (MBMS) system to measure real-time production of individual (11) Lede, J. Ind. Eng. Chem. Res. 2000, 39, 893-903. (12) Flaxman, R. J.; Hallett, L. H. Fuel 1987, 66, 607-611. (13) Fletcher, T. H. Combust. Flame 1989, 78, 223-236. (14) Fletcher, T. H. Fuel 1993, 72 (11), 1485-1495. (15) Jamaluddin, A. S.; Truelove, J. S.; Wall, T. F. Combust. Flame 1986, 63, 329-337. (16) Pollard, R. J. Anal. Appl. Pyrolysis 1997, 39, 145-160. (17) Solomon, P. R.; Serio, M. A.; Carangelo, R. M.; Markham, J. R. Fuel 1986, 65, 182-194. (18) Wagenaar, B. M.; Van den Heuvel, E. J. M. T. Biomass Bioenergy 1997, 12 (3), 185-197. (19) Westerhout, R. W. J.; Kuipers, J. A. M.; Van Swaaij, W. P. M. Chem. Eng. Sci. 1996, 51 (10), 2221-2230. (20) Sricharoenchaikul, V.; Phimolmas, V.; Frederick, W. J.; Grace, T. M. J. Pulp Paper Sci. 1998, 24 (2), 43-50. (21) Sricharoenchaikul, V.; Frederick, W. J.; Grace, T. M. J. Pulp. Paper Sci. 1997, 23 (8), J394-J400. (22) Fletcher, D. F.; Haynes, B. S.; Chen, J.; Joseph, S. D. Appl. Math. Modelling 1998, 22, 747-757.

Energy & Fuels, Vol. 15, No. 5, 2001 1277

chemical products as a function of residence time and temperature. The reactor is a bench-scale system for studying continuously fed reactants. Ultimately, the newly designed entrained flow reactor will be used to determine the rates of evolution of pyrolysis products, gasifier “tars”, and alkali metal vapors. Measurements in the reactor and predictions with computational fluid dynamics computer codes can be used to accurately characterize the reactor as it is operated under laminar flow conditions. The models and measurements facilitate the understanding of the system, and allow detailed understanding and interpretation of experimental results. It is then possible to more confidently extrapolate the bench-scale-derived information to larger scale biomass combustors and gasifiers. The ultimate goal of modeling in the bench-scale experiments is to make accurate determination of fuel dynamic behavior, so that results from the simple bench scale combustion and pyrolysis experiments can be confidently extrapolated to larger scale systems. This paper presents the modeling and characterization of a laminar entrained flow reactor (LEFR) for the study of biomass pyrolysis chemistry. A primary focus of the paper is on the capability to understand the timetemperature profile of a solid reactant. Another major focus is on the ability of the reaction system to sample kinetically controlled, and not heat transfer controlled, release of gas phase products. A combination of models and experimental measurements are used to understand the performance of the reactor. A detailed thermal analysis of biomass particles is presented to define the parameters and operating boundaries that limit the effects of transport control on experimental results. Results of the thermal decomposition kinetics of allyl ethyl ether (C5H10O) are presented and compared to literature values.23 This liquid model compound has sufficiently high vapor pressure at room temperature and local pressure (around 620 mmHg) to yield substantial signals, as detected by a MBMS system. Furthermore, it thermally decomposes to form propylene and acetaldehyde at a known rate, and decomposition occurs in a time-temperature regime similar to biomass. This paper is the first in a two-paper series and deals with characterization of the reaction system. Experimental Methods MBMS Detection. Molecular beam sampling, mass spectrometric (MBMS) detection was used to investigate the chemistry of the gas phase products. The MBMS system is ideally suited for sampling high-temperature condensable biomass pyrolysis vapors because the integrity of the sampled product gases is preserved during the free-jet expansion since chemical reactions are effectively quenched and condensation is inhibited. The nonequilibrium nature of the free jet expansion and the subsequent formation of a molecular beam allows reactive and condensable species to remain in the gas phase at temperatures far below their condensation point for long periods of time in comparison to reaction rates. The MBMS consists of a mass spectrometer housed in a three-stage differentially pumped vacuum system. The stainless steel sampling probe contains a small (0.25-0.75-mm) orifice located at the apex of a cone that is exposed to the high temperature gas stream to be studied. The gas is extractively sampled into the first stage of the vacuum system, where the (23) Egger, K. W.; Vitins, P. Int. J. Chem. Kinet. 1974, 6, 429-435.

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free-jet expansion results in a rapid quenching of the gaseous species and an abrupt transition to collisionless or molecular flow. This effectively freezes the species in their sampled states. The core of this molecular flow field is extracted with a 0.5 to 1.0-mm diameter conical skimmer located at the entrance to the second stage of the vacuum system. This forms a molecular beam that is directed into the ionization region of the mass spectrometer in the third stage. Two MBMS systems have been used in this study and both have been described in detail elsewhere. One system contains a quadrupole mass analyzer with electron impact ionization24-27 and the other system is equipped with a time-of-flight mass spectrometer that utilizes a single-photon (10.5 eV) ionization source.28 Principal component analysis (PCA), or factor analysis is a statistical technique that can be useful in interpreting large data sets containing many dependent and independent variables, such as the mass spectral data collected in this study. PCA reduces the dimensionality of the data set by finding correlations or linear combinations between variables with the greatest amount of variance. The number of principal components in a given model equals the number of variables in the system. If all of the principal components are retained in the model, the dimensionality of the system has not changed. To reduce the number of variables in the system, only those principal components explaining the majority of covariance are used to model the system. These principal components are then referred to as factors, and as soon as the number of principal components retained in the model is less than the total number of variables, the technique then becomes factor analysis. The goal of factor analysis is to determine the variables or linear combinations of variables that explain the greatest amount of covariance with a minimum distortion of the data. This technique reduces the large number of bivariate combinations to a single model explaining the multivariate combinations in the system by using a statistical procedure to provide qualitative and quantitative distinctions.29 This technique is applied to mass spectra results from the MBMS. Analysis was performed with ISMA software.30 The Reactor. Figure 1 is an illustration of the LEFR. The reactor consists of two concentric tubes, one a 4.1-cm o.d., and the other a 3.2-cm o.d. The tube walls are 0.2-cm thick. The particle-laden primary gas stream flows down the centerline of a 1.6-cm o.d., 0.435-cm i.d. water-cooled, quartz feeder tube. The height of the feeder tube is adjustable and an O-ring in a threaded ground glass joint adapter creates a gastight seal between the feeder tube and reactor. Secondary gas introduced into the bottom of the outer flow region is heated as it flows up between the two quartz walls. At the top of the reactor the secondary gas flow reverses direction and flows down the center flow region. At this point, the inner reactor tube wall and the outer wall of the water-cooled feeder tube represent the boundary walls. A 1.0-cm o.d., 0.585-cm i.d. quartz sample tube penetrates into the bulk flow region 9 cm from the bottom of the reactor. Gas flows through this tube to the sampling probe of the MBMS system for real-time, comprehensive product gas analysis. The end of the quartz sampling tube is fitted with a 90-degree cone that matches the shape of the stainless steel MBMS sampling cone to provide a physical seal between the LEFR and the MBMS. A 0.1-cm i.d. tube runs (24) Dayton, D. C.; Belle-Oudry, D.; Nordin, A. Energy Fuels 1999, 13, 1203-1211. (25) Dayton, D. C.; French, R. J.; Milne, T. A. Energy Fuels 1995, 9 (5), 855-865. (26) Evans, R. J.; Milne, T. A. Energy Fuels 1987, 1, 123-137. (27) Evans, R. J.; Milne, T. A. Energy Fuels 1987, 1, 311-319. (28) Brown, A. L.; Dayton, D. C.; Nimlos, M. R.; Daily, J. W. Chemosphere 2001, 42 (5-7), 663-669. (29) Rummel, R. J. (1970). Applied Factor Analysis; Northwestern University Press: Evanston, IL, 1972. (30) Windig, W.; Lippert, J. L.; Robbins, M. J.; Kresinske, K. R.; Twist, J. P.; Snyder, A. P. Chemom. Intell. Lab. Syst. 1990, 9, 7-30.

Brown et al.

Figure 1. A cross-sectional illustration of the LEFR and furnace. vertically along the inner wall of the inner quartz reactor tube up the length of the LEFR, opposite the quartz MBMS sample tube (not illustrated in Figure 1). A small type-K thermocouple is inserted in this tube to measure the wall temperature. The LEFR is placed inside a 26-cm diameter, 77.5-cm high, four-zone electrically heated ceramic furnace. Helium is used as the carrier gas in all pyrolysis experiments. Mass flow controllers are used to meter primary gas flows between 10 and 500 sccm of helium, and secondary gas flows between 1 and 3 slm of helium. Cellulose particles injected down the centerline feeder tube traced a smooth line from the feeder tube outlet to the reactor exit during cold flow tests. Thus for the indicated flow rate conditions the flow was observed to be laminar. The maximum calculated Reynolds number in the LEFR at the upper limits of the experimental operating conditions is less than 1000 in all tubular sections of the reactor, further indicating laminar flow conditions. As particles and gas exit the feeder tube into the reactor region, they are rapidly heated to the temperature of the nearest reactor wall. Peak heating rates are estimated to be on the order of 104 K/s. Both the height of the feeding tube (h), gas flow rates, and the furnace temperature may be adjusted to achieve a variety of reaction conditions. Unless otherwise stated, the distance between the particle injection point and the MBMS sampling point has been fixed at h ) 20 cm yielding residence times on the order of 0.5-1.0 s depending on the gas flow and temperature. The particle injection point is fixed because it is easier to model a fixed reactor with varying boundary conditions (temperatures) than to model all the different reactor configurations resulting from changes in geometry. Varying the temperature settings on the furnace controllers is sufficient to encompass the dynamic range of conditions for complete biomass thermal conversion.

Entrained Flow Reactor for Study of Biomass Pyrolysis Simulations and Modeling. Modeling fluid dynamics and heat transport enhances characterization and understanding of the reactor. It also provides a time-temperature profile for the particles, which is essential for extracting quantitative kinetic information from the experimental measurements. For the most part, the reactor is tubular and relatively simple to model with fundamental equations describing such flow. Two regions of the reactor, though, are more challenging to describe from fundamental equations. These are the mixing region, where the primary and secondary gases combine, and the 90degree turn region where the gas enters the quartz MBMS sampling tube. Computational fluid dynamics (CFD) simulations were performed to model these complex regions in relation to the tubular sections of the reactor. Simply put, CFD involves estimating a solution to the Navier-Stokes equations for a volumetric region. Fluent Inc. software version 5 was used to model the transport phenomena within the reactor. Laminar flow facilitates the modeling, as approximations in the turbulence models limit CFD accuracy in many cases. Properly designing a grid to represent the reactor is crucial for ensuring that the CFD modeling is accurate. Oversimplifying the grid can lead to loss of detail across regions of substantial gradient, and result in an inaccurate representation of a particular local region in the reactor. Propagating these errors throughout the entire simulation can lead to considerable errors in the CFD model. Grid construction, therefore, proceeds by incrementally refining the grid and comparing the results to those of the coarser grid to avoid oversimplification. Adequate grid independence for the LEFR model has been achieved with a grid made from a mixture of about 60 000 rectangular and tetrahedral cells. Standard convergence criteria are lowered, and convergence is assumed when all continuum residual values no longer change after subsequent iterations. Model accuracy is often limited by the accuracy of the known boundary conditions. The boundary conditions will be addressed further in the Modeling Results section. Particle modeling is possible in the CFD package, but including even a simple chemical model significantly increases the computational load. The complexity of current biomass pyrolysis models1,2 requires simplification. Simulated particle times, temperatures, and trajectories from the CFD package are imported into a spreadsheet, where reactions may be easily simulated across the discrete intervals of the output data through a simple explicit integration. This technique assumes that the impact of the particles and the product gases is minimal on the temperatures and flow characteristics of the bulk system. This will be a good assumption when the bulk particle mass flow is significantly below the mass flow of the gas, as is the case in the LEFR. Other aspects of this assumption are dealt with in more detail in the Modeling Results section. The Biot number is a dimensionless number that compares the rate of convective to conductive heat transport. For nonreacting particles, this quantity indicates how valid it is to assume a particle is isothermal during heat-up. This becomes more complex for biomass because thermal conversion is a reactive process, and the heat of reaction can also impact the particle temperature. Since kinetic control of the reaction rate is desired for this study, steps have been taken to understand the role heat transport plays in the dynamics of a simulated particle. A spreadsheet model has been developed that accounts for the heat transport and mass loss under conditions in the LEFR. Model equations can be derived from fundamental theories of transport. Three concentric solid zones were modeled to estimate the internal heat transport. Mass loss was modeled as the destruction of a concentric external layer of the particle. Quartz is fairly opaque in the spectral region of thermal transport corresponding to our reactor conditions. Since predicted gas temperatures are generally similar to the temperature of the neighboring wall, neglecting

Energy & Fuels, Vol. 15, No. 5, 2001 1279 radiation is not likely to be a major source of error. The energy conservation equations for a particle are as follows:

q˘ n ) q˘ conv + q˘ reac + q˘ cond + q˘ zone_transfer The term “q” is the energy [J] and the dot implies the first derivative with respect to time. The variable “n” represents the internal zone of the particle (i.e., 1,2,3). Convection occurs only in the outer zone. The convection term is

q˘ conv ) Ahθ(Tgas - Tsolid) where T is the temperature [K], A is the surface area [m2] of the particle, and h is the convective heat transfer coefficient [W m-2 K-1] that is represented by the following equation:

h)

Nu‚kf Dp

where Nu is the dimensionless Nusselt number, which is commonly assumed to have a value of 2.0 for small particles, kf is the thermal conductivity of the fluid [W/mK], and Dp is the particle diameter. θ is a correction factor to the convective coefficient to account for transpiration effects.14,31 It is defined by

θ)

B eB - 1

where the dimensionless transpiration number B is defined by

B)

Cp f m x˘ 2πDpkf i

where Cp f is the specific heat of the fluid [J/kgK], mi is the initial mass of the particle [kg], and x is the dimensionless conversion fraction. The reaction term is defined by the following equation:

q˘ reac ) ∆Hreacmi x˘ where ∆Hreac is the heat of reaction [J/kg]. The conductive term comes from integrating Fourier’s law in spherical dimensions, with a general form of the result being

[

q˘ cond ) 4πkp

(Tn - Tn-1)

(1/rn - 1/rn-1)

-

(Tn+1 - Tn)

]

(1/rn+1 - 1/rn)

where r is the particle section radius [m], T is the temperature [K], and kp is the thermal conductivity for the solid particle [W/mK]. Terms (meaning the two product groups separated by the minus sign within the brackets) are only included when both subscripts are 1, 2, or 3 in the case of a 3-zone model. The final energy term qzone_transfer accounts for the changing dimension of the internal particle zones as the particle reacts by compensating for the mass energy lost as a result of the moving boundary:

q˘ zone_transfer ) FsCp s(Tn+1V˙ n+1fn - TnV˙ nfn-1) where Fs is the particle density [kg/m3], Cp s is the specific heat of the solid particle [J/kgK], T is temperature [K], and V is the volume, which changes due to the particle reaction and may be easily calculated from the radii of the particle zones. The radii of the particle zones are a direct function of the conversion, “x”. Particle temperatures are initialized to the initial gas temperature, and particle zone energy is initialized (31) Bird, R. B.; Stewart, W. E.; Lightfoot, E. N. Transport Phenomena; John Wiley & Sons: New York, 1960.

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Table 1. Model Constants Assumed for the Numerical Study variable

symbol

value

units

Arrhenius preexponential factor activation energy particle specific heat particle thermal conductivity heat of reaction particle density

A E Cp s kp ∆Hreac Fs

6.79 × 109 140.0 2600 0.11 -538 1550

1/s kJ/mol J/kgK W/mK kJ/kg kg/m3

to the sensible energy of the solid at that temperature (internal energy is assumed to be zero at zero Kelvin). Particle conversion, “x”, is calculated from an Arrhenius rate equation:

k ) Ae-E/RT where k is the reaction rate, A is the preexponential factor [1/s], E is the activation energy [J/mol], R is the ideal gas constant [J/molK], and T is the reactant temperature [K]. Since the reactant is not at uniform temperature, the reaction rate is determined from a weighted average of the reaction rates in each heat transport zone. The conversion is then

x˘ ) -kx A time-temperature profile for a simulated 20-µm diameter particle from the CFD simulations for the LEFR with the temperature controllers set at 500 °C was used. The model steps through discrete time intervals to simulate the particle dynamics as they drop through the LEFR. The constants used in the model are listed in Table 1. The parameters in Table 1 are for cellulose, with the exception of the thermal conductivity. The thermal conductivity selected is a low-end value that is typical of thermal conductivity for a wood. This is a conservative estimate for cellulose pyrolysis, as the thermal conductivity of cellulose is thought to be higher.32,33 The heat of reaction is attributed to the centermost region of the particle, as a worst case assumption. The rate constants are based on the heated grid fast cellulose pyrolysis data of Lewellen et al.34 Gas properties are for helium from standard reference material, and are determined by a simple polynomial fit of tabulated values for those properties that have a functional temperature dependence.

Modeling Results Preliminary CFD models of the LEFR included simulations of the gas flow in the counter-flow (upward) region of the reactor. The model results indicated that the gas in the preheat region rapidly assumed the estimated wall temperatures in most regions of flow, and for a variety of flow rates. It was unnecessary, therefore, to include simulations of the counter-flow region in subsequent CFD models of the LEFR. It also means that an accurate measure of the inner wall temperatures is critical to calculating the gas temperatures in the LEFR. The temperature of the water-cooled quartz feeder tube is held relatively constant. The inner reactor wall temperatures, however, are a function of internal heat transfer processes. Wall temperatures are measured as a function of height with a thermocouple that is inserted into a small diameter (0.1-cm) quartz tube that is attached to the inner wall of the LEFR. The location of the water-cooled probe has been found to significantly (32) Curtis, L. J.; Miller, D. J. Ind. Eng. Chem. Res. 1988, 27, 17751783. (33) Di Blasi, C. Biomass Bioenergy 1994, 7, 87-98. (34) Lewellen, P. C.; Peters, W. A.; Howard, J. B. 16th Symp. (Int.) Combust. 1977, 1471-1480.

Figure 2. Predicted and measured centerline gas temperatures in the LEFR.

impact the wall temperature profiles. Temperatures near the center of the furnace also tend to be higher than at the extremities. Wall temperature measurements are used as thermal boundary conditions in the CFD modeling. Gas temperatures have been measured by inserting a thermocouple up the centerline of the reactor. Centerline gas temperature measurements have been compared to CFD predictions of the centerline gas temperature by using the measured wall temperature profile as an input parameter. Figure 2 illustrates the good agreement between the predicted and measured centerline gas temperatures. Centerline measurements are difficult to make, as the positioning of the thermocouple along the centerline is difficult to maintain precisely. This is likely the cause of the slight disagreement in the region of highest temperature gradient nearest to the water-cooled quartz feeder tube between 15 and 20 cm height. Through repetition, it has been found that the wall temperature can be accurately measured to (2 degrees. Most of this error can be attributed to the cycling of the temperature controllers and inaccuracies in the positioning of the thermocouple. Radiative correction of temperature measurements is not performed, as the gas temperature is close to that of the surrounding walls. Initially, comprehensive wall temperature measurements were taken. There is a near-linear relationship between the wall temperatures and the temperature setting on the controllers, so subsequent profiles were obtained by interpolating across detailed measurements taken at the temperature extremes. Temperature measurements in the horizontal MBMS sampling tube have also been used for boundary conditions; however, analysis of the CFD model predictions indicated that very little reaction takes place at the short residence times and moderate temperatures characteristic of this region of the reactor. The boundary condition on this wall region was subsequently simplified to be the same as the boundary condition imposed on the vertical wall region. It is desirable for all particles to have a similar history at a specified reactor condition. To understand the particle trajectories, the particle modeling capabilities in the CFD package were used to analyze the expected particle trajectories. Simulated trajectories of nonreacting 50-µm or smaller particles for established gas flow rates show that for a wide variety of starting positions within the feeding tube, the particles will all follow the gas stream-lines and exit the MBMS sam-


pling tube without colliding with any surface. Between about 50 µm and 120 µm there is a transition from most of the particles exiting without colliding with a surface to most of the particles colliding with a reactor surface, or simply dropping to the bottom of the reactor. As particles react, they decrease in mass and size, and are expected to assume the flow characteristics similar to those of smaller particles. The results of these simulations compare well to experimental observations with various grades of Avicel cellulose. Particles have been fed through the reactor under cold-flow conditions. Avicel PH-105 (20 µm mean) and Avicel PH-113 (50 µm mean) particles appear to navigate the 90° turn and exit with the gas flow, leaving no deposits on the walls. Avicel PH-112 (90 µm mean) particles mostly drop to the bottom of the reactor. Another issue in this regard is whether the particles undergo similar time-temperature histories. Although the bulk of the particles should flow in a relatively narrow region near the centerline of the reactor, not all will. Particles released horizontally further away from the sample tube exit are expected to take longer to exit the system. Representative release positions for particles have been chosen and decomposing particles released from those positions have been modeled. Resulting variation in predicted decomposition is used as an indicator of the error induced by the variation in starting position. It is assumed that particles are evenly dispersed throughout the gas in the feeding tube, and a Newtonian flow profile is assumed for the gas. The following equation is a Newtonian flow profile:

(

vy(r) ) vo 1 -

r2 R2

Figure 3. An illustration of the geometric sections assumed in the study of particle history variation due to starting location.

)

where vy is the y (vertical) velocity, vo is the velocity at the centerline, r is the radial position, and R is the radius of the cylindrical region. Integrating the flow profile over a differential area gives the volumetric flow rate in that area:

V˙ )


Figure 4. Predicted conversion for particles released in the feeding tube from various radial positions.

∫ba vy2πr dr

This expression is used to solve for the appropriate limits a and b that describe a given portion of the bulk flow. The resulting expression has been solved to determine particle starting positions that represent the average initial bulk flow rate for particles in three equalflow annular regions. Each annular region has been divided into 4 separate sections. Figure 3 illustrates the geometry of the sections used in this analysis. Figure 4 shows the results of a test based on a 460 °C temperature profile, kinetic rate constants from Table 1, 0.15 slm primary He flow, and 2.0 slm secondary He flow. This figure is representative of results achieved for similar experiments at different flow rates. Conversion is lower for particles released closer to the centerline of the system partly because of the higher velocity in that region and partly because the gas temperature lag is slightly higher close to the centerline. Particles with starting positions farther from the sample tube are expected to have higher conversion, as they must traverse a longer path to arrive at the exit. This trend is observed in the results in Figure 4. The

Figure 5. Standard deviation of predicted particle conversion for bulk flow representative sets (as illustrated in Figure 4) at various gas flow rate settings.

weighted average conversion trend line crosses the trend of the x-line around -0.42 r/R. There is a strong correlation between the gas flow rates and the resulting simulated conversion in this study. Figure 5 shows a plot of the standard deviation of the predicted conversion versus the ratio of the primary to secondary gas flow rates. Secondary flow rates were varied between 1.5 slm and 3.0 slm, and primary flow rates were between 0.01 and 0.25 slm. Lower primary flow rates

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Figure 6. Predicted mass conversion of a cellulose or biomass particle in the LEFR as a function of particle diameter.

relative to the secondary gas flow results in reduced variability. For gas flow rates of interest, this plot shows that there is a sharp increase in variability for primary to secondary flow ratios above about 0.08. Results from the detailed particle model that includes heat transport effects are illustrated in Figure 6. Figure 6 shows the predicted mass conversion of biomass/ cellulose in helium at 500 °C as a function of particle diameter, as well as an estimate of the change in conversion with respect to the particle size change (dx/ dDp) as a function of the particle diameter. As the particle size increases, the predicted conversion drops. Dp ) 0 µm is the ideal case with no heat transfer effects. The degree to which results vary from the ideal case indicates the degree to which heat transfer influences the reaction dynamics. Values for dx/dDp for small particle diameters are fairly linear with respect to the particle diameter, and theoretically go to zero at Dp ) 0 µm. This allows results to be interpolated to Dp ) 0 µm because the model did not converge for particle diameters less than about 15 µm. Decreasing the step size can help circumvent this problem. With 12 000 steps, the model would still diverge for small particles. This is believed to be due to numeric precision issues from small energy values and cubing the particle diameter in meters. Since dx/dDp is linear in that regime, results for 10 µm particles were interpolated based on the observed trend. On the basis of the model, predicted conversion for particles 20 µm and smaller can be expected to be within about 0.8% of the ideal (isothermal) values. At 50 µm, the model predicts an error of 3.3%, while at 100 µm, a 12.0% error is predicted due to particle size/heat transfer effects. For a 50-µm particle, the peak difference between the particle center temperature and the gas temperature in the reaction regime was 12 °C. In the rapid heat-up region, the peak temperature difference was about twice that, but since the particle does not react in that regime, it does not impact the kinetics. There is uncertainty as to the reaction rate constants appropriate for modeling cellulose and biomass. Other rate constants have been tested using this model at different temperature profiles, with similar results. Experimental Results As a final validation of the modeling and to complete the characterization of the LEFR, the thermal decom-

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Figure 7. Predicted time-temperature profiles for particles in the LEFR at various temperature controller settings.

Figure 8. Mass spectra of allyl ethyl ether from the quadrupole mass spectrometer and the time-of-flight mass spectrometer with the temperature controllers set at 500 °C.

position of allyl ethyl ether was monitored with the MBMS. Detailed time-temperature profiles have been generated from CFD and are plotted in Figure 7. The curves represent predictions of the time-temperature profile of a reactant with the temperature controllers set between 380 °C and 620 °C, with an increment of 20 °C. Future results from the LEFR reaction system are plotted versus “controller temperature”, which should not be confused with an isothermal reaction temperature. Reactants in the experimental system are expected to undergo a time-temperature profile similar to that illustrated in Figure 7. Time-temperature histories vary as a function of the wall temperatures, the height of the feeding tube, and the gas flow rates. The wall temperatures vary linearly with the temperature setting on the temperature controllers, and also vary slightly in response to varying gas flow rates. Wall temperature measurements that correspond to the predictions in Figure 7 are found in the Appendix. The time temperature profiles in Figure 7 correspond to the data and operating conditions from Figure 9B. Figure 8 shows sample mass spectra from the timeof-flight mass spectrometer and from the quadrupole mass spectrometer. Both correspond to typical spectra


recorded with the temperature controllers set at 500 °C. The quadrupole mass spectrum contains more peaks than the time-of-flight mass spectrum as a result of using higher ionization energy in the quadrupole system. Mass peaks at m/z ) 42 and 44 in the time-of-flight mass spectrum are assigned to the thermal decomposition products propylene and acetaldehyde, respectively. The peak at m/z ) 86 is assigned to the parent allyl ethyl ether ion. The mass peak at m/z ) 58 scales with the parent peak and is assigned to a parent ionization fragment ion. A corresponding peak at the mass difference of m/z ) 28 might be expected, but the most probable species (CO, C2H4) have ionization potentials at or above the 10.5 eV ionization source used in this instrument, and are therefore not ionized and detected. The m/z ) 58 peak is unusually broad compared to the other mass peaks, ranging from m/z ) 57.9 to about m/z ) 58.8. A curious feature of this mass spectrum is the broad, unidentified, and unexplained signal labeled with a “?” between m/z ) 50 and 56. This signal scales with the change in intensity of the m/z ) 58 and 86 peaks and therefore is thought to be related to the parent or ionization fragment ion. This broad feature is also not observed in the quadrupole mass spectrum shown in Figure 8, nor is it observed in mass spectra from the time-of-flight run in linear mode. It is probable that this broad feature in the time-of-flight mass spectrum is the result of one of the parent ions undergoing metastable decomposition in the drift region or in the reflectron.35 Varying ionization cross-sections and detection efficiencies of the various molecules may be factors that contribute to the complication of the results. There is evidence of this in the observation that the intensity of the mass peak at m/z ) 44 is typically 15% lower than that of m/z ) 42. These two reaction products exist in equal concentration. Since it is not clear how to quantitatively represent the reactants and products from the mass spectra of allyl ethyl ether, three assumptions are made. Scheme 1 assumes that the integral of the mass peak of m/z ) 42 represents the products, and m/z ) 86 represents the reactants. Scheme 2 is similar, except it includes the integral of the broad peak at m/z ) 58 along with the m/z ) 86 peak as the reactant. Scheme 3 includes m/z ) 86, 58, and the integral of the broad signal found in the mid-to-low m/z ) 50’s region. Data reduction for the quadrupole data was difficult because of extensive fragmentation of the reactant and products. As can be seen from the quadrupole mass spectrum in Figure 8, the product slate is much more complex. Peaks at m/z ) 42 and 44 exist as ionization fragments from the parent at low temperature settings. To de-convolute the data, principal component analysis of the mass spectra was performed for spectra taken at a variety of temperatures. Two principal factors were found, which correspond to the allyl ethyl ether reactant and products. The factor score for the products is expected to correlate well to the predicted kinetic conversion. Despite the difficulties with extracting quantitative information from the mass spectra using the data reduction techniques described, the measured values for reaction conversion compare well with the predicted (35) Neusser, H. J.; Krause, H. Int. J. Mass Spectrom. Ion Processes 1994, 131, 211-232.


Figure 9. A comparison between predicted and measured thermal conversion of allyl ethyl ether measured in the quadrupole and time-of-flight MBMS systems. The 3 schemes involve different assumptions as to the mass peaks involved in the reactant signal: Scheme 1, 86; Scheme 2, 86+58; Scheme 3, 86+58+mid-50’s.

conversion from the CFD generated time-temperature profiles and the measured reaction rate. The comparison is illustrated in Figure 9. Predictions are different for each case because in Figure 9A, the wall temperature profiles were different than for 9B. Figure 9B represents data with the primary gas flow set at 2 alm, and a secondary gas flow rate of 80 accm, whereas Figure 9A was with 2 alm and 10 accm. Repeated tests with the feeding tube located both at the same and different positions and for different gas flow rates give results of similar accuracy, but are omitted for brevity. Results for Scheme 2 (described above) in Figure 9B compare best to the predictions. Predictions are based on accepted values of reaction kinetics from the literature (log[A] )11.84 log[s-1], Ea ) 182.4 kJ/mol), which have been generated or tested in the temperature range of 286-500 °C.23,36 Discussion The modeling and experiments that have been performed to aid in the design and characterization of the LEFR have provided insight into the potential for this reaction system. Flow dynamics predictions and measurements indicate that 50 µm and smaller particles will adequately follow the gas stream-lines. By ensuring that the particles follow the gas stream-lines, predictions indicate that the error caused by the initial spread in particle position within the inlet feeding tube should be (36) Besseris, G. J.; Kiefer, J. H.; Zhang, Q.; Walker, J. A.; Tsang, W. Int. J. Chem. Kinet. 1995, 27, 691-701.

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small. The chance for error induced by particle collisions with reactor walls will also be minimized for smaller particles. Heat transport effects are minimized with smaller particles as well. While this result is somewhat intuitive, the modeling quantifies the advantage expected from maintaining smaller particle sizes. The results of the particle model are expected to give a “worst case” estimate of the impact of the heat transport limitations since the assumed solid thermal conductivity was low, radiation was ignored, the reaction temperature was under-estimated (by assuming the reaction occurred at the center zone), and only spherical geometries were evaluated. Uncertainties in the model constants make it difficult to draw confident conclusions, but it appears safe to conclude that pyrolysis control for particles sized 30 µm and below will be principally kinetic. Similar control for particles sized 50 µm and below can be expected based on the predictions in Figure 6. Implicit in this result is that practical systems that consume larger particles at similar heating rates may not do so under kinetic control. These guidelines were followed in the cellulose pyrolysis measurements described in the second paper on this experimental system. The model presented for the pyrolysis of biomass is capable of describing what are thought to be the most important physical phenomena for particles introduced into the LEFR. The biomass particle modeling was done to ensure that the reactor and reactant were capable of being used for kinetic measurements. A model for biomass pyrolysis under more practical industrial conditions may require more detail. This could involve larger particles with more internal zones, aspherical geometries, and a more detailed particle diffusion model. Accounting for changing density, porosity, and thermal conductivity during pyrolysis may also become important for accurate models at conditions commonly found in larger scale systems. For the experimental conditions established in the LEFR, the uncertainty in the selected physical constants used as input in the particle model likely cause greater uncertainty in the predicted results than the physical model for the size of particles being studied. Worst-case physical constants and assumptions were employed when there were doubts as to the accuracy. This reduces the chance that inaccurate assumptions will lead to an incorrect positive conclusion. Accurate physical constant data would improve the model and allow for more rigorous sensitivity analysis of the model assumptions. The close agreement between the measured and predicted centerline gas temperatures and conversion for the thermal dissociation of allyl ethyl ether helps validate the techniques presented in this paper. Fluid dynamics modeling provides a means to accurately and quantitatively compare results from the experimental system and analytical models. These results also give an indication of the expected accuracy of the techniques when applied to solid particle reactants. There are several sources of error, and the extent to which these sources contribute to the error in the overall system is difficult to quantify. Uncertainties in the data reduction technique for both instruments make comparison of the data to the model difficult. The mass spectrometers are semiquantitative, and it is very

Brown et al.

difficult to infer concentration from the resulting signals. Furthermore, what appears to be a metastable decomposition of the parent ions complicates the interpretation of the time-of-flight data. Despite these problems, it appears that the measurements and predictions of the reaction of allyl ethyl ether match well. Temperature measurements appear to be quite accurate, and are not expected to be a major source of error. Repeatedly taking temperature measurements is important, though, as the reactor can exhibit different profiles from day to day and as reaction conditions are varied. Some variation in furnace temperatures is thought to occur due to changes in the furnace insulation packing and to a lesser extent on small changes in the position of the reactor within the furnace. The error from furnace packing can be as high as (10 °C. There is also error with regard to the model. The geometry of the reactor is symmetric in the z direction. Corresponding predictions for +z and -z should ideally be the same but are not, as is seen in Figure 4. This slight disagreement could be a result of asymmetries in the grid, numeric precision, or numerical diffusion. Small variations in particle properties at the start of the particle trace can magnify as the particle trace progresses. Although the source of this error is unknown, the impact of it has been thus far observed as relatively small. Subtle differences between the numeric model and the actual geometry may also contribute to the general error of these techniques. Conclusions A laminar entrained flow reactor with peak heating rates of 104 K/s was designed, fabricated, and characterized to determine accurate chemical kinetics of biomass thermal conversion processes. The extent of biomass pyrolysis is measured by collecting residual solid and condensable products as a function of particle residence time and temperature. A molecular beam sampling mass spectrometer system can also be used in conjunction with the LEFR for real time monitoring of individual gas phase and condensable chemical products as a function of residence time and temperature. Ultimately, this newly designed reactor will be used to determine the rates of evolution of pyrolysis products, gasifier “tars”, and alkali metal vapors. Extensive modeling indicates that kinetically controlled pyrolysis is possible in the LEFR provided that the particles fed into the system are kept small. Particles below about 50 µm in diameter are expected to be sufficiently small. This also ensures that the particles closely follow the gas streamlines so their trajectories are known and collisions with the walls are minimized. Quantitative kinetic rate determination is also possible because CFD is used to adequately simulate reactor conditions and provide an estimate of the time-temperature profile within the reactor. These profiles can be used to compare pyrolysis chemistry observed by an MBMS to existing reaction rates. CFD modeling of the reactor has been important in understanding the performance of the reactor. Combining the experimental measurements with the CFD models has demonstrated that the LEFR should be useful for generating unique and important data on the hightemperature reaction chemistry of biomass and related heterogeneous systems.


Detailed modeling has proved useful in the present application. It would be extremely difficult to make accurate detailed measurements to assess many of the qualities of this LEFR that have been evaluated by analytical modeling. Modeling and measurements were pivotal in identifying the expected errors in the system. The experimental data is reproducible, however, several potential sources of error in the system have been identified, quantified, and reduced where possible. This instrument is expected to be capable of making well characterized and reproducible measurements of pyrolysis chemistry for biomass under practical conditions. Acknowledgment. The authors acknowledge support from the Solar Thermal, Biomass Power, and Hydrogen Technologies Division of the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy. Special thanks go to Richard L. Bain and Thomas A. Milne for programmatic and technical support and guidance.


Appendix Table A.1 Measured Wall Temperatures at Various Heights Above the MBMS Sample Tube at Two Furnace Temperature Settings Figure 9A

Figure 9B

height (cm)

400 °C

600 °C

400 °C

600 °C

0 3 6 9 12 15 17 19 21 23 25 30

314 375 407 422 419 405 381 330 195 161 159 176

510 576 611 627 626 610 581 510 317 276 268 300

337 388 413 421 407 380 346 281 166 149 146 163

522 577 605 610 593 562 516 419 228 210 212 263

EF010083K

Design and Characterization of an Entrained Flow Reactor for the

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