A STRUCTURAL MODEL FOR BIOMASS DEVOLATILIZATION E. Biagini1, M. Falcitelli1, L. Tognotti2,3 1 Consorzio Pisa Ricerche – Divisione Energia Ambiente, Lungarno Mediceo, 40, Pisa – ITALY 2 Dipartimento di Ingegneria Chimica – Università di Pisa, Via Diotisalvi, 2, Pisa – ITALY 3 International Flame Research Fundation, Via Orlando, 5, Livorno – ITALY
ABSTRACT: The devolatilization of solid fuels is of crucial importance for all thermo-chemical processes and is the basic step in gasification and combustion models. Here a structural model is developed to provide an Advanced tool for Biomass and Coal Devolatilization (ABCD model), even in blend. The main features are founded on the original approach of the CPD (Chemical Percolation Devolatilization) model by Fletcher [1]. The ABCD model extends the approach also to biomass fuels. Further improvements of the ABCD model are: (i) a population balance between nmers in liquid metaplast; (ii) elemental balance closure with the speciation of light gases, hetero-species and tar composition; (iii) introduction of secondary reactions of tar-cracking and crosslinking. The ABCD model results agree with a selection of experimental data (from homemade and literature works) on different biomasses. The results reported in this paper encouraged IFRF in continuing the experimental campaign for the validation of the model by extending the Solid Fuel DataBase SFDB. The inclusion of ABCD in comprehensive codes (e.g., Reactor Network Analysis, RNA [2]) and process models is valuable because it gives detailed distribution of pyrolysis products in a wide range of conditions with a low computational cost. Keywords: Solid fuels, pyrolysis, combustion model
1
INTRODUCTION
The devolatilization is the basic step of combustion and gasification processes and consequently influences the design and operation of industrial systems fed with solid fuels (biomasses, waste materials and coals). Therefore, a devolatilization model for various classes of fuels (coals and biomasses) and different operating conditions is a valuable tool for engineers and also a big challenge for scientists. Many models have been proposed to describe the devolatilization of solid fuels with different levels of complexity and accuracy [3,4]. Some complex models are difficult to extrapolate to other materials and conditions, because they do not account for the structure and chemical composition of the fuel. Recently, general models have been developed [5-8], extending macromolecular network models of coal devolatilization to biomasses. The theory is based on the depolymerization of a mixture of chain macromolecules into fragments that either volatilize into tar, partially decompose into gases, or recombine with the nascent char matrix. Previous investigators developed general devolatilization models such as FG-DVC [5], FLASHCHAIN [6, 7], CPD [1, 8]. The results showed a substantial demonstration of the efficacy of that modeling approach, but also the need of improving the quality of predictions, including further sub-models, e.g., the speciation of the gaseous product yields, the evaluation of ash catalytic effects, the closure of elemental mass balance. The Advanced Biomass and Coal Devolatilization (ABCD) Model proposed in this paper represents the evolution of the CPD model by Fletcher and co-workers, as a result of the efforts for extending it also to biomasses [9]. This issue was addressed also by Sheng and Azevedo [8] with important, but preliminary results. The new elements introduced in the ABCD model concern the following aspects: - the original CPD formulation are improved, revising some aspects of the mathematical equations. A population balance for the liquid n-mers is elaborated, allowing to differentiate the fate of n-
-
mers in the vapour phase (precursors of the tar yield) and those remaining in the liquid metaplast of the particle; new structural and chemical parameters are proposed for cellulose and hemicellulose, while they are revised for lignin and coals; secondary reactions for tar-cracking are introduced; the elemental balance is closed by developing specific procedures for light gas speciation and hetero-compound formation.
The ABCD model is used as a devolatilization submodel in comprehensive codes. It is implemented in RNA (Reactor Network Analysis), which is a zone model for optimizing advanced combustion systems for energy saving and pollution reduction [2]. It is also implemented in process models (as a “user routine” in Aspen Plus®) for the detailed model of different gasifiers to optimize the operating conditions for hydrogen production [10]. Further applications are in progress.
2
MODEL DEVELOPMENT
The original approach of the CPD model is firstly recalled, remarking the most important points recovered in the ABCD model. The improvements of this latter are then described in the subsequent sub-sections. The results of the model and its validation with experimental data on biomasses are discussed in section 3. 2.1 Original approach of the CPD model In the CPD (Chemical Percolation Devolatilization) model the coal is represented as a two-dimensional Bethe lattice of aromatic clusters linked by aliphatic bridges [1]. The CPD model distributes pyrolysis products into char, metaplast (heavy fraction of non-condensable hydrocarbons), tar (medium chain hydrocarbons which are condensable at room temperature) and light gas fractions. Percolation statistics are used to describe the network decomposition. The original version of CPD model is composed of five key elements:
1.
2. 3.
4. 5.
a description of the parent coal based on quantitative 13C NMR measurements of chemical structure; a bridge reaction mechanism with associated kinetics; percolation lattice statistics to determine the relation between bridge breaking and detached fragments (tar precursors); a vapor-liquid equilibrium mechanism to determine the fraction of liquids that vaporize; a cross-linking mechanism for high molecular weight tar precursors to reattach to the char.
Four of the parameters derived from 13C NMR analysis that describe the structure of the parent coal are used directly as input parameters to the CPD model. These include:
remaining metaplast is assumed to be fast with respect to the bridge reactions. Successive versions of the CPD model extended the use of this model. The Genetti correlation [11] allowed the structural parameters of coals to be predicted without direct measurement from 13C NMR analysis. It was based on a dataset of different rank coals and required ultimate and proximate analyses. A correlation was provided to predict the light gas composition [11]. This submodel is based on FTIR data of the pyrolysis of different rank coals as a function of the extent of devolatilization. Model predictions compared well with measured quantities of CO2, CO, H2O, CH4 released during pyrolysis experiments conducted on a variety of coals at slow and high heating rates. An undefined fraction of “other” gas species was calculated by difference, so that the elemental balance was not closed.
Mcl (the average molecular weight per aromatic cluster), M (the average side-chain molecular weight), +1 (the average number of attachments per cluster), p0 (the fraction of intact bridges). This latter parameter is the initial value of p, which is the fraction of intact bridges during the evolution of fragmentation of the infinite reticulum. The fraction of broken bridges (1-p) and the distribution of finite fragments are related by means of percolation statistics. A labile bridge £ may decompose (according to a first order reaction) giving a reactive intermediate £* that follows two competitive reaction paths (see Figure 1): (i) it can break to give two side chains connected to the aromatic clusters; in the mean time the lateral chains of every fragments may detach according to the kinetic rate kg forming light gas (g1); (ii) in the second path the bridges react and become a (stable) char bridge, c, with the release of an associated light gas product, g2.
2.2 Development of the ABCD model The original approach of the CPD model is maintained in the ABCD model. The main improvements of this model are: - the revision of mass conservation equation translating lattice statistics to macro component yields; - the extension of the model to biomass fuels; - the population balance on all the n-mers formed during the evolution of devolatilization; - the elemental balance closure, with the speciation of light gases, heteroatom species and tar composition; - introduction of secondary reactions of tar-cracking and cross-linking. The details for all these points are given in the following sub-sections. In general, the relations of the CPD model were revised, integrated with reliable correlations and procedures. Structural and kinetic parameters were optimized. Different sub-models were developed and linked to the main code. Schemes in Figures 2-4 describes the main blocks and some procedures.
ABCD fuel parameters
Solid fuel characteristic data
(coal, biomass, blend)
OUTPUT Macro-products char tar gas
Chemical Percolation Devolatilization
Thermal history
Figure 1: Kinetic scheme of the labile bridges rupture and evolution in the CPD model.
+ population balance + elemental balance + secondary reactions
Gas Speciation
Figure 2: Scheme of the ABCD model: main blocks. As bridges between neighbouring clusters are cleaved, a certain fraction of the matter becomes detached from the lattice. These detached polymeric clusters are the heavier molecular weight tar precursors that form the metaplast. The metaplast may either vaporize as tar or reattach to the char-forming lattice matrix (crosslinking). The vaporization of generated fragments is described as a flash distillation (modelled with the Raoult’s law), according to empirical correlations [1] between the vapour pressure and the molecular weight. The equilibrium between the evaporating tar and the
BLEND
BIOMASS Ultimate analysis
Chemical composition
xbiomass + ycoal
N Y Data available?
Azevedo [2002] Correlation Hypotheses on cellulose and hemicellulose composition
COAL NMR analysis
Ultimate analysis
N Y Data available?
Cellulose Hemicell. Lignin
Lignin composition from element balance
Genetti [1999] Correlation
ABCD fuel parameters
Mcl: Molecular Weight per cluster Md: Molecular Weight side chain (σ+1): Coordination Number per cluster p0: Fraction of intact bridges
Figure 3: Scheme of the ABCD model: procedure for the evaluation of structural parameters.
the n-mers fragments, on fuel d.a.f. basis, is: OUTPUT Macro-products char tar gas
Hypothesis on tar composition
GAS SPECIATION
Hetero-atoms of parent fuel (N, S, Cl) coal
Material balance
CPD correlation
light gas elemental biomass CHEMKIN composition
CO CO2 CH4 C2H4 H2 H2O HCN, NH3 COS, H2S HCl
Figure 4: Scheme of the ABCD model: procedure for the speciation of light gas.
2.3 Novel Aspects of the Mathematical Formulation Respect to the original mathematical formulation of the CPD Model, the pseudo lattice statistic is kept, but mass balances are revised and population balances for the metaplast fragments are introduced. This allows one to differentiate the fate of light fragments that, once vaporized, may produce light gases via tar-cracking, but no more via the depolimerization route. The same nomenclature of the original CPD model is used, but some new symbols and indexes are introduced. The site is the basic unit of the lattice formed by a central molecule with its lateral branches. At any stage of the depolimerization process the probability to find a number of sites Nsites belonging to fragments n-mers is by definition: Fn ( p)
N sites n - mer Ntotal sites
1 1 if p F ( p) Fn ( p) n 1 1 if p 1
(some lattice is unfragment ed) (all lattice is fragmented )
The bridge dynamic variables (p, c, ) considered in the differential equations of the kinetic scheme are related to the mass of the lattice fragments. The mass of a site can be defined from the mass of the fragment of size n, that according to the original CPD is [11]: mm frag ,n ( p,)n n ma n 1 mb cluster
£ mb p 4 1 p
Defining the mass of the site belonging to the fragments n-mers, the initial condition is:
m frag, n ( p0 ) 1 c0 1 ma 1 mb n p0 n 2
The mass of the sites belonging to the unfragmented lattice (p=1, c=0) is:
msite,unfragm ma mb
msite, n ( p)
m n 1
site, n
( p0 ) Fn ( p0 ) msite,unfragm1 Fn ( p0 ) n 1
Fn ( p)
The mass fractions of the un-fragmented lattice, on fuel d.a.f. basis, is: msite,unfragm1 Fn ( p) n 1 f unfragm( p) m ( p ) F ( p ) m 1 Fn ( p0 ) site, n 0 n 0 site, unfragm n 1 n 1
At this step, if other reactions or phase changes are not considered, except percolation depolymerization, there is no need to introduce more differential equations for the gas yields (as done in the original CPD), because the mass is conserved and the gas formed by the detachment of lateral chains or by the “stabilization” of bridges is given by difference. Actually more reactions are considered and linked to the formation of fragments (as schematized in Figure 5): tar formation via flash evaporation, gas formation via tar-cracking, C-char formation via cross-linking, so that the mass conservation equation encompasses several terms:
The percolation statistics characterize the lattice evolution through the number of bridges which remain intact p (the remaining fraction, 1-p, having been broken): at any time, the number closure equation depends on the percolation threshold 1/ as follows
msite, n ( p0 )
f fin, n ( p)
1 2
Now it is possible to express the relationship between the statistics of the rupture of the bridges with the mass fractions of the n-mers (metaplast). The mass fractions of
* * f gas f tar f C char f unfragm ( p) f fin ,n ( p ) 1 n1
The addiction of more reaction routes makes less straightforward expressing the mass fractions of the nmers fragments. These equations come from the population balances for the n-mers at the actual time and at the time step before. It is worth to underline that the population balances here proposed are not CPU time consuming, as explicit algebraic equations are formulated.
Light gas Tar (vapour) Fragments
Vapour-liquid equilibration
Metaplast (liquid)
Char
Figure 5: Reaction mechanisms in the ABCD Model.
2.4 Structural and kinetic parameters evaluation for biomass fuels The devolatilization of a generic biomass is described as the combination of the behaviour of the three idealized chemical components: cellulose, hemicellulose and lignin. If direct measurement on chemical analysis is not available, a correlation [8] is used to evaluate the chemical composition of the biomass (based on ultimate and proximate analyses). In particular, it allows the cellulose and lignin fraction to be calculated, while the hemicellulose content is obtained by difference (extractives are summed in this fraction, as justified by experimental tests [12]). The following step is the assessment of structural and kinetic parameters and vapour pressure correlation for each chemical component. Data of fast pyrolysis of biomasses, relating macro-product yields and thermal
history, are required in a wide range of operating conditions (temperature, heating rate, residence time and pressure) for a reliable evaluation. As a matter of fact an extensive dataset of uniform data are not available in literature for biomasses nor for chemical components. So a previously developed and validated model, CHL (Cellulose Hemicellulose and Lignin) model [13] on the fast pyrolysis of biomass fuels, was used as a generator of “ideal” experiments for the individual biomass components. Preliminary values of structural and kinetic parameters were initialized, based on theory and literature reviews [8, 14]. The set of the ABCD parameters was tuned with an optimization procedure, which looks for the best matching between ABCD and CHL predictions (see some examples of results in Figure 6). The resulting structural and kinetic “best” parameters are shown in Table 1.
1,0
released mass fraction
Cellulose Gas Yield
CHL CPD
0,8
973 K 0,6
0,4
873 K
0,2
773 K 0,0 0
200
400
600
800 1000 1200 1400 1600
time (ms)
(a) 1,0
released mass fraction
Hemi-cellulose Gas Yield
CHL CPD
0,8
973 K
0,6
0,4
873 K 0,2
773 K 0,0 0
(b)
of levoglucosane. The molecular weight per side chain M was supposed to be small, because it is a mean value expressed on a “per monomer basis” of the mass of the terminal chains and of the lattice amorphous defects. The high value of Mδ obtained by fitting, perhaps does not correspond to a real presence of lateral chain, but reflects the possibility for some fragments of metaplast to decompose before becoming tar vapor. Finally, cellulose pyrolysis produces much lower char when compared with hemicellulose and lignin, so the initial value for the population of char bridges (c0) is set to 0. Hemicellulose is a heteropolymer which contains several sugar monomers (C5 and C6). It is a more branched and more fragmented polymer than cellulose. For this reason, a value between 2 and 3 for σ+1 and a value of 0.79 for p0 were obtained. An average molecular weight was assigned for Mcl, calculated by averaging the molecular weights of monomers C5 and C6 (see Table 1). As well as for cellulose, the value of Mδ for hemicellulose was obtained by fitting. Lignin is similar to low rank coals with a threedimensional lattice structure of complex racemic polymer. Its structure is not well defined and depends on the biomass type. One of the main problems when studying lignin is the impossibility of extracting it from the biomass without chemically modifying it. Using 13C NMR analysis and theory research, previous investigations [14] proposed that coniferyl, sinapyl and pcoumaryl alcohols are the base clusters. The set of values for the structural parameters recently proposed in the cited reference was adopted in the present work without modifications, except for little differences in the kinetic constants produced by the optimization procedure. In particular, two lignins are assumed for Hard Wood (HW) and Soft Wood (SW), which differ only for the average molecular weight per aromatic cluster (Mcl) and the average side-chain molecular weight (M).
200
400
600
800
1000
time (ms)
Figure 6: Optimization of kinetic parameters: comparison of the ABCD model results and CHL [13] results for gas yield from (a) cellulose pyrolysis and (b) hemicellulose pyrolysis.
Cellulose is the most abundant component of biomasses, is a linear polymer with few branches and its monomer is levoglucosane (C6H10O5), a dehydrated form of glucose. Therefore its fragmentation degree is very low, the value of p0 was assigned approximately at 1 (0.999). The coordination number σ+1 is near to 2, because each monomeric unit is linked through two oxygen bridges. The molecular weight of the cluster unit Mcl is assumed 162 u.m.a, which is the molecular weight
2.4 Secondary reactions The determination of the composition of metaplast and tar is fundamental for the elemental balance closure and the evolution of product pyrolysis. As for biomasses, metaplast and tar released during pyrolysis are considered n-mer fragments whose repetitive units are levoglucosane (from cellulose), xylose (from hemicellulose) and hydroxycinnamyl alcohols (from lignin). A virtual component (C11H13O3) is assumed for metaplast and tar of lignin by averaging the three main hydroxycinnamyl alcohols (coniferyl, sinapyl and p-coumaryl alcohols [14]) constituting the lignin structure. The n-mers formed can vaporize giving tar according to the actual conditions. The tarr is involved in secondary reactions: cracking and cross-linking. Volatile tar released from the fuel particle can migrate towards the external zones, with a higher temperature. Tar cracking reactions can decompose the heavy compounds in lighter compounds (increasing the light gas yields) homogeneously or heterogeneously (reacting with the formed char and involving catalysis by ash). The model assumes a first order reaction model for tar cracking, with kinetic parameters (see Table 1) derived from optimization procedures and/or adapted from literature [15]. Vice versa, if volatile tar migrates towards colder zones (for instance, the internal pores of the particles) it can condensate on the reactive fuel, polymerize to give heavier species and produce char. In general, cross-
linking mechanisms generate a chemical rearrangement of the metaplast on the solid structure, through the occurrence of stable carbon-carbon bonds. 2.5 Elemental balance closure With the hypothesis described above, the elemental composition can be obtained (See Figure 4) for all macroproducts of pyrolysis, char, tar and gas (this latter by difference). For biomass fuels, the gas species distribution at the local equilibrium state is predicted by minimizing the free-Gibbs energy, by the routine EQUIL [16] (also included in the ChemKin code), which is connected to the ABCD model. Correlations to obtain gas speciation based on experimental data require a set of uniform tests on the pyrolysis of different biomasses. This dataset is not available in literature and will be the object of future work.
The agreement of model results with data from the pyrolysis of coals of the first two dataset was good for the macro-product yields (char, tar and gas), as well as for the gas speciation [20]. Also the evolution of macroproducts as function of the pyrolysis time was in good agreement (this was verified with fast devolatilization tests in the IPFR). As for biomass fuels, the predictions of the ABCD model are compared with the experimental data on flash pyrolysis of biomasses reported by Scott et al. [21] and Zanzi et al. [22]. These datasets were selected because the mass and elemental closures were satisfied within tight tolerances, so the product distribution was complete and self-consistent. Different feed materials were used in these tests, with properties shown in Table 2.
Table II: Ultimate analysis characterization of biomass fuels. Table I: ABCD Model: Structural and kinetic parameters optimized for cellulose, hemicellulose and lignin. Structural Cellulose Parameters 162 MCL 15.96 M σ+1 2.005 0.9998 p0 0.0 c0 α (atm.) 93718 β (g- mole 157.46 K) γ 0.58 Kinetic Parameters Ab (s-1) Eb (cal/mol) σb (cal/mol) ρ (kδ/kc) Ec (cal/mol) Ag (s-1) Eg (cal/mol) σg (cal/mol) Tar cracking Atc (s-1) Etc (cal/mol) Crosslinking Acr (s-1) Ecr (cal/mol)
3
Hemicellulose 173.45 10.28 2.7 0.79 0.267 93718
Lignin HW 207.5 39 3.5 0.71 0.1 87058
Lignin SW 186 34 3.5 0.71 0.1 87058
157.46
299
299
0.58
0.59
0.59
2.14E+15 7.96E+14 54069 2662 3.02 0.0
47636 1945 1.62 0.0
1.19E+08 1.69E+08 26743 879
18467 1802
3.0E+06
1.49E+06
26169
26169
3.00E+15 3.00E+15 65000
65000
2.60E+1 2.60E+15 5 54000 54000 3972 3972 3.9 3.9 0.0 0.0 3.00E+1 3.00E+15 5 6600 6600 4776 4776 1.49E+0 1.49E+06 6 26169 26169 3.00E+1 3.00E+15 5 55680 55680
RESULTS
The model is validated comparing the results of the simulations with different datasets from literature and homemade works: a dataset of coals of different rank, by Tomita [17, 18]; a database of solid fuels, developed by IFRF with experimental tests in the Isothermal Plug Flow Reactor (IPFR) [19]; results on flash pyrolysis of biomasses with literature works.
Fuel Eastern red maple [21] Avicel cellulose [21] Wheat straw [22] Wood birch [22] Olive waste [22] Olive waste [23] Fuel Eastern red maple [21] Avicel cellulose [21] Wheat straw [22] Wood birch [22] Olive waste [22] Olive waste [23]
and
chemical
Ultimate analysis (wt% daf) C H O N 48.5 6.1 44.9 0.5 44.4 6.2 49.4 0.0 45.6 6.5 47.4 0.5 48.6 5.6 45.6 0.2 50.9 6.5 42.1 0.5 nd nd nd nd Chemical analysis (wt% daf) Cell. Lign. Hemic. 43.5 23.2 23.3 100 0 0 43.6 21.7 34.7 42.6 21.0 36.4 44.8 28.0 27.2 23.0 46.7 30.3
Scott el a. [21] carried out tests using two fluidized bed rectors with declared heating rate of 14000 K/s and gas residence time of 0.5 s. The effective temperature of particles was calculated with the CHL code [13] because no data is available from the original work. The calculated solid residence time ranged from 0.5 to 10 s, from high (1070 K) to low (670 K) temperatures, respectively. The comparison between the experimental macroproduct yields and the ABCD model results are shown in Figures 7 and 8, for maple and cellulose, respectively. The char yield decreases with the temperature, while gas yield increases and tar yield shows a maximum. The agreement is good for both biomasses in all the temperature range. Some discrepancy at the highest temperature (1070 K in the case of maple) can be ascribed to the effective residence time of gas in the reactor. The uncertainty in the evaluation of this parameter can strongly influence the yield of tar-cracking reactions at these temperatures. The results of the light gas speciation procedure for biomasses are also reported (see Figure 7 for cellulose and Figure 8 for Maple). Yields fractions of CO, CO2, CH4 and light hydrocarbons are the composition that minimize the Gibbs Free Energy at the final temperature of pyrolysis for tests carried out up to 900 K. As for tests carried out at higher temperatures (1000-1100 K), the
standardized procedures for generating uniform data and allow an extended validation for the pyrolysis of biomasses.
120 tar gas char
Yields [wt % daf]
100 80 60 40 20 0
500
600
700
800
900
800
900
Temperature [°C]
(a) 70 CO CO2 C2H4+C2H2 CH4
60
Yields [wt % daf]
50 40 30 20 10 0
500
600
700
Temperature [°C] (b) Figure 7: Pyrolysis yields for cellulose. (a): macroproducts; (b): light gas species. Symbols denote experimental results from [21], lines denote ABCD predictions.
100 tar gas char
Yields [wt % daf]
80
60
40
20
0 400
500
600
700
800
700
800
Temperature [°C]
(a) 50 CO CO2 C2H4+C2H2 CH4
40
Yields [wt % daf]
best agreement is attained at a restricted equilibrium temperature of 900 K. This implies that the effective temperature of gas released may be lower than that of pyrolysis (this depends on the configuration of the fluidized bed used, the temperature of the freeboard being generally different from that of the bed) or that the chemical equilibrium in the gas phase is not effectively achieved and thus a lower equilibrium temperature may better represent the actual kinetic limitations. As a matter of fact, the effective temperature and residence time of gas in the reactor should be known with accuracy because the operating parameters influence strongly the yields of pyrolysis products. The fast pyrolysis of some agricultural residues performed by Zanzi et al. [22] gave another significant datasets of experimental results. They tested wheat straw, wood birch and olive waste (see composition in Table 2) in a free-fall reactor under high temperatures (800 and 10000 °C). This aspect is important because literature works on fast pyrolysis of biomasses are generally focused in the range 400-800 °C (as in the previous dataset) due to the bio-oil production interest. Data on higher temperatures are less common but indeed important for gasification and combustion processes in which the devolatilization step is thought to occur at high temperatures. The heating rate in the tests is claimed to be 500 °C/s, the particle residence time between 1 and 2 s. Unfortunately some uncertainties are present in the balance closure of the system. At 800°C a mass loss of 10-18% is reported, a lower value (3-10%) at 1000°C. The authors imputed this loss to an incomplete recovery of tar and gas products, while they are more confident in the char yield. Therefore in the graphs shown in Figure 9, the experimental data of gas yield are reported with an error bar varying from the gas yield reported by the authors (symbol) and the sum of that with the mass loss (end of bar). The agreement with ABCD model results (compared in the same Figure 9) is good at both temperatures for all materials but for olive residues. We used the data for the smaller particles for which the authors did not report the chemical composition. As a matter of fact another work is used for the chemical composition of olive residues [23]. The agreement is better this way. A bigger uncertainty is for the comparison of light gas yields and this is due to the previously mentioned mass loss for volatile products and also to the unknown thermal history inside the reactor. The temperature profile is not isothermal in most of these reactors [24]. So a sensitive comparison can be made by varying the restricted equilibrium temperature of the model. The experimental data and the ABCD model results are compared in Figure 10. Two different sets of model results are shown for two restricted temperatures, in all cases lower than the nominal reactor temperature. As can be observed, only in the case of 1000°C the agreement is acceptable, while at 800°C the experimental data can not be predicted. This can be due to the experimental uncertainties (as mentioned above) but also to the simplified modeling approach to the gas speciation. The experimental conditions are far from equilibrium (due to the low residence time and the variable temperature) so a more detailed modeling approach should be conceived. This requires a better understanding of the gas fluiddynamics and the homogeneous reactions. A dedicated experimental campaign will be programmed with
30
20
10
0 400
500
600
Temperature [°C] (b) Figure 8: Pyrolysis yields for Eastern Red Maple. (a): macro-products; (b): light gas species. Symbols denote experimental results from [21], lines denote ABCD predictions.
product yield
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
TR = 800 °C gas-exp gas-mod char-exp char-mod
product yield
straw
wood
included. The predictions were compared with a selection of experimental data from literature and homemade works, showing a good agreement as for the pyrolysis product yields of different solid fuels, reactor temperatures, residence times. The future work will be devoted towards: 1) a more extended validation of the model to refine the predictions of the minor light gas species with a dedicated experimental campaign (programmed by IFRF with standardized procedures for providing detailed information on the thermal history, with dedicated CFD-aided experimentation, the experimental error and the fuel properties, e.g., size, density, chemical analysis);
olive
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0
TR = 1000 °C gas-exp gas-mod char-exp char-mod
straw
wood
2) the integration of ABCD in more complex kinetic schemes of secondary reactions for tar cracking, soot formation and char annealing;
olive
Figure 9: Pyrolysis yields from different materials at two reactor temperatures: comparison of experimental data [22] and ABCD results.
5
Gas composition (%vol)
0.8 Wood
0.7 0.6 0.5
exp 800°C
0.4
mod 700°C
0.3
mod 650°C
0.2 0.1
H2
CH4
CO2
CO
H2
CH4
CO2
CO
Gas composition (%vol)
0.8 Wood
0.7
Wheat straw
0.6 0.5
exp 1000°C
0.4
mod 800°C
0.3
mod 750°C
0.2 0.1 0 H2
CH4
CO2
REFERENCES
Wheat straw
0
CO
H2
CH4
CO2
CO
Figure 10: Gas light speciation from the pyrolysis of different materials at two reactor temperatures: comparison of experimental data [22] and ABCD results.
4
3) the assessment of the devolatilization predictions for solid fuels in gasification environment at elevated pressure and in oxy-firing.
CONCLUSION
An upgraded version of a network devolatilization model, the Advanced Biomass and Coal Devolatilization (ABCD) model, for the rapid pyrolysis of coals and biomass feedstocks was developed. Given the proximate and ultimate analyses and/or the chemical analysis (in terms of cellulose, hemicellulose and lignin), the thermal history and pressure, the ABCD model predicts the complete distribution of devolatilization products from a wide set of coals and biomasses, including the yields of tar and all major gas species, the elemental compositions of tars and chars, and the molecular weight distribution of tar. The chemical, structural and kinetic parameters for each component (cellulose, hemicellulose and lignin) were developed based on theory literature review and curve fitting. Secondary reactions of tar-cracking and predicting methods for the gas speciation were also
[1] Fletcher T. H., A. R. Kerstein, R. J. Pugmire, D. M. Grant. Energy and Fuels 6 (1992) pag. 414. [2] Falcitelli M., Pasini S., Rossi N.,Tognotti L. CFD+reactor network analysis: an integrated methodology for the modeling and optimisation of industrial systems for energy saving and pollution reduction. Applied Thermal Engineering 22 (2002) pag. 971. [3] Di Blasi C. Modeling and Simulation of Combustion Processes of Charring and Non-Charring Solid Fuels. Progress in Energy and Combustion Science. 19 (1993) pag. 71. [4] Di Blasi C. Modeling chemical and physical processes of wood and biomass pyrolysis; Progress in Energy and Combustion Science. 34 (2008) pag. 47. [5] Chen, Y., Charpenay, S., Jensen, A., Wojtowicz, M.A. and Serio, M.A. Modelling of Biomass Pyrolysis Kinetics. 27th International Symposium on Combustion. The Combustion Institute (1998) pag. 1327. [6] Niksa, S. Predicting the Devolatilization Behavior of Any Coal from Its Ultimate Analysis. Combustion and Flame 100 (1995) pag. 384. [7] Niksa, S. Predicting the rapid devolatization of diverse forms of biomass with bio-FLASHCHAIN, Proc. 28th Int. Symp. on Combustion, Edinburgh, UK (2000) pag. 2727. [8] Sheng C., Azevedo J.L.T. Modeling Biomass Devolatilization Using the Chemical Percolation Devolatilization Model For the Main Components. Proceedings of the Combustion Institute, 29 (2002) pag. 407. [9] Biagini E., Falcitelli M., Tognotti L. 29° Meeting on Combustion – Italian Section of the Combustion Institute. Pisa (Italy) 14-17 June, 2006. [10] Biagini E., Bardi A., Pannocchia G., Tognotti L. Development of an entrained flow gasifier model for process optimization study. Ind.Eng.Chem.Res. 48 (2009) pag. 9028.
[11] Genetti, D. B., An Advanced Model of Coal Devolatilization Based on Chemical Structure, M.S. Thesis, Chemical Engineering Department, Brigham Young University, April, 1999. Available on line at http://www.et.byu.edu/~tom/Papers/Genetti_Thesis. pdf. [12] Raveendran K., Ganesh A., Khilar K.C. Pyrolysis characteristics of biomass and biomass components. Fuel 75 (1996) pag. 987. [13] Biagini E. Energy and material recovery by thermal treatments of biomasses and wastes (co-combustion, pyrolysis and gasification) PhD Thesis in Chemical Engineering – University of Pisa – Italy, 2003. [14] Pond, H. R., T. H. Fletcher, and L. L. Baxter, Prediction of Tar and Light Gas During Pyrolysis of Black Liquor and Biomass. 3rd Annual Joint Meeting of the U.S. Sections of the Combustion Institute, Chicago, IL March 16-19, 2003. [15] Rath J. Staudinger G. Cracking reactions of tar from pyrolysis of spruce wood. Fuel 80 (2001) pag.1379. [16] A.E. Lutz, F.M. Rupley, R.J. Kee, W.C. Reynolds, EQUIL: a program for computing chemical equilibria, Technical report, Sandia National Laboratories, 1996. [17] Chun Xu W., Tomita A. Effect of coal type on the flash pyrolysis of various coals. Fuel 66 (1987) pag. 627. [18] Chun Xu W., Tomita A. Effect of temperature on the flash pyrolysis of various coals. Fuel 66 (1987) pag. 632. [19] Hercog J., Tognotti L. Realisation of IFRF Solid Fuel DataBase. IFRF Doc No. E36/y02, 2008. [20] Falcitelli M., Biagini E., Tognotti L. Development of the advanced biomass and coal devolatilization (ABCD) model. 10th Conference on Energy for a Clean Environment, Lisbon 7-10 July 2009 [21] Scott D.S., Piskorz J., Bergougnou M.A., Graham R., Overend R.P. Ind. Eng. Chem. Res. 27 (1988) pag. 8. [22] Zanzi R., Sjostrom K., Bjornbom E. Rapid pyrolysis of agricultural residues at high temperature. Biomass and Bioenergy 23 (2002) pag. 357. [23] Koufopanos C.A., Maschio G. Kinetic modelling of the pyrolysis of biomass and biomass Components. The Canadian Journal of Chemical Engineering 67 (1989) pag. 75. [24] Simone M., Biagini E., Galletti C., Tognotti L. Evaluation of global biomass devolatilization kinetics in a drop tube reactor with CFD aided experiments. Fuel 88 (2009) pag. 1818.
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