CFD simulation and experimental validation of co-combustion of chicken litter and MBM with pulverized coal in a flow reactor

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w w w. e l s e v i e r. c o m / l o c a t e / f u p r o c

CFD simulation and experimental validation of co-combustion of chicken litter and MBM with pulverized coal in a flow reactor J.M. Heikkinen 1 , B.C.H. Venneker 2 , G. di Nola 3 , W. de Jong⁎, H. Spliethoff 4 Energy Technology section, Delft University of Technology, Leeghwaterstraat 44, NL-2628 CA Delft, The Netherlands

AR TIC LE I N FO

ABS TR ACT

Article history:

The influence of co-combustion of solid biomass fuels with pulverized coal on burnout and

Received 19 December 2007

CO emissions was studied using a flow reactor. The thermal input on a fuel feeding basis of

Received in revised form

the test rig was approximately 7 kW. Accompanied with the measurements, a reactor model

20 February 2008

using the CFD code AIOLOS was set up and first applied for two pure coal flames (with and

Accepted 21 February 2008

without air staging). Reasonable agreement between measurements and simulations was found. An exception was the prediction of the CO concentration under sub-stoichiometric conditions (primary zone). As model input for the volatile matter release, the HTVM (high

Keywords:

temperature volatile matter as defined by IFRF [IFRF, www.handbook.ifrf.net/handbook/

Co-combustion

glossary.html. [1]]) was used. Furthermore, a relatively slow CO oxidation rate obtained from

Coal

the literature and the ERE (Extended Resistance Equation) model for char combustion were

Meat and bone meal

selected. Furthermore, the model was used for simulating co-firing of coal with chicken

Chicken litter

litter (CL) and meat and bone meal (MBM). The conditions applied are relevant for future co-

Burnout

firing practice with high thermal shares of secondary fuels (larger than 20%). The major flue

CFD

gas concentrations were quite well described, however, CO emission predictions were only qualitatively following the measured trends when O2 is available and severely underpredicted under substoichiometric conditions. However, on an engineering level of accuracy, and concerning burnout, this work shows that co-combustion of the fuels can reasonably well be described with coal combustion sub-models. © 2008 Elsevier B.V. All rights reserved.

1.

Introduction

During the last decades, pressure to substitute fossil energy sources with renewables has increased. Industrialized countries have signed an international agreement under the Kyoto protocol to reduce the CO2 emissions. Co-firing renewable fuels with fossil fuels is a promising alternative to increase their share in energy supply. Many secondary fuels are nowadays co-fired in power plants, including components like clean biomass, agricultural

residues, animal and slaughter residues and non-renewable wastes. The fundamental advantage of co-firing is that comparatively high efficiencies in large-scale coal fired plants are allowed to be practically maintained with the addition of a minor share of secondary fuels with steam conditions that are more severe than when these would be fired in stand-alone units. Co-firing not only increases the share of renewable energy and reduces the CO2 emissions but also contributes to the tightening directives to enhance recycling and minimize land filling. Furthermore, it is considered as an economical

⁎ Corresponding author. Fax: +31 15 2782640. E-mail address: [email protected] (W. de Jong). 1 Present address: Numerola Oy, P.O. Box 126, FI - 40101 Jyväskylä, Finland. 2 Present address: Stork Thermeq, Langelermaatweg 12, NL - 7553 JD Hengelo, The Netherlands. 3 Present address: Shell, P.O. Box 38000, NL 1030 BN Amsterdam, The Netherlands. 4 Present address: TU Munich, Lehrstuhl für thermische Kraftanlagen, D-85748, Garching, Germany. 0378-3820/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.fuproc.2008.02.004

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option to increase the share of biomass in energy production within a short term as it can be implemented in existing plants without major investments. Co-combusting secondary fuels in existing pulverized coal fired utilities can influence the plant's performance. Due to different physical and chemical properties of supplementary fuels compared to coal, a number of processes including milling, feeding, combustion, heat transfer, steam production and flue gas cleaning are affected. These impacts become more significant when supplementary fuel shares are increased. Co-firing has so far been realized at several pulverized coal fired power plants, mainly in Europe. The experiences gained have demonstrated that it is an important technology to enhance renewable energy production. The share of the supple-

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mentary fuels, however, is commonly limited to about 5%th. In Denmark, though, co-firing of 20%th has been practised, see e.g. Hansen et al. [2] and Andersen et al. [3]. The technical barriers that must be overcome for higher co-firing shares include fuel supply and handling, changed combustion conditions, ash quality and emissions. In 2003 in the Netherlands, 6.7 PJ renewable energy was produced by co-firing biomass and waste with coal [4,5]. With the current knowledge on co-combustion it is not possible to predict the impact of single supplementary fuels or mixtures on the plant performance. Plant operators, therefore, carry out large-scale tests and trials to determine the suitability of a secondary fuel. This procedure is expensive and reduces the flexibility to introduce new fuels.

Fig. 1 – Schematic of the electrically heated pulverised fuelcombustion reactor, dimensions in mm.

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CFD simulations of pulverized biomass combustion have been described, both for pure biomass combustion [6,7], as well as for co-firing with coal [8–16], and with gaseous fuels [17,18]. A general assumption in biomass combustion modeling is that many aspects of coal and biomass combustion are common. Submodels for drying, devolatilization, volatile and char combustion are then similar. A number of biomass characteristics (particle size, amount of volatiles, char reactivity, nitrogen content), however, makes it necessary to re-evaluate design studies of burners and boilers for co-firing. Compared to coal properties, biomass properties are more variable. Therefore, in some CFDstudies the effect of varying certain properties is investigated. The effect of particle size was studied by [11,19,7,16], whereas the particle shape was varied in [14,18]. Char reactivity is unknown for many biomasses and the effect of it has been studied by [19,16,20]. Finally, the effect of the moisture content and devolatilization rate was investigated by [11] and [16], respectively. This paper focuses on understanding the combustion behavior of supplementary fuels. Both experiments and CFD modeling are used to study the conversion characteristics of them. The experiments are performed using an electrically heated pulverized fuel combustor, which allows for studying the impact of co-firing on combustion characteristics and emissions. In the furnace heating rates and final temperatures comparable to large-scale PC-boilers can be obtained. First, the experimental and modeling results of coal combustion are presented. The combustion field is characterized by axial and radial concentration profiles of the main gaseous components (O2, CO2, CO) and by solid char samples collected at selected locations. Furthermore, co-combustion of two agricultural wastes, chicken litter (CL) and meat and bone meal (MBM), with pulverized coal is experimentally studied. The data are used to validate combustion sub-models in a CFD code. A high biomass co-firing share of about 20%th is used, considerably higher than practised at Dutch power plants in order to investigate the possible influence of increased biomass shares. The effects of co-firing on flame characteristics, emissions and burnout are evaluated. After model validation, the CFD model can be applied to predict changes in the final burnout when biomass share and particle size are varied.

2.

Experimental

Fig. 1 shows a schematic of the down-fired electrically heated pulverized fuel combustor. The 1.94 m long heated reactor tube is composed of mullite and has an internal diameter of 0.150 m. The tube consists of five modules, which are independently heated with a total heating power of 50 kWel. The walls can be heated to 1550 °C. Fuel is fed from the top using a single screw feeder with a typical thermal input capacity of 7 kW. Separate fuel storage bunkers and screw feeders are present for biomass and coal, avoiding fuel de-mixing during co-firing. The fuels are premixed in a blending chamber, from which they are transported with primary air to the burner. The reactor is equipped with 20 ports (10 on each side in an opposite configuration) for measurement probes and staging air. Gases are sampled using air-cooled probes and the quench rate of these was measured to be about 10,000-15,000 °C/s. Sampled gas is led through a glass-fibre filter to collect char particles for following burnout at different residence times. The main species of interest are quantified on-line: O2, using a paramagnetism

based analyzer and furthermore CO2, CO and NO using standard industrial Non Dispersive InfraRed (NDIR) spectrophotometers. The wall temperature is monitored with thermocouples. All the air flows are at ambient temperature. Secondary air is injected with a swirl of 45° and primary air carrying the fuel particles is nonswirled. For the staged flames, burnout air is fed at an axial distance of 0.935 m from the burner. Radial and axial O2, CO2 and CO concentration profiles are measured. During preliminary measurements, temperature was determined with a suction pyrometer. Temperature deviations measured with the suction pyrometer at 5 different axial positions between the wall of the reactor and the inner part up to only +/- 5 K have been measured, irrespective of the axial and radial positions. An explanation forms the smoothing effect of thermal radiation [21]. Therefore, temperature measurements were not performed in the final experimental campaign.

2.1.

Fuels

Two coals, commonly fired in Dutch PC-plants, were combusted to obtain data for model validation. The coals were co-milled as a 50/ 50 wt.% blend. Also, Chicken Litter (CL) and meat and bone meal (MBM) were utilized in milled form. Table 1 presents the compositions of the coal blend and the two secondary fuels and the particle size distributions are shown in Table 2. The volatile yield of solid fuels needs special consideration. HTVM (high temperature volatile matter) expectedly provides a better estimate for the amount of volatiles released under largescale conditions compared to the more standard PVM (proximate volatile matter). HTVM of the fuels was determined by devolatilization for 250 ms at 1200 °C gas and wall temperature in the IFRF isothermal plug flow reactor [23]. Devolatilization experiments were performed in an atmosphere consisting of 6.7% CO2, 0.2% CO and the rest being N2. The volatile yield of the chars was

Table 1 – Fuel input data for CFD modeling

Moisture Volatiles Fixed carbon Ash C H N S O LHV Burnout parameter, nB Light volatiles, CmHn Heavy volatiles, H/C Heavy volatiles, O/C

True density Apparent density Porosity a

Coal blend

CL a

MBM a

%a.r. %dry %dry %dry %dry %dry %dry %dry %dry MJ/kga.r. –

9.2 35.4 52.1 12.5 71.0 4.9 1.5 0.7 9.5 25.0 0.4

8.7 71.0 4.6 24.3 37.4 4.3 3.8 0.7 29.6 11.8 0.4

2.7 80.1 2.8 17.1 43.6 6.1 9.3 1.2 22.7 18.8 0.4

–

CH4

CH4

CH4

–

0.6

0.6

0.6

–

0.17

0.17

0.17

raw

char b raw

char b raw

kg/m3 kg/m3

1403 1878 942 566

1711 – 786 507

–

32.9

54.1

69.9

–

char b

1426 – 869 715 39.1

–

Biofuels were dried and sterilised for the combustion reactor tests. b Char prepared at IFRF plug flow reactor: 250 ms at 1200 °C.

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Table 2 – Particle size distribution of the flow reactor fuels Average size (μm) Coal

31.3 45.5 58.0 76.5 107.5 118.6 82.5 μm 81.6 μm

Sauter a Mean b a b

Share w-% 5.9 16.7 8.4 24.3 39.4 5.3

Average size (mm) CL

0.13 0.34 0.64 1.02 1.82

Share w-% 30.8 21.8 38.9 7.6 1.0

Average size (mm) MBM

0.44 mm 0.46 mm

0.2 0.70 1.25 1.75

Share w-% 36.0 40.0 18.0 6.0

0.69 mm 0.68 mm

Calculated based on experimental sieve analysis. Averaged from the data used for the CFD simulations.

determined based on the ash tracer method. The weight loss X in wt.%daf reads: X¼

1  AA0t

ð1Þ

1  A0

This method only gives reliable results when the ash contains no components that volatilize easily under these conditions. For coal HTVM equalled 56%daf, versus 40%daf PVM. For the biofuels, HTVM measured at the IFRF isothermal plug flow reactor turned out to be identical to the PVM values, at least within the accuracy of the experimental methods used. The dryash-free HTVM of CL and MBM were 92% and 96%, respectively.

2.2.

Experimental conditions

All relevant experimental conditions are presented in Table 3. The air and fuel flows were adjusted so that the residence time of the gas phase in the reactor equalled 2.4-2.5 s. The thermal input of the fuels fed into the furnace was 7.1-7.5 kWth, while most of the heat was supplied by the heaters. CL was co-fired with coal under unstaged and staged conditions, while MBM co-combustion was investigated under unstaged conditions. Co-firing experiments were performed under conditions practically similar to coal combustion. The wall temperature was kept constant at 1300 °C.

2.3.

Error analysis of the validation experiments

The experimental data collected for the model validation includes radial and axial O2, CO2 and CO concentration profiles as these characterize the main features of the flame and are measured online. In addition, the char burnout of the particles collected at selected distances from the burner is determined by the ash tracer method. In this section the data confidence level is discussed.

2.3.1.

Gas concentration measurements

Slight fluctuations in the combustion conditions and concentrations were observed. These were a consequence of the fuel dosing showing variations in mass flow of up to 7.5% for CL and 15% for MBM of the set point and the nature of the secondary fuels was such that homogenization was satisfactory but not perfect on a micro-scale. The feeding of CL was more continuous than that of MBM due to the sticky nature of the latter as a consequence of its fat content of approximately 10 mass%. The authors believe that the most important factor is that a screw feeder tends to feed the coarser (bio)fuels somewhat discontinuously, as the screw is never 100% filled with the fuel. Also, the used biofuels tend to form larger agglomerates when they hit the walls of the rotating screw. Coal suffers less of such behaviour. The experimental error of the concentration measurements is estimated by the ratio of (standard deviation)/(measured value) at each measurement location. From this, the error in [CO2] is derived to be less than 5% of the measured value. This analysis is valid for [CO2] ranging from 12 to 18%. The error of [O2] is likely to be between 5-10% of the measured value over the concentration range of 2-10%. At the lower levels, the sensitivity of analyzer decreases and the error increases. Three on-line analyzers with different analysis ranges of 0800 ppmv, 0-10000 ppmv and 0-10%v were used to quantify [CO]; the appropriate signal was selected for data analysis. The CO concentration fluctuated considerably more than [O2] and [CO2]. This is because even slight changes in the combustion conditions are magnified at the low CO concentration level. The characteristic periodic time of these fluctuations (typically up to 2-3 min) referred to is longer than the residence of the gas in the furnace (few seconds) and also compared to the time constant of the sampling lines (approximately 30 s). The maximum error of the most sensitive CO analyzer (0-800 ppmv) is estimated as 30% of the measured value. The error margin of the second CO analyzer (010000 ppmv) is expected to be 15% of the measured value and that of the high concentration analyzer 10% of the measured value. The

Table 3 – Fuel and air flows of the validated co-firing flames

λPrimZone λtot Coal flow Biomass flow Primary air Secondary air Sec. air swirl number Burnout air Biomass share

– – kg/h kg/h kg/h kg/h – kg/h %th

coal-CL

coal-CL

coal-MBM

coal

coal

unstaged

staged

unstaged

unstaged

staged

1.15 1.15 0.80 0.56 1.14 9.45 0.85 – 25

0.84 1.13 0.80 0.56 1.19 6.56 0.85 2.66 25

1.15 1.15 0.80 0.31 1.11 9.11 0.85 – 22

1.23 1.23 1.0 – 1.31 9.42 0.85 – –

0.86 1.20 1.0 – 1.36 6.14 0.85 3.0 –

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concentration values reported in this work are averaged over 1020 min. per measurement point.

2.3.2.

Burnout of the particulate phase

Particles were sampled at different axial reactor positions and the carbon conversion was determined by the ash tracer technique. Char particles in the top part of the reactor were collected on a glass fiber filter while leading the flue gases to the analyzers. Ash at the outlet was collected similarly, but on a filter paper. The ash contents of the collected char and the parent fuel were determined by TGA and the ash tracer technique was applied. The measured burnout values are thus total fuel burnouts and are indicative of the volatile release and the char oxidation. Measured samples weighed a few mg. In the near burner zone, flame conditions change continuously. On the bottom, fuel particles are converted and mainly ash is collected, hence one has to sample for at least an hour to collect a representative sample. Knowing the heterogeneous nature of coal it can be understood that only trends can be measured. The experimental error in the burnout values is estimated as ±4%units in the lowest sampling port and ±7-8%-units in the ports above the burnout air inlet.

3.

Combustion model set-up

Smaller scale experimental facilities, resembling the utility-scale conditions, can provide valuable fundamental information gathered under well controlled conditions. Combustion sub-models in CFD codes can hence be evaluated and validated by measurements using such test rigs, rather than large power plants. The CFD-package AIOLOS (developed at IVD, University of Stuttgart, and currently represented by RECOM Services) [24,25] is used, which is a code tailored for simulating coal combustion in large-scale furnaces. The AIOLOS code was chosen as it has been shown to be cost-effective regarding processor and memory requirements for large geometries [26]. Moreover, the source code of the combustion sub-models was included in the licence, enabling a straightforward tailoring of the models according to user's wishes. Conservation equations for describing the flow field, turbulence, heat, combustion, etc. are solved using the finite volume method. The general form of a steady-state transport equation for variable / reads:   A A/ qui /  C ¼ S/ Axi Axi

ð2Þ

Here ρ, u, x and Γ are density, velocity, cartesian coordinate and diffusion coefficient, respectively. Depending on the conservation equation in question, / describes the local change of mass, momentum, species or energy. / can be a scalar (e.g. mass) or a vector (e.g. velocities in momentum equations). The first term on the left side of Eq. (2) accounts for the contribution caused by convection, the second term by diffusion, and the term on the right side of the equation, S/, accounts for possible sources and sinks.

3.1.

Solid fuel combustion scheme

Solid fuel combustion is a complex process including several overlapping phenomena (drying, devolatilization, combustion of volatiles, char combustion). Although it has been extensively studied, fuel conversion processes are still not thor-

oughly understood. This makes combustion modeling a challenging task. The main focus in CFD modeling is accurate flow field prediction, which is done at the expense of chemistry modeling. Due to the fact that the flow field modeling itself is time-consuming, combustion phenomena must be considerably simplified and large detailed kinetic schemes cannot be included. The reduced combustion mechanism used in this work consists of the reactions explained below. • Primary pyrolysis - single step FYC þ G þ T

Reaction 1

F, C, G and T are the fuel, char, (light) volatile compounds and (heavy) tars, respectively. The volatiles are assumed to be composed of CmHn, CO, H2O and H2; the light hydrocarbons taken as CH4. Formation of heavy volatile compounds, tars CxHyOz, and soot can be also included. Half of the volatiles were assumed to consist of heavy tars. The tar composition was calculated based on the known parent coal composition and shares of the other volatile components and char. This leads to a coal tar composition with an atomic H/C ratio of 0.6 and an atomic O/C ratio of 0.17. These reasonably agree with the experimental data reported by Freihaut et al. for coal tars in an entrained-flow reactor during pyrolysis [28]. The H/C ratios of tars derived from a high-volatile bituminous coal were found to be between 0.710.74 at 1241 °C. Soot formation was not considered in the current study, as initial computational trials showed negligible effects on flame temperature and concentration fields. The devolatilization rate is calculated according to the Arrhenius equation:   dmf E1 ¼ k1 exp  mf dt RT

ð3Þ

with mf the mass of the raw fuel. The activation energy E1 and pre-exponential factor k1 for coal primary pyrolysis, as well as for other reaction equations, are reported in Table 4. • Heterogeneous char combustion 1 C þ O2 YCO 2

Reaction 2

It is assumed that char oxidation produces only CO. Mathias et al. [29] summarized the long research history around the

Table 4 – Kinetic parameters for coal combustion. i refers to the reaction number i

Reaction

1 Primary pyrolysis 2 Char combustion, ERE adsa Char combustion, ERE desb 3 CmHn oxidation 4 CxHyOz oxidation 5 CO oxidation 6 H2 oxidation a

Ei/R [K]

ki

Ref.

8900 3775

1.5 × 105 s− 1 4.35 kgm− 2s− 1bar− 1

[22] [32]

12079

45 kgm− 2s− 1

[32]

20131 6671 15098 1000

2.33 × 1011 m3kmol− 1s− 1K− 0.5 3.80 × 107 m3kmol− 1s− 1 3.25 × 1010 m3kmol− 1s− 1 1.0 × 108 m3kmol− 1s− 1

[48] [34] [49] [41]

ERE-adsorption, b ERE-desorption.

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formed CO/CO2 ratio and stated that at temperatures above 1000 K practically only CO is produced during the primary pyrolysis. For modeling purposes, it is often assumed that purely CO is formed and apparent kinetic parameters are used to determine the char oxidation rate. In the current work, the reactor walls are kept at 1300 °C and taking CO as the only product during char oxidation seems reasonable. In AIOLOS, the char combustion rate was calculated using the Extended Resistance Equation (ERE) model, for a description see [30–32]. This model accounts for both the surface reactions and the internal oxidation reactions. It is based on a fundamental formulation of the reaction rate dependency on pressure: the char combustion rate is considered to be affected by boundary layer diffusion of oxidizer to the particle, oxidizer adsorption on the particle surface, desorption of the formed products from the particle surface and internal diffusion. dmC ¼ dt

AC 1 ek2;a

exp

E2;a  RT




po2

þ

1 ek2;d

exp

E

2;d  RT


þk

1

879

• Carbon monoxide combustion 1 CO þ O2 YCO2 2

Reaction 5

CO produced from primary pyrolysis, combustion of light volatiles and heterogeneous char combustion is further converted into CO2. The global reaction rate for CO oxidation is calculated according to rate expression by [35] and

ð4Þ

D po2

Here k2,a, E2,a, k2,d and E2,d are the kinetic parameters for adsorption and desorption, respectively. Ac is the external surface area of the char per unit mass and ε is the reaction penetration parameter for internal diffusion. It is defined as ε= 1+α/3. α is the power index in a relation describing the variation of density with diameter as the reaction proceeds: (ρ/ρ0)= (d/d0)α. The model uses the burnout parameter nB, characterizing particle diameter and density change during char combustion: nB d ¼ U2 d0

ð5Þ

3 q ¼ Uð12 nB Þ q0

ð6Þ

in which U is the unburned fuel fraction. • Combustion of light volatiles C m Hn þ

m n n n þ a O2 YmCO þ ð1  aÞ H2 þ a H2 O 2 4 2 2

Reaction 3

The combustion of light hydrocarbons is simplified as a single-step reaction, producing CO, H2 and H2O; α is calculated assuming the water-gas shift equilibrium to prevail [33]. The reaction rate is expressed in terms of concentrations:   dcCm Hn E3 ¼ k3 T0:5 exp  cCm Hn cO2 dt RT

ð7Þ

• Tar combustion Cx Hy Oz þ O2 YCO þ H2

Reaction 4

Tar is assumed to oxidize into CO and H2 in a single step. The tar combustion rate of coal and biomass is calculated based on [34]:   dctar E4 ¼ k4 exp  ctar cO2 dt RT

ð8Þ

Fig. 2 – Temperature contours of the near burner zone of the unstaged (left) and staged (right) coal flame.

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coefficient values given by [49] valid for typical coal-based combustion conditions:   dcCO E5 0:5 ¼ k5 exp  cCO c0:5 O2 cH2 O dt RT

ð9Þ

• Hydrogen combustion 1 H2 þ O2 YH2 O 2

Reaction 6

Combustion of H2 produced by primary pyrolysis and combustion of light volatiles is assumed to be infinitely fast, which is a common assumption found in the literature [24,33]. In order to simulate co-firing, the AIOLOS code was adapted for inclusion of high nitrogen content fuels such as CL and MBM. In the original approach, fuel bound N was considered as C. This is justified for most coals as they have no high N contents. In the improved AIOLOS model, fuel bound N is considered as inert N2. This is not completely true as some NOx is formed but the error on the predicted concentration fields and burnout is negligible. Moreover, a fuel drying step before the actual combustion reactions was included for the biomass fuels. If the biomass

drying was excluded, the CFD model predicted some volatile formation already in the burner that is at ambient temperature. The combustion scheme, combustion sub-models and kinetic rates of the biomass fuels are here taken identical to those for coal. In [36] it is shown that the char oxidation rate of mm-size biomass particles is governed by diffusion and can be modelled using simpler models based on surface kinetics. The internal diffusion reactions, however, for which the ERE model accounts, can slightly influence the burnout of the smaller biomass particles. Therefore the ERE model has been chosen for both biomass and coal. The importance of accurate pyrolysis modeling and knowledge of the formed products is vital. In literature, modeling studies dealing with litter or manure are found [9,37,38,15]. The reaction scheme of Raman et al. [37] used for feed-lot manure is identical to that used here (i.e. that of coal). As no experimental data on the share of light/heavy volatiles was available for the particular biomass fuels studied, it is assumed that half of the volatile compounds are released as CH4 and the other half as tars. This division is comparable to the data used by Raman and co-workers [37]. The tar composition was taken equal to that of coal as Table 1 shows.

Fig. 3 – Measured (x) and calculated oxygen concentration profiles at radial cross-sections (top four figures) and axial distances (at r = 0 m, bottom two figures) for (A) unstaged and (B) staged coal flame.

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3.2.

Modeling of turbulence-chemistry interaction

The turbulence-chemistry interaction is modeled using the Eddy Dissipation Concept (EDC) model of Magnussen [39,40]. The EDC model is based on the idea that dissipation of turbulent energy into heat and molecular mixing, and thus reactions, take place within fine structures, which are treated as well stirred reactors [39].

3.3.

Discrete phase modeling

PF combustion not only requires modeling of turbulent, reactive gaseous flow but also of solid fuel particles dispersed into it. The discrete phase can be considered dilute and its volumetric fraction neglected [27]. There are two methods to account for the discrete phase: the Eulerian and the Lagrangian approach [50]. According to the Eulerian formulation, the discrete phase is treated as a continuum similarly to the fluid phase and in one of its formulations no slip between the phases exists. Solution algorithms are identical to those of one phase flow, except that the mass and momentum in the transport equations are weighed with the volume fraction of the particle “fluid” [51].

881

The Lagrangian approach considers particles as a separate phase, not necessarily following the gas flow. For this, a userdefined number of particles are injected through the fluid phase and their particle trajectories are solved. The fluid phase is resolved by the standard approach with Favre averaged transport equations. Coupling between the phases accounts for exchange of mass, momentum and heat. The Eulerian approach is suitable for simulating flows with very small particles, likely to follow the fluid phase. Accordingly, flows with larger particles should be handled by the Lagrangian formulation. Epple et al. [52] simulated pulverized coal combustion in three different test facilities by both approaches. They demonstrated that for PF coal combustion, the Eulerian method performs as good as the Lagrangian one, while the Eulerian approach has a strong benefit on the computational effort. The computationally more expensive Lagrangian approach [52] is hence not expected to increase the accuracy of the simulations presented in this work and a Eulerian approach is used. Three particulate species are considered i.e. raw fuel, char and ash [41]. The general transport Eq. (2) is solved for the gaseous species and for the three particulate species. As the particle size distribution of the solid fuels has a major impact on

Fig. 4 – Measured (x) and calculated CO2 concentration profiles at radial cross-sections (top four figures) and axial distances (at r = 0 m, bottom two figures) for (A) unstaged and (B) staged coal flame.

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burnout, a transport equation is written for each size fraction. If one has defined 6 particle size classes for a particular fuel, 3 × 6 = 18 particle transport equations are solved.

3.4.

Radiation modeling

The dominating mechanism of heat transfer in pulverized coal fired utilities, also in our flow reactor with heated walls, is radiation [42]. For comprehensive combustion modeling, the Discrete Ordinates (DO) method has been successfully used by several researchers [43,44,27]. In this method, the radiative transfer equation is solved for a set of m discrete directions spanning the range of a 4π solid angle. Radiation intensity is assumed constant in each direction → sm. Due to the easy integration between the radiative heat transfer solution and the flow solutions, this method is becoming the choice for comprehensive combustion models [27] and is also used in this work. Particularly, the → s4 approximation outlined in [45] has been used. In the current work, a grey radiative medium is assumed and the radiation absorption coefficient of the gas phase is taken as 0.5 m− 1 [46]. Note, that in AIOLOS it is not possible to

use a variable absorption coefficient from for example the weighted-sum-of-grey-gases (WSGGM) model. The used value for the absorption coefficient accounts partly for the effect that H2O and CO2 have on the radiation. The wall emissivity is calculated for alumina at 1300 °C (Twall) based on [47] and equals 0.4. The convective wall heat transfer coefficient is taken as 30 Wm− 2 K− 1. The absorption and out-scattering coefficients are calculated as a function of the char unburned fraction, gas and particle densities, and the total particle surface [45,46]. Analogous to [45] in-scattering is neglected.

4.

CFD modeling of coal combustion

4.1.

Computational domain

For calculating the temperature, velocity, turbulence and concentration fields in the flow reactor, the transport Eq. (2) must be solved. The governing transport equations are integrated over control volumes resulting in a system of coupled algebraic equations to be solved. In the current work,

Fig. 5 – Measured (x) and calculated CO concentration profiles at radial cross-sections (top four figures) and axial distances (at r = 0 m, bottom two figures) for (A) unstaged and (B) staged coal flame.

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The differences in the gaseous concentrations were found to be explained by grid-dependent axial velocity predictions that occurred only at the level of the first measurement port [36]. As the meshes provided identical results elsewhere than in the core of the near-burner zone, the coarsest mesh was considered a suitable trade-off between required computational time and accuracy in the current study. The simulation results reported here have been obtained with the coarsest mesh (20 × 39× 99 cells in the r, θ, and z-direction).

4.2.

Model settings

Comparing the calculated results with experimental data is the only way to validate the model. Two different firing scenarios are reported here: an unstaged and a staged pure coal flame. Table 3 depicts the fuel and air flows used in the CFD modeling for the coal validation flames.

4.3.

Validation with experimental data

4.3.1.

Temperature and velocity fields

Fig. 6 – CO concentration contours of the near burner zone of the unstaged (left) and staged (right) coal flame.

Contour plots of the unstaged and staged coal flames are depicted in Fig. 2. During the first 0.05 m, the flow passes through the burner and remains cold. Hereafter pyrolysis and combustion reactions commence and temperature rapidly increases. The ignition of the unstaged flame is slightly delayed and the peak temperature slightly lower compared with the staged flame. This is due to the larger air flow of the unstaged flame via the burner, as the staged flame receives

the momentum transport equations were discretized according to the Monotonized Linear Upwind (MLU) [53] scheme and the remaining equations according to the UPWIND scheme [54]. Using the higher order MLU discretization for all equations resulted in unacceptably high elemental imbalances of about 4% for C and H, while the imbalance in the wellconverged cases was always lower than 0.5%. On the other hand, the UPWIND discretization of all equations resulted in a too weak internal recirculation zone that is formed due to the swirling secondary air. A cold primary air jet penetrated through the recirculation zone and lifted the flame. This did not agree with the measured gaseous concentrations. The standard k-ε turbulence model was used, despite the known problems regarding predicting correctly swirling flows. It was, however, used because the predictions of the Reynolds Stress Model were not consistently better and were much more time-consuming. Three different computational meshes were created to check whether the results are grid independent. Due to the swirling secondary air and the spot-like horizontal injection of the burnout air, a symmetry boundary condition cannot be utilized. The grid cell density was increased in regions where highest velocity and concentration gradients are expected i.e. in the near burner region and near the burnout air (BA) inlet. Details of the comparison are not given here but can be found in [36]. The grid independency study showed that the predicted gaseous concentrations were grid dependent only in the measurement port closest to the burner (z = 0.14 m), while the meshes provided identical results further away from the burner.

Fig. 7 – Measured (x) and calculated burnout profiles along the furnace for (A) unstaged and (B) staged coal flame.

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about 30-40% of the total air flow via the burnout air inlet at a distance of 0.935 m. The overall temperature level of the flue gases is determined by the reactor walls. The velocity contours in Fig. 2 show that an internal and an external recirculation zone are formed. These zones are stronger for the unstaged flame due to the higher mass flow and larger momentum of the swirling secondary air. Comparing the calculated velocity contours with figures from IFRF [55] reveals that the flame is of type-2. The non-swirled primary jet carrying the fuel particles partly penetrates into the internal recirculation zone (IRZ), which leads hot combustion products towards the burner tip. The swirling secondary air generates a conical cooler zone around the hot core but also these areas ignite close to the burner.

4.3.2.

Oxygen profiles

Fig. 3 shows the predicted radial O2 concentration profiles of the two coal cases and the corresponding measurements. In general, the predicted and measured O2 concentrations are in good agreement. In the first port at z = 0.14 m, the lowest [O2]

values are measured at the axis and this behavior is captured by the simulations. The increase in [O2] towards the reactor wall for the staged flame, however, is under-predicted. At a distance of z = 0.31 m for the unstaged flame (Fig. 3(A)), the simulations show no radial gradient in [O2] which is not in line with the measurements. Concerning the staged flame, at z = 0.71 m a constant [O2] approaching 0% has been reached as Fig. 3(B) demonstrates. From Table 3 it can be noted that λPrimZone of the staged coal flame equals 0.86, hence not enough O2 is provided for complete combustion. In Fig. 3, also the axial [O2] profile at the reactor axis is plotted. It can be seen that the CFD model is able to predict the trends in the [O2]. The unstaged flame in Fig. 3(A) experiences the steepest axial gradient within the first 0.1 m from the burner tip. At distances larger than 1.0 m from the burner tip [O2] remains basically constant i.e. the major part of the reactions has taken place. Multiple measurements performed at selected ports show some scatter. For the staged coal flame, [O2] in the primary and burnout zone are acceptably predicted in Fig. 3(B). The peak at z = 0.9 m is due to the burnout air injection.

Fig. 8 – Radial (top four figures) and axial profiles of (A) unstaged and (B) staged CL co-firing. Measured (x) and calculated (solid line) O2, measured (+) and calculated (dashed line) CO2.

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4.3.3.

Carbon conversion

Fig. 4 shows the radial [CO2] profiles in the same crosssections as O2. There is a reasonable agreement with the experimental CO2 data for the unstaged flame in Fig. 4(A). The near-burner CO2 levels of the staged flame in Fig. 4(B) are overpredicted by the model. This is more obvious from the axial profile. The calculated [CO2] in the burnout zone downstream of the burnout air injection agrees with the experimental data. One explanation for the too high CO2 predictions in the substoichiometric zone of the staged flame can be found in the calculated [CO] value. Fig. 5(B) shows that the measured and the calculated [CO] differs 1-3 orders of magnitude: at a distance z = 0.14 m around 7 × 104 ppm (7 vol.%) is measured and only 400700 ppm is predicted. The maximum predicted [CO] of the staged flame is about 15000 ppm and located very close to the burner (Fig. 6(B)). The calculated [CO] of the unstaged coal flame in Fig. 5(A) is also somewhat under-predicted but the discrepancy is less severe than for the staged flame. The current study concentrates on the effects of co-firing on burnout. The modeling of the near-burner zone is hence not the main focus.

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The discrepancy between the experimental and the calculated values in the burnout zone and at the reactor exit (z = 2 m) is far less severe than in the near-burner zone. The scatter in the measured [CO] of the unstaged flame is, however, large. This demonstrates the fact that [CO] is sensitive to slight changes in the measurement conditions. For example, during the measurements partly reacted particles and deposits grow on the probe tip. This can affect the measured [CO], which in the burnout zone is low, up to few hundred ppm. Besides the gaseous components, carbon in char completes the C-balance. The fuel burnouts predicted by the two cases and the experimental values calculated using ash as a tracer are shown in Fig. 7. The experimental values are averaged from 2-4 measurements. In the case of staged combustion, the smaller measured fuel burnout is consistent with the higher [CO] values. The differences are, however, smaller than the spread in the measurements. In coal combustion modeling it is common to fit the char oxidation kinetics to measured burnouts. This was not done in this work because the scatter in the measured burnout data is

Fig. 9 – Radial (top four figures) and axial (bottom two figures) profiles of (A) unstaged and (B) staged CL co-firing. Measured (*) and calculated (dash-dot line) CO.

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too high to justify any fitting and we also feel that it is better to use experimentally determined char oxidation kinetics. However, these were unfortunately not available for this coal blend.

5.

Discussion

From the above figures, it can be concluded that there is reasonable agreement between experimental data and model predictions, except for [CO]. In general, [CO] is more difficult to measure and simulate accurately than [O2] and [CO2]. Problems with the [CO] predictions have been reported by others [45,56,57]. Knaus et al. [56] simulated coal combustion in a utility boiler using both Euler/Euler and Euler/Lagrange particle description. Both approaches lead to low CO predictions but the Euler/Lagrange description considerably improved the calculated [CO] value. They concluded that the Eulerian approach predicted too low char concentrations, which in turn caused too low [CO] levels. In their other work, the calculated low [CO] levels were explained by a too fast CO oxidation rate [45]. Magel et al. [57] investigated the effect of volatile combustion model, EBU vs. EDC, on the predicted concentrations in a semi-industrial PC facility. The EDC model yielded better [CO] predictions than the EBU model that is based on pure mixing control and overpredicted the CO oxidation rate. The prediction accuracy of [CO] also depends on the accuracy of burnout description. It was pointed out in literature (see Pallares et al. [58]) that an important reason for problems with CO and burnout prediction is the treament of char burnout. The model they applied based on the approach of Hurt et al. [59] (CBK) could in principle be also implemented in different CFD codes. However, also Pallares et al. [58] do not show CO profile predictions. Kurose et al. [60] investigated coal combustion under staged conditions in a horizontal test furnace equipped with a low-NOx burner. Low [CO] values were reported throughout the computational domain. These were assigned to the single-step devolatilization model and the EDC model used for gaseous combustion. Brink [61] drew the attention to the importance of including H2 as an intermediate combustion product in the hydrocarbon oxidation mechanism. Excluding H2 formation at fuel rich conditions gives rise to an overestimated [CO] value. In the current work, including or excluding H2 as an intermediate product in CmHn oxidation (reaction 3) had no influence on the calculated [CO]. That leaves us with speculation about the origin of the discrepancy between CO model predictions and measurements. From an experimental point of view we are faced with the heterogeneous nature of the fuels: during the measurements [CO] fluctuates considerably. From the modeling point of view, a Lagrangian treatment of the solid particles might be worthwhile to test as it can not be guaranteed that larger particles will follow the gas flow field perfectly. Other changes that could improve the predictions are a better description of the volatile composition, optimized data of the homogeneous reaction kinetics (particularly tar combustion), and more realistic char oxidation kinetics for this particular coal. Finally, it is mentioned that swirling turbulent flows are difficult to predict as accurate as non-swirling flows. Nevertheless, with all limitations of the present models it is possible to study the effect of co-firing biofuels with coal, which will be the focus of the next part.

6.

Validation of co-firing flames

Experimental data of flue gas concentration profiles and char burnout data for co-firing are here compared with the CFD predictions in to validate the combustion sub-models. Radial and axial concentration profiles of the main gaseous components – O2, CO2 and CO – are used. The radial profiles were collected only in the near burner zone (NBZ) as further downstream the burner the radial gradients became gradually smaller. The axial concentration profile was measured along the reactor axis (r = 0 m). Beside the flue gas concentration, burnout of solid char sampled at selected locations was determined using the ash tracer method.

6.1.

Co-firing chicken litter

For the CL co-firing cases, approximately 25%th was replaced with biomass, corresponding to 41%wt. In Fig. 8, the measured and calculated [O2] and [CO2] profiles at z = 0.14 m and z = 0.31 m are shown. These agree well with the experimental data, though an exception is the profile at z = 0.14 m of the staged flame in Fig. 8(B), where measurements imply that O2 is depleted anywhere but close to the reactor wall. The validity of this experimental profile is questionable as at z = 0.31 m some O2 is measured. The agreement of the radial CO2 profiles for both the unstaged and staged flames is excellent. The CO2 level in the primary zone of the unstaged flame in Fig. 8(A) is lower than in the staged flame in Fig. 8(B) due to dilution by the higher secondary air flow.

Fig. 10 – Measured (x) and calculated burnout of (A) unstaged and (B) staged CL co-firing.

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Fig. 8 provides an overview of the axial concentration profiles in [O2] and [CO2]. The difference between unstaged (Fig. 8(A)) and staged combustion (Fig. 8(B)) is clearly visible. In the staged case, the peak in [O2] between z = 0.9–1.0 m is caused by burnout air injection. It can be seen that the vertical O2 profiles are in agreement with the measured data. Moreover, it is noted that CFD model is able to acceptably predict [CO2] in the reactor. Therefore it can be stated that the CFD model is able to simulate the concentrations of the major flue gas components during CL co-firing. Fig. 9(A) shows the measured and calculated [CO] of the unstaged chicken litter co-firing flame. Although some discrepancies are noted, the predictions are acceptable. In the sub-stoichiometric zone of the staged flame (Fig. 9(B)), however, the model seriously under-predicts [CO]. This was also noted and extensively studied for the coal flame. The calculated CO levels of the staged CL co-combustion flame in Fig. 9(B) shows a behavior similar to pure coal flames. Moreover, the calculated [CO] in the burnout zone of the staged flame (z N 0.935 m in Fig. 9(B)) is too low. The discrepancy in the burnout zone was less severe for the staged coal flame.

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Therefore, it is concluded that this modeling approach can be used to obtain an approximation of [CO] when sufficient O2 is available for reaction. At staged conditions, however, the calculated [CO] is about two orders of magnitude lower than the measured one and the model fails to provide reasonable data. However, it must be kept in mind that the predictions of the major flue gas components were reasonable, also in substoichiometric conditions as shown above. Fig. 10 shows the measured and calculated burnout versus the reactor height. It is noted that in the near-burner zone the discrepancy between experimental and calculated data is large although this zone is the most critical part for experimental errors. Below the near-burner zone the model somewhat overestimates the fuel burnout. For the staged CL co-firing flame, see Fig. 10(B), the model significantly over-predicts fuel burnout. There could be several reasons for the over-predicted burnout. Firstly, the volatile matter content used for biomass could be too high. Data analysis showed that more realistic estimates of the biomass VM are about 20% lower than the values reported in Table 1. Secondly, experimental data

Fig. 11 – Radial (top four figures) and axial (bottom two figures) profiles of unstaged MBM co-firing. (A) Measured (x) and calculated (solid line) O2, measured (+) and calculated CO2 (dashed line),(B) measured (*) and calculated (dash-dot line) CO.

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collected for different staging conditions showed that the experimental burnout at the exit of the staged case is most likely an outlier. According to that data (not shown here), the burnout at the exit of the staged case should lie between 90-95%. Thirdly, in the particular case of the no-slip Euler-Euler particle treatment might not be able to yield accurate results for the mm-range biomass particles. This can be of importance in staged conditions. Namely, for staging, less swirling secondary air is injected via the burner. Under staged conditions it is thus more likely that more particles are not captured in the swirl but almost fall vertically downward. Hence, the particular case of no-slip assumption between the gas phase and particles will not perfectly hold for these biomass particle sizes.

6.2.

Co-firing meat and bone meal

In the unstaged MBM co-firing flame, about 22%th MBM was used, being 28%wt. The radial O2 and CO2 profiles in the first two measurement ports are shown in Fig. 11(A). The predictions reasonably agree with the experimental data, although a bit worse than in the CL co-firing case. Comparison with the unstaged coal flame and the CL co-firing flame (Figs. 3(A) and 8(A), respectively) shows that O2 is consumed with a similar rate. The axial [O2] profile of the MBM co-firing case given in Fig. 11(A) again shows that experimental and calculated data agree. The [CO] profiles shown in Fig. 11(B) demonstrate that the CO levels can be reasonably predicted for unstaged MBM cocombustion, comparable to the unstaged CL flame. The scatter in the measured results is relatively large, but the predicted trends are acceptable. The fluctuations in the measured concentrations of the MBM flame are mainly caused by the slightly unstable biomass dosing. When pressed, MBM tends to form large agglomerates blocking the screw feeder. Furthermore, the MBM fats melt at relatively low temperatures, increasing the fuel stickiness. Comparison of the calculated fuel burnout with the experimental data implies that the CFD model over-predicts the fuel burnout, see Fig. 12. The actual measured burnouts of MBM co-firing are, however, somewhat lower than these values. When sampling particles via the reactor side ports during MBM co-combustion, a fraction of the particles was not sucked onto the filter but accumulated in the reactor bottom. Based on their appearance and relatively large particle size, these particles were thought to originate from the bone part of MBM. The burnout of this “bottom ash” equalled about 87%

(ash tracer method), which is lower than for the collected sample at the reactor exit. Therefore the actual measured burnouts are somewhat lower. It can, however, be stated that the model predicts final fuel burnout slightly too optimistically in the unstaged co-firing case.

7.

Conclusions

A CFD model for an unstaged and a staged coal flame has been validated using an externally heated flow combustor test rig. The predicted O2 profiles and final fuel burnout are in reasonable agreement with the measurements. CO2 and CO concentrations are acceptably simulated except for the substoichiometric primary zone of the staged flame. The CO concentration is under-predicted by about two orders of magnitude. Adjusting the char oxidation model, slowing down the CO oxidation rate, or including char gasification reactions in the reaction scheme do not significantly improve the CO predictions in the primary zone. The calculated CO concentrations in the burnout zone of the staged flame, however, are practically comparable for the different approaches because CO has sufficient time to oxidize to CO2 in this O2-rich zone. Moreover, the main focus of the current work is not on the near burner zone but on the effect of cocombustion on final char burnout. Therefore, the discrepancy between the measured and calculated CO levels in the substoichiometric zone is not of major importance in the current work as long as one is aware of it. Furthermore, the model has been applied to co-combustion of coal with chicken litter (CL) and with meat and bone meal (MBM) in the reactor has been validated. It has been shown that the current status of CFD models is sufficient to predict the major flue gas concentrations – O2 and CO2 – in unstaged and staged conditions. Concerning the calculated CO concentrations, the model is able to give an approximation of the CO concentration as long as there is sufficient O2 available. Under sub-stoichiometric conditions, however, the model under-predicts the CO concentration by up to two orders of magnitude. This discrepancy could be caused by an over-simplified reaction scheme. High temperature experimental or modeling data (network models) on the pyrolysis of coal and biomass might help in gaining insight about the pyrolysis of the studied fuels. Moreover, it can be stated that combustion of the two biomass types studied can be described by combustion sub-models identical to those of coal on an engineering level of accuracy.

Acknowledgement The authors are grateful to the financial support by the Netherlands Agency for Energy and Environment (SenterNovem, contract 2020-02-12-14-003). E.On Generation Benelux is greatly acknowledged for delivering the fuels.

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