Review of Pulverized Combustion of Non-Woody Residues - Energy

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Review

Review on Pulverized Combustion of Non-Woody Residues Miriam Rabaçal, Sandrina Pereira, and Mario Costa Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b03258 • Publication Date (Web): 12 Dec 2017 Downloaded from http://pubs.acs.org on December 15, 2017

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Review on Pulverized Combustion of Non-Woody Residues Miriam Rabaçal#, Sandrina Pereira, Mário Costa*

IDMEC, Mechanical Engineering Department, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

*Corresponding author: Mário Costa, email: [email protected]

#

Now at: Aerothermochemistry and Combustion Systems Laboratory, ETH Zurich, Switzerland

Submitted to Energy & Fuels December, 2017

1 Environment ACS Paragon Plus

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Abstract The intense use of wood derived fuels in (co-)firing processes results in an enormous pressure on the forest. In order to alleviate this pressure and to proceed with the CO2 emissions reduction process, it is necessary to increase the use of non-woody residues, in particular herbaceous materials and agricultural residues. (Co-)firing using such residues can cause a number of problems due to the presence of alkali metals, chlorine and other ash related impacts as well as corrosion on the metallic surfaces and particulate matter emissions. This may limit the variety of biomass residues that can actually be used in (co-)firing processes. This review aims at summarizing recent developments in the combustion of pulverized non-woody residues and includes experimental and numerical studies of single particle combustion and combustion in small- and large-scale furnaces. The review provides an overview of the properties of non-woody residues, describes the existing research facilities to study the subject and summarizes the experimental studies on the combustion of nonwoody residues, including studies on single particle combustion, drop tube furnaces and entrained flow reactors, and large-scale furnaces. The review also concentrates on numerical modelling, namely on the formulation of combustion models and their application in computational fluid dynamics. Finally, the main conclusions are summarized and the research needs listed.

Keywords: Non-woody residues, Pulverized combustion, Experimental, Numerical


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Nomenclature Acronyms 3PM

Three parallel reactions model

AAS

Atomic absorption spectroscopy

AS

Almond shells

BC

Bituminous coal

CBK

Combustion burnout kinetics

CCP

Cereal co-products

CFA

Chemical fractionation analysis

CFD

Computational fluid dynamics

CPD

Chemical percolation devolatilization

daf

Dry ash free

DAEM

Distributed activation energy model

DNS

Direct numerical simulation

DTF

Drop tube furnace

DTG

Derivative thermogravimetry

EA

Excess air

EDS/EDX

Energy dispersive X-ray spectroscopy

EFR

Entrained flow reactor

FPS

Frames per second

FTIR

Fourier transform infrared spectroscopy

GC/MSD

Gas chromatography/mass selective detector

GHG

Greenhouse gases

HHV

High heating value

HS

High speed

HSIT

High speed imaging technique

IC

Ion chromatography

ICP-OES

Inductively coupled plasma-atomic emission spectroscopy

ICP-MS

Inductively coupled plasma-mass spectroscopy

KB

Kiwi branches

LES

Large eddy simulation

LIBS

Laser induced breakdown spectroscopy

LPI

Low pressure impactor


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MFC

Multi-fuel combustor

MSW

Municipal solid waste

NG

Natural gas

OB

Olive branches

OR

Olive residues

PA

Primary air

PB

Pine branches or pine bark

PDSD

Pressure drop sintering device

PF

Pulverized fuel

PM

Particulate matter

QMS

Quadruple mass spectrometry

RANS

Reynolds averaged Navier-Stokes

RDF

Refuse derived fuels

RH

Rice husk

SA

Secondary air

SEM

Scanning electron microscopy

SFOR

Single first order reaction

SL

Soma lignite

SPT

Surface probe temperature

TDP

Tabulated devolatilization process

TG

Thermogravimetry

TGA

Thermogravimetric analysis

TL

Tunçbilek lignite

WS

Wheat straw

XFR

X-ray fluorescence spectroscopy.

XRD

X-ray diffraction

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Symbols Roman characters A

Pre-exponential factor

s-1

d

diameter

µm; mm

E

Activation energy

kJ kmol-1

k

Reaction constant

s-1


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K

Char reactivity

s-1

m

mass

kg

M

Sample mass

kg

P

Pressure

atm

rc

Kinetic term of the char reactivity

s-1

rs

Structural term of the char reactivity

s-1

R

Universal gas constant

kJ K-1 kmol-1

T

Temperature

K

V

Total evolved volatiles

kg

VM

Total volatiles

kg

X

Conversion degree

non-dimensional


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Subscripts 0

Initial

∞

Final

i

Index

p

Particle

w

Wall

Superscripts n

Order of reaction

b

Temperature exponent

Greek characters β

ºC min-1; K s-1

Heating rate


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1. Introduction Biomass can have a vegetal, human or animal origin. Wood from forest, waste from agricultural and forestry practices or waste from human, animal and industrial activities represent different forms of biomass. These resources variety added to the existing conversion processes for energy production turns biomass an attractive solution to increase the integration of endogenous and renewable energy sources in the world energy mix [1]. Direct combustion is widely used to convert biomass into heat and/or electricity, namely in co-firing processes with coal. The co-combustion of biomass with coal contributes to the reduction of power plants greenhouse gas (GHG) emissions. Demirbas [1], Kuo and Wu [2] and Zhang et al. [3] also refer that the combustion of biomass instead of coal can reduce the emissions of SOx and NOx, comparatively to pure coal firing, due to factors such as the lower content of sulphur and nitrogen present in the biomass, retention of the sulphur by the alkali/alkaline earth compounds present in the biomass or, in case of the NOx emissions, due to the lowering of the flame temperature as a consequence of the relatively high moisture content present in the biomass. At economic level, Karampinis et al. [4] refer that the investment costs needed to implement cofiring in a power plant are lower than the investment costs in a new hydro power plant or in a new onshore wind power plant. Nevertheless, the co-firing of coal with biomass, namely in pulverized fuel (PF) power plants poses some difficulties. Firstly, ash that results from biomass combustion, especially for biomass residues with higher levels of alkalis and chlorine than coal, may increase phenomena such as slagging, fouling and corrosion. Secondly, in comparison to coal, biomass presents low heating values due to its high moisture and high oxygen contents [3,5]. Despite these drawbacks, a number of biomass residues are currently used in co-firing and, more recently, in pure biomass firing processes; specifically, forestry and sawmill residues, short rotation coppice wood and other wood materials; solid waste materials from olive, palm, sunflower and rape seed oil and other industries; dried sewage sludge; cereal straws and other baled agricultural residue materials [6].


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The woody residues have low ash contents and can be fired at relatively high co-firing ratios with coal or in pure firing processes. In contrast, non-woody residues, such as those processed from agriculture and related industries, present generally high ash and high alkali metal contents, which make their use in co-firing processes with coal, even at relatively low co-firing ratios, or in pure pulverized firing processes rather difficult. For example, grasses, reeds and straws have high ash content, rich in silica, lime, potash and phosphate, which, together with their low ash fusion temperatures, potentiate both slagging and fouling formation in conventional pulverized combustion systems.

There is a need to improve the current understanding of the combustion of pulverized non-woody residues in order to increase their penetration in co-firing and pure firing processes. Recent important related reviews include those of Niu et al. [7], Chen et al. [8], Toftegaard et al. [9], Williams et al. [10] and Scheffknecht et al. [11], which focused on various aspects of the combustion of solid fuels, based on both experimental [7-9] and modelling [10,11] studies. Niu et al. [7] reviewed the research on ash-related issues during biomass combustion, with emphasis on the formation mechanisms, needs and potential countermeasures. Chen et al. [8] discussed the impacts of the wildfire and anthropogenic emissions from biomass burning in China on public health and climate, and identified the research priorities and insights on biomass burning in the country. Toftegaard et al. [9] revised the current knowledge on oxy-fuel combustion, with focus on flame temperature, heat transfer, ignition, burnout, emissions and fly ash characteristics. Williams et al. [10] reviewed the formation of pollutants during the combustion of solid biomass fuels and, finally, Scheffknechtet al. [11] concentrated on modelling studies, including oxy-fuel pulverized coal studies.

The present manuscript aims at summarizing recent developments in the combustion of pulverized non-woody residues and includes experimental and numerical studies of single particle combustion


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and combustion in small- and large-scale furnaces. The review is organized in the following manner. Section 1 introduces the subject, section 2 provides an overview of the properties of non-woody residues, section 3 describes the existing research facilities to investigate the various aspects associated with the combustion of pulverized non-woody residues, section 4 summarizes some of these experimental studies, including works on single particle combustion, drop tube furnaces and entrained flow reactors, and large-scale furnaces, section 5 concentrates on numerical modelling, namely on the formulation of combustion models and their application in computational fluid dynamics and, finally, section 6 summarizes the main conclusions of the present review and identifies the research needs.


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2. Properties of non-woody residues Dermibas [1] suggested the division of the biomass resources in three categories: i) wastes, which include agricultural production and processing wastes, crusher wood wastes and urban wastes; ii) forest products, which encompass wood, trees, shrubs and wood residues, sawdust and bark from forest cleaning; and iii) energy crops, which comprise short rotation woody, herbaceous, grasses, starch, sugar, forage and oilseed crops. Jenkins et al. [12] emphasized that vegetal biomass includes mainly cellulose, hemicellulose, lignin, lipids, proteins, simple sugars, starches, water, hydrocarbons and ash, among others, and that the concentration of each component depends on the species, plant tissue, stage of growth and growing conditions. The physical properties and the organic, inorganic and energy contents of biomass are different than those of coal. Biomass has usually less carbon (depending on ash content, about 30 to 60 wt.% of the dry matter is carbon), more oxygen (around 30 to 40 wt.% of the dry matter is oxygen), more silica and potassium, less aluminium and iron, lower heating value, higher moisture content and lower density than coal. In regard to the organic components, hydrogen represents the third major constituent, representing 5 to 6 wt.% of the dry matter. Nitrogen, sulphur and chlorine represent usually less than 1 wt.% of the dry matter [12,13]. Table 1 presents the composition and high heating values (HHV) of several non-woody residues and, for comparison purposes, Table 2 presents the same characteristics for coal and woody residues [5,12,14-20]. In addition, Figures 1 and 2 show the ultimate analysis and ash main components for each fuel presented in Tables 1 and 2. As for the proximate analysis, coffee husk is the solid fuel that presents the lowest fixed carbon content (1.4%), while the walnut shells is the one that presents the highest content (37.9%). The average value of fixed carbon content of the non-woody residues included in Table 1 is 17.5%. The ash content of these residues varies between 1.4% for pistachio shells and 20.4% for yard waste. The coal fixed carbon content varies between 47.6% and 62.1%, while for woody biomass it varies between 18.1% and 30.1%. The ultimate analysis reveals that biomass residues present higher oxygen content and coals higher carbon content.


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The ash elemental composition of the three coals included in Table 2 is similar, with high levels of SiO3 and Al2O3, but the non-woody residues present a very heterogeneous composition. High levels of SiO2 are present in the ash composition of the rice husk (88.2%), switch-grass (65.2%), yard waste (59.7%), miscanthus (55.9%) and wheat straw (55.3%). In contrast, the ash compositions of the coffee husk and grape pomace have only 2.5% and 5.5% of SiO2, respectively, being the CaO and K2O compounds that are present in larger quantities in the ash composition of these two biomass residues. The ash composition of the grape pomace has a significant amount of P2O5 (19.7%), as the miscanthus and pistachio shells with 12.3% and 11.8%, respectively, but in the ash composition of biomass residues such as wheat straw, rice husk and yard waste, its weight percentage is lower than 2%. These examples illustrate well the large diversity and heterogeneity of the non-woody residues and emphasise the need to characterize them at a fundamental level prior to their use in combustion systems.


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Table 1 – Non-woody biomass composition and HHV. Data taken from references [5,12,14-20] (continued). Non-woody biomass

Alfalfa stems

Proximate analysis (wt.%, dry) Fixed carbon 15.8 Volatile matter 78.9 Ash 5.3 Ultimate analysis (wt.%, daf) Carbon 47.2 Hydrogen 6.0 Oxygen 38.2 Nitrogen 2.7 Sulphur 0.2 Ash elemental composition (wt.%, daf) SiO2 5.8 Al2O3 0.07 Fe2O3 0.3 CaO 18.3 MgO 10.4 Na2O 1.1 K2O 28.1 SO3 1.9 P2O5 7.6 Others 26.4 18.7 HHV (MJ/kg)

Wheat straw

Rice husk

Coffee husk

Switch grass

Sugar cane bagasse

Almond shells

Almond hulls

17.7 75.3 7.0

16.3 73.2 10.5

1.4 91.7 6.8

14.3 76.7 9.0

12.0 85.6 2.4

20.7 76.0 3.2

20.1 73.8 6.1

44.9 5.5 41.8 0.4 0.2

40.7 6.0 32.9 0.5 < 0.02

38.2 5.1 36.8 2.4 0.05

46.7 5.8 37.4 0.8 0.2

48.6 5.9 42.8 0.02 0.04

49.3 6.0 40.6 0.8 0.04

47.5 6.0 39.2 1.1 0.06

55.3 1.9 0.7 6.1 1.1 1.7 25.6 4.4 1.3 1.9 17.9

88.2 0.3 0.2 2.8 1.3 0.7 3.7 0.8 1.6 0.4 15.7

2.5 2 2.5 37 12.1 0 35 2.5 3.8 2.6 17.7

65.2 4.5 2.0 5.6 3 0.6 11.6 0.4 4.5 2.6 18.1

46.6 17.7 14.1 4.5 3.3 0.8 0.2 2.1 2.7 8.0 19.0

8.7 2.7 2.3 10.5 3.2 1.6 48.7 0.9 4.5 16.9 19.5

9.3 2.1 0.8 8.1 3.3 0.9 52.9 0.3 5.1 17.3 18.9

12 ACS Paragon Plus Environment

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Table 1 – Non-woody biomass composition and HHV. Data taken from references [5,12,14-20] (concluded). Pistachio shells

Olive stones

Yard waste

Walnut shells

Sunflower shells

Miscanthus

Cynara

Grape pomace

Fixed carbon

17.0

16.3

13.6

37.9

19.8

15.6

14.1

26.7

Volatile matter Ash Ultimate analysis (wt.%, daf)

81.6 1.4

82.0 1.7

66.0 20.4

59.3 2.8

76.2 4.0

79.3 5.2

77.0 8.9

69.3 4.0

Carbon

50.2

52.8

41.5

53.5

47.4

48.9

46.3

51.1

Hydrogen

6.3

6.7

4.8

6.6

5.8

5.4

4.9

6.7

Oxygen

41.2

38.3

31.9

45.4

41.3

39.8

46.7

40.1

Nitrogen 0.7 Sulphur 0.2 Ash elemental composition (wt.%, dry)

0.3 0.05

0.9 0.2

1.5 0.1

1.4 0.05

0.7 0.1

1.05 0.1

1.9 0.2

SiO2

8.2

30.4

59.7

23.1

29.3

55.9

20.6

5.5

Al2O3

2.2

10.6

3.1

2.4

2.9

3.1

3.0

1

Fe2O3

35.4

9.9

2.0

1.5

2.1

2.1

1.5

1.2

CaO

10.0

9.9

23.8

16.6

15.8

8.8

30.1

37.8

MgO

3.3

7.2

2.2

13.4

6.1

3.8

6.1

7.2

Na2O

4.5

2.9

1

1

1.5

0.5

8.0

0.4

K2O

18.2

18.7

3.0

32.8

35.6

12.7

10.1

24.7

SO3

3.8

0.7

2.4

2.2

1.3

0

0

1.7

P2O5 Others HHV (MJ/kg)

11.8 2.7 18.2

7.4 2.3 21.6

2.0 1.1 16.3

6.2 0.8 n/a

4.8 0.6 n/a

12.3 0.9 17.8

0 20.3 19.3

19.7 0.8 21.2

Non-woody biomass Proximate analysis (wt.%, dry)


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Table 2 – Coal and woody biomass composition and HHV. Data taken from references [5,12,14-20]. Coal 1

Coal 2

Coal 3

Poplar

Pine shells

Pine branches

Forest residue

Fixed carbon

52.3

47.6

62.1

18.1

30.1

24.2

24.5

Volatile matter Ash Ultimate analysis (wt.%, daf)

45.4 2.3

19.7 32.8

23.3 14.6

81.0 0.9

68.4 1.5

72.8 3.0

74.8 0.7

Carbon

79.3

86.8

77.8

45.8

47.8

46.6

71.0

Hydrogen

5.9

4.0

4.5

6.5

5.6

6.3

5.3

Oxygen

8.4

5.8

14.4

47.4

46.3

31.1

43.8

Nitrogen 1.9 Sulphur 0.5 Ash elemental composition (wt.%, dry)

1.8 1.6

1.8 1.5

0.3 0.03

0.3 0.0

0.9 < 0.02

< 0.3 0.1

SiO2

39.4

50.9

48.3

4.9

9.6

6.1

6.6

Al2O3

23.2

26.9

31.4

2.3

11.9

4.4

2.0

Fe2O3

24.7

7.9

3.6

0.9

2.1

1.9

1.8

CaO

3.5

4.7

6.4

33.5

50.9

50.3

43.3

MgO

1

1.3

1.5

12.2

12.1

12

8.4

Na2O

2.6

0.2

0.2

2.9

0.8

2.7

1.0

K2O

1.2

1.0

0.6

10.6

4.1

10.2

25.8

SO3

1.5

4.7

4.1

8.2

2

2.3

2.2

P2O5 Others HHV (MJ/kg)

0.2 2.7 35.0

0.4 2.0 20.8

1.5 2.5 26.9

23.9 0.7 18.2

5.6 0.9 18.8

7.2 2.9 18.3

3.8 4.9 21.0

Solid fuel Proximate analysis (wt.%, dry)


SiO2

Al2O3

Fe2O3

CaO

MgO

Na2O


K2O

SO3

P2O5

Forest residue

Pine branches

Pine shells

Poplar

Coal 3

Coal 2

Nitrogen

Coal 1

Grape pomace

Cynara

Oxygen

Miscanthus

Sunflower shells

Hydrogen

Walnut shells

Yard waste

Olive stones

Pist. shells

Carbon

Almond hulls

Almond shells

Forest residue

Pine branches

Pine shells

Poplar

Coal 3

Coal 2

Coal 1

Grape pomace

Cynara

Miscanthus

Sunflower shells

Walnut shells

Yard waste

Olive stones

Pist. shells

Almond hulls

Almond shells

Sugar cane bagasse

Switch-grass

Coffee husk

Rice husk

Wheat straw

Alfalfa stems

0

Sugar cane bagasse

Switch-grass

Coffee husk

Rice husk

Wheat straw

Alfalfa stems

(wt.%, daf)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 (wt.%, dry)

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90

80

70

60

50

40

30

20

10

Sulphur

Figure 1 – Ultimate analysis for different solid fuels. Data taken from references [5,12,14-20].

100

90

80

70

60

50

40

30

20

10

0

Others

Figure 2 – Ash composition for different solid fuels. Data taken from references [5,12,14-20].

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3. Research facilities 3.1 Single particle experiments Experimental studies on single solid particle combustion are typically carried out in drop tube furnaces (DTFs) [21-26] or in entrained flow reactors (EFRs) [27-35]. Table 3 presents their main characteristics.

Table 3 – Summary of the main characteristics of the equipment used in particle ignition studies [21-35]. Characteristic

Equipment Drop tube reactor

Entrained flow reactor

Dominant heat transfer mechanism

Radiation

Convection

Maximum temperature (K)

Up to 1300 3

4

Up to 1800

Maximum heating rate (K/s)

~ 10 to 10

~ 104 to 105

Residence time

Order of milliseconds

Order of milliseconds

Gas composition

Broad variation

Limited variation

Optical accessibility

Difficult

Easy

DTFs, where radiation plays the main role as far as heat transfer is concerned, allow an easy control of the combustion atmosphere, concerning the gas composition, but do not permit an easy optical access to the reaction zone or reaching temperatures relevant for industrial applications. The group of Levendis, at Northeastern University, USA, has been extensively using DTFs with optical windows [21,22,24,36-39].

EFRs, where the dominant heat transfer mechanism is convection, are used mainly by the group of Shaddix, at Sandia, USA [27,28], by the group of Li, at Tshingua University, China [29-32,40], and by the group of Costa, at Instituto Superior Técnico, Lisboa, Portugal [33-35]. The combustion conditions in these reactors are comparable to those conditions encountered in industrial combustion equipment since cold particles are injected into a very hot atmosphere, composed of combustion


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products, enabling high heating rates. These reactors have the advantage of allowing an easy optical access, but do not allow a broad variation of the gas composition due to the stability limits of the flames used to produce the hot combustion products.

The typical residence time of particles in both rectors (DTFs and EFRs) is of the order of milliseconds. As such, these facilities are most adequate to study the early stages of the particle combustion, particularly the ignition behavior, provided that the experimental methods allow for a considerable time resolution. According with the measuring techniques employed, authors have used different criteria to define the onset and the mode of ignition. The early studies of Howard and Essenhigh [41] in the 1960’s defined the ignition event based on the percentage of loss of carbon and volatiles during the early instants of combustion. Not much work was carried out until very recently. The interest on single particle ignition studies was recovered mainly due to the significant advances in non-intrusive optical diagnostics that occurred in the past decade or so. Molina and Shaddix [27] defined the onset of ignition based on the CH* chemiluminescence (although some interference of blackbody emission from hot soot and from the coal/char particle was noted). In a more recent work, Shaddix and Molina [28] measured the ignition delay time of individual coal particles by capturing the visible light signal emitted by igniting particles, and defined the ignition onset as the point at which 60% of the maximum luminosity intensity was reached. The definition of ignition based on a fraction of the maximum of the visible light was also used by Li and co-workers [29-32,40]. Although soot and char emission are also included in the visible light signal captured, this has proved to be a good indicator of ignition [28]. Khatami and co-workers [23,24] also use the visible light to characterize ignition, but simply define it as the time between injection and the first visible light signal. More recently, Köser et al. [42,43] used high-speed OH-PLIF and defined the onset of ignition as the instant where an increase of the OH-signal beyond the background is first observed.


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In these studies, particles are injected either in jets [27,28,28-32,40] or as single particles [21,22,24,36-39,33-35]. Jets have the advantage of being closer to industrial applications; however, inter-particle effects become important (for example radiation exchange between particles), and these effects are not trivial to interpret and model. To fully understand and model the combustion behavior of blends or jets of particles, it is first essential to comprehend the combustion process of isolated particles.

3.2 Drop tube furnace and entrained flow reactor experiments Biomass burning takes place by devolatilization followed by homogeneous oxidation of the devolatilization products and by heterogeneous oxidation of the remaining char. Much of the current knowledge of the basic reaction processes such as biomass particle heating, pre-ignition and ignition behavior, devolatilization, combustion of the volatiles and char oxidation has emerged from studies of small quantities of particles under laminar flow conditions or low levels of turbulence. Two types of apparatus have been generally used for these studies: DTFs and EFRs, whose main characteristics are given in Table 3.

As it is heated, biomass starts to devolatilize. The amount of volatile matter released and product composition for a given biomass depend on many factors such as the particle heating rate, final temperature, holding time at the final temperature and particle size. Discrepancies in experimental findings are common in the literature, reflecting, perhaps, not only the different techniques and range of experimental conditions used, but also the extreme complexity and diversity of the biomass itself.

The char particle remaining after devolatilization consists mostly of carbon and ash. Studies of reaction rates of non-woody biomass chars with various gases (O2, CO2, steam and H2) are scarce. Although reactions of char with CO2 and steam are important in gasification applications, only the


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heterogeneous char oxidation reaction with O2, whose rate is generally much higher than char-CO2 and char-steam reaction rates, is typically considered in conventional PF combustors. Recent studies have been challenging this assumption, particularly for the case of the biomass chars, which are highly reactive. The oxidation of the char is much slower than the devolatilization process and the rate-limiting step may be chemical or gaseous diffusion. The reaction rate is influenced by a number of variables such as oxygen concentration, reaction temperature, char particle size and porosity and concentration of catalytic metallic impurities in the char.

There are a large number of DTFs and EFRs scattered around the world [18,44-55], as discussed in section 4.2, which allow to study complex aspects associated to solid fuels combustion such as char reactivity, particle fragmentation, PM formation and emission and ash deposits formation.

3.3 Large-scale experiments The apparatus described in the two previous sections permit to gather a great deal of fundamental knowledge on solid fuels combustion, but they do not reproduce important features that occur in real combustors. Mathematical models demand reliable and detailed data for their validation. Unfortunately, full-scale plants are not entirely suited to the collection of this data, being very expensive to operate, very difficult to access and differing greatly one from another. Such drawbacks reinforce the importance of large-scale furnaces as a valuable means of obtaining data of special value to the modelers. Large-scale laboratory furnaces are large enough to ensure that the essential physics of full-scale combustors are simulated. In particular, they are large enough to ensure fully turbulent flow combined with significant thermal radiation transfer, but small enough to enable the collection of detailed and reliable data.


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Despite the existence of some large-scale furnaces scattered around the world [17,56-58], as discussed in section 4.3, its costs of operation and maintenance are relatively high so that the studies available are relatively scarce, particularly as far the combustion of non-woody residues is concerned.


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4. Experimental studies on pulverized combustion of non-woody residues 4.1 Single particle combustion Most of the work on the early stages of single particle combustion using optical measurements has been focused on coal ignition. Howard and Essenhigh [41] pioneered coal ignition studies in the 1960’s, and, more recently, Molina and Shaddix [27,28] applied optical non-intrusive diagnostics for the first time to study coal ignition. Later on, the group of Levendis focused not only in the ignition of single coal particles, but also in the subsequent stages of combustion, with particular focus on particle surface temperature measurements, which were scarce up to that point [21,22,24,36-39]. More recently, the group of Li focused their efforts on developing an affordable and reliable threecolor pyrometry technique, while enhancing the understanding of the ignition modes of coal [2932,40]. In what concerns biomass, the group of Williams at University of Leeds, United Kingdom, has been focusing their studies mainly on the effects of potassium on the early stages of the combustion of relatively large woody and non-woody biomass particles. Levendis and co-workers used the experimental setup and experimental methods developed for coal to study the early stages of combustion of mainly non-woody biomass. More recently, the group of Costa at Instituto Superior Técnico, Lisboa, Portugal, focused on the ignition delay time and mode of mostly non-woody biomass residues, with particular emphasis on the effects of biomass composition, atmosphere temperature, particle size and the presence of potassium and calcium [33-35]. Table 4 shows a summary of the experimental studies on the early stages of the combustion of single biomass particles, including the materials and methods, experimental conditions, fuels and the main results of each study. Several types of biomass residues are listed in the table, namely woody residues (willow, pine, sawdust, eucalyptus, kiwi branches and sycamore branches), and non-woody residues (sugar cane bagasse, olive residue, wheat straw, rape straw, miscanthus, coffee waste, sewage sludge, pine bark, almond shells and grape pomace), along with some types of coal and lignite. The experimental studies listed in Table 4 are based on single particle experiments performed in DTFs and ERFs, of


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which the main characteristics and differences are discussed in section 3.1 of this review. In the remainder of this section, the effects of different parameters on the early stages of the combustion of biomass residues are discussed; specifically, the effect of the surrounding temperature, atmosphere composition, particle size and shape, biomass composition and presence of potassium and calcium on the ignition delay time, ignition mode, volatile combustion time and particle temperature.


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Table 4 – Summary of experimental studies on the early stages of the combustion of single biomass particles (continued). Reference

Materials and methods

Experimental conditions

Fuels

Main results

Jones et al. [59]

EFR. HS cinematography. K-emission spectroscopy. SEM.

Tg ~ 1500 K. xO2 = 2.75 vol.%.

Short rotation willow coppice (raw, demineralized and doped with K). Size range: 0.005-4 mm.

Duration of volatile combustion. Duration of char combustion. High magnification images of the particles during the combustion process. K release profiles.

Levendis et al. [22]

DTF with optical access. 3-color pyrometry. HS cinematography. SEM.

Tw = 1400 K. ug = 4.55 cm/s. tr = 5.5 s.

Sugar cane bagasse and coal.

Particle temperature as a function of time. High magnification sequential images of the particles during the combustion process. SEM images of chars.

Khatami et al. [23]


Tw = 1400 K. O2/N2 atmosphere. O2/CO2 atmosphere. 20 < xO2 < 100 vol.%.

Sugar cane bagasse and coal.

Momeni et al. [60]

EFR. HS cinematography.

1473 < Tg < 1873 K. 5 < xO2 < 20 vol.%.

Pine wood. Spherical particles: dp = 3 mm. Cylindrical particles: 1.31 < d < 3 mm; 4.16 < L < 18 mm.

Size range: 75-90 µm.



Particle temperature as a function of time. High magnification sequential images of the particles during the combustion process. SEM images of chars. Ignition temperature for different modes. Critical diameter for the ignition mode. Burnout time. High magnification images of the particles during the combustion process. Ignition, devolatilization, char burning, and total burnout times.

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Table 4 – Summary of experimental studies on the early stages of the combustion of single biomass particles (continued). Reference



Fuels

Main results

Riaza et al. [21]


Tw = 1400 K. O2/N2 atmosphere. O2/CO2 atmosphere. 21 < xO2 < 50 vol.%.

Olive residues, sugarcane bagasse, pine sawdust, torrefied pine sawdust.

Particle temperature as a function of time. High magnification sequential images of the particles during the combustion process. Burnout times.

Fatehi et al. [61]

EFR. LIBS. Numerical model of K release.

1408 < Tg < 1596 K.

Swedish wood pellets: d = 8 mm, L = 4 mm.

K release profiles. Mass loss profiles. Percentage of K mass released from particle at each stage.

Mason et al. [62]

EFR. HS cinematography. Thermometric imaging. Numerical model.

Tg = 1823 K. xO2 = 10.75 vol.%.

Pine, eucalyptus, willow. Cuboid/cylindrical particles. Size range: 0.005-4 mm.

Ignition delay time. Volatile flame duration. Char combustion duration.

Li et al. [63]

DTF with photo density elements and solid residue sampling. TG-DTG. Single particle model.

Tw = 1173 K. Air.

TG-DTG: Mass loss yields and rates. DTF: Ignition delay time. Volatile release time.

Manson et al. [64]

EFR. K-emission spectroscopy.

Tg ~ 1800 K.

Straw, softwood, torrefied softwood. Size ranges: 63-90 µm, 180200 µm, 305-500 µm, 630-800 µm. Pine, eucalyptus, wheat straw, rape straw, miscanthus, olive residues, willow doped with 0.1, 0.25, 0.5, 0.75, 1.5 wt.% of K. Cuboids: woody: 1×1×2 mm; herbaceous: 1×1×3 mm.



Profiles of K release. Burnout times. Partitioning of K between combustion stages and ash.

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Table 4 – Summary of experimental studies on the early stages of the combustion of single biomass particles (concluded). Reference



Fuels

Main results

Mock et al. [65]

EFR. HS cinematography. SEM.

Tg = 1090, 1340 K. 10 < xO2 < 40 vol.%.

Torrefied wood, coffee waste, sewage sludge. Size ranges: 150-215 µm, 425500 µm.

Sequential images of the particles during the combustion process. Radii of effective flame to particle size plotted as a function of the oxygen concentration. Average duration of heat-up and the end of volatile combustion.

Simões et al. [33]

EFR. HS cinematography. SEM.

Tg = 1500, 1575, 1650, 1700, 1800 K. x O2 = 3.5, 5.1, 6.5 vol.%.

Manson et al. [66]

EFR. Thermal imaging. K-emission spectroscopy. Single particle model.

Tg = 1823 K. xO2 = 10.7 vol.%.

Wheat straw, kiwi branches, vine branches, sycamore branches, pine bark, coal. Size ranges: 80-90 µm, 212224 µm, 224-250 µm. Pine, willow doped with 0.1, 0.25, 0.5, 0.75 wt.% of K.

Shan et al. [67]

DTF. HS cinematography. Thermocouple fixed to pellets.

Tw = 873, 973 and 1073 K. O2/CO2 atmospheres. xO2 = 21, 30, 40, 50 and 100 vol. %.

Lacebark pine, rice husk. Pellets: d = 9 mm; L = 10 mm.

Magalhães et al. [34]

TG-DTG. EFR. HS cinematography.

TG-DTG: 20 K/min. N2, air atmospheres. EFR: Tg = 1460, 1560 1660 K. xO2 = 3.5, 5.2, 6.5 vol.%.

Almond shells, olive residues, Tunçbilek lignite, Soma lignite. Size ranges: 80-90 µm, 106125 µm, 224-250 µm.

High magnification sequential images of the particles during the combustion process. Ignition mode. Ignition delay time. Devolatilization and burnout times. Particle surface temperature profiles. K release profiles. Comparison between measurements and predictions. High magnification sequential images of the particles during the combustion process. Ignition delay time. Internal particle temperature. Internal ignition temperature. Volatile combustion time. TG-DTG: Ignition temperature. Risk of self-ignition map. Ignition mode. EFR: Ignition mode. Ignition delay time.


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4.1.1 Effect of the surrounding temperature The ignition delay time decreases with the increase of the surrounding gas temperature [33,34,60,63]; in fact, all biomass combustion stages are accelerated with an increase of the surrounding temperature [60]. Simões et al. [33] and Magalhães et al. [34] observed that for high surrounding gas temperatures (Tg > 1650 K) the ignition delay time of different types of biomass converge to similar values as of those of coal and lignite, regardless of the size, indicating that the properties of the particles become less dominant as the temperature increases. Figure 3 shows a compilation of ignition delay times of single particles of one bituminous coal, two lignites and four biomass residues from Simões et al. [33] and Magalhães et al. [34].

Figure 3 – Compilation of ignition delay times of single particles of bituminous coal (BC), Tunçbilek lignite (TL), Soma lignite (SL), pine bark (PB), wheat straw (WS), almond shells (AS) and olive residues (OR) [33,34].

In what concerns the ignition mode, Simões et al. [33] observed that for low temperatures (1500 < Tg < 1650 K) it was governed by the biomass type, while for high temperatures (Tg > 1650 K) the ignition mode was governed by the particle size. Small particles tended to ignite on the surface, while large particles tend to ignite on the gas-phase. Li et al. [63] observed that at sufficiently high


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temperatures, the ignition of biomass particles changes from gas-phase ignition to surface ignition. Yuan et al. [29,30] also observed the transition of the ignition mode of coals depending on the surrounding temperature and could relate the transition with the variation of the heating-up and the variation of the devolatilization characteristic times with the temperature. As for the ignition delay time, there are also various definitions in the literature associated with the specific experimental technique used, which hinders the usage of a universal criterion and a direct comparison between studies using different diagnostics techniques.

4.1.2 Effect of the atmosphere composition Increasing the oxygen mole fraction in an O2/N2 atmosphere, increases the flame and the char surface temperatures, and decreases the burnout times [23,68]. Shan et al. [67] observed that for the same temperature, the combustion time of the volatiles decreased with the increase of the oxygen concentration. However, when the oxygen concentration was ≥ 50%, the volatile combustion time decreased marginally. Khatami et al. [22] and Shan et al. [67] consistently observed an increasing tendency of the volatiles to burn closer to the char surface with the increase of the oxygen concentration. Khatami et al. [23], Riaza et al. [21] and Shan et al. [67] consistently observed that when N2 was substituted by CO2, i.e. when changing from conventional to oxy-fuel combustion, the combustion intensity decreased with an increase of the volatile combustion and burnout times. In fact, at moderate O2 concentrations (< 30%), Khatami et al. [23] observed that most particles did not ignite. As the oxygen concentration increased, the combustion intensity of the biomass was enhanced, and both the volatile combustion time and the burnout time decreased [21,23,67]. Molina and Shaddix [27] studied the ignition and combustion behavior of bituminous coal under both conventional and oxy-fuel combustion conditions. To explain the effect of changing the atmosphere they examined the phenomena occurring after injection of the coal into the flow reactor. They argued that for the case of a non-reactive particle the only gas property that affects the initial particle heat-up


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is the thermal conductivity of the gas. Comparing the thermo-physical properties of N2 and CO2 they observed that the ratio of the thermal conductivities was close to one, meaning that during the initial instants there should be no significant difference in particle heating rates for the two atmospheres. After ignition has taken place, the heat capacity of the reacting mixture gives a measure of the thermal sink for any heat that is chemically released. The authors observed that at high temperatures the heat capacity was 1.7 times larger for CO2 than for N2, indicating that in the O2/CO2 atmosphere more energy needs to be released to reach the self-sustained combustion reaction.

4.1.3 Effect of the particle size and shape As expected, large particles require more time for the occurrence of ignition [33,34,63]. Simões et al. [33] observed that the effect of the particle size is more pronounced at low temperatures, as seen in Figure 3. In these conditions, the ignition delay time of the large particles presented a larger deviation between different biomass residues than those of the small particles. For the highest temperatures considered in Figure 3, the ignition delay time of the biomass residues converge to similar values, regardless of the size. In what concerns the ignition mode, the results of Simões et al. [33] suggest that the critical diameter for the ignition mode transition varies with the residue type. The ignition mode of large wheat straw particles changed from surface to gas-phase with increasing temperature, consistent with the critical diameter decreasing with increasing temperature. The results also suggest that the pine bark particle diameter is above the critical value for all tested conditions, but in small particles heterogeneous reactions onset may be promoted by fast heating-up. Furthermore, Magalhães et al. [34] observed that the small size particles of biomass residues exhibited an increasing tendency for surface ignition in comparison with the large size particles, particularly at low temperatures. Finally, Momeni et al. [60] observed that spherical particles have the longest conversion time, as compared with non-spherical, because they have the lowest surface area/volume ratio. Indeed, highly non-spherical particles favor a fast and complete conversion.


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4.1.4 Effect of the biomass composition Levendis et al. [22], Riaza et al. [21], Simões et al. [33] and Magalhães et al. [34] consistently observed that biomass appears to have a somewhat unified combustion behavior, despite being very heterogeneous in composition and in source, contrary to coal, whose combustion behavior differs widely with the rank. In general, the volatile flames of the biomass particles are less sooty than those of bituminous coal particles [21,33], and the particles experience shrinking core mode of conversion during both devolatilization and char combustion [22]. The ignition mode of biomass particles occurs predominantly in the gas-phase [33] and variations in the ignition delay time arising from differences in the composition are more pronounced at low temperatures [33,35], see Figure 3.

4.1.5 Effect of the presence of potassium and calcium Jones et al. [59] demonstrated a catalytic effect of potassium at conditions of high heating rates and temperature, influencing both devolatilization and char burnout. Potassium release occurs in three stages, associated with the devolatilization, char combustion, and particle shrinking. Manson et al. [64] further showed that the release of potassium during the devolatilization stage of combustion is small when compared with the subsequent release during char combustion for a total of 13 biomass residues. The maximum release rate of potassium during char combustion was correlated to the potassium content in the particle, and the proportion of potassium released during combustion to that retained in the ash was correlated to the initial potassium content, although the latter relationship differed between wood and herbaceous materials. More recently, Carvalho et al. [35] showed that both the ignition delay time and the volatile combustion time increased with the demineralization process, further confirming the catalytic effect of the presence of the minerals on the early stages of the combustion of biomass particles. The particles impregnated with the same concentration of K as the raw biomass particles showed a similar ignition delay time as the raw biomass particles, but a higher volatile combustion time. The particles impregnated with the same concentration of Ca as the


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raw biomass particles showed higher ignition delay time and volatile combustion time than the raw biomass particles. The ignition delay time decreased as the potassium concentration increased, but in the case of the calcium, the ignition delay time first increased and then decreased as the calcium concentration increased. Both impregnated potassium and calcium had an impact on the volatile combustion by decreasing its duration as the concentrations of impregnated potassium and calcium increased. This effect was more prominent in the case of the calcium impregnation. The impact of the concentration of potassium and calcium was more significant on the volatile combustion time than on the ignition delay time.

Fatehi et al. [61] proposed a reaction mechanism based on experimental observations in which the potassium is released during the devolatilization stage in the form of KCl. Not all of the potassium is released and the remaining is retained as char bounded potassium. The potassium released during the char reaction and ash-cooking stages is in the form of KOH. The rate of release of potassium during char reaction and ash-cooking stages follows a first order Arrhenius expression. The authors estimated the kinetic constants along with the reaction path, and were able to predict the potassium release with an acceptable accuracy. However, a deeper phenomenological understanding of the impacts of potassium on the conversion of biomass is still needed.

4.2 Combustion studies in drop tube furnaces and entrained flow reactors DTFs and EFRs can be used to study ignition, as discussed in the previous section, but also flame stability, pollutant formation and emission, devolatilization, char morphology and burnout evolution, and kinetic parameters [69]. Table 5 presents a summary of the experimental studies on combustion of biomass, including non-woody residues, performed in DTFs and EFRs, whose main characteristics are discussed in section 3.2 of this review. The table includes information on the materials and methods, experimental conditions, fuels and the main results of each study.


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Table 5 – Summary of experimental studies on combustion of biomass performed in DTFs and EFRs (continued). Reference


Combustion behavior Riaza et al. [21] DTF. Thermocouple type S. Three-color optical pyrometer. HS cinematography. Torrefaction device.

Luan et al. [46]

DTF. PDSD. ICP-OES. SEM/EDS/XRD.

Pohlmann et al. [70]

DTF. Spectroscopic techniques. SEM. TGA.

Botelho et al. [18]

DTF/TGA/QMS. Thermocouple type R. Water-cooled stainless-steel probe. Gas analysers. Isokinetic probe. Tecora total filter holder.


Fuels

Main results

Torrefaction: Nitrogen flow rate = 50 mL/min. T = 240 ºC. Heating rate = 10 ºC/min. DTF: Tw = 1127 ºC. O2/CO2 atmospheres = 21, 30, 35, 50 vol.% of O2. DTF: Tw = 1500 ºC. Air flow rate = 15 L/min. Excess air coefficient = 1.3. PDSD: Ash sample: 0.4 g. Flow rate = 5 mL/min. Heating rate = 10 ºC/min. Torrefaction: T = 250 ºC. Carbonization: T = 450 ºC. Heating rate = 5 ºC /min. Nitrogen flow rate = 50 mL/min. DTF: Tw = 1300 ºC. Fuel feed rate = 1 g/min. O2 = 2.5-5 vol.% in N2 or CO2 TGA: T = 50-900 ºC. Heating rate = 20 ºC/min. Air flow rate = 50 mL/min. Tw = 1100 ºC. Fuel feed rate = 23 g/h. Air flow rate = 4 L/min.

Sugarcane bagasse, pine sawdust, torrefied pine sawdust, olive residues (75 < dp < 150 µm).

HS cinematography images. Temperature and burnout data.

Wheat straw (dp < 1 mm), pine sawdust (dp < 0.8 mm), coal (dp < 100 µm).

SEM/EDS images. XRD patterns. Parity diagrams.

Torrified woody (36 < dp < 150 µm), carbonized samples, torrified olive stones (36 < dp < 80 µm).

Combustion and burnout data. Optical micrographs. Mesopore and micropore surface areas.

Raw and torrified grape pomace (dp < 1000 µm).

TG and DTG curves. Kinetic parameters.


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Jimenez et al. [71]

EFR. Isokinetic probe. Laser diffractometer. SEM. Ash, PM and gaseous emissions Luan et al. [68] DTF (horizontal and vertical chamber). Thermocouple type K. Gas analysers. GC/MSD. Lu et al. [72] EFR (primary and reburning zone). Water-cooled stainless-steel probe. Gas analysers. SEM. De Fusco et al. [47]

DTF. SEM/EDS.

Yao et al. [73]

DTF. Isokinetic probe. 13 stages LPI. SEM/EDX. AAS. DTF. Isokinetic probe. 13 stages LPI. XFR.

Wang et al. [74]


Fuels

Main results

Tw = 1040, 1175, 1300 ºC at 4 vol.% of O2. Tw = 1175 ºC at 8 vol.% of O2. Fuel feed rate = 40 g/h.

Cynara cardunculus (dp = 500 µm).

Kinetic parameters. Particle size distribution evolution. SEM images.

Reburning ratio = 10-25%. Equivalence ratio of reburning zone = 0.7-1.

Rice husk (SMD = 707 µm), corn straw (SMD = 531 µm), bituminous coal (SMD = 49 µm). Cotton stalk, wheat straw, rice husk, straw (coarse particles: 180 < dp < 425 µm; fine particles: dp < 180µm). Sunflower hulls without and with H3PO4 and H3PO4 + CaCO3 (dp < 2 mm). Dried sewage sludge (dp < 150 µm).

Reburning mechanisms.

Primary zone: NO concentration = 800 ppm. Excess air coefficient = 1. Reburning zone: T = 900-1000 ºC. Tw = 500-900 ºC. Fuel flow rate = 1.25 kg/h.

Tw = 450-950 ºC. Excess air coefficient = 1.5. Fuel feed rate = 0.5 g/min. Kaolin = 5% mass of sludge. Tw = 1150 ºC. N2/O2 = 4/1, 1/1 (volume basis). Coal/biomass = 75/25 (mass basis). Fuel feed rate = 0.3 g/min.


Sawdust, straw (dp < 200 µm), coal (100 < dp < 200 µm).

NO reduction efficiency. SEM images.

Organic and inorganic matter composition and concentration. SEM images. SEM images. Metals transformation. Particle size distribution evolution.

PM characterization and composition.

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Fuels

Main results

Wang et al. [19]

DTF. Laser diffractometer. Thermocouples type K and R. Water-cooled stainless-steel probe. Gas analysers. Isokinetic probe. Tecora total filter holder. 3 stages LPI. 13 stages LPI. SEM/EDS. As in Wang et al. [19].

Tw = 1100 ºC. Fuel feed rate = 23 g/h. Air flow rate = 4 L/min.

Rice husk, pine branches, wheat straw, coffee husk, RDF (dp < 1128 µm), coal (dp < 1000 µm).

Temperature and burnout data. SEM images. Elemental composition of ash particles.

As in Wang et al. [19].

Temperature and burnout data. PM concentrations. SEM images.

Costa et al. [20]; Costa and Costa [75]

As in Wang et al. [19].

Wang et al. [48]

DTF. Burning and burning-out sections. Two-stage ash removal section. Thermocouple type R. SEM/EDX/XRD. EFR. Air-cooled probe. SEM/EDX. CFA.

Torrefaction: T = 280-300 ºC. Nitrogen atmosphere = 3 L/min. DTF: Tw = 900-1100 ºC. Fuel feed rate = 23 g/h. Air flow rate = 4 L/min. Tw = 850 ºC. Excess air coefficient = 1.25-1.5. Coal in the blend = 0, 5, 15, 20, 40, 60, 90, 100 wt.%.

Wheat straw, rice husk (size fractions: 100 < dp< 200 µm; 400 < dp < 600 µm; 800 < dp < 1000 µm; dp < 1000 µm). Torrefied and raw pine shells, olive stones, wheat straw (dp < 1000 µm).

Branco and Costa [52]

Theis et al. [49,50]

Tw = 1000 ºC. Fuel feed rate = 12.5 g/min. Flue gas velocity = 2 m/s. STP = 550 ºC.


Temperature and burnout data. Kinetic parameters. PM concentrations. SEM images.

Straw, coal.

SEM images. XRD spectrum diagrams.

Eucalyptus bark, oat straw, peat (dp < 1 mm).

SEM images. Ash composition. Deposition rates and composition versus biomass fraction.

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Table 5 – Summary of experimental studies on combustion of biomass performed in DTFs and EFRs (concluded). Reference



Fuels

Main results

Ren et al. [76]

DTF. Thermocouple type S. Gas analysers.

Corn straw, miscanthus, sugarcane bagasse, corn, olive residues, rice husk, beech wood (75 < dp < 150 µm).

Fluidization and combustion efficiency.

Moço et al. [51]

DTF. Laser diffractometer. Thermocouples type K and R. Tecora total filter holder. Stainless-steel deposition probe with a removable stainless-steel capsule. SEM/EDS. Conventional DTF and a two-stage pyrolysis/combustion DTF. TGA/IC/ICP-OES/ICP-MS. 13 stages LPI.

Torrefaction: T = 270 ºC. Heating rate = 10 ºC/min. Duration = 30 min. DTF: Tw = 1127 ºC. Air flow rate = 2 L/min. Fuel feed rate = 0.2 g/min. Tw = 1200 ºC. Air flow rate = 0.0031 Nm3/min. Fuel feed rate = 23 g/h.

Grape pomace (dp < 1000 µm).

Deposition rates and composition versus time. SEM images.

Fast pyrolysis of biosolid at T = 800, 900, 1000 °C. Combustion of biosolid and its derived products (char or volatiles) at Tw = 1300 ºC. DTF biosolid feed rate = 0.05 g/min (equivalent char and volatiles feed rates).

Biosolid (75 < dp < 150 µm), biosolid char and biosolid volatiles.

Fast pyrolysis of biosolid, cellulose and polyethylene at T = 800, 1000 °C. Combustion of biosolid, biosolid char and volatiles of biosolid, cellulose and polyethylene at Tw = 1300 ºC. Biosolid, cellulose and polyethylene feed rate = 0.05 g/min.

Biosolid (90 < dp < 150 µm), biosolid char and biosolid, cellulose and polyethylene volatiles

Partitioning of trace elements in char and volatiles during biosolid fast pyrolysis. Yields and particle size distributions of PM emitted during the combustion of biosolid, char and volatiles. Elemental particle size distributions and yields of individual trace elements in PM10. Direct evidence on the importance of volatile-char interactions in PM emission. Changes in major and trace elements distribution in PM due to volatile–char interactions.

Liaw et al. [54]

Chen et al. [55]

A three-stage pyrolysis/combustion reactor. TGA/IC/ICP-OES/ICP-MS. 13 stages LPI.


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4.2.1 Impact of the torrefaction on biomass combustion Botelho et al. [18], Costa et al. [20], Costa and Costa [75] and Ren et al [76] studied the impact of biomass pre-treatments, like torrefaction and carbonization, on the biomass combustion process (Table 5). Botelho et al. [18] evaluated the combustion characteristics of raw and torrefied grape pomace using both a TGA and a DTF and verified that the burnout values along the DTF were always lower for the torrefied grape pomace and the NOx concentrations always higher. In contrast, Ren et al. [76] carried out combustion experiments in a DTF using raw and torrefied woody, herbaceous and crop-derived wastes and concluded that that the torrefaction pre-treatment decreased the NOx emissions. Additionally, Ren et al. [76] also verified that the combustion of the torrefied biomass residues originated lower SO2 emissions, particularly for bagasse, corn straw, miscanthus, rice husk and beech-wood. Costa et al. [20] examined the impact of the torrefaction on particle fragmentation of raw and torrefied pine shells and olive stones in the same DTF used by Botelho et al. [18], and concluded that the torrefaction process did not affect the particle fragmentation process during the last stages of the combustion process. Costa and Costa [75] extended the work of Costa et al. [20] to other non-woody residues, confirming that no fragmentation occurred in the combustion of the raw and torrefied olive stones, but it occurred in the last stages of the combustion of raw and torrefied wheat straw.

4.2.2 Biomass combustion under oxy-fuel conditions CO2 emissions from fuel combustion have a negative impact on the environment and needs to be reduced. Oxy-fuel combustion is a very attractive technology for reducing emissions of CO2 if it is sequestered (carbon capture and storage). Since the combustion of biomass is considered CO2 neutral, the combustion of biomass under oxy-fuel conditions will result in ‘negative’ CO2 emissions. Pholmann et al. [70] conducted combustion tests with three woody biomass chips and olive stones, previously torrefied at 250 °C or carbonized at 450 °C, in a DTF under different O2/N2 and O2/CO2


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atmospheres. The authors verified that the burnout values were always higher under oxy-fuel conditions than under conventional combustion conditions. Usually, the carbonized residues presented lower burnout values than the torrefied ones, and the reactivity of the biomass chars decreased with the increase of the pre-treatment temperature. Riaza et al. [21] examined the combustion of single particles of sugarcane bagasse, raw and torrefied pine sawdust and olive residues in a DTF in air and O2/CO2 atmospheres. The authors observed that the combustion intensity of the biomass residues was stronger in air than under oxy-fuel conditions, and that replacing N2 by CO2 reduced the combustion temperature and increased the burnout time, but with the increase of the oxygen concentration in mixture to 30-35% the combustion intensity was restored. Fryda et al. [44] studied the impact on the ash behavior of the co-combustion of coal with shea meal in a DTF under oxy-fuel conditions. The results obtained showed that the deposition rate and the deposition propensity were higher under oxy-fuel conditions, and that blends of coal with shea meal presented always lower deposition propensity that coal alone. Ruscio et al. [45] evaluated the impact of combustion under oxy-fuel conditions on the gaseous emissions in a DTF firing three pulverized biomass residues (olive residues, corn residues and torrefied pine sawdust). The results showed that, in the case of the olive stones and torrefied pine sawdust, the PM yields were analogous to the biomass ash contents, but they also observed that under oxy-fuel conditions lower submicrometer PM are obtained.

4.2.3 Ash deposition The high levels of alkalis and chlorine present in some biomass residues can have a negative impact on the metal surfaces of industrial boilers due to their high propensity for slagging and fouling, which reduce heat transfer and facilitate the initiation of corrosive reactions. As shown in Table 5, Wang et al. [19], Luan et al. [46], De Fusco et al. [47], Wang et al. [48], Theis et al. [49,50] and Moço et al. [51] examined the ash deposition characteristics of a number of non-woody residues in


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DTFs fired alone, mixed with other biomass residues or co-fired with coal. Wang et al. [19] studied the combustion behavior and ash characteristics of rice husk, straw, coffee husk, RDF, pine and coal in a DTF. The authors concluded that RDF has some potential for corrosion and slagging, and that the combustion of rice husk and straw induces slagging formation due to the formation of low melting temperature ashes. Luan et al. [46] used a DTF to evaluate the ash deposition propensity during the co-firing of coal with wheat straw and pine sawdust and to assess the suitability to use the ash sintering temperature as an index of the deposition propensity. The authors verified a non-linear increase of the deposition propensity with the increase of the mass of biomass in the blend, and also noticed that the impact of the use of wheat straw on the ash sintering temperature is higher than the impact of the use of sawdust. De Fusco et al. [47] fired raw sunflower hulls and sunflower hulls with additives (different mixtures of phosphoric acid and calcium carbonate) in a DTF to assess its fouling deposition propensity and behavior. The results indicated that the fouling resulting from the combustion of sunflower hulls can be reduced by optimizing the addition of phosphorus and alkaline-earth metals. Wang et al. [48] performed tests in a DTF using different co-firing ratios of coal and straw, and noticed that the co-firing ratio affected the ash quantity and that the chlorine had a significant impact on the ash components and quantity. Theis et al. [49,50] used an EFR to evaluate the deposition rate and deposits composition of the combustion of mixtures of peat, which has low fouling propensity, with eucalyptus bark and with oat straw, both residues with high fouling propensity. Theis et al. [49] verified that the combustion of blends of peat with bark up to 30% and blends of peat with straw up to 70% does not increase the deposition rates. Theis et al. [50] verified that, for mixtures of peat and eucalyptus bark, the higher the Cl/S ratio, the higher the deposition rate. For mixtures of peat and straw the deposition rates are independent of the Cl/S ratio. Moço et al. [51] studied the ash deposit formation during the combustion of pulverized grape pomace in a DTF using sampling times between 2 and 14 hours. The results showed no impact of the sampling time on the concentrations of K, P and Mg, but it increased the concentrations of Ca and Si.


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4.2.4 Particulate matter formation and emission Branco and Costa [52] examined the effect of the particle size on the burnout and PM emissions from the combustion of wheat straw and rice husk in a DTF. The authors observed that the burnout values for the rice husk are higher than those for the wheat straw, being the PM concentrations comparable. Particle fragmentation is observed during the last stages of the combustion of the higher particle size class of the wheat straw, but no fragmentation occurs during the combustion of the rice husk regardless of the size fraction. Wang et al. [74] co-fired rice husk with coal in a DTF in two different N2/O2 atmospheres (4:1 and 1:1), being the coal/biomass mass ratio kept at 75/25 (mass basis). The authors verified that the particle size distribution of the PM10 is bimodal, with one peak at about 4.3 µm and the other at about 0.1 µm; the increase of the oxygen concentration in the atmosphere originates an increase of the total concentration of PM10, but a decrease of the percentage of PM1 in the PM10.

The group of Wu, at Curtin University, Australia [53,54] used a two-stage pyrolysis/combustion DTF to study the PM emissions during the combustion of biosolids from wastewater treatment plants and its derived products (char or volatiles). The authors concluded that PM produced from the combustion of volatiles produced in situ from biosolid fast pyrolysis is dominantly PM1 and has a unimodal distribution, while char combustion produces both PM1 and PM1-10, with the PM having a bimodal distribution. Moreover, authors observed significant differences in the PM between direct biosolid combustion and the sum of PM from char and volatile combustion, which suggest that direct biosolid combustion may have produced substantially different char and volatiles. Very recently, the same group [55] used a three-stage pyrolysis/combustion DTF to demonstrate the importance of volatile-char interactions in PM emissions from the combustion of biosolid volatiles and concluded that the volatile-char interactions enhance the emission of PM1 (mainly PM0.1).


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To face the problems related to the high amount of sludge and the difficulties to find sustainable final disposal sites in Japan, Yao et al. [73] performed combustion tests in a DTF to assess the combustion behavior, ash deposition and emission of sludge alone and sludge blended with kaolin. The results obtained indicate that lead and cadmium concentrate in the fine PM and that the addition of kaolin can shift the lead and the cadmium from fine PM to coarse PM.

4.2.5. Use of biomass residues as a reburn fuel Luan et al. [68] and Lu et al. [72] assessed the aptitude of the non-woody residues to reduce NO emissions through reburning. Luan et al. [68] performed combustion tests in a DTF using corn straw and rice husk as reburn fuels, and showed that both residues have a better aptitude to reduce NO than bituminous coal, because of their higher content of volatiles. Lu et al. [72] used an EFR to demonstrate that the biomass type has a significant impact on the NO reduction efficiency as a secondary fuel in reburning processes.

4.3 Combustion studies in large-scale furnaces In 1998, Heinzel et al. [77] referred the high potential of using biomass residues in co-combustion processes with coal as a promising solution for CO2 reduction. Five years later, Nussbaumer [56] reinforced the importance of using biomass residues and wastes for energy production, stating that combustion is one of the most known and important technologies for biomass conversion. These authors, however, underlined the need to continuously improve the knowledge regarding combustion efficiency and pollutant emissions. Additionally, biomass combustion can increase the propensity for the formation of slagging and fouling in industrial equipment, particularly when non-woody residues are used. All these and other issues related with the use of biomass residues have been the focus of a number of experiments undertaken in large-scale furnaces, like the works of Heinzel et al. [77], Robinson et al. [78], Abreu et al [57], Wang et al. [79], Bartolomé et al. [17] and Nordgen et al. [80],


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among others. Table 6 presents a summary of the experimental studies on combustion of biomass, including non-woody residues, performed in large-scale furnaces, whose main characteristics are discussed in section 3.3 of this review. The table includes information on the materials and methods, experimental conditions, fuels and the main results of each study.


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Table 6 – Summary of experimental studies on combustion of biomass performed in large-scale facilities (continued). Materials and methods


Heinzel et al. [77]

Reference

Large-scale furnace. Air-cooled deposition probes.

Large-scale MFC. Air-cooled stainless-steel probe. ICP-OES. SEM. Large-scale laboratory furnace. Air-cooled deposition probe. Uncooled ceramic deposition probe. XFR. SEM/XRD. As in Abreu et al. [57].

Bituminous coal, straw, miscanthus, beach wood, oat, sewage sludge. Coal, two types of straw, switchgrass, wood (dp < 1 mm).

Deposits quantity. Deposits composition and fusion temperature.

Robinson et al. [78]

Thermal input = 300 kW. Blends: 0, 25, 50% biomass (energy basis). SPT = 500-900 ºC. Test duration = 180-1200 min. Thermal input = 30 kW. Blends: 15% biomass (energy basis). SPT = 540 ºC. Test duration = 1 h. Thermal input = 150 kW. Blends: 10-50% biomass (energy basis). SPT = 500 ºC. Test duration = 2 h.

Bituminous coal, pine sawdust, olive stones (dp < 1000 µm).

Deposition rate. SEM images. Deposits composition.

Thermal input = 100, 140 kW. Blends: 40% biomass (energy basis). SPT = 500, 850 ºC. Test duration = 2 h. Thermal input = 500 kW. Blends: 0, 5, 10 and 15% cynara (energy basis). SPT = 550 ºC. Test duration = 50 min.

Coal, pine branches, olive stones, peach stones, wheat straw (dp < 2 000 µm). Cynara (dp < 0.5 mm), two coals (dp < 0.3 mm).


Thermal input = 500 kW. Blends: 0, 5, 10, 15% biomass (energy basis).

Cynara, poplar (dp < 0.5 mm), coal (dp < 0.3 mm). Miscanthus (dp < 2 mm), Daw Mill coal.

DTG curves. Gases concentration vs. biomass share. Gaseous emissions. SEM images. Deposits composition.

Abreu et al. [57]

Wang et al. [79]

Bartolomé et al. [17,58]

Bartolomé et al. [58]

Khodier et al. [16]

Large-scale laboratory furnace. Air-cooled deposition probe. Gas analysers. SEM/EDS. TGA. As in Bartolomé et al. [17].

Laboratory furnace. Infra-red pyrometers. Air-cooled probes. FTIR. SEM/EDX/XRD.

Thermal input = 100 kW. Blends: 0, 20, 40, 60, 80, 100% biomass (mass basis). SPT: 500-700 ºC. Test duration = 3 h.


Fuels

Main results


SEM images. Deposits composition.

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Table 6 – Summary of experimental studies on combustion of biomass performed in large-scale facilities (concluded). Reference Jurado et al. [81]

Fuller et al. [82]

Nordgren et al. [80]



Laboratory furnace. Air-cooled deposition probes. FTIR/SEM/EDX/XRD. TGA. Industrial furnace. Large-scale laboratory furnace. ICP-OES. SEM/EDX. XFR. Laboratory furnace. Air-cooled deposition probe. 13 stage LPI. FTIR. SEM/EDS. XRD.

Thermal input = 100 kW Blends: 0, 25, 50, 100% CCP (mass basis). SPT = 650, 750 ºC. Test under air- and oxy-fuel combustion conditions. Thermal input = 0.5 MW. Blends: 0, 10, 50, 100% cardoon (energy basis).

Thermal input = 150 kW. Blends: 0, 25, 50% (mass basis) STP = 250, 550 ºC. Test duration = 5-6 h.

.


Fuels

Main results

CCP (dp < 2 mm), coal (dp < 0.5 mm).

SEM images. Deposits composition. Ash XRD charts.

Cardoon (d10 = 33.72 µm, d50 = 244.01 µm, d90 = 838.34 µm), lignite (d50 = 150 µm, d90 = 475 µm). Wheat straw, pine, stem wood, softwood bark (dp < 1 mm).

Fly ash composition. SEM images. Fly ash quality.

Deposits composition. Bottom ash composition. Fly ash particle mass size distribution.

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4.3.1 Ash deposition Heinzel et al. [77] assessed the potential of slagging and fouling from the co-combustion of straw, miscanthus, beech wood, oat and sewage sludge with coal in a large-scale laboratory furnace. The authors verified that most deposit samples softened at higher temperatures, and slagging occurred for higher biomass ratios in the blend on uncooled refractory and when the biomass particles were not completely burned and sticky before reaching the probes. Robinson et al. [78] discussed the cocombustion of biomass with coal as a short-term option for CO2 reduction, which feasibility depends, in addition to other factors, on ash deposition. To study this issue, the authors developed experiments with blends of eight solid fuels (two types of straw, switchgrass and wood and different coals) in a down-fired large-scale multi-fuel combustor (MFC). The authors emphasized that, even samples from the same type of biomass can have distinct characteristics, namely alkali and chlorine contents due to, for example, agricultural practices, making crucial the analysis of the fuel characteristics before to extrapolate the results obtained in their study to other biomass residues. Abreu et al. [57] and Wang et al. [79] performed experiments in a large-scale laboratory furnace to assess the effect on ash deposition of the co-combustion of coal with different woody and non-woody residues (pine sawdust, olives stones, wheat straw and peach stones). In line with other studies, these authors [57,79] observed that the ash deposition rate depends on the biomass characteristics used in the cocombustion with coal. Blends of coal with woody biomass have lower impact on ash deposition, in contrast with blends of coal with olive stones or with wheat straw, which can originate deposits with a high degree of adherence to metal and refractory surfaces. Bartolomé et al. [17] also performed cocombustion tests in a large-scale furnace to assess the impact on the ash formation of the cocombustion of cynara with two different coals (South African and Spanish). The authors observed that the quantity of deposits for blends with biomass up to 15% (energy basis) remains similar, and that the deposits formed were soft, loose and easy to remove. Finally, Nordgen et al. [80] assessed the ash behavior during the co-firing of different mixtures of straw and woody biomass, which is Ca43 ACS Paragon Plus Environment

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rich, in a laboratory furnace. The authors observed that the slagging and fouling propensities of all co-firing mixtures studied were reduced, as compared to that of the pure straw firing.

4.3.2 Fly ash and gaseous emissions In addition to ash deposits related problems, the combustion of biomass can also have a severe negative impact on the environment and on the human health due to the PM and gaseous emissions. Bartolomé et al. [58] examined the gaseous emissions during the co-combustion of coal with cynara and poplar in the facility used by Bartolomé et al. [17], and observed that the NOx and SO2 emissions decreased with the introduction of both biomass residues. Khodier et al. [16] performed tests on a large-scale furnace to evaluate the impact of co-firing coal with miscanthus on the gaseous emissions. The authors observed that the increase of the miscanthus share in the blend resulted in a reduction of the SOx, NOx and HCl levels.

Jurado et al. [81] performed co-combustion tests in a laboratory furnace with different coals with agricultural sub-products produced from wheat and straw briquettes blends, under air and oxy-fuel conditions, to examine the ash deposition, fly ash and gaseous emissions. The authors concluded that the impact of the oxy-fuel combustion on the parameters examined is insignificant, in contrast with the results obtained in DTF experiments. Fuller et al. [82] evaluated the fly ash quality from the cocombustion of woody and herbaceous residues with coal to verify their suitability to be used as raw material in some industries, like cement and concrete production industries. The co-firing tests of coal with woody biomass were performed in an industrial boiler and the co-firing tests of coal with cardoon pellets were performed in a large-scale laboratory furnace. The authors concluded that cofiring coal with herbaceous residues up to 50% (energy basis) or co-firing coal with woody biomass at a very high thermal share do not have any negative effect on the current fly ash use.


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5. Numerical modelling on pulverized combustion of non-woody residues Numerical work focused on non-woody residues is extremely limited. With the purpose of presenting a global overview of the modelling strategies available to study the combustion of non-woody residues, it is helpful to present and discuss studies focusing on woody biomass and coal combustion. The limitations of the strategies applied to woody biomass or coal combustion, as well as the necessary modifications to model non-woody residues, are discussed when appropriate.

Before discussing model strategies, it is also convenient to define the main steps of biomass combustion and how to approach each step from the modelling point of view. It is widely accepted that biomass conversion follows the general steps: (i) inert heating, (ii) drying, (iii) devolatilization, and (iv) char combustion [36,83,84]. Note that, as discussed in 4.1, ignition may occur in the gasphase or on the surface of the particle. Therefore, it may occur after step (iii) or after step (iv) depending on the conditions of the combustion atmosphere, more than on the properties and composition of the biomass. The inert heat phase duration is essentially dependent on the thermophysical properties of the particle and the surrounding gas. Even though drying may have an important impact on flame stabilization of biomass with high moisture content, its mathematical modelling is not addressed here. The term devolatilization is used for the thermal decomposition of biomass in an oxidizing atmosphere. Under these conditions, a gaseous flame may be established in the surroundings of the particle and act as an external source of heat that accelerates the process of thermal decomposition and consumes the primary gaseous products. In the interest of developing kinetic models that can accurately describe the decomposition of biomass, including weight loss and the formation of primary products, it is convenient to exclude external sources of heat and oxidizing reactions. Therefore, detailed kinetic models have been developed under pyrolysis conditions and this is the focus of this section in what concerns thermal decomposition of biomass. Empirical models for pyrolysis are discussed, with a focus on the limitations of the methods used to calibrate 45 ACS Paragon Plus Environment

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the model constants. The gaseous products originated during the thermal decomposition may undergo successive decomposition or combustion reactions in the surrounding gas-phase. Secondary or successive gas-phase reactions of the released volatile species are promoted by increased residence time and increased temperature. The gas-phase reactions are outside the scope of this review. Char combustion is a very complex process involving intra-porous gas diffusion and absorption of gaseous reactions, reactions between absorbed reactants and the solid matrix, and desorption of products. Given the typical low value of fixed carbon content of biomass, devolatilization is the governing step of biomass combustion, so little attention has been given to the numerical modelling of biomass chars. Furthermore, biomass chars are highly reactive and, as such, it has been usual to describe biomass char combustion as diffusion controlled. There are still a number of open questions in what concerns biomass char combustion that are addressed in this chapter, along with the modelling of char fragmentation, which is an open topic with significant importance and potential for substantial developments. The non-woody residues may have a significant amount of inorganic material so that interactions between the inorganic and the organic phase during thermal decomposition and char combustion may become relevant. The subject of inorganic chemistry is highly complex, and an in-depth discussion is outside the scope of this review.

The combustion process of biomass particles leads to mass loss and variations in particle density and diameter that are combustion step related. The particle mass balance governing total mass loss rate can be written as:

ௗ௠೛ ௗ௧

= −݉ሶ௩௢௟ − ݉ሶ௖௛௔௥

(1)

The first term of Eq. 1 corresponds to the devolatilization rate, which may be further decomposed to encompass several individual rates depending on the choice of devolatilization model. The second 46 ACS Paragon Plus Environment

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term corresponds to the char combustion rate. Modelling biomass combustion is essentially a matter of closing the modelling terms ݉ሶ௩௢௟ and ݉ሶ௖௛௔௥ , which are the focus of this section and the methods used to achieve the closure are discussed below. Eq. 1 applies to the simplest case of neglecting internal temperature gradients. However, the equation can be extended for cases where temperature gradients are non-negligible, i.e., for large particles. In numerical studies of practical applications, namely large-scale combustion of pulverized biomass, the assumption of neglecting internal temperature gradients is usually accepted [85-88]. The interested reader in modeling large particles is referred to the review of Di Blasi [89], where intra-particle transport phenomena and unreactedshrinking core model are discussed along with a summary of results available in the literature.

5.1. Pyrolysis modelling The simplest form of modelling biomass pyrolysis is through the empirical single first order reaction (SFOR) model, in which the overall volatile rate is calculated as follows:

݉ሶ௩௢௟ =

ௗ௏ ௗ௧

ா

= ݇ሺܸெ − ܸሻ௡ = ‫ ܶܣ‬௕ exp ቀ− ோ்ቁ ሺܸெ − ܸሻ௡

(2)

where VM and V are the total volatile content and the released volatile gases, respectively. It follows a n-order kinetic rate expression, in the Arrhenius form, with the following kinetic parameters: the preexponential factor A, the temperature exponent b, and the activation energy E. The SFOR model is easy to implement and has an associated reduced cost. This model can be extended to multiple noncompetitive parallel independent rates. The contribution of the reaction is described by the first-order reaction:

ௗ௏೔ ௗ௧

௡

ா

= ݇௜ ൫ܸெ,௜ − ܸ௜ ൯ = ‫ ܶܣ‬௕ exp ቀ− ோ்೔ ቁ ൫ܸெ,௜ − ܸ௜ ൯

௡

(3)


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where i denotes one particular reaction, Vi is the mass fraction of volatiles evolved up to time t and VMi is the maximum amount of volatiles released from reaction i. The review of Di Blasi [89] presents a comprehensive overview on measurements of pyrolysis rates and kinetic constants of one- and multi-component mechanisms. When formulating one-component mechanisms, linear forms of the mass conservation equations, usually combined with analytical methods for the evaluation of the kinetic constants, are typically used. However, when formulating multi-component mechanisms and estimating the related kinetic, this method presents serious drawbacks deriving from data manipulation and applicability limited to single measurements. Numerical solutions of the conservation equations are more adequate. Metaheuristics are emerging as interesting alternatives, specifically optimization methods such as the genetic algorithm [90], the shuffle complex evolution [91], or the particle swarm optimization [92]. These methods can also be more accurate and convenient than analytical methods for the formulation of one-step mechanisms, apart from being applicable to the formulation of multi-step mechanisms. Figures 4 and 5 show the TG and DTG experimental curves of slow pyrolysis of non-woody residues, specifically rice husk and wheat straw, respectively, and predicted curves with the SFOR model and the three parallel reactions model (3PM), using a genetic algorithm for the estimation of the kinetic parameters [90]. The SFOR model captures the pyrolysis behavior in a satisfactory way, but the 3PM reproduces it significantly better. The authors compared the fitting errors obtained with the SFOR model and with the 3PM and concluded that the latter describes better the pyrolysis process. Nevertheless, the fitting error of the SFOR model was very satisfactory, evidencing the appropriateness of the fitting procedure method. It should be noted that only the 3PM was capable to predict correctly the maximum devolatilization rate for all biomass fuels.


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Figure 4 – TG (top), DTG (bottom) experimental curves of slow pyrolysis of rice husk (RH) and predicted curves with the SFOR model and the 3PM, using a genetic algorithm for the estimation of

wt.%, db

the kinetic parameters [90].

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Figure 5 – TG (top), DTG (bottom) experimental curves of slow pyrolysis of wheat straw (WS) and predicted curves with the SFOR model and the 3PM, using a genetic algorithm for the estimation of the kinetic parameters [90].


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The distributed activation energy model (DAEM) assumes that lignocellulosic biomass has many chemical groups. The complexity of lignocellulosic biomass pyrolysis is such that a continuous ாା∆ா

distribution f(E) of activation energies is assumed where ‫׬‬ா

݂ሺ‫ܧ‬ሻ ݀‫ ܧ‬describes the probability

that chemical groups of a sample have an activation energy in the range E to E+∆E. A popular choice for the activation energy distribution is the symmetric Gaussian distribution, but actual reactivity distributions tend to be asymmetric. Alternative asymmetric distributions are the Weibull and the logistic distributions. Solving for the DAEM numerically can require a significant computational cost. The reader is referred to the review of Cai et al. [93] for a more in-depth discussion of the derivation of the DAEM, the numerical calculation and parameter estimation methods and a summary of recent results available in the literature for the application of the DAEM to the pyrolysis kinetics of lignocellulosic biomass.

The empirical models described above have the disadvantage of being based on non-competitive reactions. As such the kinetic parameters calibrated for a certain data set are only valid for the conditions at which that data set was obtained. A mechanism with competitive reactions that allow for a prediction of different yields for different heating rates, including product speciation, based on the commonly available ultimate and proximate analysis, is more desirable. In general, biomass fuels can be simply described in terms of the three major constituents: hemicellulose, cellulose and lignin, and extractives and mineral matter. This allows for a universal description of the biomass composition regardless of its origin. A non-interaction hypothesis between the components during the pyrolysis process is commonly assumed [89]. In this way, the overall pyrolysis yield of a biomass can be described as the weighted sum of the yields of each component, considering the mass fraction of each component in the raw biomass. Some recent works at low heating rates have shown that interactions between the components during pyrolysis may actually occur [94,95]. Giudicianni et al. [95] studied the interactions between the three main components of biomass on Arundo donax steam 50 ACS Paragon Plus Environment

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assisted pyrolysis by comparing the pyrolysis behavior of the raw biomass, a mixture of xylan, cellulose and lignin resembling Arundo donax, a simulated mixture of xylan, cellulose and lignin resembling Arundo donax canes composition, and demineralized Arundo donax. The results showed that the interactions mainly affect the speciation of the pyrolysis products (namely light gases) and a more reduced impact is observed for the total pyrolysis yields and rate profiles.

Phenomenological network models were developed to describe the process of coal pyrolysis, specifically the chemical percolation devolatilization (CPD) model by Fletcher and co-workers [94,96], the FG-DVG by Solomon and co-workers [97], and the FLASHCHAIN by Niksa and Kerstein [98]. All these models include network modeling, coal structure characterization, depolymerization reactions, cross-linking reactions, and the formation of non-condensable gas, tar and char. However, they differ in describing and modeling these phenomena. In particular, these models differ in terms of the network models employed to describe the interrelationships in the macromolecular lattice characteristics of coal, tar and char. The FG-DVC model uses a twoparameter Bethe lattice; the FLASHCHAIN uses a straight chain model, with no three-dimensional cross-linking; and the CPD model uses the percolation theory and a three-dimensional Bethe lattice to approximate a coal network. These models were extended for biomass pyrolysis; specifically, the Bio-CPD [94], the FG-biomass [99], and the Bio-FLASHCHAIN [100]. The most notable adaptation consists in the description of the biomass as being a mixture of three reference components, cellulose, hemicellulose and lignin. Contrary to the coal models, their biomass counter parts have not been thoroughly validated.

Ranzi et al. [101] developed the Bio-PoliMi mechanism, based on conventional multistep devolatilization models of the three reference components of biomass, which predicts the yields and lumped composition of gas, tar and solid residue. More recently, the model was extended to include 51 ACS Paragon Plus Environment

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extractives in order to broaden the range of biomass composition compatibility [102]. The model considers 46 species, with a significant increase in complexity when compared to the empirical models discussed earlier. Due to the complexity of the lignin structure, three species identified as LIG-C, LIG-O and LIG-H are used, being respectively rich in carbon, oxygen and hydrogen. The composition of the biomass in terms of reference components is not usually available, and given that the model considers lignin pseudo species, Ranzi and co-workers [101-103] developed a method to estimate the reference components based on the ultimate analysis of biomass. Three reference biomass fuels (S1, S2 and S3), with specific oxygen to carbon ratio and hydrogen to carbon ratio, are defined as a linear combination of the five components (cellulose, hemicellulose and three lignin species), defining a triangle. Any biomass contained in the range of hydrogen and carbon enclosed by the triangle vertices can be described as a linear combination of the reference biomass fuels. In this respect, a significant limitation of the model is still a too narrow compatibility with non-woody residues. In particular, non-woody residues have typically a high hydrogen content that falls outside the triangulation area [104].

The evolution of the gas and tar volatile yields and rates is a function of the process conditions. The decomposition yields phenolic components, phenoxy components, among other organic compounds, as well as light gases such as CO, H2 and CH4. A few studies, comparing Bio-PoliMi predictions with experimental measurements of biomass slow pyrolysis, have shown that the mechanism is capable of predicting with a high degree of accuracy the mass loss curve of the major components of biomass and of some biomass residues [104-106]. Furthermore, total product yields (light gas, tars and char residue) from the pyrolysis of biomass have been predicted with a good degree of success by the mechanism. Figure 6 shows the experimental main product yields obtained from the slow pyrolysis of olive branches (OB), kiwi branches (KB), rice husk (RH), wheat straw (WS) and pine bark (PB), and predictions obtained with the Bio-PoliMi [104], which are very good. 52 ACS Paragon Plus Environment

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70 60

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Figure 6 – Experimental main product yields obtained from the slow pyrolysis of olive branches (OB), kiwi branches (KB), rice husk (RH), wheat straw (WS) and pine bark (PB), and predictions obtained with the Bio-PoliMi [104].

However, the mechanism is not yet capable of accurately predict the composition of the light gas species, which can be attributed to two reasons [104]: (i) discrepancies between the predicted and measured composition of the light gases evolving from each reference component, and (ii) uncertainties in defining the reference composition based on the triangulation method. The predictive capabilities of the model in what concerns tar speciation have not been thoroughly verified yet.

The char porosity and the structural organization of the carbon matrix will go through modifications during the pyrolysis process. More specifically, the char yield, the structural features and the surface chemistry of the biochars are affected by the pyrolysis temperature, heating rate and residence time [104-109]. The char matrix structure becomes more organized when increasing the pyrolysis


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temperature and residence time, and tends to evolve to a graphite-like structure [110,111]. The increase in the structural ordering of the carbon-matrix (thermal annealing) and a reduction in the oxygen containing groups lowers the concentration of active sites, and thus the reactivity of the biochar. Many other authors have correlated several char properties, especially those relating the structural parameters of the char and respective reactivity [110,112-114]. In general, the gasification and combustion reactivity of the char decreases when the pyrolysis temperature and residence time increase and/or the heating rate decreases. Di Blasi presents an extensive review on the variation of the yield of char and the variation of the char reactivity with the pyrolysis conditions [115]. More recently, Guizani et al. [116] performed beech wood pyrolysis experiments in an EFR between 500 ºC and 1400 ºC for different gas residence times. The recovered chars were characterized in terms of their morphological, structural, surface chemistry properties as well as their reactivity towards oxygen. The authors observed an increasing ordering of the carbonaceous structures as the pyrolysis temperature increases, with a higher content of condensed aromatic at the expense of the amorphous ones. The char reactivity expressed by means of various parameters was also correlated with the pyrolysis temperature. For instance, the mean reactivity decreased nearly threefold for the char produced at 1400 ºC compared to the char produced at 500 ºC. The authors established correlations that evidenced intimate relationships between chemical composition, structure and reactivity.

The interactions between the organic components (cellulose, hemicellulose, lining and extractives) and the alkali and alkali-earth metals present in biomass can affect the pyrolysis behavior by catalyzing or inhibiting chemical reactions. Alkali and alkali-earth metals are difficult to handle from the numerical point of view, especially when the thermo-chemistry and kinetics related to these species are still uncertain or even unavailable [117]. Van Lith et al. [118,119] studied the release of inorganic elements to the gas-phase during pyrolysis and, subsequently, the combustion of woody biomass. The authors proposed a few reaction steps that describe the possible transformations and 54 ACS Paragon Plus Environment

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release of these organically bounded elements during the pyrolysis and combustion of woody biomass based on their experimental findings. Fatehi et al. [120] studied the kinetics of alkali metals release during the pyrolysis of Swedish wood pellets, at high temperatures, and the experimental measurements were used to extract the global kinetic parameters to model the release of the alkali species during the pyrolysis stage. Leng et al. [121] focused on the effect of KCl and CaCl2 loading on the formation of reaction intermediates during cellulose fast pyrolysis in a wire mesh reactor. These authors proposed a modified kinetic mechanism for the cellulose pyrolysis based on the work of Ranzi et al. [101] that takes into account the primary catalysis of KCl and CaCl2. Trendewicz et al. [122] studied the effect of potassium on the cellulose pyrolysis reaction kinetics under fast pyrolysis conditions. In this work, correlations were proposed to determine the activation energies of some of the reactions of cellulose mechanism proposed by Ranzi et al. [101], as a function of the potassium mass fraction, based on a fitting procedure to the experimental data. The above studies represent the current attempt of using a combination of experimental and kinetic work with the goal of constructing predictive mechanisms. However, a deeper phenomenological understanding is still needed, especially at temperatures relevant for pulverized biomass combustion.

5.2. Char combustion modeling In contrast with biomass char combustion, coal char combustion has been a subject of research for many years, and excellent reviews include those of Smith [123], Bews et al. [124] and Hurt and Mitchell [125]. These are recommended for the readers not familiar with carbonaceous char combustion. It can be assumed that the mechanisms of char combustion for coal chars are also, in principle, applicable for those originated from lignocellulosic fuels. More recently, Di Blasi published a review on the combustion and gasification rates of lignocellulosic biomass [115]. In this highly recommend review, Di Blasi critically examines the state-of-the-art of rate laws and kinetic constants for the gasification, with carbon dioxide and steam, and the combustion of chars produced 55 ACS Paragon Plus Environment

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from lignocellulosic fuels. The review also includes a brief outline on the yields and composition of pyrolysis products. The role of the relevant conditions on the char reactivity, namely heating rate, temperature and pressure of the pyrolysis stage, feedstock and content/composition of the inorganic matter, is also addressed.

In essence, the heterogeneous char conversion depends on the char surface area, the surface accessibility, the carbon active sites and catalytic active sites created by indigenous or added inorganic matter, and the local gaseous reactant concentration [126] or, in more simple terms, the chemical structure, the inorganic material and the porosity. These char characteristics are very difficult to measure from the experimental point of view, especially at high conversion rates. Therefore, in the case of biomass char conversion, the most used approach has been based on the global apparent reactivity that implicitly takes into account the particular characteristics of a certain char burning at specific conditions. The overall kinetics of char conversion is measured through the reactivity:

ܴ=−

ଵ ௗெ ெ ௗ௧

=

ଵ ௗ௑ ଵି௑ ௗ௧

(4)

where M is the mass of the organic fraction, dM/dt is the conversion rate and X is the degree of conversion given by X = (M-M0)/(M0-M∞), with M0 and M∞ representing the initial and final values of the organic fraction in the initial and final sample. Although this mathematical tool may appear simple, it is very useful to determine the variation of the reactivity of a certain char with the degree of conversion.

The char reactivity is not only dependent on kinetics, but also on mass transfer phenomena, particularly the transport of oxidizing and product molecules in and out of the char particle. In this 56 ACS Paragon Plus Environment

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respect, the morphological structure of the char has an important role on the mass transfer phenomena. The morphological structure is influenced by the release rate of volatiles, essentially the pyrolysis conditions, and the amount and composition of inorganic matter. This aspect was addressed in the previous subsection, and here the focus is on the mathematical description of the structural and kinetic formulation in modelling char reactivity.

The reactivity of a char can be expressed by a kinetic term rc(T, Pox), which accounts for the effect of the temperature and the reactant partial pressure on the char reactivity, and a structural term rs(X), which implicitly or explicitly describes the available internal surface area, available active or reactive sites and pore evolution. The available internal surface area and the pore evolution can be described by means of correlations that are a function of the degree of conversion X [85,115,127]. The overall reactivity can be expressed as:

ܴ = ‫ݎ‬௖ ሺܶ, ܲ௢௫ ሻ × ‫ݎ‬௦ ሺܺሻ

(5)

This expression allows formulating the chemical term as intrinsic kinetics that, contrary to apparent kinetics, are scale independent and exclude transport phenomena. When formulating the kinetic parameters corresponding to intrinsic kinetics, the conversion conditions should be carefully chosen to ensure that conversion occurs in the kinetically controlled regime. In particular, low temperatures and high flow rates of the oxidizer, and low amount of the char sample mass and small particle size should be guaranteed. In coal char combustion, it is usually assumed that the rate of the oxidation reactions is much faster than that of the gasification reactions [127], but that is not necessarily the same case for biomass.


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The oxidation reaction may be formulated using the Haynes turnover mechanism [128], requiring the tracking of free sites on the carbon surface; semi-global kinetics typically in form of a two-step Langmuir-Hinshelwood mechanism [129,130] describing the competing adsorption and desorption reactions on the char surface expression; or global kinetics in the form of a nth-order Arrhenius reaction model [131]. Di Blasi [115] presents an overview of the expressions for the rates of char conversion from the literature, with the estimated kinetic parameters for one-step models and the quantitatively most important combustion reaction of the multi-step models used for biomass chars. A discussion of the viability of the kinetic parameters is also included. Most of the studies listed by Di Blasi use a one-step nth-order Arrhenius reaction model. The gasification of chars has been extensively studied, and detailed mechanisms based on the carbon active sites have been proposed for carbon dioxide gasification [132] and steam gasification [133]. However, most studies of biomass gasification are based on the one-step nth-order Arrhenius reaction model. Again, Di Blasi [115] presents an overview of expressions for the char gasification rates from the literature, along with the respective parameters.

The most sophisticated coal char combustion model developed so far is the combustion burnout kinetics (CBK), developed by Hurt et al. [134]. The CBK model is capable of predicting, in good agreement with experimental observations, (i) the low reactivity for unburned carbon residues extracted from commercial ash samples, (ii) the reactivity loss in the late stages of laboratory combustion, (ii) the observed sensitivity of char reactivity to high-temperature heat treatment on second and sub-second time scales, and (iv) the global reaction inhibition by mineral matter in the late stages of combustion observed in single particle imaging studies. However, the CBK model contains empirical terms for the description of the structural development of the coal that need to be calibrated based on experimental data.


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There are still many open issues in the modelling of biomass char combustion and gasification. Literature provides significant information for wood, whereas considerable less work has been carried out for non-woody residues. One recurrent issue in the literature is that only a very few kinetic investigations have given the due consideration to the combustion conditions, specifically if they correspond to the kinetically controlled regime. Failing to do so can introduce flaws in the measurements and analysis of the data. Careful consideration of these aspects in future investigations is recommended.

The effect of the presence of inorganic material during char conversion, which may be significant in non-woody residues, is still poorly understood. The catalytic effects of several metals on the gasification of biomass was reviewed by Nzihou et al. [135], including inorganics that are naturally present in biomass; however, the authors still resort to referencing considerable work on coal gasification, given the lack of studies on biomass. More recently, Dupond et al. [136] studied the effects of inorganic elements of 19 biomass chars on the steam gasification kinetics. The authors found that the reactivity varied by a factor of more than thirty and this large difference appeared to be correlated with the biomass inorganic elements. The authors also highlighted the catalytic effect of the potassium, as well as the inhibiting effect of the silicon and phosphorus. Finally, the authors used very simple models to account for the effect of the inorganics on the reactivity of the chars as a function of the conversion. Still, there is a need of more research in this area; in particular, a deeper phenomenological understanding is still needed.

Another issue that has received very limited attention is the fragmentation of the char, both in the case of coal and biomass. Fragmentation leads to an increase of surface area and hence affects the reactivity of the char to a non-negligible extent, but is extremely difficult to characterize from the experimental point of view. Mitchell [137] proposed an empirical formulation to model 59 ACS Paragon Plus Environment

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fragmentation of coal chars during combustion. In this formulation, the size of the particle chars can vary due to char oxidation, which can be described using a phenomenological formulation, and particle fragmentation, which is described using an empirical formulation. The type of fragmentation that occurs during char combustion is of the percolation type, in which the fragmenting particle produces fragments in all smaller size classes. The fragmentation rate is power law dependent on particle size and density. Three model parameters, a fragmentation rate coefficient, an apparent density sensitivity parameter and a dependency of fragmentation rate on the apparent density parameter are adjusted such that the model gives fragmentation rates that are consistent with the experimental observations for both size and apparent density variations during char conversion. In a more recent study, Tilghman and Mitchell [138] showed that the inclusion of fragmentation effects improved the burnout predictions. The authors concluded that for the derivation of kinetic parameters from char oxidation proceeding in the mixed regime controlled, it is necessary to account for fragmentation. This inclusion results in a broadening of the temperature distribution with conversion, as typically observed experimentally, and a more accurate prediction of the size distribution of the ash particles produced during char conversion.

5.3. Application of biomass combustion models in computational fluid dynamics Numerical studies of pulverized biomass combustion or co-firing of coal and biomass using computational fluid dynamics (CFD) under realistic conditions are scarce. Realistic conditions entail high level of turbulent intensity and heat transfer dominated by radiation. Considering that the development and validation of numerical models requires extensive and reliable experimental data, the scarcity of numerical studies of large-scale pulverized biomass combustion is not surprising given the shortage of detailed large-scale experimental measurements. Table 7 presents a summary of CFD studies, based in Reynolds Averaged Navier-Stokes (RANS), of large-scale pulverized biomass (co-)combustion. Typically, the same modeling strategies that are used for coal can be 60 ACS Paragon Plus Environment

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employed in biomass combustion modelling, given that the combustion process is analogous. The reader not familiar with the general framework used to model pulverized coal or biomass combustion is referred to the reviews of Williams et al. [127] and of Tabet and Gökalp [139]. In the remainder of this section, key findings of the studies listed in Table 7 are presented followed by a discussion on emerging strategies, developed in the context of coal and applied to large eddy simulations (LES), but transferable to biomass, to overcome the main limitations of the numerical models used in those studies. Direct numerical simulations (DNS) are emerging as an interesting tool to study fundamental aspects of biomass pyrolysis, biomass combustion and coal combustion, which are presented at the end of this section.


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Table 7 – Summary of CFD studies of large-scale pulverized biomass (co-)combustion. Reference

Case study

Pyrolysis model

Char combustion model

Backreedy et al. [85]

Co-firing of coal and biomass blends. 1 MW research combustion facility. Biomass combustion. 1 MW research combustion facility Co-firing of coal and biomass. 350 MWe utility boiler. Co-firing of coal and biomass. 150 kW large-scale laboratory facility. Co-firing of coal and biomass. Utility boiler. Co-firing of coal and biomass under oxyfuel conditions. 500 MWe utility boiler.

The FG-biomass was used in a pre-processing step to calibrate a SFOR expression. Several heating rates were used, resulting in several (A, E) pairs that were tested. As in Backreedy et al. [85]. Only one (A, E) pair was used.

The intrinsic char combustion model, developed for coal char combustion, was used. The same activation energy was applied for the coal and biomass chars, but the pre-exponential factor was increased by a factor of 2 in the case of biomass. As in Backreedy et al. [85]. The reaction rate was increased by a factor of 4.

The pair (A, E) of SFOR was calibrated based on TGA experiments.

Char combustion proceeds in the mixed regime. The parameters for the kinetic rate expression were obtained from Hurt and Mitchell's correlations [125] that relate the activation energy and preexponential factor with the fuel elemental analysis, developed for coal. Char combustion proceeds in the diffusion limited rate without any enhancement factor.

Ma et al. [86]

Pallarés et al. [140]

Yin et al. [141]

Ghenai and Janajreh [87] Black et al. [88]

The pair (A, E) of SFOR retrieved from a numerical study of the same biomass fuel burned in a fixed bed grate [143]. Kobayashi model developed for coal [144].

Char combustion proceeds in the mixed regime. The parameters of the kinetic rate expression not given.

The FG-biomass was used in a pre-processing step to calibrate a SFOR expression based on an assumed heating rate.

The intrinsic char combustion model, developed for coal char combustion, was used. The parameters were obtained from a single particle reactor study from Lu et al. [145].

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The studies of Ma et al. [86], Pallarés et al. [140], Ghenai and Janajreh [87] and Black et al. [88] focused on purely numerical results of RANS simulations of industrial-scale biomass combustion and co-firing coal with biomass. While these studies offer valuable insights to the phenomena occurring inside industrial-scale facilities that otherwise would not be possible through experimental measurements, they offer limited insights on the validity of the combustion models employed. Backreedy et al. [85] investigated the effect of varying the devolatilization and char combustion rate constants, but no comparisons with experimental data are provided. The authors point out to the uncertainties about the kinetic parameters of biomass devolatilization and char combustion, as there are little data available at high temperatures. It is important to note that the information available from low temperature studies is influenced by catalysis and cannot be easily extrapolated to the higher temperature regime. Furthermore, there is a lack of information on the reaction order for the reaction of biomass char with oxygen because of its inherent oxygen and the likelihood of char fragmentation. Yin et al. [141] compared RANS predictions with experimental data from Damstedt et al. [142] and obtained a reasonable agreement. The discrepancies between the measurements and the predictions, especially with respect to the flame stabilization, may arise from the uncertainty associated with the kinetic parameters used for devolatilization. With exception of the work of Backreedy et al. [85] and Ma et al. [86], simple empirical models were used with the devolatilization and char combustion kinetic parameters taken from the literature, obtained for biomass residues and under heating conditions not necessarily representative of the case studies. While Backreedy et al. [85] and Ma et al. [86] used the FG-biomass to calibrate the kinetic constants to the exact biomass properties, there is a significant uncertainty associated with the heating rates used for the calibration. Ideally, the kinetic parameters would be calibrated to the exact biomass properties and representative heating rates of the case study; however, this is not usually feasible from the experimental point of view.


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The direct coupling of detailed pyrolysis and char combustion in CFD is prohibitive, whether one considers RANS, LES or DNS. In what concerns devolatilization, two strategies have been recently developed that make use of the predictive model discussed previously for the calibration of the kinetic parameters. Hashimoto et al. [146] used a tabulation strategy where the predictions from the FLASHCHAIN or CPD are stored in a table for a range of particle temperature histories. The table is accessed during the calculation and, for each particle, the kinetic parameters are adjusted according to the closest particle temperature history in the data base. This strategy was used to perform RANS studies of coal combustion in a small-scale laboratory jet [147] and in a large-scale laboratory facility [147,148]. The predictions of the tabulated devolatilization process (TDP) model were compared with those obtained with empirical models from the literature. In general, it was found that the TDP model lead to improved predictions of the flame characteristics, namely flame lift-off and flame length.

Vascellari et al. [149] developed an optimization procedure where a set of multiple particle temperature histories are retrieved from the CFD simulation, that are then used as inputs for the three network models and an empirical model is fitted to the predictions, yielding a new set of kinetic parameters to the next simulation. The procedure is repeated iteratively until convergence is achieved, which typically takes three steps. Here a summary of the process is given, for more details the interested reader is referred to Vascellari et al. [149]. The properties of the fuels, in terms of ultimate and proximate analysis, and optionally structural properties, are used to calculate the reference components to be used as inputs to the predictive models. An initial guess of particle heating rate is also given as input to the predictive models. Then, the results obtained with the predictive models, namely yields and rates, are used as reference data for calibration. The calibration is performed on the basis of an objective function that quantifies the difference between the reference data and the curves obtained with the empirical models. The objective function can exhibit several


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local minima when multiple particle heating laws are considered. A genetic algorithm is used to minimize the objective function, given that genetic algorithms are suited to find the global optima. The authors compared RANS simulations using calibrated SFOR models and using the CPD model directly coupled to the simulations. The authors found that using three rates for the optimization process resulted in a good agreement with the simulation using the CPD model directly coupled. This strategy was later applied to LES of large-scale coal combustion by Rabaçal et al. [150], and to LES of a laboratory coal jet by Rieth et al. [151]. In the latter study, Rieth et al. [151] performed LES using SFOR models calibrated using the CPD model as a reference data (SFOR), LES with the CPD model directly coupled (CPD), and LES using the Kobayahi model (CRM). It is debatable whether the predictions using the Kobashai model are worse or better than those obtained with the CPD approaches. However, the authors obtained an excellent agreement between the LES using the CPD model directly coupled and the LES with the calibrated SFOR models, confirming the good accuracy of this approach.

This strategy could be easily transferable to biomass combustion using one of the advanced models discussed previously to obtain reference data. However, the application of those advanced models to high heating rates has been extremely limited. Rabaçal et al. [152] tested the predictive capabilities of the Bio-PoliMi and Bio-CPD models using woody and non-woody biomass pyrolysis data obtained in high temperature drop tube reactors as reference. Figure 7 shows the total volatile yield of sawdust measured and predicted with the Bio-PoliMi and Bio-CPD using two different methodologies to define the reference composition. The authors found that both the Bio-PoliMi and the Bio-CPD models underestimated the total volatile yield, although the latter captures the trend of increasing yield with increasing temperature observed in experiments. Using different methods to estimate the fractions of hemicellulose, lignin and cellulose of the biomass did not produce a significant difference in the investigated cases when using the Bio-CPD model. A sensitivity analysis


Energy & Fuels

showed that the total volatile yield predictions using the Bio-CPD model did not vary significantly within the whole spectra of the biomass composition, contrary to the use of the Bio-PoliMi model.

Total volatile yield [wt%]

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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100 80 60 40 20 0 0.0

0.5

1.0 1.5 Time [s]

2.0

Expt. 1073 K Expt. 1273 K Bio-PoliMi 1073 K Bio-PoliMi 1273 K Bio-CPDi 1073 K Bio-CPDi 1273 K Bio-CPDii 1073 K Bio-CPDii 1273 K i) Correlation ii) Triangulation

Figure 7 – Total volatile yield of sawdust measured (Expt.) and predicted with the Bio-PoliMi and Bio-CPD models using two different methodologies to define the reference composition [152].

Similar to the case of the advanced devolatilization models, it is prohibitive to couple the description of all processes affecting char reactivity directly with CFD. More recently, a calibration technique, analogous to one used for devolatilization by Vascellari et al. [149], was developed by Vascellari et al. [153] to fit the constants of nth-order Arrhenius models of char combustion using predictions of the CBK model. However, the CBK model contains empirical terms that required fitting with controlled experimental data using the targeted solid fuel, see previous discussion on char combustion modelling, and as such this model is not fully predictive.

DNS has been emerging as a tool to study the fundamentals of biomass pyrolysis [154], biomass combustion [155], coal ignition [156-158] and coal char combustion. The advantage of DNS is that the phenomena at high temperatures can be studied under controlled turbulent conditions, obtaining an insight that otherwise would not be possible from the experimental measurements and a data set can be used for further model validation. The disadvantage of DNS is its very high computational


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cost. As a consequence, Brosh and co-workers [156-158] have resorted to an empirical model for coal pyrolysis retrieved from the literature, focusing the value of their work in the insights obtained concerning gas-phase ignition. Even though Russo et al. [154] and Awasthi et al. [155] modelled biomass particles as Lagrangian source points, the authors described in detail the intra-particle heat and mass transfer in biomass particles along the radius of the particle. But the pyrolysis process itself was extremely simplified – the authors considered that decomposition occurs instantaneously in the radius coordinate where the temperature equals the pyrolysis temperature. The choice of a fixed pyrolysis temperature is rather controversial. The authors based their choice of a suitable value for the pyrolysis temperature on the work of Galgano and Di Blasi [159]. It is clear that these studies open the door for further developments in modeling biomass (and coal) combustion using DNS, and it is foreseen that the main focus will be on the combustion of volatiles in the gas-phase, specifically on the occurrence of ignition, flame propagation and combustion mode.


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6. Summary and research needs This manuscript provides an overview of the recent developments in the combustion of pulverized non-woody residues based on both experimental and numerical studies. First, the properties of the most common non-woody residues are summarized, revealing the large diversity and heterogeneity of these residues and, thereby, highlighting the need to characterize them at a fundamental level prior to their use in combustion systems. Second, despite the excellence of many of the existing research facilities, from DTFs and EFRs to large-scale laboratory furnaces, along with the significant advances in non-intrusive optical diagnostics that occurred in the past decade, experimental studies on the (co-)combustion of pulverized non-woody residues are scarce in relation to the complexity of the problem.

Little data have been reported for the ignition delay time and mode of non-woody residues, with particular emphasis on the effects of the biomass composition, atmosphere temperature, particle size and the presence of potassium and calcium. There is a general consensus that biomass combustion can increase the propensity for the formation of slagging and fouling in industrial equipment, particularly when non-woody residues are used. Moreover, co-firing coal with non-woody residues seems to originate deposits with a high degree of adherence to metal and refractory surfaces. In addition to ash deposits related problems, the (co-)combustion of non-woody residues may also originate higher PM and gaseous emissions than the (co-)combustion of woody residues, but there is some experimental evidence that non-woody residues have a better propensity to reduce NO through reburning than bituminous coals. Nonetheless, a number of non-woody residues are currently used in co-firing and, more recently, in pure biomass firing processes, including solid waste materials from olive, palm, sunflower and rape seed oil and other industries, dried sewage sludge, cereal straws and other baled agricultural residue materials.


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Apart from the obvious necessity to extend the existing data base for non-woody residues, from the experimental point of view the research needs include studies on ignition and combustion of single (and streams) non-woody particles, on the influence of the ash composition, particularly of the K and Ca elements, on the devolatilization and char oxidation rates of non-woody residues, on PM formation and emission and occurrence of particle/char fragmentation during the combustion of nonwoody residues. Finally, there is an absolute need to study pure non-woody flames in large-scale furnaces as far as flame stability, pollutants formation and emission, ash deposition rates and ash characteristics, and overall combustion efficiency are concerned.

Modelling solid fuels is a relatively mature field in the case of coal and, to some extent, woody biomass. While coal and biomass have distinctive molecular structures and particle morphology, both fuels experience the same general steps of combustion: (i) inert heating, (ii) drying, (iii) devolatilization, and (iv) char combustion. As such, the general numerical framework is similar for both solid fuels. Modelling biomass combustion is essentially a matter of closing the source terms for devolatilization (or pyrolysis) and for char combustion. Pyrolysis can be modelled using empirical or predictive models. The models from first category have the advantage of having a low numerical cost and, depending on the complexity of the formulation, can be very effective for modelling pyrolysis under various conditions. However, the models are based on constants that need to be calibrated to the targeted specific conditions. As such their applicability is limited to the availability of quality reference data for calibration. The models from the second category have substantially higher cost, but being predictive in nature allows for a broader application based on the biomass composition, without the need of reference pyrolysis data. In more recent years there have been substantial efforts towards developing this type of models. Since the composition of the non-woody residues diverges quite significantly from that of the woody biomass, such as higher amounts of extractives and ashes, there is a need to extend the range of applicability of the predictive models. Furthermore, the


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simplifications applied to woody combustion are challenged by the significant impact that the presence of extractives and ashes has on product yields and product speciation due to interactions between the organic components and catalytic effects of the inorganics present in the ashes. The pyrolysis conditions affect the final composition and structure of the char, with a direct impact on its reactivity. Char combustion of biomass is analogous to that of coal; however, its overall reactivity is several orders of magnitude higher and gasification reactions have a preponderant role during this stage of conversion. As for the pyrolysis, there have been considerable efforts to develop models that describe char oxidation and gasification, particularly kinetic mechanisms. In what concerns the structural evolution of the char, empirical models are still the state-of-the-art. While predictive models are preferable to empirical models, it is not feasible to directly couple them to CFD simulations of large-scale practical applications. Empirical modelling is still the common practice, but while in the past empirical models calibrated under non-realistic conditions were used, nowadays a more consistent method for calibration is used. The predictive models are used to create a highquality database for calibration, using realistic heating rates for a broad range of solid fuels. Finally, metaheuristic methods can be used to calibrate the empirical models at very reasonable computational costs. This method was originally developed for coal, but can be easily extended to biomass.

Modelling solid particle conversion is a relatively mature topic, but there are still many open questions. In regard to pyrolysis/devolatilization, in recent years there have been substantial improvements on the phenomenological description of the process, concerning detailed kinetics and product speciation. However, current models are based on simplifications that hinder a wider spread of their use, particularly in the case of non-woody residues. In one hand, the applicability of the models needs to be expanded to a wider range of biomass compositions and, in the other hand, the interactions between the organic components need to be better understood in order to include them in


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the detailed mechanisms. Furthermore, non-woody residues have typically high amounts of inorganic compounds that can have a catalytic or inhibiting effect on the pyrolysis/devolatilization process. There is a need of a more phenomenological understanding of these effects to also include them in the detailed kinetics mechanisms. It is well known that during pyrolysis/devolatilization the porosity and the structure of the particle vary, with a direct impact on the reactivity of the subsequent char. However, this effect is typically modelled using empirical formulations, and little phenomenological understanding is really available to formulate predictive models. In what concerns char combustion, many efforts have been dedicated in recent years to formulate detailed kinetic mechanisms for oxidation and gasification. Particle fragmentation and ash chemistry are phenomena with a significant impact on the char reactivity, but there is little phenomenological understanding of these and a significant potential for future research. Finally, in the last years, it has been shown, in the field of coal combustion, that it is possible to bridge the gap between advanced complex conversion models and computationally demanding CFD simulations. This can be achieved by calibrating the constants of simple and effective empirical models using the predictions of advanced predictive models under heating rates and fuels properties that match the CFD test cases. This approach can be used also in the field on non-woody residues, though one needs to critically consider the current limitations of the advanced conversion models for biomass as discussed in this review.

Acknowledgments This work was supported by Fundação para a Ciência e a Tecnologia, through IDMEC, under LAETA, project UID/EMS/50022/2013 and PTDC/EMS-ENE/5710/2014.


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Review of Pulverized Combustion of Non-Woody Residues - Energy

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