Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak)

Journal of the Energy Institute xxx (xxxx) xxx

Contents lists available at ScienceDirect

Journal of the Energy Institute journal homepage: http://www.journals.elsevier.com/journal-of-the-energyinstitute

Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak) €lle Trouve  a, *, Gontrand Leyssens a, Guillaume Schmidt a, Gwenae € nnenbeck a, Alain Brillard a, Mohammad Ebrahim Olya b, c, Cornelius Scho e Dewaele d, Fabrice Cazier d Dorothe Laboratoire Gestion des Risques et Environnement EA2334 - Universit e de Haute-Alsace, 3 bis rue Alfred Werner, 68093 Mulhouse Cedex, France Groupe G enie des Proc ed es de traitements des effluents, Laboratoire LPI, Ecole Nationale Sup erieure de Chimie de Mulhouse, 3 rue Alfred Werner, 68093 Mulhouse Cedex, France c Environmental Research Department, Institute for Color Science and Technology, Teheran, Iran d ^te d’Opale MREI 1, 145 Avenue Maurice Schumann, 59140 Dunkerque, France Centre Commun de Mesures, Universit e du Littoral Co a

b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 21 September 2018 Received in revised form 13 March 2019 Accepted 18 March 2019 Available online xxx

Nowadays, biomass increasingly replaces fossil fuels for domestic heating production. But this leads to gaseous and particulate pollutant emissions. Wood washing is a process which can be applied to reduce such emissions. In the present study, the impact of this process on the extraction of mineral and organic compounds from wood sawdust from three different species (beech, fir and oak) was analyzed, as well as the influence on the wood thermal reactivity. Wood washing leads to a decrease in several ionic elements such as potassium and sodium, which can be largely removed from biomass. Globally, mineral extracts range between 17 and 40% of the initial mass depending of the particle size (ships or sawdust) and of the specie (beech, fir and oak). Moreover, 2% of wood extractives can also be removed from wood. The impacts of wood granulometry, wood drying and washing temperature have been studied to understand the different extraction processes. Wood demineralization has been modelled through pseudo first- and second-order models to derive kinetic parameters of ionic exchanges between water and wood. The pseudo first-order model gives poor results. The second-order model show rapid exchanges with half time reactions approximately equal to 23, 24 and 40 min for beech, fir and oak samples, respectively, washed during a week. © 2019 Published by Elsevier Ltd on behalf of Energy Institute.

Keywords: Wood washing Conductivity Minerals extraction Kinetic modelling

1. Introduction Wood has become an important fuel for domestic heat generation, since the cost of fossil fuels is rising. The biomass combustion is nearly CO2 neutral. All households are incited to turn to the use of biomass as energy source for domestic heating. But wood combustion is well-known to be a source of fine particles (PM2.5) and gaseous compounds such as carbon monoxide (CO) or other incomplete combustion gas such as nitrogen oxides (NOx) or Non-Methane Volatile Organic Compounds (NMVOC) [1e6]. Nowadays, several techniques are developed to reduce pollutants from wood combustion. Primary and secondary technologies must be distinguished: on the one hand, primary solutions focus on fuel quality and conception of stoves and boilers, on the other hand, secondary solutions focus on the post-treatment of fumes [7e9]. Secondary technologies largely dominate using technologies like electrostatic filter, catalytic filter, or cyclones [10,11]. Secondary technologies aim principally at reducing particle emissions, but some techniques like catalytic filter allow to also reduce gaseous emissions such as carbon monoxide and VOCs [11]. Concerning primary techniques, important efforts from the 2000's were done to complain with different National and European standards [12e15] when conceiving stoves and boilers [16], choosing wood species and optimizing the air/fuel ratio [17].

* Corresponding author. ). E-mail address: [email protected] (G. Trouve https://doi.org/10.1016/j.joei.2019.03.008 1743-9671/© 2019 Published by Elsevier Ltd on behalf of Energy Institute.

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

2

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

Primary solutions to reduce pollutant emissions from biomass combustion may be drying, bark-removal and demineralization by washing of the wood. Indeed, through a washing process, some compounds like minerals or extractives are removed from wood. Deng et al. [18] studied the demineralization of few biomass after a washing process with deionized water and their results showed that minerals like potassium and calcium can be extracted. For example, from rice straw, the extraction efficiency reaches respectively 87 and 19% at 30
C for these elements. Jiang et al. [19] also studied rice straw demineralization by deionized water and solutions of chloride acid or phosphoric acid for example. They showed that, depending on the solution, the demineralization efficiency varies: calcium can be removed by 17% using deionized water versus 98% using chloride acid. Some extractives like phenols or carboxylic acids are also removed by wood washing [19e22]. Previous studies show the role of demineralization process on combustion kinetics and emissions [23,24]. Investigations concerning the influence of wood preparation, particularly its washing process, on gaseous and particulate pollutants emitted in the exhaust are scarce at real domestic scale [20]. Reducing the mineral content from wood, the mineral part and more generally the particle emission factors of biomass combustion decrease. As shown in our previous work on the influence of the washing process on pellets combustion emissions [25], some compounds and particularly particulate emissions are affected. Indeed, Total Suspended Particle (TSP) can be reduced by 50% after wood demineralization. Moreover, carbon monoxide and organic compounds as Polycyclic Aromatic Compounds (PAH) emissions decreased up to 65%. This work was a part of a French project supported by the National Agency of Energy and Environment (ADEME) to study the impacts of wood pre-treatments on emissions during the heat production at a domestic scale. The results of this project were previously described in Ref. [25], which describe the impact of the washing on gaseous and particulate emissions. Kinetic data on demineralization are currently not available in the literature. To fully understand the kinetics of demineralization exchanges and to get free of the variability observed at domestic scale (nature, humidity and size of the fuel, bark content, etc.), it was decided to study the influence of the washing process applied to sawdust at a laboratory scale. The present study aims at really understanding and clearly explaining the role of minerals and the exchange mechanisms between wood and water before combustion tests. Already known kinetic models are applied to simulate these exchanges between the solid (biomass) and the liquid (water). During wood washing, water conductivity and inorganic compounds have been measured to follow the wood demineralization. The total organic compounds extracted have also been measured during the washing protocol. Thermogravimetric analyses have been performed on the raw and washed samples under oxidative conditions (temperature ramp of 5
C. min1 from room temperature to 900
C with an air flow rate of 6 NL. h1). The differences between the TG and DTG of the washed samples and that of the raw one are small, whatever the wood specie. Therefore, the optimal values of the kinetic parameters (pre-exponential factors and activation energies) determined using the EIPR model do not present important variations. Their values are available in Ref. [26]. 2. Experiments 2.1. Washing protocols The experimental protocol developed to study the demineralization of biomass is represented in Fig. 1. Three wood species were studied: beech, fir and oak. These species are locally produced. Domestic heat production must be in agreement with the European DIN EN CERTCO 13240 regulation [27]. This certification scheme is applicable to room heaters (solid fuel stove) with low-pollution combustion and which also fulfill the test criteria associated to the quality label “DIN plus”. This certification scheme lays down the requirements for the room heater itself as well as for the testing and monitoring. It imposes requirements in terms of materials, design, construction, safety and performance of the stoves but also of the wood species to be burnt. If hardwoods are recommended by this certification, softwoods and wood waste as pellets continue to be used for domestic heat production. To represent this kind of solid fuel, the French agency ADEME asked us to perform combustion tests with pellets made of fir.

Fig. 1. Washing protocol.

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

3

The mass ratios of water and biomass are taken according to the standard leaching protocol EN 12457/2 of May 2002, [28]. Thus, a quantity of 50 g on dry basis of wood was mixed with a volume of 500 mL of demineralized water. The mixture was placed under agitation during 24 h, which corresponds to one washing cycle, on a leaching table Stuart model SSL1 at 145 rpm. Each 24 h washing cycle, the mixture was filtered and 500 mL of demineralized water were added for the next washing cycle. This operation was repeated during 96 h to realize a total of 4 washing cycles. Several tests were carried out to analyze the influence of granulometry, through the washing of chips or sawdust (D50 < 4 mm), as well as the influence of the drying of the biomass before washing (Fig. 2). Moreover, the influence of the temperature of the washing water was tested on raw biomass ground at 4 mm. The biomass washing was only performed at room temperature. However, as the ultimate goal of this work is to study the influence of natural leaching by rainwater, the temperature under which the previously described experiments have been performed has been changed. The influence of the washing temperature was indeed carried out choosing three temperatures (5, 20 and 30
C), to simulate cold and warm weather conditions of autumn/winter and spring/summer, respectively. The washing contain was immersed in a water circulation vessel connected to a thermostatic bath LabWorks Model 2000 series. The water was then regulated at 5, 20 and 30
C.

2.2. Characterization of wood and water samples To measure the exchanges of minerals, the conductivity was continuously monitored, as soluble minerals under ionic forms contribute to its signal. This continuous measurement of the conductivity of the washing water was carried out using a conductivity meter PCE model PHD1 to obtain a measure point each 5 s. This conductivity meter has a temperature compensation of 2%.
C1 as default entry. Each washing water was analyzed by ICP/OES (Thermo Scientific model ICAP 6300 DUO) and ionic chromatography with conductivity detector (Thermo Scientific model ICS-5000þ), for the quantification and speciation of minerals. Raw and washed (after 4 washing cycles)

Fig. 2. Water conductivities for beech (a), fir (b) and oak (c) sawdust, after washing during 24 h (blue crosses), 48 h (red points), 72 h (black lines) and 96 h (green triangles). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

4

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

woods were both mineralized and analyzed by ICP/OES. The Total Organic Carbon (TOC) of residual washed solutions was determined by catalytic oxidation of the organic carbon into dioxide carbon using a SHIMADZU TOC-VCSN device to quantify the extracted organic part. The physical and chemical properties of the raw and washed wood species are commented in the paper by Schmidt et al. [25]. Globally, the washing process decreases ash contents and modifies the elemental composition with the extraction of minerals and organic compounds, as discussed later on. 2.3. Modelling the ionic exchange between wood and water To simulate the ionic exchange between solid and liquid, a pseudo first-order model and a pseudo second-order model are available. These two kinetic models need the values of the conductivity of the washing water versus time and the value of the equilibrium conductivity. They return the value of the kinetic constant from which it is possible to compute a half-reaction time. To correlate the mineral analysis of the washing water with the measured equilibrium conductivity, a theoretical conductivity has been calculated using Kohlrausch's law expressed as:

X  o zi Ci l

s ¼ 10

(1)

i

i

where s is the total conductivity of the washing water, zi the value of charge of the element i, Ci the concentration of the element i and loi the limit ionic molar conductivity of the element i. A pseudo first-order kinetic of mineral desorption is given by the Largergren equation expressed as:

dst ¼ k1 ðse  st Þ dt

(2)

where st and se are the conductivity of the solution, respectively at time t and at equilibrium, and k1 is the reaction constant of the pseudo first-order model. The resolution of the differential equation (2) between to ¼ 0 and t leads to:

lnðse  st Þ ¼ lnðse  s0 Þ  k1 t

(3)

and the simulated conductivity st can expressed as:

st ¼ se  ðse  s0 Þexpðk1 tÞ

(4)

From the expression (3), when t increases to large values, st increases to se , which means that the equilibrium conductivity se is the limit conductivity when t increases to large values. In the present study, an equilibrium is generally never reached, whatever the wood under consideration, as the conductivity goes on increasing, see Fig. 2a) and b) or c). An approximate value of the equilibrium conductivity has been computed, as the mean value of the experimental conductivities after their rapid increase. The value of k1 is then deduced from (3). In this pseudo first-order case, the half-reaction time t1/2, defined by st ¼ 0.5se is finally calculated and expressed as:

t1=2 ¼

lnð2Þ k1

(5)

A pseudo second-order model of mineral desorption is expressed as:

dst ¼ k2 ðse  st Þ2 dt

(6)

where k2 is the reaction constant of the pseudo second-order model. The resolution of this differential equation (4) between to ¼ 0 and t leads to:

1

se  st

¼

1

se  s0

þ k2 t

(7)

from which the conductivity can be expressed as:

st ¼ se 

1 se  s0 ¼ se  1 þ ðse  s0 Þk2 t þ k2 t

1 se  s0

(8)

Again, when t increases to large values, this simulated conductivity st tends to se , which means that the equilibrium conductivity se is again the limit of the simulated conductivity when t increases to large values. Assuming s0 ¼ 0, the expression (8) leads to:

st ¼ se 

se

1 þ k2 t se

¼ se

1 þ k2 t se  1 k2 t se ¼ se 1 þ k2 t se 1 þ k2 t se

(9)

which implies:

t

st

¼

1 þ k2 t se k2 ðse Þ2

¼

1 k2 ðse Þ2

þ

t

se

(10)

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

5

Plotting t=st . with respect to time allows to derive, through a linear regression, an approximate value of 1=se hence of se and of 1=k2 ðse Þ2 hence of k2 . In this pseudo second-order case, the half-reaction time t1/2, defined by st ¼ 0.5se is calculated and expressed as:

t1=2 ¼

1

(11)

se k2

3. Results and discussion 3.1. Influence of the wood granulometry and drying In each exchange process, the contact surface plays an important role. In the present case, this observation suggests that the more finely the wood is divided, the faster and/or more important the demineralization will be. The influence of the particle size of the fuel was studied by washing wood with demineralized water on biomass ground at 4 mm and on standardized P40 wood chips, as received. Because of the large amounts of biomass to wash, this process could only be performed on the biomass in the form of wood chips. Therefore, the possible gap due to the two wood granulometries has to be analyzed. First, the washing protocol was only tested on the raw biomass ground at 4 mm. The continuous measurement of the conductivity of each species during the four washing cycles is available in Fig. 2 for beech, fir and oak, respectively. During the first two washing cycles (24 h and 48 h), the conductivity increases following a logarithmic shape without reaching an equilibrium. This increase is also observed, with a more moderate intensity, on the last two washing cycles (72 h and 96 h). There is a real interest in renewing the washing water, to allow the maximization of exchanges between the wood and demineralized water. For each water renewal, the maximal conductivity reached for the new washing cycle is lower than the previous one, which confirms the gradual minerals depletion of the biomass. At the end of the four washing cycles, whatever the biomass, a factor 7 occurs between the conductivities of the initial (24 h) and the final (96 h) washing water. The conductivity of the last washing solution is systematically close to 20 mS. cm1 and even if the number of washing cycles is greater than 4, i.e. 96 h of wood immersion. The removal of ionic soluble components from woods is totally achieved and new exchanges cannot further occur. In addition, for each water change, the initial conductivity of the solution is never equal to zero because deionized water also contributes to a part of the conductivity, even if it is small. The conductivity tests being conclusive on raw biomass ground at 4 mm by considering the ionic exchanges, these tests were then carried out on P40 wood chips (Fig. 1). For the washing of wood chips or sawdust, the tests were carried out on raw and dry basis always maintaining the same ratio of mass of dry wood (mdw) to the volume (v) of water mdw/v. As these conductivity curves also exhibit a logarithmic shape, only the maximal conductivities of each test and washing cycle are presented in Table 1. Except for oak, dry wood allows a better extraction of minerals leading to higher water conductivity values than raw woods. Because raw woods contain a part of water as relative humidity, this could contribute to a slight dilution of ionic species and allow a diminution of the conductivity values. Other parameters are also to be taken into account. For example, the limit ionic molar conductivity loi of the elements differs from one ion to another one and this could modify the conductivity of the solution. The solubility of metallic elements indeed depends on the chemical speciation under chlorides, nitrates, nitrites, sulphates, carbonates, etc. and can differ from one wood specie to another one. If the speciation of elements varies with the woods, consequently the conductivity changes. Woods contain a part of organic extractives molecules ranging from 8 to 20% of the dry mass that are soluble [25]. Some molecules as phenols and carboxylic acids exist under ionic form and consequently contribute to the conductivity of the solution. This conductivity difference between raw and dried woods is only significant for the first two washing cycles. Indeed, from the third cycle, the conductivities of raw and dried wood converge to the same values. Wood ground at 4 mm, presenting the higher exchange surface, is expected to present a higher conductivity than that of wood chips, the wood being dried or not. The results presented in Fig. 2 and Table 2 prove the transfer of ionic species from biomass to water. This conductivity approach acts that the wood drying process does not alter the mineral extraction, but on the contrary, improves the extraction rate. As conductivity does not specify individual ionic elements, samples were analyzed by ICP/OES and ionic chromatography to verify this hypothesis. Minerals contained in raw and final washed (after 96 h of washing) biomass and in each washing water were quantified. Table 2 shows the total masses of minerals extracted by the washing process after the fourth cycle (96 h). The values gathered in Table 2 are obtained Table 1 Influences of the granulometry and of the drying process on mineral extraction.

mS/cm Beech 1st cycle 2nd cycle 3rd cycle 4th cycle Fir 1st cycle 2nd cycle 3rd cycle 4th cycle Oak 1st cycle 2nd cycle 3rd cycle 4th cycle

Conductivity Raw chips

Dried chips

Raw 4 mm

Dried 4 mm

144 76 50 39

285 77 47 36

224 106 64 47

358 105 64 47

30 19 12 8

81 28 15 10

63 30 19 13

135 20 16 16

105 48 35 28

151 56 37 31

249 90 42 26

304 109 54 32

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

6

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

by summing the concentrations of the elements of the four filtrates obtained after each washing test. The values of minerals are expressed in mg of element per kg of dry wood for both solid (initial wood sample) and washing solutions. Comparisons of their amounts from initial wood to waters show that most of the elements are removed from the biomass. Values being in the same order of magnitude between the initial wood and waters prove the high removal occurring during the washing process. As an example, the removal of potassium from raw sawdust of beech is close to 100% (1223 mg. kg1dw in the solid raw material compared to 1212 mg. kg1dw in the water). In some cases, the values obtained in water are higher than that obtained in initial woods. This is due to the heterogeneity of the wood sampling from the fuel big bag. Whatever the wood specie, potassium is the most soluble metal extracted during the washing process, followed by sodium, magnesium, phosphorus and sulfur. Aluminum, calcium, iron and silica are less extracted, probably due to less soluble speciation in the woods. These values are very consistent with the extraction ratios presented in the literature for different biomass and agricultural residues [18,19,29e31]. Considering the total masses of extracted minerals, the differences between a raw and a dry wood (chips or sawdust) are not significant in the case of oak and fir. For beech, the extraction ratio is higher for a dried biomass with a 10% higher extraction (Table 2). Considering element by element (Table 2) drying the wood allows a larger extraction for some ionic species, such as phosphates or sulphates for the three species (higher extraction close to 50%). These observations are coherent with the results obtained by ICP (Table 2). An effect of wood granulometry also occurs during washing. Indeed, because of a high exchange surface for wood ground at 4 mm compared to chips, it is possible to extract up to twice minerals in mass in comparison with wood chips washing. The biomass washing also promotes the extraction of some organic compounds from wood, including light molecular weight molecules such as extractives. The loss of carbonaceous molecules decreases the calorific value of the biomass as explained in our previous work [25]. To estimate these losses, the washing waters were firstly analyzed to determine the Total Organic Carbon (TOC) content extracted. Experiments were carried out on the four washing cycles only in the case of raw biomass ground at 4 mm at three temperatures (5, 20 et 30
C). These data are summarized in Table 3. As the biomass batches may differ from one experiment to another one, the values may vary accordingly. For the four washing cycles, the results show that it is possible to extract between 1 and 2% in mass of organic carbon. The maximal values of TOC were obtained for oak for 20 and 30
C compared to fir and beech, because oak contains more extractives, mostly tannins [31e33]. Oak and beech present low TOC values at the low temperature of 5
C. The demineralization of hardwoods is not favored at low temperatures compared to softwoods. As the extraction was only carried out at room temperature, i.e. 20
C, these values are in agreement with that of the literature, the extractives of wood ranging between 2 and 8% [34,35]. 3.2. Influence of wood washing temperature A continuous measurement of the conductivity of each washing water was carried out on raw biomass ground at 4 mm to characterize the influence of the washing temperature. To be able to compare the conductivity results at the different temperatures, a compensation factor has been applied, because the conductivity meter compensates by default the conductivity up to 2%.
C1, with a reference temperature of 25
C [36]. This compensation factor aims at correcting the conductivity values because ionic molar conductivities depend on the temperature. However, this compensation factor is only an approximation and to really determine the compensation factor for the different biomass at the different temperatures, this compensation factor was determined through calculations from experiments. The conductivity of the filtrates was measured at different temperatures and the compensation factor determined to obtain the same conductivity regardless of the temperature. The default value of this compensation factor is equal to 2%.
C1 imposed by the conductivity meter model PHD1 from PCE. For each wood specie and each temperature, it was decided to evaluate by the experience the value of the compensation factor. These determinations of the compensation factors are given in the Appendix (Figures A1 to A3). The compensation coefficients are taken equal to 1, 2 and 0 2%.
C1 for beech, fir and oak, respectively. It is interesting to note that experiments show that it is necessary to apply a zero compensation factor in the case of oak. Mean conductivities measured for oak at both 5
C and 30
C were close to 658 and 660 mS. cm1, respectively. This could be due to the fact that, depending on the temperature, the ionic elements in the oak filtrate form salts, thus reducing the conductivity value. In Table 2 Ionic elements of wood and washing waters analyzed by ICP/OES and IC (±4 mg/kgdw by element). mg/kgdw

BEECH Raw beech wood Dried chips waters Raw chips waters Dried 4 mm waters Raw 4 mm waters FIR Raw fir wood Dried chips waters Raw chips waters Dried 4 mm waters Raw 4 mm waters OAK Raw oak wood Dried chips waters Raw chips waters Dried 4 mm waters Raw 4 mm waters

ICP measurement

IC measurement

Al

Ca

Fe

K

Mg

Na

P

S

Si

Zn

Total

7 <1 <1 <1 <1

2264 93 38 154 75

8 <1 <1 <1 <1

1223 1095 815 1353 1212

386 49 15 99 32

<5 3 3 5 4

47 50 16 54 34

115 67 51 73 59

194 69 69 78 72

2 <1 <1 <1 <1

/ 1430 1010 1815 1490

3 <1 <1 <1 <1

893 93 62 224 104

6 <1 <1 <1 <1

279 279 236 389 336

68 14 7 34 13

<5 4 3 7 6

18 <11 <5 <13 <5

50 29 28 53 38

683 <6 <6 50 48

5 1 <1 2 1

/ 440 350 775 550

27 <1 >1 >1 >2

3247 48 84 217 339

25 1 2 2 5

991 696 694 1303 1335

136 7 12 52 59

6 7 6 9 8

68 25 17 59 37

158 12 10 29 26

47 13 15 18 18

2 <1 <1 <1 <1

/ 810 ± 40 840 ± 40 1690 ± 40 1830 ± 40

± ± ± ±

± ± ± ±

40 40 40 40

40 40 40 40

Cl

SO42-

PO43-

/ 8 7 11 12

/ 81 45 85 40

/ 140 36 148 88

/ 7 5 12 9

/ 0 0 0 0

/ 25 9 30 9

/ 10 10 17 11

/ 14 7 25 10

/ 65 45 151 101

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

7

Table 3 Influence of the washing temperature on conductivity values for sawdust.

mS/cm Beech 1st cycle 2nd cycle 3rd cycle 4th cycle Fir 1st cycle 2nd cycle 3rd cycle 4th cycle Oak 1st cycle 2nd cycle 3rd cycle 4th cycle

Compensated conductivity 5
C

20
C

30
C

119 ± 4 70 ± 4 50 ± 4 41 ± 4

146 ± 4 79 ± 4 49 ± 4 39 ± 4

147 ± 4 103 ± 4 57 ± 4 45 ± 4

127 ± 4 68 ± 4 50 ± 4 42 ± 4

136 ± 4 65 ± 4 42 ± 4 34 ± 4

138 ± 4 58 ± 4 35 ± 4 29 ± 4

156 ± 4 62 ± 4 36 ± 4 27 ± 4

257 ± 40 104 ± 4 50 ± 4 32 ± 4

339 ± 40 133 ± 4 67 ± 4 40 ± 4

parallel with the increase of the ionic molar conductivities, which depends on the temperature, it is therefore possible that different effects compensate, thus leading to a conductivity compensation factor equal to 0 (see Figures A1 to A3 in the Appendix). The maximal values of compensated conductivity, corrected according to the corresponding factor, for each biomass, each washing cycle and each temperature are summarized in Table 3. Whatever the temperature, the behavior remains the same, the more the number of washing cycles increase, lower the conductivity is. Considering the standard deviations, the temperature favors the mineral extraction for the first cycle. During the three last cycles, as shown in Fig. 3 in the case of oak, the second cycle poorly extracts some ions but the extraction process is mainly stopped after 48 h. In the case of hardwoods, the increase of the washing temperature from 5 to 30
C leads to an extraction efficiency approximately equal to two, in the case of oak, considering the conductivity values during the first two washing cycles. The conductivity then converges to similar values for the last two washing cycles. If some exchanges occur during the last two cycles, their contribution to the total conductivity is low as shown in Table 3, because the conductivity values are very close to limits of the sensibility of the conductivity meter. It is well known that the temperature favors the leaching of ionic species in water. Only in the case of oak, as shown in Fig. 3, the conductivity is proportional to the temperature for a given washing cycle. The two other woods do not allow to build a mathematical model. The organic fraction extracted was carried out on the three raw biomass ground at 4 mm. The influence of the temperature on TOC extraction was also studied. The results of the TOC analyses are summarized in Table 4. Whatever the temperature, the more the number of washing cycles increases, the more the TOC extracted decreases. This behavior is similar to that of minerals extraction as previously observed. Further, softwoods and hardwoods exhibit opposite behaviors. In the case of fir, the temperature does not seem to increase the organic extraction, but it slowdowns this organic extraction from the second cycle. In the case of hardwoods, increasing the washing temperature from 5 to 30
C allows to extract up to 2 times more in the case of oak, considering the TOC values during the first two washing cycles. The TOC values then converge to similar values for the last two washing cycles with values close to the limit of sensibility. The extraction of both mineral and organic compounds could be increased with an increase of the temperature but mainly during the two first cycles. Temperature favors ionic exchanges between water and solid woods because it increases the solubility of ionic species. 3.3. Modelling Firstly, thanks to the inorganic analyses of the washing waters (Al, Ca, Fe, K, Mg, Na, P, Zn, Cl, SO42 et PO43) of raw and ground wood at 4 mm, and according to Kohlrausch's law, the theoretical conductivities of the filtrates were calculated which are reported in Table 5. In

Fig. 3. Compensated conductivity evolution of oak sawdust versus washing temperature for first (blue diamonds), second (red squares), third (green triangles) and fourth (violet crosses) washing cycle. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

8

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx Table 4 Influence of washing temperature on TOC extraction for sawdust wood. TOC (mass %)

Beech 1st cycle 2nd cycle 3rd cycle 4th cycle TOTAL Fir 1st cycle 2nd cycle 3rd cycle 4th cycle TOTAL Oak 1st cycle 2nd cycle 3rd cycle 4th cycle TOTAL

5
C

20
C

30
C

0.28 0.23 0.26 0.16 0.93 ± 0.01

0.32 0.38 0.29 0.15 1.13 ± 0.01

0.45 0.31 0.43 0.22 1.41 ± 0.01

0.31 0.34 0.23 0.45 1.33 ± 0.01

0.43 0.40 0.23 0.08 1.15 ± 0.01

0.52 0.23 0.12 0.08 0.90 ± 0.01

0.35 0.26 0.13 0.16 0.90 ± 0.01

0.66 0.41 0.31 0.28 1.66 ± 0.01

0.80 0.46 0.19 0.23 1.68 ± 0.01

Table 5 Comparison of calculated and measured conductivities at room temperature (20
C) for sawdust washing. Conductivity (mS/cm)

1st cycle 2nd cycle 3th cycle 4th cycle TOTAL

Beech

Fir

Oak

Measured

Calculated

Measured

Calculated

Measured

Calculated

158 ± 4 56 ± 4 47 ± 4 35 ± 4 296 ± 16

130 58 37 26 251

61 ± 4 28 ± 4 17 ± 4 10 ± 4 116 ± 16

56 21 11 7 95

247 ± 40 87 ± 4 40 ± 4 24 ± 4 397 ± 52

210 69 31 20 329

Fig. 4. Logarithmic conductivity scale versus the washing time (a) or versus logarithmic scale washing time (b), in the case of beech sawdust.

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

9

Table 6 Pseudo first-order kinetic constants for each biomass washed at room temperature. Beech

SAWDUST k1 (h1) t1/2 (h) t1/2 (eq. cycle)

Fir

Oak

Measured

Calculated

Measured

Calculated

Measured

Calculated

0.020 35.4 1.5

0.022 31.5 1.3

0.025 28.1 1.2

0.029 24.2 1.0

0.033 21.2 0.9

0.033 21.0 0.9

CHIPS k1 (h1) t1/2 (h) t1/2 (eq. cycle)

Beech

Fir

Oak

Measured

Measured

Measured

0.019 36.8 1.5

0.018 38.2 1.6

0.018 39.0 1.6

Table 7 Kinetic constants for temperature influence and one week cycle for sawdust washing. Beech k1 (h1)

Fir t1/2 (h)

k1 (h1)

t1/2 (eq. cycle)

Pseudo first-order kinetic constants for each biomass and temperature 0.015 47 2.0 0.015 5
C 20
C 0.018 37.5 1.6 0.019 30
C 0.017 40.4 1.7 0.022 Beech k1 (h1)

t1/2 (eq. cycle)

k1 (h1)

t1/2 (h)

t1/2 (eq. cycle)

45.9 36.2 32.1

1.9 1.5 1.3

0.024 0.029 0.030

28.7 23.8 23.4

1.2 1 1

Fir t1/2 (h)

k1 (h1)

Pseudo first-order kinetic constants for one week washing cycle 2.1 0.33 5.4 Beech

Oak t1/2 (h)

Oak t1/2 (h)

k1 (h1)

t1/2 (h)

0.13

1.2

0.60

t1/2 (h)

k2 (h1)

t1/2 (h)

0.40

0.005

0.68

Fir

t1/2 (h) k2 (h1) k2 (h1) Pseudo second-order kinetic constants for one week washing cycle 0.014 0.39 0.019

Oak

general, the measured and calculated conductivity values for each wood species are very close and in the same order of magnitude. Differences between measured and calculated conductivity values on the total are equal to 15, 18 and 17% for beech, fir and oak, respectively. These differences are mainly observed for the two first cycles, which suggests that the inorganic elements analyzed are those that predominate. The measured conductivity values are systematically higher than the calculated ones, because other inorganic elements and some organic compounds (light molecular weight ones containing oxygen as carboxylic acids, phenols, methoxyphenols…) are contained in the washing waters which also contribute to the total conductivity [19,22]. It was impossible to take into account the conductivities of organics because their speciations were not performed in water solution by ionic chromatography. This minimizes the calculated conductivity values. Secondly, a washing test was carried out on only beech wood for 256 h, representing 10 washing cycles, to model the kinetics of the biomass after a time longer than 96 h. According to the measured conductivity, the majority of minerals are extracted during the first four washing cycles. After these four first washing cycles, the extraction process is strongly less efficient. To determine a trend of these data, the conductivity is plotted on a log-decimal scale versus the washing time (Fig. 4a). The position of few points and the standard deviations equal to r2 ¼ 0.80 show that first-order kinetics lead to poor simulations. However, this kinetics is applicable on the first five washing cycles, and more particularly on the first four cycles. Fig. 4b shows the conductivity versus washing time with both axes in logarithmic scale. The position of the points and the standard deviation r2 ¼ 0.94 indicate that a model written as a power law: s ¼ at b , where a and b are two constants, can be applied to the ten washing cycles. Applying this model only to the first four washing cycles, it is possible to predict the conductivity value of the further cycles. Indeed, the two simulated curves (red and blue) perfectly superimpose (Fig. 4b). In conclusion, a model written as a power law: s ¼ at b , can be applied on all the points, or only on the first four washing cycles, thus allowing to predict the conductivity values. In addition, for the first four washing cycles, the pseudo-first-order model can be applied and allows determining the constants (k1) and half-reaction times (t1/2) for each specie. Table 6 summarizes these values determined from the experimental and calculated data of Table 5. Concerning the expression of the half-reaction times, the modelling concerning the four washing cycles, it would be more rigorous to express this value in equivalent number of washing cycles (eq. cycle) and not in hours. The two units are thus shown in Table 6. In general, the orders of magnitude of the kinetic constants (k1) and half-reaction times t1/2 are similar, whatever the wood species for both sawdust and wood chips. It is possible to observe, considering the values of these parameters, that the extraction of minerals is faster in the case of oak in comparison with fir and beech, beech (in the sawdust form) presenting the slowest behavior. The same experiments carried out at the wood chips scale (Table 6) lead to half-reaction times higher than sawdust, because of the granulometry and more specifically of the smallest exchange area. In addition to the influence of the granulometry, the washing temperature also plays an important role in mineral extractions. From the maximal conductivities recorded during the experiments, and as previously seen, it was possible to apply a pseudo-first-order model, to

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

10

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

Fig. 5. Kinetic of mineral extraction versus washing time in the case of beech sawdust. Silica is below the detection limit and does not appear in the figure.

determine the reaction constants and half-reaction times of each washing test, these values being gathered in Table 7. At 20
C, in the case of sawdust and from the experimental values of the conductivity, small differences between the values of these parameters from Tables 6 and 7 appear, because the sampling of each biomass batch could differ leading to heterogeneity. Whatever the temperature, the trend is the same: the orders of magnitude of the kinetic constants k1 and of the half-reaction times t1/2 are similar, whatever the wood species. The kinetics of extraction accelerate when the temperature increases, especially in the case of fir, for which the half-reaction time decreases by 30% when increasing the washing temperature to 25
C. In the case of beech, experimental uncertainties explain the variations of the values versus the washing temperature.

Fig. 6. Modelling of pseudo-first (left) and second (right) order in the case of beech (a), fir (b) and oak (c) sawdust.

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

11

Focusing on the kinetics of extraction of each inorganic element, in the case of beech, for example (Fig. 5), each mineral except sodium is extracted following a similar trend. In general, for each new washing cycle, 50% of each chosen element is extracted from the previous cycle. This is also confirmed for oak and fir (Figures A.4 and A.5 in the Appendix). This trend is identical to that followed by the total conductivity of each washing cycle. From the inorganic element concentrations of each washing cycle, pseudo-first order kinetics can then be applied to each element of each biomass. The values of the kinetic constants being quite similar for each element, they were averaged for each wood, thus giving for beech, fir and oak, average half-reaction times respectively equal to 29.3, 22.2 and 33.6 h (1.2, 0.9 and 1.4 equivalent washing cycles). These values are similar to those described in Table 6 for the conductivity showing that the ten chosen inorganic elements are largely predominant in the evolution of the conductivity. After focusing on the four washing cycles, modeling will now focus on the behavior of the conductivity of one long washing cycle. For each biomass, a single washing cycle during 168 h (7 days) was carried out to study the final evolution of the conductivity and to be able to determine the value of the equilibrium conductivity se by the pseudo first- and pseudo second-order models. These experiments were performed on sawdust. On the last days of washing, noise appears on conductivity measurements. The analyses were therefore limited to the first 30e40 h. Experimental and simulated curves in the case of beech, fir and oak are presented in Fig. 6a), b) and c), respectively. The profile of the curves is the same, whatever the wood. Equilibria are achieved for all species very quickly, in less than one day. A pseudo second-order model fits better with the experimental curves than a first-order one, even if there is a loss of information on the first minutes. Observe that the pseudo first-order model accelerates the reaction. The two models applied to the three species lead to the values of the kinetic constants gathered in Table 7. Even if the pseudo first-order model does not correctly fit, the values of the half-reaction times are similar to that obtained applying the second-order model. The kinetics of mineral extraction are identical for beech and fir, oak showing a slower kinetics. The values of the half-reaction times obtained with the pseudo second-order model are indeed equal to 23.4, 24.0 and 40.8 min for beech, fir and oak, respectively, washed during one week. 4. Conclusions Leaching of woods is well known to extract some organic compounds (tannins, phenols, etc.) and minerals. But kinetics of ionic exchanges between the solid (biomass) and the liquid (water) are currently not available in the literature. The records of the conductivity of water washing solutions versus time were helpful to simulate these exchanges. Tests were performed at a laboratory scale with different wood sawdust and chips. The impact of various parameters (granulometry, drying process or temperature washing) were tested. The results show that the nature of the biomass plays an important role. It clearly appears that washing the biomass extracts large amounts of the minerals contained in the wood. Most soluble elements were found to be potassium, sodium, magnesium, phosphorus and sulfur. Demineralization can reach an efficiency close to 100% for some ionic elements as potassium for example, which is one of the main minerals contained in wood. The washing protocol allows to decrease by 2% the rate of extractive wood compounds as TOC. For both organic and mineral parts, the influence of the wood granulometry shows that the more the wood is finely ground, the more the extraction yields increase. Similarly, increasing the washing temperature from 5
C to 30
C in order to simulate cold and warm climate conditions improves the extraction yields and favors the extraction of soluble compounds. During a short time of 24 h, in the case of the oak specie, the conductivity could be multiplied by a factor 2 with an increase of 25
C of the temperature. Biomass drying highly influences mineral extractions. Indeed, wood drying before washing shows an increase in the mineral extraction yields, especially on the first washing cycle. This can probably be explained by the affinity of minerals with water. When minerals are still again in contact with water, in the case of dried wood, they migrate more easily to the solution, unlike raw wood where minerals are already in an aqueous phase because of the residual moisture. Modelling the wood washing process highlights that the ionic exchanges between the biomass and the aqueous middle are governed by kinetic laws. Diffusional limitations appear during the exchanges and thus show the importance of the renewal of the washing water to increase the extraction yields. Considering the second-order model which gives the better simulations, when compared to the first-order one, the kinetic constant k2 evaluated after one week washing is almost the same for beech and fir (0.017 and 0.019 h1) and is lower for oak (0.005 h1). The half-reaction time t1/2 is almost the same for beech and fir (0.39 and 0.40 h) and is higher for oak (0.68 h). Acknowledgments The authors thank Mrs. F. Proharam from the Agence De l’Environnement et de la Maitrise de l’Energie (ADEME) for the financial support rieure de Chimie de Mulhouse, Mrs D. Kehrli from of the PREPABOIS project (n
1501C0039), M H. Aleboyeh from the Ecole Nationale Supe  and F. Balay from LORFLAM GRE laboratory for her technical assistance, M. P. Genevray from the CCM for his technical support, MM. S. Labbe industry, M. Y. Denance and Mrs. E. Faivre from INOVALP industry and M. N. Adam from AGRIVALOR industry. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.joei.2019.03.008. References [1] S. Ozgen, S. Caserini, S. Galante, M. Giugliano, E. Angelino, A. Marongiu, F. Hugony, G. Migliavacca, C. Morreale, Emission factors from small scale appliances burning wood and pellets, Atmos. Environ. 94 (2014) 144e153, https://doi.org/10.1016/j.atmosenv.2014.05.032.  de l’air inte rieur et exte rieur, 2008. [2] INERIS, Evaluation de l’impact des appareils de chauffage domestique  a bois sur la qualite € nen, J. Lyyr€ [3] J. Tissari, K. Hyto anen, J. Jokiniemi, A novel field measurement method for determining fine particle and gas emissions from residential wood combustion, Atmos. Environ. 41 (2007) 8330e8344, https://doi.org/10.1016/j.atmosenv.2007.06.018. €m, A. Nordin, Stove performance and emission characteristics in residential wood log and pellet combustion, Part 2: [4] E. Pettersson, C. Boman, R. Westerholm, D. Bostro wood stove, Energy Fuels 25 (2011) 315e323, https://doi.org/10.1021/ef1007787. €m, A. Nordin, Stove performance and emission characteristics in residential wood log and pellet combustion, Part 1: [5] C. Boman, E. Pettersson, R. Westerholm, D. Bostro pellet stoves, Energy Fuels 25 (2011) 307e314, https://doi.org/10.1021/ef100774x.

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008

12

G. Schmidt et al. / Journal of the Energy Institute xxx (xxxx) xxx

[6] C. Schmidl, M. Luisser, E. Padouvas, L. Lasselsberger, M. Rzaca, C. Ramirez-Santa Cruz, M. Handler, G. Peng, H. Bauer, H. Puxbaum, Particulate and gaseous emissions from manually and automatically fired small scale combustion systems, Atmos. Environ. 45 (2011) 7443e7454, https://doi.org/10.1016/j.atmosenv.2011.05.006. , The “zero-CO” domestic fireplace: a catalytic solution to reduce pollutants, Manag. Environ. Qual. 22 (2011) 429e439. [7] F. Ozil, V. Tschamber, F. Haas, G. Trouve [8] M. Omara, P.K. Hopke, S. Raja, T.M. Holsen, Performance evaluation of a model electrostatic precipitator for an advanced wood combustion system, Energy Fuels 24 (2010) 6301e6306, https://doi.org/10.1021/ef101031u. [9] S. Ghafghazi, T. Sowlati, S. Sokhansanj, X. Bi, S. Melin, Particulate matter emissions from combustion of wood in district heating applications, Renew. Sustain. Energy Rev. 15 (2011) 3019e3028, https://doi.org/10.1016/j.rser.2011.04.001. duction des e missions polluantes (gazeuses et particulaires) d’appareils de chauffages re sidentiels alimente s a  la biomasse - Etude [10] ADEME, Evaluation de solutions de re bibliographique, 2009. duction des e missions polluantes (gazeuses et particulaires) d’appareils de chauffages re sidentiels alimente s  [11] ADEME, Evaluation de solutions de re a la biomasse sultats de l’e tude des performances de syste mes commercialise s, 2011. Re [12] AFNOR, NF EN 12809/A1 - Residential Independent Boilers Fired by Solid Fuel - Nominal Heat Output up to 50 kW - Requirements and Test Methods, 2005. ^les a  combustibles solides - Exigences et me thodes d’essai, 2005. [13] AFNOR, NF EN 13240/A2 - Poe  convection a  granule s de bois - Exigences et me thodes d’essai - Appareils de chauffage domestique a  [14] AFNOR, NF EN 14785 - Appareils de chauffage domestique a s de bois, 2006. convection granule cologique, performant, e conomique et contemporain !, Doss. Presse (2015) 1e8. [15] Flamme Verte, Le label Flamme Verte : un chauffage au bois e  des parame tres de fonctionnement des appareils de chauffage domestique au bois sur la composition et l’e mission des particules fines [16] ADEME, Etude de la sensibilite Etude bibliographique, 2013. [17] S. Ozgen, S. Becagli, V. Bernardoni, S. Caserini, D. Caruso, L. Corbella, M. Dell'Acqua, P. Fermo, R. Gonzalez, G. Lonati, S. Signorini, R. Tardivo, E. Tosi, G. Valli, R. Vecchi, M. Marinovich, Analysis of the chemical composition of ultrafine particles from two domestic solid biomass fired room heaters under simulated real-world use, Atmos. Environ. 150 (2017) 87e97, https://doi.org/10.1016/j.atmosenv.2016.11.048. [18] L. Deng, T. Zhang, D. Che, Effect of water washing on fuel properties, pyrolysis and combustion characteristics, and ash fusibility of biomass, Fuel Process. Technol. 106 (2013) 712e720, https://doi.org/10.1016/j.fuproc.2012.10.006. [19] L. Jiang, S. Hu, L. Sun, S. Su, K. Xu, L. He, J. Xiang, Influence of different demineralization treatments on physicochemical structure and thermal degradation of biomass, Bioresour. Technol. 146 (2013) 254e260, https://doi.org/10.1016/j.biortech.2013.07.063. [20] I.-Y. Eom, K.-H. Kim, J.-Y. Kim, S.-M. Lee, H.-M. Yeo, I.-G. Choi, J.-W. Choi, Characterization of primary thermal degradation features of lignocellulosic biomass after removal of inorganic metals by diverse solvents, Bioresour. Technol. 102 (2011) 3437e3444, https://doi.org/10.1016/j.biortech.2010.10.056. [21] D. Mourant, Z. Wang, M. He, X.S. Wang, M. Garcia-Perez, K. Ling, C.-Z. Li, Mallee wood fast pyrolysis: effects of alkali and alkaline earth metallic species on the yield and composition of bio-oil, Fuel 90 (2011) 2915e2922, https://doi.org/10.1016/j.fuel.2011.04.033. [22] R.C. Pettersen, The chemical composition of wood, in: R. Rowell (Ed.), Chem. Solid Wood, American Chemical Society, Washington, DC, 1984, pp. 57e126, https://doi.org/ 10.1021/ba-1984-0207.ch002. [23] P. Ravichandran, K. Corscadden, Comparison of gaseous and particle emissions produced from leached and un-leached agricultural biomass briquettes, Fuel Process. Technol. 128 (2014) 359e366, https://doi.org/10.1016/j.fuproc.2014.07.030. [24] I.-Y. Eom, J.-Y. Kim, T.-S. Kim, S.-M. Lee, D. Choi, I.-G. Choi, J.-W. Choi, Effect of essential inorganic metals on primary thermal degradation of lignocellulosic biomass, Bioresour. Technol. 104 (2012) 687e694, https://doi.org/10.1016/j.biortech.2011.10.035. , G. Leyssens, C. Scho € nnenbeck, P. Genevray, F. Cazier, D. Dewaele, C. Vandenbilcke, E. Faivre, Y. Denance, C. Le Dreff-Lorimier, Wood washing: [25] G. Schmidt, G. Trouve influence on gaseous and particulate emissions during wood combustion in a domestic pellet stove, Fuel Process. Technol. 174 (2018) 104e117, https://doi.org/10.1016/ j.fuproc.2018.02.020. paration du combustible utilise  par des appareils domestiques de chauffage au bois sur la pollution atmosphe rique [26] G. Schmidt, Etude de l'impact de la nature et de la pre se de l'Universite  de Haute-Alsace, 2018. particulaire, The [27] DIN EN 13240, Certification Scheme for Room Heaters for Solid Fuels with Low-Pollution Combustion, DIN CERTCO, Berlin, Germany, 2011. risation des de chets - Essai de conformite  pour lixiviation des de chets fragmente s et des boues - Partie 2 : essai en b^ e unique avec [28] AFNOR, NF EN 12457-2 - Caracte ache trie infe rieure a  4mm (sans ou avec re duction de la granularite ), 2002. un rapport liquide-solide de 10L/kg et une granulome [29] C. Yu, P. Thy, L. Wang, S.N. Anderson, J.S. VanderGheynst, S.K. Upadhyaya, B.M. Jenkins, Influence of leaching pretreatment on fuel properties of biomass, Fuel Process, Technol. 128 (2014) 43e53, https://doi.org/10.1016/j.fuproc.2014.06.030. [30] D.C. Dayton, B.M. Jenkins, S.Q. Turn, R.R. Bakker, R.B. Williams, D. Belle-Oudry, L.M. Hill, Release of inorganic constituents from leached biomass during thermal conversion, Energy Fuels 13 (1999) 860e870, https://doi.org/10.1021/ef980256e. [31] L. Deng, J. Ye, X. Jin, D. Che, Transformation and release of potassium during fixed-bed pyrolysis of biomass, J. Energy Inst. 91 (2018) 630e637, https://doi.org/10.1016/ j.joei.2017.02.009. [32] T. Müller, M. Müller, U. Behrendt, B. Stadler, Diversity of culturable phyllosphere bacteria on beech and oak: the effects of lepidopterous larvae, Microbiol. Res. 158 (2003) 291e297. [33] D.J. Shure, P.D. Mooreside, S.M. Ogle, Rainfall effects on plant-herbivore processes in an upland oak forest, Ecology 79 (1998) 604, https://doi.org/10.2307/176957. cules chimiques issues du bois en France - Rapport final, 2015. [34] ADEME, Etat de l’art sur la production de mole veloppement - Le bioraffinage forestier : Possibilite  pour les entreprises que  be coises de pa ^tes et papiers, 2009. [35] FPInnovations-Paprican, Guide de de , JUMO, 2007. [36] R. Manns, Guide de la mesure de conductivite

Please cite this article as: G. Schmidt et al., Influence and modelling of wood washing on mineral and organic compositions of three woods (beech, fir and oak), Journal of the Energy Institute, https://doi.org/10.1016/j.joei.2019.03.008