# Alkali deactivation of high-dust SCR catalysts used for NOx 

# reduction exposed to flue gas from 100 MW-scale biofuel and peat fired boilers: Influence of flue gas composition

A  ̊ sa Kling a , 

## * , Christer Andersson b 

## , A  ̊ se Myringer c 

## , David Eskilsson d 

## , Sven G. Ja  ̈ ra  ̊ s e

a Vattenfall AB Va  ̈rme Uppsala, SE-753 82 Uppsala, Sweden
b Vattenfall AB Va  ̈ rme Norden, SE-814 26 A  ̈ lvkarleby, Sweden

c Vattenfall Utveckling AB, SE-162 87 Stockholm, Sweden
d Swedish National Testing and Research Institute, SP, Box 857, SE-501 15 Bora  ̊ s, Sweden

e KTH, Royal Institute of Technology, Department of Chemical Engineering and Technology, Chemical Technology, SE-100 44 Stockholm, Sweden Received 8 November 2005; received in revised form 15 March 2006; accepted 24 March 2006 Available online 24 August 2006

Abstract

Deactivation of vanadium–titanium deNOx SCR (selective catalytic reduction) catalysts in high-dust position have been investigated in three 100 MW-scale boilers during biofuel and peat combustion. The deactivation of the catalyst samples has been correlated to the corresponding flue gas composition in the boilers. Too investigate the effect on catalyst deactivation a sulphate-containing additive was sprayed into one of the furnaces. Increased alkali content on the SCR catalyst samples decreased the catalytic deNOx activity. The study has shown a linear correlation between exposure time in the boilers and alkali concentration (mainly potassium) on the samples. The results imply that mainly alkali in ultra fine particles ( < 100 nm) in the flue gas increased the alkali accumulation on the catalyst samples. Low correlation was found between particles larger than 100 nm and the catalyst deactivation. It was not possible to decrease the deactivation of the catalyst samples by the sulphate-containing additive. Although the additive had an effect in sulphating potassium chloride to potassium sulphate, it did not decrease the amount of potassium in ultra fine particles or the deactivation of the catalyst samples. # 2006 Elsevier B.V. All rights reserved.

Keywords: Selective catalytic reduction; Nitrogen oxides; Combustion; Biofuel; Alkali; Potassium; Particles; Deactivation

1. Introduction

Selective catalytic reduc tion (SCR) of nitrogen oxides (NOx) has been recognised as an effective post-combustion method for reducing NOx from stationary sources. One of the commercial SCR catalysts contains TiO 2 for high surface area support and V 2 O 5 as active catalytic compound. The catalyst is promoted by tungsten or molybdenum. By addition of ammonia, the SCR catalyst can reduce more than 90% of the NOx emissions in the flue gas. At least five biofuel-fired combined heat and power plants in Sweden are today using this kind of SCR in high-dust positions for NOx reduction. The experiences from these boilers are high catalyst deactivation, 2–4 times higher compared with coal-combustion and the main deactivating compound was found to be potassium [1]

. Similar deactivation of titanium–vanadium SCR-catalysts has been observed during co-combustion of straw and wood in Denmark [2]

. One of the strongest poisons for SCR catalysts is potassium [1–9] . Potassium preferentially co-ordinates to the hydroxyl groups of V 2O 5 and probably also to the promoter and the catalytic activity decreases with the decrease of both the number and the strength of the Brønsted acid sites [9] . A number of studies on wet impregnation with different potassium compounds on SCR catalysts showed that K2O, K2 SO 4 and KCl all give high catalyst deactivation [4–7,9–11]

. The combustion method has been found to have a major impact on the catalyst deactivation in wood-fired boilers [1]

. Catalyst samples showed a more severe deactivation during pulverised fuel combustion exposure than with exposure during www.elsevier.com/locate/apcatb Applied Catalysis B: Environmental 69 (2007) 240–251

* Corresponding author. Tel.: +46 18 269788; fax: +46 18 146657.

E-mail address: asa.kling@vattenfall.com (A  ̊ . Kling).

0926-3373/$ – see front matter # 2006 Elsevier B.V. All rights reserved. doi: 10.1016/j.apcatb.2006.03.022circulating fluidised bed combustion. The results indicated that only specific forms of potassium deactivated the catalyst [1]

. The total amount of potassium in the flue gas did not correlate to the catalyst deactivation. Potassium is usually the dominant alkali metal in biofuels, unlike in coal where sodium dominates. The potassium is also, in biomass, bound predominantly as salts or organically bound in ion-exchangeable form, which results in high volatilisation during combustion. Depending on the fuel composition the potassium is bound mainly to either sulphate or chloride in the flue gas. As the flue gas temperature decreases the potassium forms submicron particles by homogeneous or heterogeneous condensation. At SCR catalyst operation temperatures, between 300 and 400 8 C, no or low amounts of potassium vapour are thermodynamically, stable [12] . The smallest particles ( < 0.5 mm) will however be dominated by potassium, sulphur and chloride [12–17]

. The aim of this work was to investigate, in full-scale boilers, the correlation between SCR catalyst deactivation and the different alkali aerosol components in the flue gas. The aim was also to investigate the possibility of using a sulphate-additive to decrease the deactivation and prolong the catalyst lifetime.

2. Experimental

2.1. Catalyst exposure

Three types of honeycomb V 2O 5/TiO 2 deNOx SCR catalysts have been used in the study (type A, B and C). The main difference between the types is the vanadium content. The type A catalyst has an activity typical for coal-fired applications (high SO 2 content in the flue gas) with low vanadium content and low deNOx activity per m 3 . The type B is a ‘bio-optimised’ catalyst with increased vanadium content for catalysts used in applications with low SO 2 content in the flue gas. Type C is a catalyst tested for high alkali metal flue gas environments with very high vanadium content. The samples have been exposed from 100 to 3000 h using a simple testing device called a sample bench. The sample-bench can expose up to 36 honeycomb samples with a size of 20 mm 20 mm inlet area and a length of 90 mm. The samples each consist of nine channels. The bench holds the samples directly in the flue gas channel, Fig. 1 . The sample bench was placed between the economisers, at a temperature of 300– 400 8 C, in ‘high-dust’ position before dust collection. The pressure drop over the catalyst samples influences the flue gas flow through the samples. However, at least 50% of the flue gas passing over the sample area will go through the samples (calculated on the pressure balance in the system). Exposure of this kind of short samples gives a larger deactivation compared to a full-length catalyst [1,4] . That is mainly a consequence of turbulence at the inlet of the catalyst, before the laminar flow is attained. Three full-scale combined heat and power plants have been used for the tests. Johannes, situated in Ga  ̈ vle, has a thermal capacity of 90 MW and is using bubbling fluidised bed combustion with a fuel-mix of bark and demolition wood. The combustion temperature is about 800 8 C. Brista, situated outside Stockholm, has a thermal capacity of 120 MW and is using forest residues as fuel with circulating fluidised bed combustion. The combustion temperature is 850–880 8 C. Brista has a combined deNO x cleaning system with a SNCR (Selective Non-Catalytic Reduction) in combination with a SCR (35 m 3 ). The Uppsala combined heat and power plant has a thermal capacity of 400 MW burning a fuel-mix of peat and wood powder with pulverised fuel combustion in a Benson boiler. The combustion temperature is about 1500 8 C. An SCR-catalyst (100 m 3 ) was installed during 2005 in combination with the existing SNCR-system. In Table 1 the different boilers, their thermal capacity, combustion principle, fuels, furnace additive, A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 241

Fig. 1. Schematic overview of a catalytic sample bench in a plant with bubbling fluidised bed combustion.sample bench exposure temperatures, flue gas velocities and SCR sample space velocities (flue gas in relation to volume of catalyst) are given. The SCR samples have been exposed during a total period of 5 years but only during the cold period of the year, from December until April called combustion season in the paper, these seasons have been named A, B, etc. No complete fuel analyses were conducted during the sample exposure periods. Average fuel compositions have been summarised in Table 2 . Fuel samples (forest residue) from the Brista combined heat and power plant were analysed for chlorine content. The fuel (0.1–0.3 g) was adsorbed with hydrogen peroxide and sodium hydroxide and combusted with oxygen (Scho  ̈ niger combustion method). The solution was analysed using ion chromatography. Sulphate, a patented additive [22] , was added in the Brista combined heat and power plant for two catalyst sample test periods. The additive was primarily used to reduce superheater corrosion by sulphation of KCl to K2 SO 4 , which decreases chlorine-induced corrosion in biofuel boilers [21,22] . The sulphate was added to the furnace before the superheaters at a temperature of 800–900 8 C, in a water suspension. As the water suspension reaches the furnace it dissolves into (gaseous) SO 3 . The sulphate was added with sulphur to chloride mol ratio of 1:1 (chloride content in the form of potassium chloride compared with the added sulphate). The catalytic activity was measured using a small laboratory test-rig. The gas stream contained (on a volume basis) 500 ppm NO, 600 ppm NH 3 and 4% of oxygen in N2 . No water was added. The total flow rate was 7.5 l/min. Mass flow controllers that provide a constant flow controlled the flow rates of the individual gases, Fig. 2 . The space velocity of the experiments was 6000 h
 1 . The reactor was aluminium-based with an internal surface of stainless steel. The activity of the catalyst was measured at 300 8 C. The catalytic activity was calculated according to the rate expression (1)

kNO 1⁄4  AV ln ð 1

 h Þ (1)

where k NO is the catalytic activity, AV = Q/ A ; Q flow (cm 3/min) and A area (cm2) and 1

 h = NO out/NO in . As the flue gas in the test rig was not representative for the flue gases in the full-scale boilers the absolute activity has not A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 242

Table 1 Boilers used in the study: thermal capacity, fuel, additive, sample exposure temperature, flue gas velocity over the sample bench and SCR sample space velocity

Name Season a Boiler type Maximal thermal capacity (MW) Fuel Furnace additive SCR-sample temperature ( 8 C) Flue gas velocity (m/s) Sample space velocity (h  1 )

Johannes A BFB b 90 Bark + 15% demolition wood (B + 15% DW) 270 3.9 156000

Johannes B BFB b 90 Bark + 30% demolition wood (B + 30% DW) 270 3.9 156000

Brista A CFB c 120 Forest residues (FR) 370 3.2 128000 Brista B CFB c 120 Forest residues (FR) 370 3.2 128000 Brista C CFB c 120 Forest residues (FR) Sulphur (S) 370 3.2 128000 Brista D CFB c 120 Forest residues (FR) Sulphur (S) 370 3.2 128000 Uppsala A PC d 400 Peat + 30% wood (P + 30% W) 370 10 400000

Uppsala B PC d 400 Peat + 20% wood (P + 20% W) 370 10 400000

Uppsala C PC d 400 Peat (P) 370 10 400000

a The cold period of the year from December until April.
b Bubbling fluidised bed.

c Circulating fluidised bed.
d Pulverised combustion.

Fig. 2. Measurement equipment for catalytic deNOx activity. Table 2 Standard fuel content of potassium, chlorine, sulphur, sodium and ash for the different fuels used in the study, the different shares of fuels have been calculated based on energy content [19,20]
Fuel K (mg/kg ds) Cl (mg/kg ds) S (mg/kg ds) Na (mg/kg ds) Ash content (%)
Peat + 15% wood (1) 434 100 2300 218 5.4 Forest residues (11) 1500  480 211  66 384  130 275  190 3.1 Demolition wood (32) 1165  580 641  550 788  790 665  300 5be used for the evaluation of data. Instead the relative activity has been used in the paper. The relative activity has been defined as the activity of a deactivated catalyst compared to the corresponding fresh sample.

2.2. Catalyst characterisation

The distribution of different chemical species on the catalyst samples was investigated by scanning electron microscope/ energy-dispersive X-ray (SEM-EDX) on a Hitachi S-3000N, with an Oxford 7021 X-ray diffractor. A standard resolution of 100 was used but up to 3500 was used for some samples. Wet chemical analyses were made for potassium, sodium, chlorine, sulphate, ammonium, lead and zinc. The samples were analysed with ion chromatography after water leaching of the samples (285 ml of deionised water in 16 h at a temperature of 90 8 C). For lead and zinc the samples were also placed in (100 ml, 7 M) nitric acid and analysed using atom absorption spectrophotometer. Surface area and nitrogen adsorption data at 77 K were measured on a Micrometrics ASAP 2000 automatic surface area and pore-size distribution apparatus. The total surface area was evaluated by the Brunauer–Emmett–Teller (BET) method, while the average pore diameter was calculated from the surface area and the BET pore volume.

2.3. Flue gas characterisation

The flue gas composition of SO 2 , O 2 and CO was continuously analysed by the instruments for operations monitoring in all plants. Measurement of alkali chloride content was performed in the Brista CHP using in situ alkalichloride monitoring (IACM), developed at Vattenfall Utveckling AB [23] . The instrument measures the sum of the sodium and potassium chloride concentrations and cannot differentiate between these two species. The instrument also measures sulphur dioxide. IACM is an optical UV instrument based on absorption spectroscopy and has a sampling time of 5–10 s. Sodium chloride absorption is seen between 194 and 235 nm in the temperature interval 850–1350 8 C, the corresponding absorption for potassium chloride appears at 198–246 nm and is observed between 800 and 1300 8 C. Each absorption spectrum was evaluated by DOAS, differential optical absorption spectroscopy, a technique for evaluating absorption spectra of complex mixtures. The light source in IACM is a xenon lamp; the light is sent across the furnace or flue gas channel by several optical units, Fig. 3

. Particles were sampled in the same measurement position as the catalyst sample bench was placed. The fine particles originating from combustion were sampled iso-kinetically through a heated sampling line. The mass size distributionswere measured using two pre-cyclones in series with a lowpressure impactor (DLPI, dekati low-pressure impactor) and a filter. The pre-cyclones were used for separating the larger particles ( > 2.0 mm) in high concentration in order to obtain a longer measurement time with the DLPI. Nine different filter sizes were used with an aerodynamic cut diameter from 0.030 to 2.0 mm. An additional filter was used to collect the smallest particles leaving the impactor (0.008–0.030 mm). The sample line, the pre-cyclones, the DLPI and the filter were heated to 120 8 C. After collecting the particles, mass size distributions were obtained by weighing the filters and the particles were analysed by scanning electron microscope/energy-dispersive X-ray (SEM-EDX) on a Hitachi S-3000N, with an Oxford 7021 X-ray diffractor. The particle mass collected on the filter was however so low that the filter could not be weighed. Instead, the filter was chemically analysed in order to determine mass of the collected particles using inductive coupled plasma-mass spectroscopy, ICP-MS.

3. Results

3.1. Catalyst deactivation

The SCR catalyst samples decreased between 5 and 50% in catalytic activity after 100–3000 h of exposure in the flue gas, Table 3 . No or little plugging was found in the catalyst samples during the exposure time. The deactivation of the catalyst samples cannot directly be compared with the deactivation of a full-length catalyst. The poisonous components accumulate at the inlet of the catalyst and the catalyst deactivation will be higher on the short SCR samples. The deactivation rate in different boilers and with different fuels can however be compared with this method. To compare the different samples the loss of activity was calculated as a linear decrease where the various measurements (exposed more than 500 h) have been interpolated to 1500 h. During combustion of peat the loss of activity for the samples was 5–15%, see Fig. 4 , an increased co-combustion A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 243

Fig. 3. Schematic sketch of a system for IACM monitoring, the light source is placed in the boiler wall opposite the receiver.with wood fuel (up to 30%) did not increase the deactivation of the samples. The loss of activity during combustion of forest residues was between two and three times higher compared with peat combustion after 1500 h. An increased deactivation was measured during co-combustion of bark and demolition wood (in a bubbling fluidised bed) compared with combustion of forest residues (in a circulating fluidised bed). The effect of the sulphur-containing additive on the catalyst deactivation was small; the same deactivation was measured during the periods with and without additive. A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 244

Table 3 Fuels, boiler types, additive, sample exposure season, exposure time and relative catalytic activity for SCR sample exposures
Fuel Boiler type Additive Season Exposure time (h) Relative catalytic activity (%)
Type B Type A Type C

Peat + 30% wood PC a A 864 91  1 (4) Peat + 30% wood PC a A 1340 92  1 (2) Peat + 30% wood PC a A 2850 93  6 (3) Peat + 15% wood PC a B 768 88  2 (2) Peat + 15% wood PC a B 1488 85  2 (4) 80  2 (2) Peat PC a C 526 96 (1) 91 (1) Peat PC a C 929 93 (1) 84 (1) Peat PC a C 1455 95 (1) Forest residues CFB b A 1230 70 (1) Forest residues CFB b A 1650 71  4 (4) Forest residues CFB b A 2880 55  3 (4) 46 (1) 81  8 (4) Forest residues CFB b B 120 83 (1) Forest residues CFB b B 864 88 (1) 105 (1) Forest residues CFB b B 1584 64  6 (2) 49 (1) 89  6 (2) Forest residues CFB b C 648 73  2 (2) Forest residues CFB b C 984 79  3 (4) Forest residues CFB b Sulphate C 1320 73  5 (4) 56  2 (2) Forest residues CFB b Sulphate C 2820 55 (1) Forest residues CFB b Sulphate D 502 84  1 (2) 71  1 (2) Forest residues CFB b Sulphate D 1038 76  1 (2) 60  1 (2) Forest residues CFB b Sulphate D 1540 71  2 (2) 54  1 (2) Bark + 15% demolition wood BFB c A 720 72  1 (2) 97 (1) Bark + 15% demolition wood BFB c A 1150 95  11 (2) 94 (1) Bark + 15% demolition wood BFB c A 1870 66  3 (3) 87  19 (2) Bark + 30% demolition wood BFB c B 100 93  3 (3) 93  4 (3) Bark + 30% demolition wood BFB c B 500 86  7 (3) 85  4 (3) Bark + 30% demolition wood BFB c B 1000 67  5 (3) 88  4 (2) Bark + 30% demolition wood BFB c B 1400 59  4 (2) Bark + 30% demolition wood BFB c B 1500 58  0 (3) 48  1 (3) 79  4 (2)
All the activity analyses were conducted at a temperature of 300 8 C.

a Pulverised combustion.
b Circulating fluidised bed.

c Bubbling fluidised bed.

Fig. 4. Loss of catalytic activity (%) of type B samples per 1500 h of exposure in flue gas, the activity analyses have been conducted at a gas temperature of 300 8 C. P, peat; W, wood; FR, forest residues; B, bark; DW, demolition wood.The most commonly used catalyst type in biofuel boilers in Scandinavia is a bio-optimised type with a higher vanadium content than for applications with higher sulphur content in the flue gas, such as coal combustion. One bio-optimised catalyst is the type B—used in this study. The increased amount of vanadium in the sample increases the deNOx capacity of the catalyst. When the deactivation of the bio-optimised sample (type B) was compared with the deactivation of the low vanadium content sample (type A) the deactivation was 1.2–1.9 times faster for the sample with the lower vanadium content, Fig. 5 . The same result was observed when the type B samples were compared with a sample with high vanadium content (type C). The deactivation of the type C sample was 0.4–0.7 times the deactivation of the type B sample after a 1500-h exposure period (type C had 30–70% lower deactivation compared with type B). This indicates that these bio-optimised samples not only have a higher initial deNOx activity, the deactivation is also considerable less, which gives a longer lifetime and higher deNOx capacity per hour in the flue gas. The elementary analyses using SEM-EDX on the catalyst samples showed an increased amount of mainly potassium but also of sodium. Lead was found on the samples exposed to fluegas from co-combustion of demolition wood. Higher concentrations of the deactivating compounds were found at the inlet of the catalyst samples compared with the outlet. As the SEM-EDX also detects the metals in the catalyst material the signals from the deactivating compounds were very low. Therefore, the SEM-EDX investigations were mainly used as guidance for choice of wet chemical analyses and not for any quantitative measurement. By splitting the catalyst wall the distribution of the deactivating compounds through the wall was detected by SEM-EDX line-scan. As seen in earlier studies [1,4] the potassium was relatively evenly distributed throughout the wall, Fig. 6 . No accumulations of potassium were found on the outer surface of the samples. Mapping (correlation between the different ions on the catalyst surface) was done with the SEM-EDX for potassium, sodium, sulphur, chlorine, oxygen, titanium and vanadium on samples exposed in all plants in the study. No correlation was found between the deactivating compounds and the different possible corresponding anions. Wet chemical analyses were conducted on samples from Uppsala (seasons B and C), Brista (seasons C and D) and A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 245

Fig. 5. Loss of catalytic activity of type A and type C samples compared with type B samples after 1500 h of exposure in flue gas. The analyses were conducted at a temperature of 300 8 C.

Fig. 6. Distribution of potassium through the catalyst wall on a type B sample exposed for 1500 h during combustion of forest residues in a circulating fluidised bed (season C).Johannes (seasons A and B). The results are presented in Fig. 7

. Potassium and sodium were accumulated linearly on the samples. The highest accumulation of potassium was measured on the samples exposed to co-combustion of bark and demolition wood, about 0.2 wt.% after 1500 h of exposure in the flue gas. The accumulation of potassium from forest residues (with and without sulphate additive) was about 0.10– 0.13 wt.% after 1500 h while combustion of peat gave a potassium accumulation of 0.05 wt.% after 1500 h. The accumulation of sodium was low for all samples (less than 0.05 wt.%). About 1.5 wt.% of sulphur was found on the freshcatalyst samples. During combustion of peat and co-combustion of bark and demolition wood the amount of sulphur on the samples decreased by about 0.1–0.5 wt.%. During combustion of forest residues (both with and without sulphate additive) the amount of sulphur on the samples decreased by about 1 wt.%. The sulphur level decreased during the first 500 h of exposure, after that the sulphur level on the samples was more or less constant. A low amount of chlorine was detected on the samples. Wet chemical analyses on lead and zinc were only conducted on the samples exposed to flue gas from combustion of demolition wood; the accumulation of both zinc and lead was low on the samples. No wet chemical analysis on zinc and lead were conduced on the other samples, earlier studies have not shown an accumulation of these metals during biofuel combustion nor did the analyses with the SEM-EDX. Surface area and pore-size distribution were measured on two different samples exposed during co-combustion bark and 30% demolition wood. These samples were compared with a fresh sample. No analysis was conducted on the samples that had been exposed during combustion of peat and forest residues. Earlier studies have shown that the loss in surface area was low on similar samples [1,2] . The average pore size for the fresh sample was 13 nm, 85% of the pores were in the size range of 10–23 nm and 99% of the pores were smaller than 100 nm. The BET analyses showed a small decrease in surface area, 6%, on the sample exposed to bark and demolition wood, Table 4 . No change in micropore volume was detected. A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 246

Fig. 7. Accumulation of potassium, sodium, chlorine, sulphur, zinc and lead on catalyst samples as function of the catalyst exposure time in flue gas: W , wood; FR, forest residues; B, bark; DW, demolition wood.3.2. Flue gas analysis

The flue gas content of sulphur varied between the different fuels. During peat combustion the sulphur dioxide content in the flue gas was about 200 ppmwg (measured on volume basis) and during combustion of forest residues and co-combustion of bark and demolition wood about 3–10 ppmwg . The alkali (sum of potassium and sodium) chloride content was continuously measured during combustion of forest residues for two seasons. During the period when no additive was used the average level of alkali chloride was about 6 ppmwg and the concentration of SO 2 4 ppmwg . This can be compared to the two periods when the sulphate additive was used when the alkali chloride content decreased to 2 and 1 ppmwg , respectively, and the SO 2 concentration was 3 and 7 ppmwg , respectively. This was probably a consequence of the sulphation of alkali chlorides and the resulting formation of alkali sulphates. Impactor measurements were conducted in Uppsala (peat and 15% wood powder, season B), Brista (forest residues with and without sulphate additive, seasons C and D) and Johannes BFB (bark and 30% demolition wood, season B). The amount of particles (mg/N m 3 ) varied between the different boilers and between the different size fractions (0.008–2 mm), see Fig. 8

. The largest amount of submicron particles (particles with a size smaller than 1 mm) was found in Johannes BFB during cocombustion of bark and demolition wood. The lowest amountof particles was found in the Uppsala PC boiler during cocombustion of peat and wood. There was a significant difference in the total amount of particles smaller than 2 mm with and without sulphur additive during combustion of forest residues, larger amounts or particles were found during the periods when the additive was used. However, the amount of ultra fine particles (smaller than 100 nm) was the same during the periods with and without the additive, Table 5

. By using the results from the low pressure impactor; the different chemical compounds analysed with SEM-EDX and the weighted amount of particles in each filter step, the amounts of potassium, sodium, chlorine, sulphur, lead, zinc, phosphorus and calcium was calculated, Fig. 9 and Table 5 . The dominating chemical compounds in the ultra fine particles were potassium, sodium, chlorine, sulphur and oxygen. In particles larger than 0.5 mm calcium and phosphorus was also found. The amount of potassium in the flue gas was, for the ultra fine particles, highest during combustion of bark and demolition wood, Table 5 . The sulphate additive did not change the amount of potassium in the ultra fine particles significantly. The amount of alkali in the fine particles during peat combustion was low. The amount of sodium, in the fine particles, was low compared with the A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 247

Fig. 8. Fly ash mass size distribution (mg/N m 3 ) in the flue gas: P, peat; W, wood; FR, forest residues; B, bark; DW, demolition wood.

Table 5 Fly ash mass size distribution (mg/N m3 ) of particles in flue gas (detected with DLPI) with an aerodynamic particle diameter smaller than 100 nm (mg/N m 3 ) and corresponding measurements with IACM during the impactor-sampling period: alkali chloride and sulphur dioxide

Peat + 15% wood Forest residues (season C) a Forest residues (season D) Forest residues + sulphate (season C) Forest residues + sulphate (season D) Bark + 30% demolition wood (season B)
Potassium (mg/N m 3 ) 4.4 10.5 6.7 6.2  0.2 a 6.8 16.7 Sodium (mg/N m 3 ) 0.8 1.1 1.3 0.9  0.1 a 2.5 5.8 Chlorine (mg/N m3 ) 0.6 2.5 1.1 0.0  0 a 0 4.3 Sulphur (mg/N m 3 ) 2.7 4.9 2.8 3.3  0.3 a 3.8 2.1 Lead (mg/N m 3 ) 00 0 0 a 0 0 Zinc (mg/N m3 ) 0 0 0.1 0 a 0.2 0.1 Phosphorous (mg/N m 3 ) 1.1 0 0 0 a 0 0.1 Calcium (mg/N m 3 ) 2.2 0 0 0 a 0 0 Total particle amount (mg/N m 3 ) 17.9 8.6  5.0 a 11.6 6.0  0.9 a 12.5 32.7 (K + Na)Cl (ppmwg) 5.2 8.0 1.6 2.4 SO 2 (ppmwg) 5.4 0.0 7.1 6.3

a Average of two measurements during the same day. Table 4 BET surface on fresh sample, sample exposed during co-combustion of bark and demolition wood (500 h) and exposed during combustion of refuse energy fuel (100 h)
Catalyst. type Fuel Relative catalytic activity (%) Surface area (m2/g) Micro pore volume (cm3/g) Average pore diameter (A  ̊ )
Type B Fresh 100 66.8 0.0016 128 Type B Bark + 30% demolition wood 79 62.7  1.7 0.0024 138amount of potassium during combustion of all fuels. The highest amount of sodium was found during combustion of bark and demolition wood. There was a significant decrease in chlorine content in the fine particles during the periods when the sulphate additive was used. This can be compared with the amount of KCl measured in the gas phase using IACM, see Table 5 . A decreased amount of KCl in the gas phase corresponded to a decreased amount of chlorine in the fine particles. The amount of chlorine in the ultra fine particles was two times higher during combustion of bark and demolition wood compared with the combustion of forest residues. The amount of sulphur in the ultra fine particles was highest during combustion of forest residues. There was low correlation between the amount of sulphur in the fine particles and the amount of sulphur dioxide in the flue gas. The sulphur in the particles probably correlates with the particle sulphate compounds such as potassium sulphate, sodium sulphate or other metal sulphates. Low amounts of lead, zinc, phosphorus and calcium were found in the ultra fine particles. Five forest residues fuel samples from Brista, season C, were analysed for chlorine content. The samples were then compared with the corresponding amount of alkali chloride in the gas A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 248

Fig. 9. Fly ash mass size distribution (mg/N m 3 ) for potassium, sodium, chlorine and sulphur, lead, zinc, phosphorus and calcium: P, peat; W, wood; FR, forest residues; B, bark; DW, demolition wood.phase measured in the flue gas when the samples were collected. There was a strong correlation between the chlorine content in the fuel and the alkali chloride concentration in the flue gas, see Fig. 10 . The chlorine content in the fuel varied between 0.005 and 0.045 wt.% during the period.

4. Discussion

4.1. Catalyst deactivation

The wet chemical analyses showed that potassium and sodium were the main chemical components that accumulate on and in the catalyst material. There was a strong correlationbetween the alkali content on the samples and the corresponding catalytic activity, Fig. 11 , which implies that the main deactivation effect probably was alkali poisoning. This has been concluded in earlier studies on SCR catalyst deactivation during biofuel combustion [1–4] . It was difficult to conclude in what form the alkali penetrates and reacts with the catalyst material by the analyses conducted on the catalyst material. No anion was found that correlates to the potassium on the catalyst surface. The fact that the potassium, also in this study, was found relatively evenly distributed throughout the catalyst material and not in higher concentrations on the outer surface of the catalyst material indicates that it is probably transported into the material in small particles or in gas phase. Sulphur has a promoting effect on the SCR catalyst deNOx activity [27–32] . The decrease in sulphur content in the catalyst material, during exposure of the catalyst samples in the lowsulphur gas flow, was probably a part of the catalyst deactivation during the biofuel combustion. Studies on regeneration of catalyst deactivated during biofuel combustion have shown that sulphation, exposure of the samples to gaseous SO 2 , increased the activity of the catalyst samples from a relative activity of about 50% up to a relative activity of about 80% [29]

. 4.2. Flue gas composition

By measuring the flue gas composition before and near the catalyst surface differences in flue gas composition can be detected for different fuel types and combustion techniques. Earlier studies have shown that the flue gas chemical compositions are highly dependent on fuel, combustion temperature and combustion technique [12,14–35] . The combustion aerosols from fuels with a high content of volatile sodium or potassium compounds are often dominated by a large submicron fraction [12,14] . These particles are formed by recondensation of alkali volatilised in the combustion zone, and their main constituent components are chlorides and sulphates of the alkali metals. The occurrence of chlorine in the submicron particles has been found to mainly represent the occurrence of alkali chlorides during combustion of biomass [16] . The chlorine content in the submicron particles differed between the different boilers and for the different fuels. The chlorine content in the fuel seems to correlate with the chlorine content in the submicron particles; increased amounts of chlorine in the fuel corresponded to increased amounts of chlorine in the submicron particles. These results correspond to the fuel analyses and the on-line measurements of alkali chlorides during combustion of forest residues in the circulating fluidised bed. Increased amount of chloride in the fuel corresponded to increased amount of alkali chlorides in the flue gas. When sulphur in the form of SO 3 was added to the boiler at a temperature of 800–900 8 C this resulted in a sulphation of the potassium chlorides to potassium sulphate, the chlorine content in the submicron particles decreased to very low values and the on-line measurement with IACM indicated a decrease in gaseous alkali chlorides by more than 50%. The addition of the sulphate additive did however not decrease the total amount of alkali in the fine particles.

4.3. Impact of flue gas composition on SCR catalyst deactivation

As can be seen in Fig. 12 there was a linear correlation between the alkali content in the ultra fine particles and the alkali accumulation on the catalyst samples with an R 2 value of 0.76. The linear correlation between the accumulation of alkali accumulation on the samples and the amount of alkali in the flue gas decreases with increased particles sizes. The correlation for particles smaller than 200 nm had a R 2 value of 0.56, the correlation with particles smaller than 300 nm had a R 2 value of 0.14, particles smaller than 450 nm had a R 2 value of 0.05 and for particles smaller than 2000 nm the R 2 value was A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 249

Fig. 10. Chlorine content in the forest residue fuel (Brista, season C) compared with the amount of alkali chloride in the flue gas monitored by IACM.

Fig. 11. Accumulation of alkali on the SCR catalyst samples as function of the corresponding catalytic deNOx activity (measured at a temperature of 300 8 C). P, peat; FR, forest residues; B, bark; DW, demolition wood; S, sulphur.0.10. The correlation between the particles smaller than 450 nm and the accumulation of alkali on the samples are presented in Fig. 13 . The largest catalyst pores are about 100 nm, larger alkali components can only be transported into the catalyst by solid-state diffusion. This should lead to a concentration profile in the material with higher concentrations in the outer part of the material, which was not found in this study. The uncertainty in the correlation between the impactor measurements and the alkali accumulation on the catalyst samples were however large. Only one or two impactor measurement of flue gas composition, each with about 20 min sampling time, has been conducted for each catalyst exposure season—in total up to 3000 h. Only a very small fraction, less then 0.3% calculated for combustion of forest residues in Brista, of the total alkali content in the ultra fine particles smaller than 100 nm accumulated on the catalyst surfaces. The fuel analyses during season C in the Brista CHP imply that the variation in fuel quality and the resulting variation in flue gas composition could be large.

5. Conclusions

The study has shown that alkali, mainly potassium, accumulates linearly on the catalyst samples during the exposure time in the biofuel boilers. Increased alkali content on the catalyst samples decreased the catalytic deNOx activity; 40% loss in catalytic activity correlated to an alkali accumulation of about 2 wt.%. An increased amount of vanadium in the catalyst material decreased the catalyst deactivation rate significantly. The results imply that large amounts of alkali in ultra fine particles ( < 100 nm) in the flue gas increased the alkali accumulation on the catalyst. Low correlation was foundbetween particles larger than 100 nm and the catalyst deactivation. It was not possible to decrease the deactivation of the catalyst samples by the sulphate-containing additive. Although the additive had an effect in sulphating potassium chloride to potassium sulphate, this did not decrease the amount of potassium in ultra fine particles or the deactivation of the catalyst samples.

Acknowledgements

Financial support from the European Commission through the project CATDEACT and Va  ̈ rmeforsk, Sweden, is gratefully acknowledged. We also wish to thank the staff at Brista, Johannes and Uppsala CHP for kind help with the full-scale deactivation tests.

References

[1] R. Khodayari, C. Andersson, C.U.I. Odenbrand, L.H. Andersson, in: Proceedings of the 5th European Conference on Industrial Furnaces and Boilers, vol. VII, Espinho, Porto, Portugal, 2000. [2] Y. Zheng, A.D. Jensen, J.E. Johnsson, Appl. Catal. B: Environ. 60 (2005) 253. [3] Y. Zheng, A.D. Jensen, J.E. Johnsson, Ind. Eng. Chem. Res. 43 (2004) 941.[4] R. Khodayari, Doctoral dissertation, Department of Chemical Engineering, Lund Institute of Technology, Sweden, 2001. [5] F. Moradi, J. Brandin, M. Sohrabi, M. Faghihi, M. Sanati, Appl. Catal. B: Environ. 46 (2003) 65. [6] J.P. Chen, R.Y. Yang, J. Catal. 125 (1990) 411. [7] J.P. Chen, M.A. Buzanowski, R.T. Yang, J.E. Cichanowica, J. Air Waste Manage. Assoc. 40 (1990) 1403. [8] M. Tokarz, S.G. Ja  ̈ ra

 ̊ s, B. Persson, Catalyst Deactivation, Elsevier Science Publishers B.V., 1991, pp. 523–532. [9] H. Kamata, K. Takahashi, C.U.I. Odenbrand, J. Mol. Catal. A: Chem. 139 (1999) 189. [10] S. Pritchard, C. Difrancesco, S. Kaneko, N. Kobayashi, K. Suyama, K.Lida, in: Proceedings of the EPA/EPRI 1995 Joint Symposium on Stationary Combustion NOx Control, Kansas City, USA, May 16–19, 1995. [11] L. Lisi, G. Lasorella, S. Malloggi, G. Russo, Appl. Catal. B: Environ. 50 (4) (2004) 251. [12] K.A Christensen, PhD Thesis, Technical University of Denmark, 1995. [13] B. Coda, M. Aho, R. Berger, K. Hein, Energy Fuel 15 (2001) 680. [14] E. Latva-Somppi, E.I. Kauppinen, T. Valmari, P. Ahonen, in: Proceedingsof the Engineering Foundation Conference on Biomass Utilization, Snowbird, Utah, USA, April 28 to May 3, 1996. [15] T. Lind, E. Kauppinen, J. Jokiniemi, W. Maenhaut, T. Pakkanen, in:Proceedings of the Engineering Foundation Conference, Solihull, England, June 20-25, 1993. [16] T. Valmari, T. Lind, E. Kauppinen, G. Sfiris, K. Nilsson, W. Maenhaut, Energy Fuels 13 (2) (1999) 379. [17] T. Brunner, M. Joeller, I. Obernberger, F. Frandsen, in: Proceedings of the 12th European Conference on Biomass for Energy Industry and Climate Protection, Amsterdam, The Netherlands, 17–21 June, 2002.[18] P.F.B. Hansen, K.H. Andersen, K. Wieck-Hansen, P. Overgaard, I. Rasmussen, F.J. Fradsen, L.A. Hansen, K. Dam-Johansen, Fuel Process. Technol. 54 (1998) 207. A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 250

Fig. 13. Accumulation of alkali on the catalyst samples (per 1500 h of exposure in the flue gas) wt.% as function of the mass concentration of alkali for ultra fine particles ( < 450 nm) mg/N m3 : P, peat; W, wood; FR, forest residues; B, bark; DW, demolition wood. Fig. 12. Accumulation of alkali on the catalyst samples (per 1500 h of exposure in the flue gas) wt.% as function of the mass concentration of alkali for ultra fine particles ( < 100 nm) mg/N m3 : P, peat; W, wood; FR, forest residues; B, bark; DW, demolition wood.[19] J. Jemer, A. Ekvall, C. Tullin, Va  ̈ rmeforskrapport nr 732 (2001) (in Swedish). [20] Vattenfall fuel data base, 2004. [21] Fo  ̈ rfarande vid drift av va  ̈ rmeproducerande anla  ̈ ggningar fo  ̈ r fo  ̈ rbra  ̈ nning av klorinneha  ̊ llande bra  ̈ nslen. Swedish Patent SE 0100220-3, 2003 (in Swedish). [22] P. Henderson, C. Andersson, H. Kassman, VGB PowerTech (2004) 58. [23] IACM, Swedish Patent SE 99036 56-8, (2001); IACM, International Patent PCT WO0127593 (2001); IACM, European Patent EP1221036 (2002). [24] T. Lind, E. Kauppinen, G. Sfiris, K. Nilsson, W. Maenhaut, in: Proceedings of the Engineering Foundation Conference, Kona, Hawaiian Islands, November 2–7, (1997), p. 14. [25] K. Peltola, M. Hiltunen, J.P. Blomqvist, B.J. Skrifvars, J. Kurkela, J.Latva-Soumppi, E.I. S Kauppinen, in: Proceedings of the 15th International Conference on Fluidized Bed Combustion, Proceedings of the 15th International Conference on Fluidized Combustion, Savannah (GA), (1999), p. 1733. [26] E. Hums, in: Proceedings of the 2nd International Seminar on Monolith Honeycomb Supports and Catalysts, Kinetics and Catalysis, vol. 39 (5), 1998, p. 603. [27] J. Svachula, L.J. Alemany, N. Ferlazzo, P. Forzatti, E. Tronconi, F. Bregani, Ind. Eng. Chem. Res. 32 (1993) 826. [28] R. Khodayari, C.U.I. Odenbrand, Appl. Catal. B: Environ. 30 (1/2) (2001) 87. [29] R. Khodayari, C.U.I. Odenbrand, Appl. Catal. B: Environ. 33 (2001) 277. [30] J.P. Chen, R.T. Yang, J. Catal. 125 (1990) 411. [31] W. Kijlstra Sjoerd, J. Komen Niels, A. Andreini, K. Poels Eduard, A. Bliek, in: Proceedings of the 11th International Congress on Catalysis, Studies in Surface Science and Catalysis, vol. 101, 1996, p. 951. [32] J.P. Dunn, H.G.Jr. Stenger, I.E. Wachs, Catal. Today 51 (1999) 301. [33] J.R. Jensen, L.B. Nielsen, C. Schultz-Mo  ̈ ller, S. Wedel, H. Livbjerg, Aerosol Sci. Technol. 33 (2000) 490. [34] K. Iisa, Y. Lu, Energy Fuels 13 (1999) 1184. [35] K. Wieck-Hansen, P. Overgaard, O.H. Larsen, Biomass Bioenergy 19 (6) (2000) 395. A  ̊ . Kling et al. / Applied Catalysis B: Environmental 69 (2007) 240–251 251