Article pubs.acs.org/EF
Prediction of Bed Agglomeration Propensity Directly from Solid Biofuels: A Look Behind Fuel Indicators Mirja H. Piispanen, Matti E. Niemela,̈ Minna S. Tiainen, and Risto S. Laitinen* Department of Chemistry, University of Oulu, P.O. Box 3000, 90014 University of Oulu, Finland ABSTRACT: Determination of ash-forming elements directly from solid biofuels is essential in the prediction and prevention of ash-related problems like slagging, fouling, agglomeration, and corrosion in FB-boilers. In this contribution, we report the characterization of reed canary grass, straw, pine and spruce needles, as well as two peat samples by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and scanning electron microscope energy dispersive spectroscopy (SEMEDS). For comparison, their ash-forming elements have been determined by inductively coupled plasma optical emission spectrometry (ICP-OES) after digesting in a microwave oven or, in case of chlorine, after leaching with tetramethyl ammonium hydroxide. The analytical data are in agreement with those recorded previously for the ash of the biofuels in question and are consistent with their relative slagging, fouling, and agglomeration propensities. The prediction of the onset of ash-related problems is facilitated by use of fuel indicators that are conventionally based on bulk analytical data. The extension of the use of these indicators by considering the compositional distribution of discrete particles in the fuel by SEM-EDS and LA-ICP-MS provides more detailed understanding of the properties of different fuels and their mixtures upon combustion. In addition to laboratory tests, fuel and bed samples were received from a full-scale 315 MW CFB boiler during its normal operation using a mixture containing 60% of peat, 20% of woodchips and sawdust, and 20% of forest residue as fuel. While the operation of the boiler was unproblematic, occasional formation of coating layers on the bed particles has been reported. This behavior can be explained by the distribution of numerical values of selected fuel indicators.
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INTRODUCTION Whereas biofuels provide for environmentally friendly sources of energy, their fluidized bed combustion often leads to slagging, fouling, and agglomeration. The agglomeration of bed material, in particular, is a serious problem in fluidized bed combustion, since the defluidization of the bed may lead to a nonscheduled shutdown of the power plant with significant economic losses in the whole energy production chain. The tendency toward agglomeration depends on bed material, fuel, additives, and on the boiler conditions.1−5 The mechanism of the bed agglomeration has been actively explored during the recent decades.6−10 Ö hman et al.,9,11−13 in particular, have studied the agglomeration caused by biofuels. They deduced that the agglomeration process is dependent on the chemical characteristics and the melting behavior of the coatings, which was found to be sensitive to the relative amounts of calcium and potassium in the fuel. The modeling of the combustion process provides information on the behavior of different fuels during the combustion14−16 and can be utilized in reducing or preventing the problems inherent in the energy use of biofuels. Similarly, the characterization of emissions provides information of gaseous compounds and the particles formed during combustion.17−20 All these methods rely on the knowledge of the physical and chemical characteristics of fuel. The characterization of inorganic material in biofuel has conventionally been carried out indirectly by studying the composition of ash, as well as the coating layers on bed particles, and the adhesive material in the agglomerates (see refs 1, 5, and 21, and references therein). The fuel is ashed in the laboratory or the ash is collected from the boiler, and its © 2012 American Chemical Society
composition is analyzed. Scanning electron microscope energy dispersive spectroscopy (SEM-EDS) together with automated image processing, in particular, has proven to be a useful method, since it provides information about compositional distribution of ash, agglomerates, and coating layers on bed particles.22 Since molten phases play a role in the onset and development of agglomeration or slag, it is also important to establish the phase-relationships in ash. The powder X-ray diffraction (XRD) offers a possibility to identify the crystalline phases and to estimate the content of amorphous material in ash.23−25 Ash-related problems are also studied by chemical fractionation of ash by utilizing different leaching agents.26−28 This is a very versatile and informative technique but unfortunately also very labor-intensive and time-consuming. It is difficult to determine the content of the ash-forming elements directly from solid biofuels due to their low abundance. Generally, fuel samples are digested, for instance in the microwave oven, dissolved, and determined by CVAAS, FAES, GFAAS, ICP-MS, ICP-OES, or in more restricted applications by HPLC, or direct Hg-determination.29,30 Care has to be taken that elements such as chlorine do not volatilize upon digestion. The ash-related problems have been explored by use of different indicators, which are based on the bulk analysis of digested fuel samples. In the case of biofuels, it has been reported that potassium is one of the main elements in the coating layers on the bed particles as well as in the adhesive Received: January 29, 2012 Revised: April 2, 2012 Published: April 2, 2012 2427
dx.doi.org/10.1021/ef300173w | Energy Fuels 2012, 26, 2427−2433
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Table 1. Operation Parameters of LA-ICP-MS laser param. output (%) repetition rate (Hz) spot size (μm)
a
ICP-MS param. 100 20 160
RF power (W) nebulizer gas (mL/min) auxliary gas (mL/min) cooling gas (mL/min) quadrupole bias (V) hexapole bias (V) focus reaction gas (mL/min)c
24
Mg, 27Al, 28Si, 40Ca, 48Ti, 56Fe
31
P, 34S, 35Cl, 81Br,
1100 1.00b/1.02−1.07a 0.78 12.0 −1.2 1.3 8.0 5.5
1000 0.98b/1.06a 0.98b/1.0 12.0 −2.4 −2.0 16.0
127
I
23
Na, 39K
1100 1.00−1.07a 0.78−0.99 12.0 −10.0 −8.0 5.0 1.25
55
Mn
1000a/1100b 0.98b/1.1a 0.98 12.0 −2.4 −2.0 16.0
Fuel. bBed sample. cReaction gas: 7% H2/He (Mg, Si, Ca, Ti, and Fe) and H2 (Na and K). K, P, S, Na, Mn, Ti, Cl, Br, and I. The ashing was carried out according to the modified ASTM-standard D 3174-8942 by heating the samples to 550 °C in one hour and keeping the temperature constant for another hour. We also obtained fuel and bed samples from a 315 MW CFB-boiler during its normal operation. They were collected over eight hours at intervals of one hour. The fuel during the test was a mixture consisting of 60% of peat, 20% of woodchips and sawdust, and 20% of forest residue. The six laboratory fuel samples were mounted with epoxy resin, Struers Epofix (15:2). Mounts were cross-sectioned by grinding with 250, 600, and 1200 mesh SiC paper. The samples were coated with a thin carbon layer to eliminate the electrostatic effects. The fuel and bed samples from the full-scale boiler test were dried and milled before mounting in Struers Epofix resin. The fuel samples were ground and polished by using MD Largo and MD Allegro 9 μm diamond grinding discs, and the bed material samples were mounted in a similar fashion, but ground with MD Piano 220 and polished with MD Largo 9 μm grinding discs. LA-ICP-MS. A Thermo Elemental X7 ICP-MS equipped with a laser unit Thermo New Wave UP/213 was used for the multielement analyses of the solid samples. The operation of the laser unit was controlled daily with a glass standard sample NIST 61243 before each analysis. The operation conditions of ICP-MS were optimized, and a short-term stability test was carried out to ensure the sensitivity of instrument. The ICP-MS was optimized by using collision and reaction cell technology for analysis to maximize the sensitivity and minimize the interferences. The operating parameters are given in Table 1. Moss and humus reference materials were used as calibration standards for fuel samples in LA-ICP-MS determinations.44 Two different straw samples were used as calibration standards for the determination of chlorine in our straw sample, because the moss and humus standards do not have appropriate concentration levels for straw. NIST 2691,45 1633b,46 and CRM 03847,48 calibration standards were used in the analysis of bed material. SEM-EDS. SEM-EDS analyses were carried out with a JEOL JSM6400 scanning electron microscope equipped with an Inca energy dispersive X-ray analyzer and Feature image processing software. The acceleration voltage was 15 kV and the beam current was 120 × 10−8 A. The sample distance was 15 mm and the magnification was 80×. The compositional distribution was determined by analyzing ca. 1000 particles or domains as described by Virtanen et al.49 The SEM-EDS results were visualized by use of quasiternary diagrams utilizing a locally constructed program package.50−52 They are a logical extension of conventional ternary diagrams in which each corner has been defined in terms of a content of a single element. The normalized content of this element in the corner is 100%, and it decreases toward the edge between the other two corners, being 0% at the opposite edge. Two other corners provide analogous information in terms of two other elements. At any point in the ternary diagram the sum of the contents of the three elements is 100%. In a similar fashion, the corners of a quasiternary diagram are defined in terms of the sum of the normalized contents of the several elements, their combined contents being 100% at the corner and zero at the opposite edge. Therefore, the quasiternary diagram provides
material of the agglomerates (see ref 22 and references therein). Visser agglomeration indicators constitute one way to explore the agglomeration propensity of fuel ashes:31 I1 =
(Na + K) (2S + Cl)
I2 =
(Na + K + Si) (Mg + P + Ca)
The element symbol in the formula indicates the content of the element in fuel (g/kg fuel). I1 has been defined to reflect the chance that with an excess of alkali metals over sulfur and chlorine, alkali metal silicates are formed in the bed. Similarly, I2 describes the chance of the formation of sticky outer coating.31 Although Visser agglomeration indicators were developed for combustion of wood in a quarz bed, they will be used in this paper as possible indicators for other fuels. Sommersacher et al.32 have reported a very comprehensive study of the significance of various indicators in the evaluation of fuel properties and their utility in the prediction of combustion-related problems ranging from bed agglomeration to corrosion and NOx emssions. They have shown that timeconsuming and expensive combustion can be mostly avoided by pre-evaluation of the fuel. The direct determination of inorganic material from solid biofuel samples has utilized XRF, XRD, and SEM-EDS.33−36 Recently, laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been shown to be a potential technique to study solid samples.37−41 It provides information for compositional distribution in a similar fashion to SEM-EDS. In this work, LA-ICP-MS and SEM-EDS have been used to analyze a number of solid biofuels and peat. The ash-forming elements have been determined directly in reed canary grass, straw, pine and spruce needles, and two peat samples. Samples of fuel and bed material were also collected from a 315 MW CFB-boiler during its normal operation and were analyzed by LA-ICP-MS and SEM-EDS. Various solution techniques have been used for comparison. The objective of the study was to investigate further the suitability of the direct solid fuel analysis for the prediction of ash-related problems. In this connection, we also explore how the compositional distribution in the solid fuel particles can be used to extend the deductions made from agglomeration indicators based on bulk analyses of the digested fuel samples.
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EXPERIMENTAL SECTION
Samples. Laboratory-scale investigation of four biofuels (reed canary grass, straw, and pine and spruce needles) as well as two peat samples from two different bogs was carried out in this work. All fuel samples and their laboratory ashes were analyzed for Si, Ca, Fe, Al, Mg, 2428
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compositional information of all elements defined in the three corners. For a point analysis to appear in the diagram, the sum of the normalized contents of the chosen elements has to exceed a minimum limit, usually 80%.53 The column heights in the quasiternary diagrams represent the relative contents of the material with the elemental composition as controlled by the definitions in the corners (for a more detailed description, see ref 22 and references therein). The X-ray maps and the phase relationship images were created with the Feature software. The phase relationship images were produced from three elemental X-ray maps by coloring each map with one color (red, green, or blue) and integrating the colored maps into one image. Bulk Analyses. The fuel samples were digested in a CEM MARS 5X microwave oven (CEM Corp.) using XP-1500 plus high pressure Teflon TFM vessels (CEM Corp.) according to the standard CEN/TS 15290.54 Ash and bed material samples were also digested according to the microwave sample preparation note 5MS-6 of MARS 5X (CEM Corp.).55 The digested samples were analyzed by a sequential Pye Unicam 7000 ICP-OES or by a Thermo Elemental X7 Series ICP-MS. For comparison, the determination of chlorine, bromine, and iodine in all samples involved leaching by tetramethylammonium hydroxide (TMAH) followed by their determination with ICP-MS.56
The composition of the standard laboratory ashes of each fuel determined by ICP-OES is also shown in Figure 1. The LAICP-MS determinations have been carried out directly from the solid samples, while the ICP-OES determinations involved digestion and were carried out from the dissolved samples. Halogens are excluded from the quasiternary diagram of Figure 1. It can be seen from Figure 1 that the averaged LA-ICP-MS results from the solid fuel samples agree well with those determined by ICP-OES from the corresponding digested solutions. It can also be seen that the normalized elemental compositions determined from the four fuels agree well with those determined from the corresponding ash. Figure 1 also clearly shows that the bulk compositions of different fuels vary significantly, as expected from their different behavior upon combustion. Determination of Halogens. It is well-known that halogens cause corrosion, and it is therefore important to be able to determine their contents reliably directly from the fuel prior to combustion. Their determination, however, is demanding. In this work, halogens were determined directly from the solid samples by LA-ICP-MS and for comparison, from solution by ICP-MS after leaching with tetramethylammonium hydroxide. The halogen contents are presented in Table 2. It can be seen that the halogen abundance in a solid fuel can be determined with a reasonable reliability by LA-ICP-MS. However, even though the average values show a fair agreement with those determined from solution by ICP-MS after the TMAH leaching, the standard deviations in the LA-ICP-MS determinations are relatively high as a result of the variation of the composition of discrete fuel particles. Chlorine is a dominant element in straw, whereas bromine and iodine mainly occur in peat samples. We note that the chlorine contents of the peat samples determined by ICP-MS after TMAH leaching are somewhat smaller compared to the LA-ICP-MS determinations. According to Tagami et al.,56 TMAH does not leach chlorine completely, if the samples contain rock or soil material. However, this method can routinely be used for bromine and iodine. Agglomeration Indicator I2 and Compositional Distribution. In this contribution, we have considered the Visser indicator I231 as an example and extend the information from the bulk fuel analysis to that obtained from the considerations of compositional distribution in the ash-forming material in fuel. The indicator I2 should be below 1 for the formation of nonsticky outer coating layers on the bed particles.31
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RESULTS AND DISCUSSION General. The quasiternary diagram of the normalized bulk contents of the eleven elements, as determined from the fuel samples by LA-ICP-MS and ICP-OES, is shown in Figure 1.
Figure 1. Quasiternary diagram showing the comparison of normalized average bulk contents of solid fuels, as well as determinations from digested fuel solutions and digested solutions of standard laboratory ash of each fuel.
Table 2. Comparison of the Halogen Contents of Four Biofuels and Two Peat Samplesa Cl (ppm)
Br (ppm)
LA-ICP-MS
I (ppm)
LA-ICP-MS
LA-ICP-MS
sample
xb̅
σc
RSD%d
ne
TMAH
x̅
σ
RSD%
n
TMAH
x̅
reed canary grass straw pine needles spruce needles peat I peat II
296 6062 259 432 375 619
58 1595 39 62 36 56
19 26 15 14 10 9
13 20 10 10 10 10
300 5900
