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Influence of fuel blend ash components on steam co-gasification of coal and biomass – Chemometric study
Energy 78 (2014) 814e825
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Influence of fuel blend ash components on steam co-gasification of coal and biomass e Chemometric study ski* Natalia Howaniec, Adam Smolin w 1, 40-166 Katowice, Poland Central Mining Institute, Department of Energy Saving and Air Protection, Pl. Gwarko
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 May 2014 Received in revised form 12 August 2014 Accepted 26 October 2014 Available online 18 November 2014
The process of co-gasification of coal and biomass offers the benefits of stable supplies of a primary energy resource e coal, with a partial replacement of a fossil fuel with a “zero-emission”, renewable energy source e biomass. The main objective of the experimental steam co-gasification study focused on hydrogen-rich gas generation, as a prospective clean energy carrier, was the determination of the impact of a fuel blend composition and process temperature on the yield and composition of gas generated. The identification of the synergy effects observed in the co-gasification process and their sources was also made with an application of chemometric methods of data analysis, such as the Principal Component Analysis and the Hierarchical Clustering Analysis. Based on the results it was concluded that the synergy effects observed were related to the presence of biomass-derived fuel blends components, such as K2O, Al2O3, Fe2O3, Na2O, TiO2. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Co-gasification Hydrogen Coal Biomass Ash Synergy
1. Introduction The process of coal and biomass co-gasification offers several environmental and economic benefits when compared to gasification. It secures a continuous operation of large scale energy systems based on reliable supplies of a fossil fuel, and at the same time, an application of a “zero-emission”, renewable energy resource. It makes the biomass utilization in energy generation systems more efficient, and at lower production costs, than it can be achieved in present-day biomass gasification systems. Production of hydrogen in the process of steam co-gasification, as a clean, environmental friendly energy carrier, may be also considered as the environmental sustainability added value. The IGCC (Integrated Gasification Combined Cycle) installation in Buggenum, in the Netherlands, is an example of one of few industrial scale co-gasification plants, where thermochemical conversion of fuel blends of up to 50% w/w of biomass content is planned to be performed to improve the CO2 balance of the installation. In most installations, in which coal and biomass are processed for energy purposes, coal is, however, still cocombusted with raw gas from biomass gasifiers. The recent studies on co-pyrolysis and co-gasification are reported to be performed predominantly with an application of laboratory scale fluidized bed
* Corresponding author. Tel.: þ48 32 2592252; fax: þ48 32 2596533. ski). E-mail address: [email protected] (A. Smolin http://dx.doi.org/10.1016/j.energy.2014.10.076 0360-5442/© 2014 Elsevier Ltd. All rights reserved.
and fixed bed reactors, under various operating conditions. In some of them synergy effects in pyrolysis and gasification of fuel blends were observed, which involved e.g. increase in fuel chars reactivity [1e4], carbon conversion rate [4e6], reduction of tars yield [3,4,7] and variation in product gas composition, e.g. in hydrogen to carbon monoxide ratio [7,8]. Interpretations of the impact of biomass content in a fuel blend on product gas composition are ambiguous. In the studies on cogasification of coal and biomass, an increase in hydrogen yield [8,9], or hydrogen content in a product gas [10,11] were observed. An increase in carbon dioxide content with increasing biomass content in a fuel blend [2,5,10], and in carbon monoxide yield [9] or carbon monoxide content in product gas [5,7,12e14] were also reported. The opposite trends, that is, a decrease in concentrations of particular gas components with increasing biomass content in a fuel blend were also found. These include examples of reduced hydrogen [5,7,11e15], carbon monoxide [2,10,11,13], and carbon dioxide [7,11,13,14] contents. With increasing biomass content in a fuel blend, increases in char reactivity [1,3,6], the total gas efficiency [1,3,5,13], carbon conversion rate [1,5e8] and process efficiency [7e9,12e14] were also reported. The vagueness of the data on the impact of biomass content in a fuel blend on gas composition, yields of individual gas components and synergy effects observed in co-pyrolysis and co-gasification results from substantial variations in process conditions, including types of gasifiers and fuels as well as operating
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parameters applied. Therefore, the synergy effects themselves and identification of the mechanisms behind them, require further research before the wide implementation of the co-gasification process is commercially viable. The PCA (Principal Component Analysis), and the HCA (Hierarchical Clustering Analysis) data exploration methods were applied in determination of the influence of a fuel blend composition on steam co-gasification results and in the interpretation of the synergy effects observed in relation to the content of particular metal oxides in a fuel blend.
process: particular gas components volumes and the total gas volume, chars reactivity: at 50% of carbon conversion, R50, and the maximum reactivity, Rmax, time required to reach 50% carbon conversion, t50, and the maximum reactivity, tmax, gas calorific value and carbon conversion rate, as well as the parameters defining the relative increase in the total gas, ngp, and hydrogen, nH2, volume in the co-gasification process when compared to the values reported in the gasification process at 700, 800 and 900 C were taken into account in the PCA and the HCA analyses (see Table 2).
2. Materials and methods
2.2. Methods
2.1. Materials and data organization
The Principal Component Analysis [18,19] is one of the most commonly applied chemometric techniques of exploratory analysis of multivariate data sets. It allows reduction of data dimensionality, its visualization and interpretation of the relationships between objects (fuel blends) and measured parameters. In the PCA the data
The energy crops selected for the experimental study included: SV (Salix Viminalis), AG (Andropogon Gerardi), HT (Helianthus Tuberosus), SH (Sida Hermaphrodita), MXG (Miscanthus X Giganteus) and SP (Spartina Pectinata). Hard coal was supplied by Piast coal mine (KWK Piast, Kompania Weglowa S.A.). Fuels were characterized in terms of proximate and ultimate analyses, heat of combustion and calorific value. The analyses were performed in accordance with the relevant standards and test procedures by the accredited Laboratory of the Department of Solid Fuels Quality Assessment of the Central Mining Institute. These included: moisture content analysis (PN-G-04560: 1998 and PB-4, 11th edition, using automatic thermogravimetric analyzers LECO: TGA 701 and MAC 500), ash content (PN-G-04560:1998 and PB5, 11th edition, with an application of automatic thermogravimetric analyzers LECO: TGA 701 and MAC 500), volatile content (PN-G-04516: 1998 and PB-6, 11th edition, based on DIN ISO 562: 2000 with the use of automatic thermogravimetric analyzers: LECO TGA 701 and MAC 500), heat of combustion and calorific value (PN-G-04513: 1981 and PB-7, 11th edition, with an application of LECO calorimeters: AC-600 and AC-350), total sulphur content (PN-G-04584: 2001 and PB-15, 11th edition, using an automatic analyzer TruSpec S by LECO), carbon, hydrogen, and nitrogen contents (PN-G-04571: 1998 and PB-19, 11th edition, with the use of a TruSpecCHN analyzer). Oxygen content was calculated as: 100% e Wa e Aa e Cat e Hat e Sac e Na (PN-G-04510:1991), and fixed carbon as: 100% e Wa e Aa e Va (PN-G-04516:1998). The content of metal oxides in ash of fuels tested was determined in compliance with the test procedure SC-1/ PB-05 according to DIN EN ISO 12677 in the accredited Laboratory of Solid Waste Analysis of the Department of Environmental Monitoring of the Central Mining Institute, with an application of a wavelength-dispersive X-ray fluorescence spectrometry. The results are given in Table 1. In general coal was characterized by higher content of fixed carbon, sulphur and ash, and lower content of volatiles, oxygen and hydrogen than biomass (see Table 1). Mineral matter of coal was rich in aluminium and iron oxides, and contained significantly less alkali and alkali earth metals than biomass ash. The detailed results of steam gasification and co-gasification of coal and biomass of selected energy crops in the laboratory scale fixed bed gasifier were published previously [16,17]. In the paper the relationships between physical and chemical parameters of fuel blends, ash composition and content in a fuel blend, co-gasification process parameters, and synergy effects observed in the process with an application of the data exploration methods, such as the PCA and the HCA are presented. The studied data set was organized in a matrix X(24 60), in which the rows correspond to fuel blends of various SV, AG, HT, SH, MXG and SP biomass content (20, 40, 60 and 80% w/w), whereas the columns represent parameters listed in Table 2. The physical and chemical parameters of fuel blends, ash composition and experimental data on the steam co-gasification
Table 1 Physical and chemical characteristics of fuel samples. Parameter (unit)
Moisture content, Wa (%w/w) Ash content, Aa (%w/w) Volatiles content, Va (%w/w) Heat od combustion, Qsa (J/g) Calorific value, Qia (J/g) Total sulphur content, Sta (%w/w) Carbon content, Cta (%w/w) Hydrogen content, Hta (%w/w) Nitrogen content, Na (%w/w) Oxygen content, Oa (%w/w) Fixed carbon (%w/w) SiO2 content in ash (%w/w) Al2O3 content in ash (%w/w) Fe2O3 content in ash (%w/w) CaO content in ash (%w/w) MgO content in ash (%w/w) Na2O content in ash (%w/w) K2O content in ash (%w/w) SO3 content in ash (%w/w) TiO2 content in ash (%w/w) P2O5 content in ash (%w/w) ZnO content in ash (%w/w)
Fuel sampleb 1
2
3
4
5
6
7
4.74
9.72
8.81
8.76
6.78
8.69
6.02
1.51
3.87
3.18
2.63
1.60
4.31
5.69
73.16
70.26
69.24
71.47
76.00
69.89
31.12
18,171 16,132 15,989 16,484 16,546 16,920 28,805
16,697 14,242 14,543 15,030 14,942 15,481 27,616 0.05
0.06
0.04
0.04
0.05
0.12
0.50
52.19
53.3
46.62
47.18
53.71
45.77
70.64
6.22
7.57
5.64
5.68
6.59
5.62
4.08
0.98
35.29
25.54
35.74
35.73
31.27
35.58
12.09
20.59 14.67
16.15 65.18
18.77 32.54
17.14 3.65
15.62 69.01
17.11 64.82
57.17 46.55
3.18
0.45
1.24
0.064
0.38
0.33
25.65
0.93
0.28
0.52
0.28
0.19
0.32
8.63
37.10
11.74
34.20
42.43
15.27
9.99
7.34
3.46
3.60
2.94
4.66
1.79
1.50
3.82
0.53
0.93
1.02
1.75
0.73
1.53
2.60
22.13
7.26
12.93
21.77
2.98
11.48
1.92
4.12
3.85
5.90
5.56
4.95
4.95
1.78
0.15
0.06
0.08
0.05
0.05
0.05
1.08
13.17
5.97
7.87
17.30
3.99
4.45
0.36
1.07
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Table 2 Parameters of fuel blends and co-gasification process applied in PCA. No.
Parameter
Unit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Moisture, Wa Ash, Aa Volatiles, Va Heat of combustion Qsa Calorific value Qia Total sulphur, Sta Carbon, Cta Hydrogen Hta Nitrogen, Na Oxygen, Oa Fixed carbon SiO2 content in a fuel blend Al2O3 content in a fuel blend Fe2O3 content in a fuel blend CaO content in a fuel blend MgO content in a fuel blend Na2O content in a fuel blend K2O content in a fuel blend SO3 content in a fuel blend TiO2 content in a fuel blend P2O5 content in a fuel blend Hydrogen volume at 700 C Carbon monoxide volume at 700 C Carbon dioxide volume at 700 C Methane volume at 700 C The total gas volume at 700 C Increase in the total gas volume at 700 C, ngp Increase in hydrogen volume at 700 C, nH2 Gas calorific value at 700 C, Qg Reactivity at 50% coal conversion at 700 C, R50 Maximum reactivity at 700 C, Rmax Time of reaching 50% coal conversion at 700 C, t50 Time of reaching maximum reactivity at 700 C, Rmax, tmax Carbon conversion rate at 700 C Hydrogen volume at 800 C Carbon monoxide volume at 800 C Carbon dioxide volume at 800 C Methane volume at 800 C The total gas volume at 800 C Increase in the total gas volume at 800 C, ngp Increase in hydrogen volume at 800 C, nH2 Gas calorific value at 800 C, Qg Reactivity at 50% coal conversion at 800 C, R50 Maximum reactivity at 800 C, Rmax Time of reaching 50% coal conversion at 800 C, t50 Time of reaching maximum reactivity at 800 C, Rmax, tmax Carbon conversion rate at 800 C Hydrogen volume at 900 C Carbon monoxide volume at 900 C Carbon dioxide volume at 900 C Methane volume at 900 C The total gas volume at 900 C Increase in the total gas volume at 900 C, ngp Increase in hydrogen volume at 900 C, nH2 Gas calorific value at 900 C, Qg Reactivity at 50% coal conversion at 900 C, R50 Maximum reactivity at 900 C, Rmax Time of reaching 50% coal conversion at 900 C, t50 Time of reaching maximum reactivity at 900 C, Rmax, tmax Carbon conversion rate at 900 C
%w/w %w/w %w/w J/g J/g %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w Nm3 Nm3 Nm3 Nm3 Nm3 % % MJ/Nm 1/s 1/s s s % Nm3 Nm3 Nm3 Nm3 Nm3 % % MJ/Nm3 1/s 1/s s s % Nm3 Nm3 Nm3 Nm3 Nm3 % % MJ/Nm3 1/s 1/s s s %
a
Analytical state.
matrix, X(m n), is decomposed into the matrices, S(m fn), D (n fn) and E(m n), where m and n denote, respectively, the number of objects and parameters, fn denotes the number of significant factors. S represents the scores matrix, whereas D represents the loading matrix and E is the residuals matrix. The columns of the matrix S are called the PCs (Principal Components) or eigenvectors. Scores and loading matrices are orthogonal. Effective reduction of data dimensionality enables to use scores vectors and loadings vectors (i.e. the columns of the matrix S and D, respectively) to visualize and interpret the relationships between the
objects and the parameters, respectively. The Hierarchical Clustering Analysis allows for the study of similarities or dissimilarities between objects in the variables space or parameters in the objects space [20e23]. The HCA is characterized by a similarity measure applied and the way the resulting sub-clusters are linked. The Euclidean distance is the prevalent similarity measure applied when continuous variables are considered, and among the linkage methods the single linkage, complete linkage, average linkage, centroid linkage and Ward's linkage may be mentioned. The single linkage method defines the distance between two clusters as the smallest dissimilarity between an object from the first and the second cluster. In the complete linkage method the distance between studied clusters is defined as the longest distance between two objects belonging to the first and the second cluster, respectively. In the average linkage method the distance between the clusters is defined as an average distance determined by the single linkage and the complete linkage method, whereas the centroid linkage method is based on the distance between the mass centres of two clusters analyzed. The Ward's linkage method is most often applied, and is based on the inner squared distance between the studied clusters; at each stage these two clusters are merged, for which the minimum increase in the total within-group error sums of squares is observed. The results of the HCA are presented in a form of a dendrogram where, on axis x clustered objects (or variables) are displayed, whereas on axis y the corresponding linkage distances (or similarity measures) between the two objects or clusters, are presented. The disadvantage of the HCA is that it does not allow the interpretation of the observed patterns in terms of the original variables (parameters). This is a considerable limitation, since the purpose of the analysis is not only to identify the similarities and dissimilarities between objects, but also to identify the reasons behind them. This drawback may be, however, overcome by an application of a visualization method [24,25]. The studied data set is then organized in a matrix form containing m objects and n variables, X(m n). The HCA analysis results in a construction of two dendrograms: the dendrogram of objects in the variables space and the dendrogram of parameters in the objects space, respectively. At the dendrograms m objects and n parameters are ordered according to their similarity. In the same way the objects and parameters in a data matrix X(m n) are sorted. The resulting image of the data set attains a smoother appearance, since the adjacent objects and variables are ordered according to their similarity. 3. Results and discussion 3.1. PCA of fuel blends samples in the space of measured parameters A percentage of the data variance was applied in order to determine the correct complexity of the PCA model for the standardized data X(24 60). The data compression with the PCA model developed was ineffective, since as many as five Principal Components were required to describe 82.42% of the total data variance. However, the general conclusions could be drawn on the similarities and dissimilarities between individual samples. Score plots and loading plots resulting from this analysis, are presented in Fig. 1. The first two PCs describe 64.00% of the total data variance. Along the PC1, describing 51.72% of the total data variance, significant variations between samples are visible, resulting from fuel blends composition. A clear difference can be observed between the fuel blends of 20% and 40% w/w content of SV, AG, HT, SH, MXG and SP biomass (objects nos. 1, 5, 9, 13, 17, 21 and 2, 6, 10, 14, 18 and 22, respectively), and the fuel blends of 60 and 80% w/w of biomass content (objects nos. 3, 7, 11, 15, 19, 23 and 4, 8, 12, 16, 20 and 24,
Fig. 1. Score plots (a) and loading plots (b) resulting from PCA for centred and standardized data X(24 60).
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respectively). The most significant dissimilarity is observed between the fuel blends of 20% w/w (objects nos. 1, 5, 9, 13, 17 and 21) and 80% w/w of biomass content (objects nos. 4, 8, 12, 16, 20 and 24). Based on the parameters projection onto the plane defined by PC1 and PC2 it was found that the fuel blends of 20 and 40% w/w of biomass content are characterized by higher ash content in a sample, higher values of heat of combustion, calorific value, total sulphur, carbon, nitrogen, fixed carbon, Al2O3, Fe2O3, MgO, Na2O, SO3 and TiO2 content in a sample (parameters nos. 2, 4e7, 9, 11, 13, 14, 16, 17, 19, 20), as well as higher volumes of hydrogen, carbon monoxide and carbon dioxide in gas generated in the process of steam co-gasification at 700, 800 and 900 C, higher values of the total gas volume (parameters nos. 22e24, 26, 35e37, 39, 48, 49, 50 and 52) and lower content of volatiles, hydrogen and oxygen in a fuel blend (parameters nos. 3, 8 and 10) than the fuel blends of 60 and 80% w/w of biomass content. Moreover, samples of 20% w/w of biomass content are characterized by the highest values of parameters nos. 2, 4e7, 9, 11, 13, 14, 16, 17, 19, 20, 22e24, 26, 35e37, 39, 48e50 and 52 and the lowest values of parameters nos. 3, 8 and 10. The fuel blends of 80% w/w of biomass content are characterized by the highest content of volatiles, hydrogen and oxygen in a fuel blend (parameters nos. 3, 8 and 10). The PC2 describes 12.28% of the total data variance, resulting from the differences between the fuel blends of 40% w/w content of SH, HT and SP biomass (objects nos. 14, 10 and 22), and the fuel blends of 20% w/w of SV and MXG biomass content (objects nos. 1, 5 and 17), 60% w/w of SV, AG and MXG biomass content (objects nos. 3, 7 and 9) and 80% w/w of SV, AG and MXG biomass content (objects nos. 4, 8 and 20). These variations are related to the values of the total gas volume increase, ngp, and hydrogen volume increase, nH2, at 700, 800 and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54) and the time of reaching 50% of carbon conversion, t50, at 700 and 900 C (parameters nos. 32 and 58). The fuel blends of 40% w/w of SH, HT and SP biomass content (objects nos. 14, 10 and 22, respectively), are characterized by the highest increase in the total gas volume, ngp, and hydrogen volume, nH2, at 700, 800 and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54) and the shortest time required to reach 50% of carbon conversion, t50, at 700 and 900 C (parameters nos. 32 and 58) in comparison with the remaining fuel blends. Furthermore, these fuel blends are characterized by high contents of CaO and K2O (parameters nos. 5 and 18), high values of carbon conversion rate at 800 C and reactivity R50 at 900 C (parameters nos. 47 and 56). The fuel blends of 20% w/w of SV and MXG biomass content (objects nos. 1 and 5), 60% w/w of SV and AG biomass content (objects nos. 7 and 3) and 80% w/w of SV, AG and MXG biomass content (objects nos. 4, 8 and 20) are characterized by the longest time required to reach 50% of carbon conversion, t50, at 700 and 900 C (parameters nos. 32 and 58) and the lowest increase in the total gas volume, ngp, and hydrogen volume, nH2, at 700, 800 and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54). The PC3, describing 7.02% of the total data variance, displays the difference between the fuel blends of 20, 40, 60 and 80% w/w of SV biomass content (objects nos. 1e4), and fuel blends of the remaining energy crops biomass. Additionally, the PC3 reveals the uniqueness of the fuel blends of 60% w/w of AG biomass content, and 60 and 80% w/w of SP biomass content (objects nos. 7, 23 and 24). The fuel blends of 20, 40, 60 and 80% w/w of SV biomass content (objects nos. 1e4) are characterized by high values of tmax and the highest carbon dioxide content in gas generated at 900 C (parameters nos. 33 and 50). Furthermore, the fuel blends of 60% w/w of AG biomass content as well as 60 and 80% w/w of SP biomass content (objects nos. 7, 23 and 24) are characterized by high moisture, SiO2 and SO3 contents in a sample (parameters nos. 1, 12 and 19) and high gas calorific value at 900 C (parameter no 55) in comparison with the remaining fuel blends.
The PC4, describing 6.44% of the total data variance, shows the uniqueness of the fuel blend of 20% w/w of HT biomass content (object no 9), which can be attributed to high values of CaO content in a sample and tmax (parameters nos. 15 and 46, respectively). The PC5, describing 4.96% of the total data variance, shows the difference between the fuel blends of 40% w/w of HT and AG biomass content (objects nos. 10 and 6, respectively). The fuel blend of 40% w/w of HT biomass content (object no 10) differs from the remaining fuel blends by the highest value of R50 at 800 C (parameter no 43), while the fuel blend of 40% w/w of AG biomass content (object no 6) is characterized by high values of moisture, hydrogen and MgO content in a sample and gas calorific value at 800 C (parameters nos. 1, 8, 16, 42). The PCA analysis allowed also the investigation of correlations between particular parameters. The largest contribution to the PC1 have positively correlated parameters nos. 17, 37, 29 and 25 (describing Na2O content in a sample, methane volume and gas calorific value at 700 C and carbon dioxide volume at 800 C), the parameters nos. 24, 26, 35, 39, 48 and 52 (describing carbon dioxide and the total gas volume at 700 C, and hydrogen and the total gas volume at 800 and 900 C) and the parameters nos. 4, 6, 9, 11, 13, 14, 20, 23 and 36 (describing heat of combustion, the total sulphur, nitrogen, fixed carbon, Al2O3, Fe2O3 and TiO2 content in a sample and carbon monoxide volume at 700 and 800 C). In addition, a negative correlation can be observed between the parameters describing content of volatiles in a fuel blend (parameter no 3), carbon dioxide and the total gas volume at 700 C, and hydrogen and the total gas volume at 800 and 900 C (parameters nos. 24, 26, 35, 39, 48 and 52). A negative correlation is also observed between carbon and oxygen content in a sample (parameters nos. 7 and 10, respectively). 3.2. HCA of fuel blends samples in the space of measured parameters The difficulty of ineffective data compression was solved with an application of the Hierarchical Clustering Analysis, enabling to trace dissimilarities between objects in the space of the measured parameters and between the measured parameters in the objects space. The Euclidean distance was applied as a similarity measure. The results of the HCA are presented in a form of dendrograms constructed with an application of the Ward's linkage method (see Fig. 2). The dendrogram shown in Fig. 2a allows splitting the examined fuel blends into two clusters. All fuel blends of 60 and 80% w/w of biomass content are grouped in the cluster A, while those of 20 and 40% w/w of biomass content belong to the cluster B. Additionally, within each of the two clusters two subgroups can be divided. The subgroup A1 in the cluster A includes the fuel blends of 60 and 80% w/w of HT, SP and SH biomass content (objects nos. 11, 12, 15, 16, 23 and 24), whereas the subgroup A2 clusters the fuel blends of 60 and 80% w/w of SV, AG and MXG biomass content (objects nos. 3, 4, 7, 8, 19 and 20). Within the cluster B, the subgroup B1 may be distinguished, composed of the fuel blends of 20% w/w of biomass content and the subgroup B2, containing all fuel blends of 40% w/w of biomass content. Furthermore, fuel blends similar to each other, and connected the lowest along the axis of ordinates can be indicated. These are, for instance, the fuel blends of 60% w/w of HT and SP biomass content (objects nos. 11 and 23), 60 and 80% w/w of SV biomass content (objects nos. 3 and 4), 20% w/w of HT and MXG biomass content (objects nos. 9 and 17), 20% w/w of SH and SP biomass content (objects nos. 13 and 21) and 40% w/w of HT and MXG biomass content (objects nos. 10 and 18).
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Fig. 2. Dendrograms for: (a) fuel blends in the space of measured parameters (see Table 2), and (b) variables in the space of objects, obtained by the Ward's linkage method.
Fig. 2b shows a dendrogram constructed for 60 variables in the space of 24 objects, which allows grouping the variables into four main classes: class A, composed of the parameters nos. 2, 4e7, 9, 11e14, 16, 17, 20, 22e26, 29, 32, 35e37, 39, 42, 45, 48e50, 52, 55, 58
(describing ash content, heat of combustion, calorific value, content of: total sulphur, carbon, nitrogen, fixed carbon, SiO2, Al2O3, Fe2O3, MgO, Na2O and TiO2 in a sample, volume of: hydrogen, carbon monoxide, carbon dioxide and methane in gas generated at 700, 800 and 900 C, total gas volume and calorific value as well as time t50 at 700, 800 and 900 C, respectively),
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class B, containing the parameters nos. 27, 28, 40, 41, 53, 54, 60 (describing respectively: the increase in the total gas, ngp, and hydrogen, nH2, volume at 700, 800 and 900 C and carbon conversion rate at 900 C), class C, which includes the parameters nos. 1, 3, 8, 15, 18, 19, 21, 34, 44, 47, 56, 57 (describing moisture, volatiles, hydrogen, CaO, K2O, SO3 and P2O5 content in a sample and carbon conversion rate at 700 and 800 C, as well as the reactivity Rmax at 800 and 900 C and R50 at 900 C, respectively), class D, which groups the parameters nos. 30, 31, 33, 38, 43, 46, 51, 59 (describing respectively: reactivity R50 at 700 and 800 C, Rmax at 700 C, tmax at 700, 800 and 900 C, and methane volume at 800 and 900 C). The results of the cluster analysis clearly differentiates the fuel blends of 20 and 40% w/w of biomass content from the samples of 60 and 80% w/w of biomass content. 3.3. HCA of fuel blends samples in the space of measured parameters supplemented with a colour data map The disadvantage of the HCA is the inability to simultaneously track dependencies between objects in the variables space and
between and variables in the objects space, respectively. The HCA was complemented with a colour map of experimental data showing the measured values of parameters, ranked in accordance with the order of the organization of objects and variables, as shown in Fig. 2. The colour data map with the dendrogram showing the objects in the space of the measured parameters allowed for the analysis of the similarities and dissimilarities between the analyzed objects in the parameter space (see Fig. 3). On the basis of the simultaneous interpretation of the dendrogram presenting the fuel blends in the space of the parameters and the colour map of the experimental data it can be concluded that the samples of 60 and 80% w/w of biomass content, grouped in the cluster A (objects nos. 3, 4, 7, 8, 11, 12, 15, 16, 19, 20, 23 and 24) are characterized by lower values of heat of combustion, calorific value, the total sulphur, carbon, nitrogen, fixed carbon, Al2O3, Fe2O3 and TiO2 content, lower total gas as well as hydrogen and carbon dioxide volume at all temperatures tested (parameters nos. 4e7, 9, 11, 13, 14, 20, 22, 24, 26, 35, 37, 39, 52, 48 and 50), higher content of volatiles, hydrogen and oxygen in a sample, carbon conversion rate at 700 C and maximum reactivity, Rmax, (parameter nos. 3, 8, 10, 34, 44) than the fuel blends of 20 and 40% w/w of biomass content included in the cluster B (objects nos. 1, 2, 5, 6, 9, 10, 13, 14, 17, 18, 21 and 22, respectively).
Fig. 3. Dendrograms for fuel blends in the space of measured parameters (see Table 2) and for variables in the space of objects, with a colour map showing the values of the variables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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The subgroup A1 in the cluster A, composed of the fuel blends of 60 and 80% w/w of HT, SP and SH biomass content (objects nos. 11, 12, 15, 16, 23 and 24), is characterized by higher values of moisture, CaO, K2O, SO3 and P2O5 content in a sample, carbon conversion rate at 800 C and reactivity R50, and Rmax at 900 C (parameters nos. 1, 15, 18, 19, 21, 47, 56 and 57) as well as higher value of Rmax at 700 C and R50 at 800 (parameters nos. 31 and 43) in comparison with the remaining samples. Furthermore, within the subgroup A1, a significant dissimilarity may be observed between the fuel blends of 60% w/w of HT, SH and SP biomass content (objects nos. 11, 15 and 23), and the fuel blends of 80% w/w of HT, SH and SP biomass content (objects nos. 12, 16 and 24). Objects nos. 11, 15 and 23 are characterized by the highest value of the maximum reactivity, Rmax, at 700 C (parameter no 31), while objects nos. 12, 16 and 24 by high oxygen and the lowest carbon content in a sample (parameters nos. 10 and 7). Furthermore, the fuel blend of 80% w/w of SH biomass content (object no 16) has the highest content of CaO, K2O and P2O5 and the highest value of tmax (parameters nos. 15, 18, 21 and 46), while the fuel blend of 80% w/w of SP biomass content (object no 24) is characterized by the highest SO3 concentration in a sample and the highest carbon conversion rate at 800 C (parameters nos. 19 and 47). The fuel blend of 80% w/ w of HT biomass content (object no 12) is characterized by the lowest values of carbon monoxide volume and gas calorific value at 800 C (parameters nos. 36 and 42) among all the samples tested. The subgroup A2, including the fuel blends of 60 and 80% w/w of SV, AG and MXG biomass content (objects nos. 3, 4, 7, 8, 19 and 20) is characterized by the highest content of volatiles in a sample (parameter no 3) in comparison with all the remaining samples tested. Furthermore, within the subgroup A2, a specificity of the fuel blends of 60 and 80% w/w of SV biomass content (objects nos. 3 and 4), may be observed, resulting from the lowest moisture content in a sample (parameter no 1) and the lowest gas calorific value at 900 C (parameter no 55) in comparison with the remaining samples. A specificity of the fuel blend of 80% w/w of SV biomass content (object no 4) is also reported which may be attributed to low ash, MgO and Na2O content (parameters nos. 2, 16 and 17), the lowest SiO2 and SO3 content (parameters 12, and 19) in a sample, low methane volume, the lowest gas calorific value at 700 and 800 C, as well as the lowest value of carbon conversion rate at 900 C (parameters nos. 25, 29, 42 and 60) among all the samples tested. Within the subgroup A2, the fuel blends of 80% w/w of AG and MXG biomass content (objects nos. 8 and 20) are characterized by the lowest hydrogen, carbon dioxide and the total gas volume at 700, 800 and 900 C, as well as the shortest time t50 at 800 C (parameters nos. 22, 24, 26, 35, 37, 39, 45, 48, 50 and 52) and high values of volatiles and hydrogen content in a sample and Rmax at 800 C (parameter nos. 3, 8 and 44) in comparison with the remaining fuel blends. The fuel blend of 80% w/w of AG biomass content (object no 8) is characterized by the highest moisture and hydrogen content in a sample (parameter nos. 1 and 8) among all the samples tested. The fuel blends of 20 and 40% w/w of biomass content (objects nos. 1, 2, 5, 6, 9, 10, 13, 14, 17, 18, 21 and 22) included in the cluster B, are characterized by high values of moisture content, heat of combustion, heating value, the total sulphur, carbon, nitrogen, fixed carbon, SiO2, Al2O3, Fe2O3, MgO, Na2O and TiO2 content in a sample and hydrogen, carbon monoxide, carbon dioxide and the total gas volume, as well as gas calorific value at 700, 800 and 900 C (parameters nos. 2, 4e7, 9, 11e14, 16, 17, 20, 22e24, 26, 29, 35e37, 39, 42, 48e50, 52 and 55) and lower values of volatiles and oxygen content in a sample and Rmax at 800 C (parameters nos. 3, 10 and 44) in comparison with the remaining samples. The Subgroup B1, including the fuel blends of 20% w/w of biomass content is characterized by the highest values of moisture content, heat of combustion, calorific value, the total sulphur,
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carbon, nitrogen, fixed carbon, SiO2, Al2O3, Fe2O3, MgO, Na2O, TiO2 and SiO2 content in a sample and high hydrogen, carbon monoxide, carbon dioxide and the total gas volume and gas calorific value at 700, 800, and 900 C (parameters nos. 2, 4e7, 9, 11e14, 16, 17, 20, 22e24, 26, 29, 35e37, 39, 42, 48e50, 52 and 55) as well as the lowest content of volatiles and oxygen in a sample and low value of Rmax at 800 C (parameters nos. 3, 10 and 44). Furthermore, the uniqueness of the fuel blend of 20% w/w of SV biomass content (object no 1) results from the highest carbon dioxide volume generated at 800 and 900 C (parameters nos. 37 and 50) and the lowest carbon conversion rate at 700 C (parameter no 34) among all the samples tested. Moreover, a specificity of the fuel blends of 20% w/w of HT, AG and SH biomass content (objects nos. 9, 5 and 13) is also observed. The fuel blend of 20% w/w of HT biomass content is characterized by the highest gas calorific value at 800 C (parameter no 42), the fuel blend of 20% w/w of AG biomass content e by the highest value of t50 at 800 C (parameter no 45) and low value of carbon conversion rate at of 900 C (parameter no 60), and the fuel blend of 20% w/w of SH biomass content e by the highest value of tmax (parameter no 59) in comparison with the remaining fuel blends. The specificity of the objects clustered in the subgroup B2 results from high value of the increase in the total gas, ngp, and hydrogen, nH2, volume at 700, 800, and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54) in comparison with the values reported in cogasification of the remaining samples. Furthermore, the fuel blend of 40% w/w of SH biomass content (object no 14) is characterized by the highest value of the total gas, ngp, and hydrogen, nH2, volume increment at all process temperatures applied (parameters nos. 27, 28, 40, 41, 53 and 54). For the fuel blend of 40% w/w of AG biomass content (object no 6) the highest value of Rmax and carbon conversion rate are observed (parameters nos. 57 and 60), while for the fuel blends of 40% w/w of HT and MXG biomass content the highest methane volume at 900 C (parameter no 51) of all the samples tested is reported. Furthermore, the fuel blend of 40% w/w of HT biomass content is characterized by the highest value of R50 at 700 C and methane volume at 800 C (parameters nos. 30 and 38) among all the samples tested, as well as the lowest value of t50 (parameter no 32) of the samples grouped in the cluster B. 3.4. Synergy effects study with application of HCA The HCA method was also applied in order to analyze the potential relation between the synergy effects observed, that is the relative increases in the total gas, ngp, and hydrogen, nH2, volume in Table 3 Parameters of fuel blends and co-gasification process applied in HCA. No.
Parameter
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Content of SiO2 in a fuel blend Content of Al2O3 in a fuel blend Content of Fe2O3 in a fuel blend Content of CaO in a fuel blend Content of MgO in a fuel blend Content of Na2O in a fuel blend Content of K2O in a fuel blend Content of SO3 in a fuel blend Content of TiO2 in a fuel blend Content of P2O5 in a fuel blend Increase in the total gas volume at 700 C, ngp 700 C Increase in hydrogen volume at 700 C, nH2_700 C Increase in the total gas volume at 800 C, ngp_800 C Increase in hydrogen volume at 800 C, nH2_800 C Increase in the total gas volume at 900 C, ngp_900 C Increase in hydrogen volume at 900 C, nH2_900 C
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Fig. 4. Dendrograms for: (a) fuel blends in the space of selected measured parameters (listed in Table 3), and (b) variables in the space of objects, obtained by the Ward's linkage method.
the process of steam co-gasification at 700, 800, and 900 C (see Table 3) and metal oxides content in the fuel blends tested. Fig. 4 shows the dendrograms constructed with an application of the Ward's linkage method.
The distribution of the fuel blends presented in the dendrogram constructed for objects in the space of 16 selected parameters (see Fig. 4a) deviates from the one resulted from the dendrogram developed for objects in the space of 60 parameters. Namely, in
ski / Energy 78 (2014) 814e825 N. Howaniec, A. Smolin
Fig. 2 two main clusters were distinguished, which differentiate the fuel blends of 20 and 40% w/w of biomass content from the fuel blends of 60 and 80% w/w of biomass content, while in the dendrogram constructed for objects in the space of 16 parameters (Fig. 4a) three major clusters were identified (A, B and C, respectively). In the latter case there was also a considerable variation observed among the fuel blends of 20 and 40% w/w of biomass content grouped in the clusters A and B, respectively. Within the cluster C the fuel blends of 60 and 80% w/w of biomass content were included. The dendrogram in Fig. 4b enabled to group the parameters in three classes: class A: parameters nos. 1, 2, 3, 5, 6 and 9 (describing the contents of SiO2, Al2O3, Fe2O3, MgO, Na2O and TiO2 in a fuel blend), class B: parameters nos. 11e16 (describing the increase in the total gas, ngp, and hydrogen, nH2, volume at 700, 800 and 900 C), and class C: parameters nos. 4, 7, 8 and 10 (describing the contents of CaO, K2O, SO3 and P2O5 in a fuel blend).
3.5. Synergy effects study with application of HCA supplemented with a colour data map Supplementing the HCA with a colour map of the data enabled to trace the variations in metal oxides content in a sample and the synergy effects observed in steam co-gasification at 700, 800 and 900 C (see Fig. 5). The fuel blends of 20 and 40% w/w of biomass content differ from the ones of 60 and 80% w/w of biomass in terms of Al2O3, Fe2O3, MgO, Na2O and TiO2 concentration in a sample (parameters nos. 2, 3, 5, 6 and 9), and to a lesser extent, in terms of SiO2 and K2O content in a sample (parameters nos. 1 and 7). The fuel blends of 20% w/w of biomass content are characterized by the highest values of Al2O3, Fe2O3 and TiO2 content in a sample (parameters nos. 2, 3 and 9) and high content of MgO and Na2O in a sample (parameters nos. 5 and 6) in comparison with the remaining samples tested. However, no significant increase in the total gas, ngp, and hydrogen, nH2, volume was observed in co-gasification of these samples at 700, 800 and 900 C in comparison with other samples. A significant increase in the total gas, ngp, and hydrogen, nH2, volume is, however, observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature applied. The difference in the values of the increase in the total gas and hydrogen volume may be attributed primarily to the content of Al2O3, Fe2O3, Na2O, K2O and TiO2 in a sample (parameters nos. 2, 3, 6, 7 and 9). The highest increase in the total gas, ngp, and hydrogen, nH2, volume was observed in the co-gasification of the fuel blends of 40% w/w of HT, SP and SH biomass content. Based on the colour data map it can be concluded that these samples differ from the remaining fuel blends of 40% w/w of biomass content in terms of higher K2O content (parameter no 7). This demonstrates the significant impact of a certain amount of K2O on the composition and volume of gas produced in the process of co-gasification. The above findings are in line with the reports on catalytic effect of alkali and alkali earth metal compounds [26e30], especially potassium salts [31e35], on the gasification process. The cluster C groups the fuel blends of 60 and 80% w/w of biomass content. These fuel blends differ from the remaining samples mainly by lower values of Al2O3, Fe2O3, TiO2 and Na2O content in a sample (except for the fuel blend of 60% w/w of SP biomass content e object no 23) and MgO (except for the fuel blends of 60 and 80% w/w of AG biomass content e objects nos. 7 and 8). Within the cluster C, three following subgroups may be
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distinguished, which differentiate the fuel blends of 60 and 80% w/ w of biomass content: subgroup C1 composed of the fuel blends of SV and MXG biomass content (objects nos. 3, 4, 19 and 20), subgroup C2a including the fuel blends of AG and SP biomass content (objects nos. 7, 8, 23 and 24), and subgroup C2b composed of the fuel blend of HT and SH biomass content (objects nos. 11, 12, 15 and 16). The fuel blends of the subgroup C1 are characterized by lower increase in the total gas, ngp, and hydrogen, nH2, volume at each process temperature tested and lower values of metal oxides content in a sample (parameters nos. 11e16) than the remaining samples. The specificity of the fuel blends included in the subgroup C2a results from the highest content of SiO2 (parameter no 1) and high concentrations of K2O, SO3 and P2O5 in a sample (parameters nos. 7, 8 and 10) in comparison with the remaining samples tested. In the case of the subgroup C2b, the highest values of CaO, K2O, SO3 and P2O5 content in a sample (parameters nos. 4, 7, 8, and 10) were observed among the all samples tested. However, no significant influence of high concentrations of these metal oxides on the synergy effects discussed (parameters nos. 11e16) was observed. 3.6. Summary To sum up, the analysis of the physical and chemical parameters of the fuel blends as well as the parameters of the steam cogasification process with an application of the PCA enabled to observe the variations between the fuel blends of 20% and 40% w/w of biomass content, and of 60 and 80% w/w of biomass content, respectively. The first group was characterized by higher values of ash content in a sample, heat of combustion, calorific value, the total sulphur, carbon, nitrogen, fixed carbon, Al2O3, Fe2O3, MgO, Na2O, SO3 and TiO2 content in a sample, higher volume of hydrogen, carbon monoxide and carbon dioxide generated at 700, 800 and 900 C, higher values of the total gas volume, and lower content of volatiles, hydrogen and oxygen in a sample in comparison with the fuel blends included in the second group. The fuel blends of 40% w/w of SH, HT and SP biomass content were also characterized by the largest increase in the total gas and hydrogen volume generated at 700, 800 and 900 C, and the lowest value of t50 at 700 and 900 C, among all the samples tested. The ineffective data compression in the PCA analysis was overcome with an application of the HCA method, enabling to track the dissimilarities between fuel blends in the parameters space and between the measured parameters within the studied fuel blends space. A division of samples tested into two groups, including the fuel blends of 20 and 40% w/w of biomass content and 60 and 80% w/w of biomass content, was confirmed based on the HCA results. The highest value of the total gas and hydrogen volume increase in the process of steam co-gasification in comparison with the values reported for coal and biomass gasification was observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature applied. The synergy effect observed in the co-gasification process was associated with the catalytic effects of biomass ash e derived metal oxides present in a sample. Further differentiation between the fuel blends of 20 and 40% w/w of biomass content was also made based on the HCA results. It was found that the fuel blends of 20% w/w of biomass content were characterized by the highest values of Al2O3, Fe2O3 and TiO2 content in a sample and high content of MgO and Na2O in a sample, in comparison with the remaining fuel blends. However, no significant increase in the total gas and hydrogen volume for these
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Fig. 5. Dendrograms for fuel blends in the space of selected parameters (see Table 3) and for variables in the space of objects, with a colour map showing the values of the variables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
samples was reported in comparison with the results reported for the remaining samples. The significant increase in the total gas and hydrogen volume in the process of steam co-gasification was observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature tested. The results indicate therefore, that the synergy effect observed may be related to the impact of biomass-derived fuel blends components: Al2O3, Fe2O3, Na2O, K2O and TiO2 on the co-gasification process. The highest increase in the total gas and hydrogen volume was observed in the process of co-gasification of the fuel blends of 40% w/w of HT, SP and SH biomass, characterized by the highest content of K2O among all the samples of 40% w/w of biomass content, irrespective of the process temperature applied. This proves that there is an impact of a certain amount of K2O on the synergy effects observed in the cogasification process, and that the biomass compounds of catalytic properties may be considered as alternative to expensive catalysts applied in the process. 4. Conclusions The highest increase in the total gas and hydrogen volume in the process of steam co-gasification was observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature applied. Based on the HCA results, it was associated with the
catalytic effects of biomass ash e derived metal oxides. Further differentiation between the fuel blends of 20 and 40% w/w of biomass content proved that the most significant synergy effects were observed for the fuel blends of HT, SP and SH biomass, of the highest K2O content among all the 40% w/w biomass content fuel samples.
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Energy journal homepage: www.elsevier.com/locate/energy
Influence of fuel blend ash components on steam co-gasification of coal and biomass e Chemometric study ski* Natalia Howaniec, Adam Smolin w 1, 40-166 Katowice, Poland Central Mining Institute, Department of Energy Saving and Air Protection, Pl. Gwarko
a r t i c l e i n f o
a b s t r a c t
Article history: Received 16 May 2014 Received in revised form 12 August 2014 Accepted 26 October 2014 Available online 18 November 2014
The process of co-gasification of coal and biomass offers the benefits of stable supplies of a primary energy resource e coal, with a partial replacement of a fossil fuel with a “zero-emission”, renewable energy source e biomass. The main objective of the experimental steam co-gasification study focused on hydrogen-rich gas generation, as a prospective clean energy carrier, was the determination of the impact of a fuel blend composition and process temperature on the yield and composition of gas generated. The identification of the synergy effects observed in the co-gasification process and their sources was also made with an application of chemometric methods of data analysis, such as the Principal Component Analysis and the Hierarchical Clustering Analysis. Based on the results it was concluded that the synergy effects observed were related to the presence of biomass-derived fuel blends components, such as K2O, Al2O3, Fe2O3, Na2O, TiO2. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Co-gasification Hydrogen Coal Biomass Ash Synergy
1. Introduction The process of coal and biomass co-gasification offers several environmental and economic benefits when compared to gasification. It secures a continuous operation of large scale energy systems based on reliable supplies of a fossil fuel, and at the same time, an application of a “zero-emission”, renewable energy resource. It makes the biomass utilization in energy generation systems more efficient, and at lower production costs, than it can be achieved in present-day biomass gasification systems. Production of hydrogen in the process of steam co-gasification, as a clean, environmental friendly energy carrier, may be also considered as the environmental sustainability added value. The IGCC (Integrated Gasification Combined Cycle) installation in Buggenum, in the Netherlands, is an example of one of few industrial scale co-gasification plants, where thermochemical conversion of fuel blends of up to 50% w/w of biomass content is planned to be performed to improve the CO2 balance of the installation. In most installations, in which coal and biomass are processed for energy purposes, coal is, however, still cocombusted with raw gas from biomass gasifiers. The recent studies on co-pyrolysis and co-gasification are reported to be performed predominantly with an application of laboratory scale fluidized bed
* Corresponding author. Tel.: þ48 32 2592252; fax: þ48 32 2596533. ski). E-mail address: [email protected] (A. Smolin http://dx.doi.org/10.1016/j.energy.2014.10.076 0360-5442/© 2014 Elsevier Ltd. All rights reserved.
and fixed bed reactors, under various operating conditions. In some of them synergy effects in pyrolysis and gasification of fuel blends were observed, which involved e.g. increase in fuel chars reactivity [1e4], carbon conversion rate [4e6], reduction of tars yield [3,4,7] and variation in product gas composition, e.g. in hydrogen to carbon monoxide ratio [7,8]. Interpretations of the impact of biomass content in a fuel blend on product gas composition are ambiguous. In the studies on cogasification of coal and biomass, an increase in hydrogen yield [8,9], or hydrogen content in a product gas [10,11] were observed. An increase in carbon dioxide content with increasing biomass content in a fuel blend [2,5,10], and in carbon monoxide yield [9] or carbon monoxide content in product gas [5,7,12e14] were also reported. The opposite trends, that is, a decrease in concentrations of particular gas components with increasing biomass content in a fuel blend were also found. These include examples of reduced hydrogen [5,7,11e15], carbon monoxide [2,10,11,13], and carbon dioxide [7,11,13,14] contents. With increasing biomass content in a fuel blend, increases in char reactivity [1,3,6], the total gas efficiency [1,3,5,13], carbon conversion rate [1,5e8] and process efficiency [7e9,12e14] were also reported. The vagueness of the data on the impact of biomass content in a fuel blend on gas composition, yields of individual gas components and synergy effects observed in co-pyrolysis and co-gasification results from substantial variations in process conditions, including types of gasifiers and fuels as well as operating
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parameters applied. Therefore, the synergy effects themselves and identification of the mechanisms behind them, require further research before the wide implementation of the co-gasification process is commercially viable. The PCA (Principal Component Analysis), and the HCA (Hierarchical Clustering Analysis) data exploration methods were applied in determination of the influence of a fuel blend composition on steam co-gasification results and in the interpretation of the synergy effects observed in relation to the content of particular metal oxides in a fuel blend.
process: particular gas components volumes and the total gas volume, chars reactivity: at 50% of carbon conversion, R50, and the maximum reactivity, Rmax, time required to reach 50% carbon conversion, t50, and the maximum reactivity, tmax, gas calorific value and carbon conversion rate, as well as the parameters defining the relative increase in the total gas, ngp, and hydrogen, nH2, volume in the co-gasification process when compared to the values reported in the gasification process at 700, 800 and 900 C were taken into account in the PCA and the HCA analyses (see Table 2).
2. Materials and methods
2.2. Methods
2.1. Materials and data organization
The Principal Component Analysis [18,19] is one of the most commonly applied chemometric techniques of exploratory analysis of multivariate data sets. It allows reduction of data dimensionality, its visualization and interpretation of the relationships between objects (fuel blends) and measured parameters. In the PCA the data
The energy crops selected for the experimental study included: SV (Salix Viminalis), AG (Andropogon Gerardi), HT (Helianthus Tuberosus), SH (Sida Hermaphrodita), MXG (Miscanthus X Giganteus) and SP (Spartina Pectinata). Hard coal was supplied by Piast coal mine (KWK Piast, Kompania Weglowa S.A.). Fuels were characterized in terms of proximate and ultimate analyses, heat of combustion and calorific value. The analyses were performed in accordance with the relevant standards and test procedures by the accredited Laboratory of the Department of Solid Fuels Quality Assessment of the Central Mining Institute. These included: moisture content analysis (PN-G-04560: 1998 and PB-4, 11th edition, using automatic thermogravimetric analyzers LECO: TGA 701 and MAC 500), ash content (PN-G-04560:1998 and PB5, 11th edition, with an application of automatic thermogravimetric analyzers LECO: TGA 701 and MAC 500), volatile content (PN-G-04516: 1998 and PB-6, 11th edition, based on DIN ISO 562: 2000 with the use of automatic thermogravimetric analyzers: LECO TGA 701 and MAC 500), heat of combustion and calorific value (PN-G-04513: 1981 and PB-7, 11th edition, with an application of LECO calorimeters: AC-600 and AC-350), total sulphur content (PN-G-04584: 2001 and PB-15, 11th edition, using an automatic analyzer TruSpec S by LECO), carbon, hydrogen, and nitrogen contents (PN-G-04571: 1998 and PB-19, 11th edition, with the use of a TruSpecCHN analyzer). Oxygen content was calculated as: 100% e Wa e Aa e Cat e Hat e Sac e Na (PN-G-04510:1991), and fixed carbon as: 100% e Wa e Aa e Va (PN-G-04516:1998). The content of metal oxides in ash of fuels tested was determined in compliance with the test procedure SC-1/ PB-05 according to DIN EN ISO 12677 in the accredited Laboratory of Solid Waste Analysis of the Department of Environmental Monitoring of the Central Mining Institute, with an application of a wavelength-dispersive X-ray fluorescence spectrometry. The results are given in Table 1. In general coal was characterized by higher content of fixed carbon, sulphur and ash, and lower content of volatiles, oxygen and hydrogen than biomass (see Table 1). Mineral matter of coal was rich in aluminium and iron oxides, and contained significantly less alkali and alkali earth metals than biomass ash. The detailed results of steam gasification and co-gasification of coal and biomass of selected energy crops in the laboratory scale fixed bed gasifier were published previously [16,17]. In the paper the relationships between physical and chemical parameters of fuel blends, ash composition and content in a fuel blend, co-gasification process parameters, and synergy effects observed in the process with an application of the data exploration methods, such as the PCA and the HCA are presented. The studied data set was organized in a matrix X(24 60), in which the rows correspond to fuel blends of various SV, AG, HT, SH, MXG and SP biomass content (20, 40, 60 and 80% w/w), whereas the columns represent parameters listed in Table 2. The physical and chemical parameters of fuel blends, ash composition and experimental data on the steam co-gasification
Table 1 Physical and chemical characteristics of fuel samples. Parameter (unit)
Moisture content, Wa (%w/w) Ash content, Aa (%w/w) Volatiles content, Va (%w/w) Heat od combustion, Qsa (J/g) Calorific value, Qia (J/g) Total sulphur content, Sta (%w/w) Carbon content, Cta (%w/w) Hydrogen content, Hta (%w/w) Nitrogen content, Na (%w/w) Oxygen content, Oa (%w/w) Fixed carbon (%w/w) SiO2 content in ash (%w/w) Al2O3 content in ash (%w/w) Fe2O3 content in ash (%w/w) CaO content in ash (%w/w) MgO content in ash (%w/w) Na2O content in ash (%w/w) K2O content in ash (%w/w) SO3 content in ash (%w/w) TiO2 content in ash (%w/w) P2O5 content in ash (%w/w) ZnO content in ash (%w/w)
Fuel sampleb 1
2
3
4
5
6
7
4.74
9.72
8.81
8.76
6.78
8.69
6.02
1.51
3.87
3.18
2.63
1.60
4.31
5.69
73.16
70.26
69.24
71.47
76.00
69.89
31.12
18,171 16,132 15,989 16,484 16,546 16,920 28,805
16,697 14,242 14,543 15,030 14,942 15,481 27,616 0.05
0.06
0.04
0.04
0.05
0.12
0.50
52.19
53.3
46.62
47.18
53.71
45.77
70.64
6.22
7.57
5.64
5.68
6.59
5.62
4.08
0.98
35.29
25.54
35.74
35.73
31.27
35.58
12.09
20.59 14.67
16.15 65.18
18.77 32.54
17.14 3.65
15.62 69.01
17.11 64.82
57.17 46.55
3.18
0.45
1.24
0.064
0.38
0.33
25.65
0.93
0.28
0.52
0.28
0.19
0.32
8.63
37.10
11.74
34.20
42.43
15.27
9.99
7.34
3.46
3.60
2.94
4.66
1.79
1.50
3.82
0.53
0.93
1.02
1.75
0.73
1.53
2.60
22.13
7.26
12.93
21.77
2.98
11.48
1.92
4.12
3.85
5.90
5.56
4.95
4.95
1.78
0.15
0.06
0.08
0.05
0.05
0.05
1.08
13.17
5.97
7.87
17.30
3.99
4.45
0.36
1.07
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Table 2 Parameters of fuel blends and co-gasification process applied in PCA. No.
Parameter
Unit
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Moisture, Wa Ash, Aa Volatiles, Va Heat of combustion Qsa Calorific value Qia Total sulphur, Sta Carbon, Cta Hydrogen Hta Nitrogen, Na Oxygen, Oa Fixed carbon SiO2 content in a fuel blend Al2O3 content in a fuel blend Fe2O3 content in a fuel blend CaO content in a fuel blend MgO content in a fuel blend Na2O content in a fuel blend K2O content in a fuel blend SO3 content in a fuel blend TiO2 content in a fuel blend P2O5 content in a fuel blend Hydrogen volume at 700 C Carbon monoxide volume at 700 C Carbon dioxide volume at 700 C Methane volume at 700 C The total gas volume at 700 C Increase in the total gas volume at 700 C, ngp Increase in hydrogen volume at 700 C, nH2 Gas calorific value at 700 C, Qg Reactivity at 50% coal conversion at 700 C, R50 Maximum reactivity at 700 C, Rmax Time of reaching 50% coal conversion at 700 C, t50 Time of reaching maximum reactivity at 700 C, Rmax, tmax Carbon conversion rate at 700 C Hydrogen volume at 800 C Carbon monoxide volume at 800 C Carbon dioxide volume at 800 C Methane volume at 800 C The total gas volume at 800 C Increase in the total gas volume at 800 C, ngp Increase in hydrogen volume at 800 C, nH2 Gas calorific value at 800 C, Qg Reactivity at 50% coal conversion at 800 C, R50 Maximum reactivity at 800 C, Rmax Time of reaching 50% coal conversion at 800 C, t50 Time of reaching maximum reactivity at 800 C, Rmax, tmax Carbon conversion rate at 800 C Hydrogen volume at 900 C Carbon monoxide volume at 900 C Carbon dioxide volume at 900 C Methane volume at 900 C The total gas volume at 900 C Increase in the total gas volume at 900 C, ngp Increase in hydrogen volume at 900 C, nH2 Gas calorific value at 900 C, Qg Reactivity at 50% coal conversion at 900 C, R50 Maximum reactivity at 900 C, Rmax Time of reaching 50% coal conversion at 900 C, t50 Time of reaching maximum reactivity at 900 C, Rmax, tmax Carbon conversion rate at 900 C
%w/w %w/w %w/w J/g J/g %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w %w/w Nm3 Nm3 Nm3 Nm3 Nm3 % % MJ/Nm 1/s 1/s s s % Nm3 Nm3 Nm3 Nm3 Nm3 % % MJ/Nm3 1/s 1/s s s % Nm3 Nm3 Nm3 Nm3 Nm3 % % MJ/Nm3 1/s 1/s s s %
a
Analytical state.
matrix, X(m n), is decomposed into the matrices, S(m fn), D (n fn) and E(m n), where m and n denote, respectively, the number of objects and parameters, fn denotes the number of significant factors. S represents the scores matrix, whereas D represents the loading matrix and E is the residuals matrix. The columns of the matrix S are called the PCs (Principal Components) or eigenvectors. Scores and loading matrices are orthogonal. Effective reduction of data dimensionality enables to use scores vectors and loadings vectors (i.e. the columns of the matrix S and D, respectively) to visualize and interpret the relationships between the
objects and the parameters, respectively. The Hierarchical Clustering Analysis allows for the study of similarities or dissimilarities between objects in the variables space or parameters in the objects space [20e23]. The HCA is characterized by a similarity measure applied and the way the resulting sub-clusters are linked. The Euclidean distance is the prevalent similarity measure applied when continuous variables are considered, and among the linkage methods the single linkage, complete linkage, average linkage, centroid linkage and Ward's linkage may be mentioned. The single linkage method defines the distance between two clusters as the smallest dissimilarity between an object from the first and the second cluster. In the complete linkage method the distance between studied clusters is defined as the longest distance between two objects belonging to the first and the second cluster, respectively. In the average linkage method the distance between the clusters is defined as an average distance determined by the single linkage and the complete linkage method, whereas the centroid linkage method is based on the distance between the mass centres of two clusters analyzed. The Ward's linkage method is most often applied, and is based on the inner squared distance between the studied clusters; at each stage these two clusters are merged, for which the minimum increase in the total within-group error sums of squares is observed. The results of the HCA are presented in a form of a dendrogram where, on axis x clustered objects (or variables) are displayed, whereas on axis y the corresponding linkage distances (or similarity measures) between the two objects or clusters, are presented. The disadvantage of the HCA is that it does not allow the interpretation of the observed patterns in terms of the original variables (parameters). This is a considerable limitation, since the purpose of the analysis is not only to identify the similarities and dissimilarities between objects, but also to identify the reasons behind them. This drawback may be, however, overcome by an application of a visualization method [24,25]. The studied data set is then organized in a matrix form containing m objects and n variables, X(m n). The HCA analysis results in a construction of two dendrograms: the dendrogram of objects in the variables space and the dendrogram of parameters in the objects space, respectively. At the dendrograms m objects and n parameters are ordered according to their similarity. In the same way the objects and parameters in a data matrix X(m n) are sorted. The resulting image of the data set attains a smoother appearance, since the adjacent objects and variables are ordered according to their similarity. 3. Results and discussion 3.1. PCA of fuel blends samples in the space of measured parameters A percentage of the data variance was applied in order to determine the correct complexity of the PCA model for the standardized data X(24 60). The data compression with the PCA model developed was ineffective, since as many as five Principal Components were required to describe 82.42% of the total data variance. However, the general conclusions could be drawn on the similarities and dissimilarities between individual samples. Score plots and loading plots resulting from this analysis, are presented in Fig. 1. The first two PCs describe 64.00% of the total data variance. Along the PC1, describing 51.72% of the total data variance, significant variations between samples are visible, resulting from fuel blends composition. A clear difference can be observed between the fuel blends of 20% and 40% w/w content of SV, AG, HT, SH, MXG and SP biomass (objects nos. 1, 5, 9, 13, 17, 21 and 2, 6, 10, 14, 18 and 22, respectively), and the fuel blends of 60 and 80% w/w of biomass content (objects nos. 3, 7, 11, 15, 19, 23 and 4, 8, 12, 16, 20 and 24,
Fig. 1. Score plots (a) and loading plots (b) resulting from PCA for centred and standardized data X(24 60).
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respectively). The most significant dissimilarity is observed between the fuel blends of 20% w/w (objects nos. 1, 5, 9, 13, 17 and 21) and 80% w/w of biomass content (objects nos. 4, 8, 12, 16, 20 and 24). Based on the parameters projection onto the plane defined by PC1 and PC2 it was found that the fuel blends of 20 and 40% w/w of biomass content are characterized by higher ash content in a sample, higher values of heat of combustion, calorific value, total sulphur, carbon, nitrogen, fixed carbon, Al2O3, Fe2O3, MgO, Na2O, SO3 and TiO2 content in a sample (parameters nos. 2, 4e7, 9, 11, 13, 14, 16, 17, 19, 20), as well as higher volumes of hydrogen, carbon monoxide and carbon dioxide in gas generated in the process of steam co-gasification at 700, 800 and 900 C, higher values of the total gas volume (parameters nos. 22e24, 26, 35e37, 39, 48, 49, 50 and 52) and lower content of volatiles, hydrogen and oxygen in a fuel blend (parameters nos. 3, 8 and 10) than the fuel blends of 60 and 80% w/w of biomass content. Moreover, samples of 20% w/w of biomass content are characterized by the highest values of parameters nos. 2, 4e7, 9, 11, 13, 14, 16, 17, 19, 20, 22e24, 26, 35e37, 39, 48e50 and 52 and the lowest values of parameters nos. 3, 8 and 10. The fuel blends of 80% w/w of biomass content are characterized by the highest content of volatiles, hydrogen and oxygen in a fuel blend (parameters nos. 3, 8 and 10). The PC2 describes 12.28% of the total data variance, resulting from the differences between the fuel blends of 40% w/w content of SH, HT and SP biomass (objects nos. 14, 10 and 22), and the fuel blends of 20% w/w of SV and MXG biomass content (objects nos. 1, 5 and 17), 60% w/w of SV, AG and MXG biomass content (objects nos. 3, 7 and 9) and 80% w/w of SV, AG and MXG biomass content (objects nos. 4, 8 and 20). These variations are related to the values of the total gas volume increase, ngp, and hydrogen volume increase, nH2, at 700, 800 and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54) and the time of reaching 50% of carbon conversion, t50, at 700 and 900 C (parameters nos. 32 and 58). The fuel blends of 40% w/w of SH, HT and SP biomass content (objects nos. 14, 10 and 22, respectively), are characterized by the highest increase in the total gas volume, ngp, and hydrogen volume, nH2, at 700, 800 and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54) and the shortest time required to reach 50% of carbon conversion, t50, at 700 and 900 C (parameters nos. 32 and 58) in comparison with the remaining fuel blends. Furthermore, these fuel blends are characterized by high contents of CaO and K2O (parameters nos. 5 and 18), high values of carbon conversion rate at 800 C and reactivity R50 at 900 C (parameters nos. 47 and 56). The fuel blends of 20% w/w of SV and MXG biomass content (objects nos. 1 and 5), 60% w/w of SV and AG biomass content (objects nos. 7 and 3) and 80% w/w of SV, AG and MXG biomass content (objects nos. 4, 8 and 20) are characterized by the longest time required to reach 50% of carbon conversion, t50, at 700 and 900 C (parameters nos. 32 and 58) and the lowest increase in the total gas volume, ngp, and hydrogen volume, nH2, at 700, 800 and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54). The PC3, describing 7.02% of the total data variance, displays the difference between the fuel blends of 20, 40, 60 and 80% w/w of SV biomass content (objects nos. 1e4), and fuel blends of the remaining energy crops biomass. Additionally, the PC3 reveals the uniqueness of the fuel blends of 60% w/w of AG biomass content, and 60 and 80% w/w of SP biomass content (objects nos. 7, 23 and 24). The fuel blends of 20, 40, 60 and 80% w/w of SV biomass content (objects nos. 1e4) are characterized by high values of tmax and the highest carbon dioxide content in gas generated at 900 C (parameters nos. 33 and 50). Furthermore, the fuel blends of 60% w/w of AG biomass content as well as 60 and 80% w/w of SP biomass content (objects nos. 7, 23 and 24) are characterized by high moisture, SiO2 and SO3 contents in a sample (parameters nos. 1, 12 and 19) and high gas calorific value at 900 C (parameter no 55) in comparison with the remaining fuel blends.
The PC4, describing 6.44% of the total data variance, shows the uniqueness of the fuel blend of 20% w/w of HT biomass content (object no 9), which can be attributed to high values of CaO content in a sample and tmax (parameters nos. 15 and 46, respectively). The PC5, describing 4.96% of the total data variance, shows the difference between the fuel blends of 40% w/w of HT and AG biomass content (objects nos. 10 and 6, respectively). The fuel blend of 40% w/w of HT biomass content (object no 10) differs from the remaining fuel blends by the highest value of R50 at 800 C (parameter no 43), while the fuel blend of 40% w/w of AG biomass content (object no 6) is characterized by high values of moisture, hydrogen and MgO content in a sample and gas calorific value at 800 C (parameters nos. 1, 8, 16, 42). The PCA analysis allowed also the investigation of correlations between particular parameters. The largest contribution to the PC1 have positively correlated parameters nos. 17, 37, 29 and 25 (describing Na2O content in a sample, methane volume and gas calorific value at 700 C and carbon dioxide volume at 800 C), the parameters nos. 24, 26, 35, 39, 48 and 52 (describing carbon dioxide and the total gas volume at 700 C, and hydrogen and the total gas volume at 800 and 900 C) and the parameters nos. 4, 6, 9, 11, 13, 14, 20, 23 and 36 (describing heat of combustion, the total sulphur, nitrogen, fixed carbon, Al2O3, Fe2O3 and TiO2 content in a sample and carbon monoxide volume at 700 and 800 C). In addition, a negative correlation can be observed between the parameters describing content of volatiles in a fuel blend (parameter no 3), carbon dioxide and the total gas volume at 700 C, and hydrogen and the total gas volume at 800 and 900 C (parameters nos. 24, 26, 35, 39, 48 and 52). A negative correlation is also observed between carbon and oxygen content in a sample (parameters nos. 7 and 10, respectively). 3.2. HCA of fuel blends samples in the space of measured parameters The difficulty of ineffective data compression was solved with an application of the Hierarchical Clustering Analysis, enabling to trace dissimilarities between objects in the space of the measured parameters and between the measured parameters in the objects space. The Euclidean distance was applied as a similarity measure. The results of the HCA are presented in a form of dendrograms constructed with an application of the Ward's linkage method (see Fig. 2). The dendrogram shown in Fig. 2a allows splitting the examined fuel blends into two clusters. All fuel blends of 60 and 80% w/w of biomass content are grouped in the cluster A, while those of 20 and 40% w/w of biomass content belong to the cluster B. Additionally, within each of the two clusters two subgroups can be divided. The subgroup A1 in the cluster A includes the fuel blends of 60 and 80% w/w of HT, SP and SH biomass content (objects nos. 11, 12, 15, 16, 23 and 24), whereas the subgroup A2 clusters the fuel blends of 60 and 80% w/w of SV, AG and MXG biomass content (objects nos. 3, 4, 7, 8, 19 and 20). Within the cluster B, the subgroup B1 may be distinguished, composed of the fuel blends of 20% w/w of biomass content and the subgroup B2, containing all fuel blends of 40% w/w of biomass content. Furthermore, fuel blends similar to each other, and connected the lowest along the axis of ordinates can be indicated. These are, for instance, the fuel blends of 60% w/w of HT and SP biomass content (objects nos. 11 and 23), 60 and 80% w/w of SV biomass content (objects nos. 3 and 4), 20% w/w of HT and MXG biomass content (objects nos. 9 and 17), 20% w/w of SH and SP biomass content (objects nos. 13 and 21) and 40% w/w of HT and MXG biomass content (objects nos. 10 and 18).
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Fig. 2. Dendrograms for: (a) fuel blends in the space of measured parameters (see Table 2), and (b) variables in the space of objects, obtained by the Ward's linkage method.
Fig. 2b shows a dendrogram constructed for 60 variables in the space of 24 objects, which allows grouping the variables into four main classes: class A, composed of the parameters nos. 2, 4e7, 9, 11e14, 16, 17, 20, 22e26, 29, 32, 35e37, 39, 42, 45, 48e50, 52, 55, 58
(describing ash content, heat of combustion, calorific value, content of: total sulphur, carbon, nitrogen, fixed carbon, SiO2, Al2O3, Fe2O3, MgO, Na2O and TiO2 in a sample, volume of: hydrogen, carbon monoxide, carbon dioxide and methane in gas generated at 700, 800 and 900 C, total gas volume and calorific value as well as time t50 at 700, 800 and 900 C, respectively),
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class B, containing the parameters nos. 27, 28, 40, 41, 53, 54, 60 (describing respectively: the increase in the total gas, ngp, and hydrogen, nH2, volume at 700, 800 and 900 C and carbon conversion rate at 900 C), class C, which includes the parameters nos. 1, 3, 8, 15, 18, 19, 21, 34, 44, 47, 56, 57 (describing moisture, volatiles, hydrogen, CaO, K2O, SO3 and P2O5 content in a sample and carbon conversion rate at 700 and 800 C, as well as the reactivity Rmax at 800 and 900 C and R50 at 900 C, respectively), class D, which groups the parameters nos. 30, 31, 33, 38, 43, 46, 51, 59 (describing respectively: reactivity R50 at 700 and 800 C, Rmax at 700 C, tmax at 700, 800 and 900 C, and methane volume at 800 and 900 C). The results of the cluster analysis clearly differentiates the fuel blends of 20 and 40% w/w of biomass content from the samples of 60 and 80% w/w of biomass content. 3.3. HCA of fuel blends samples in the space of measured parameters supplemented with a colour data map The disadvantage of the HCA is the inability to simultaneously track dependencies between objects in the variables space and
between and variables in the objects space, respectively. The HCA was complemented with a colour map of experimental data showing the measured values of parameters, ranked in accordance with the order of the organization of objects and variables, as shown in Fig. 2. The colour data map with the dendrogram showing the objects in the space of the measured parameters allowed for the analysis of the similarities and dissimilarities between the analyzed objects in the parameter space (see Fig. 3). On the basis of the simultaneous interpretation of the dendrogram presenting the fuel blends in the space of the parameters and the colour map of the experimental data it can be concluded that the samples of 60 and 80% w/w of biomass content, grouped in the cluster A (objects nos. 3, 4, 7, 8, 11, 12, 15, 16, 19, 20, 23 and 24) are characterized by lower values of heat of combustion, calorific value, the total sulphur, carbon, nitrogen, fixed carbon, Al2O3, Fe2O3 and TiO2 content, lower total gas as well as hydrogen and carbon dioxide volume at all temperatures tested (parameters nos. 4e7, 9, 11, 13, 14, 20, 22, 24, 26, 35, 37, 39, 52, 48 and 50), higher content of volatiles, hydrogen and oxygen in a sample, carbon conversion rate at 700 C and maximum reactivity, Rmax, (parameter nos. 3, 8, 10, 34, 44) than the fuel blends of 20 and 40% w/w of biomass content included in the cluster B (objects nos. 1, 2, 5, 6, 9, 10, 13, 14, 17, 18, 21 and 22, respectively).
Fig. 3. Dendrograms for fuel blends in the space of measured parameters (see Table 2) and for variables in the space of objects, with a colour map showing the values of the variables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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The subgroup A1 in the cluster A, composed of the fuel blends of 60 and 80% w/w of HT, SP and SH biomass content (objects nos. 11, 12, 15, 16, 23 and 24), is characterized by higher values of moisture, CaO, K2O, SO3 and P2O5 content in a sample, carbon conversion rate at 800 C and reactivity R50, and Rmax at 900 C (parameters nos. 1, 15, 18, 19, 21, 47, 56 and 57) as well as higher value of Rmax at 700 C and R50 at 800 (parameters nos. 31 and 43) in comparison with the remaining samples. Furthermore, within the subgroup A1, a significant dissimilarity may be observed between the fuel blends of 60% w/w of HT, SH and SP biomass content (objects nos. 11, 15 and 23), and the fuel blends of 80% w/w of HT, SH and SP biomass content (objects nos. 12, 16 and 24). Objects nos. 11, 15 and 23 are characterized by the highest value of the maximum reactivity, Rmax, at 700 C (parameter no 31), while objects nos. 12, 16 and 24 by high oxygen and the lowest carbon content in a sample (parameters nos. 10 and 7). Furthermore, the fuel blend of 80% w/w of SH biomass content (object no 16) has the highest content of CaO, K2O and P2O5 and the highest value of tmax (parameters nos. 15, 18, 21 and 46), while the fuel blend of 80% w/w of SP biomass content (object no 24) is characterized by the highest SO3 concentration in a sample and the highest carbon conversion rate at 800 C (parameters nos. 19 and 47). The fuel blend of 80% w/ w of HT biomass content (object no 12) is characterized by the lowest values of carbon monoxide volume and gas calorific value at 800 C (parameters nos. 36 and 42) among all the samples tested. The subgroup A2, including the fuel blends of 60 and 80% w/w of SV, AG and MXG biomass content (objects nos. 3, 4, 7, 8, 19 and 20) is characterized by the highest content of volatiles in a sample (parameter no 3) in comparison with all the remaining samples tested. Furthermore, within the subgroup A2, a specificity of the fuel blends of 60 and 80% w/w of SV biomass content (objects nos. 3 and 4), may be observed, resulting from the lowest moisture content in a sample (parameter no 1) and the lowest gas calorific value at 900 C (parameter no 55) in comparison with the remaining samples. A specificity of the fuel blend of 80% w/w of SV biomass content (object no 4) is also reported which may be attributed to low ash, MgO and Na2O content (parameters nos. 2, 16 and 17), the lowest SiO2 and SO3 content (parameters 12, and 19) in a sample, low methane volume, the lowest gas calorific value at 700 and 800 C, as well as the lowest value of carbon conversion rate at 900 C (parameters nos. 25, 29, 42 and 60) among all the samples tested. Within the subgroup A2, the fuel blends of 80% w/w of AG and MXG biomass content (objects nos. 8 and 20) are characterized by the lowest hydrogen, carbon dioxide and the total gas volume at 700, 800 and 900 C, as well as the shortest time t50 at 800 C (parameters nos. 22, 24, 26, 35, 37, 39, 45, 48, 50 and 52) and high values of volatiles and hydrogen content in a sample and Rmax at 800 C (parameter nos. 3, 8 and 44) in comparison with the remaining fuel blends. The fuel blend of 80% w/w of AG biomass content (object no 8) is characterized by the highest moisture and hydrogen content in a sample (parameter nos. 1 and 8) among all the samples tested. The fuel blends of 20 and 40% w/w of biomass content (objects nos. 1, 2, 5, 6, 9, 10, 13, 14, 17, 18, 21 and 22) included in the cluster B, are characterized by high values of moisture content, heat of combustion, heating value, the total sulphur, carbon, nitrogen, fixed carbon, SiO2, Al2O3, Fe2O3, MgO, Na2O and TiO2 content in a sample and hydrogen, carbon monoxide, carbon dioxide and the total gas volume, as well as gas calorific value at 700, 800 and 900 C (parameters nos. 2, 4e7, 9, 11e14, 16, 17, 20, 22e24, 26, 29, 35e37, 39, 42, 48e50, 52 and 55) and lower values of volatiles and oxygen content in a sample and Rmax at 800 C (parameters nos. 3, 10 and 44) in comparison with the remaining samples. The Subgroup B1, including the fuel blends of 20% w/w of biomass content is characterized by the highest values of moisture content, heat of combustion, calorific value, the total sulphur,
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carbon, nitrogen, fixed carbon, SiO2, Al2O3, Fe2O3, MgO, Na2O, TiO2 and SiO2 content in a sample and high hydrogen, carbon monoxide, carbon dioxide and the total gas volume and gas calorific value at 700, 800, and 900 C (parameters nos. 2, 4e7, 9, 11e14, 16, 17, 20, 22e24, 26, 29, 35e37, 39, 42, 48e50, 52 and 55) as well as the lowest content of volatiles and oxygen in a sample and low value of Rmax at 800 C (parameters nos. 3, 10 and 44). Furthermore, the uniqueness of the fuel blend of 20% w/w of SV biomass content (object no 1) results from the highest carbon dioxide volume generated at 800 and 900 C (parameters nos. 37 and 50) and the lowest carbon conversion rate at 700 C (parameter no 34) among all the samples tested. Moreover, a specificity of the fuel blends of 20% w/w of HT, AG and SH biomass content (objects nos. 9, 5 and 13) is also observed. The fuel blend of 20% w/w of HT biomass content is characterized by the highest gas calorific value at 800 C (parameter no 42), the fuel blend of 20% w/w of AG biomass content e by the highest value of t50 at 800 C (parameter no 45) and low value of carbon conversion rate at of 900 C (parameter no 60), and the fuel blend of 20% w/w of SH biomass content e by the highest value of tmax (parameter no 59) in comparison with the remaining fuel blends. The specificity of the objects clustered in the subgroup B2 results from high value of the increase in the total gas, ngp, and hydrogen, nH2, volume at 700, 800, and 900 C (parameters nos. 27, 28, 40, 41, 53 and 54) in comparison with the values reported in cogasification of the remaining samples. Furthermore, the fuel blend of 40% w/w of SH biomass content (object no 14) is characterized by the highest value of the total gas, ngp, and hydrogen, nH2, volume increment at all process temperatures applied (parameters nos. 27, 28, 40, 41, 53 and 54). For the fuel blend of 40% w/w of AG biomass content (object no 6) the highest value of Rmax and carbon conversion rate are observed (parameters nos. 57 and 60), while for the fuel blends of 40% w/w of HT and MXG biomass content the highest methane volume at 900 C (parameter no 51) of all the samples tested is reported. Furthermore, the fuel blend of 40% w/w of HT biomass content is characterized by the highest value of R50 at 700 C and methane volume at 800 C (parameters nos. 30 and 38) among all the samples tested, as well as the lowest value of t50 (parameter no 32) of the samples grouped in the cluster B. 3.4. Synergy effects study with application of HCA The HCA method was also applied in order to analyze the potential relation between the synergy effects observed, that is the relative increases in the total gas, ngp, and hydrogen, nH2, volume in Table 3 Parameters of fuel blends and co-gasification process applied in HCA. No.
Parameter
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Content of SiO2 in a fuel blend Content of Al2O3 in a fuel blend Content of Fe2O3 in a fuel blend Content of CaO in a fuel blend Content of MgO in a fuel blend Content of Na2O in a fuel blend Content of K2O in a fuel blend Content of SO3 in a fuel blend Content of TiO2 in a fuel blend Content of P2O5 in a fuel blend Increase in the total gas volume at 700 C, ngp 700 C Increase in hydrogen volume at 700 C, nH2_700 C Increase in the total gas volume at 800 C, ngp_800 C Increase in hydrogen volume at 800 C, nH2_800 C Increase in the total gas volume at 900 C, ngp_900 C Increase in hydrogen volume at 900 C, nH2_900 C
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Fig. 4. Dendrograms for: (a) fuel blends in the space of selected measured parameters (listed in Table 3), and (b) variables in the space of objects, obtained by the Ward's linkage method.
the process of steam co-gasification at 700, 800, and 900 C (see Table 3) and metal oxides content in the fuel blends tested. Fig. 4 shows the dendrograms constructed with an application of the Ward's linkage method.
The distribution of the fuel blends presented in the dendrogram constructed for objects in the space of 16 selected parameters (see Fig. 4a) deviates from the one resulted from the dendrogram developed for objects in the space of 60 parameters. Namely, in
ski / Energy 78 (2014) 814e825 N. Howaniec, A. Smolin
Fig. 2 two main clusters were distinguished, which differentiate the fuel blends of 20 and 40% w/w of biomass content from the fuel blends of 60 and 80% w/w of biomass content, while in the dendrogram constructed for objects in the space of 16 parameters (Fig. 4a) three major clusters were identified (A, B and C, respectively). In the latter case there was also a considerable variation observed among the fuel blends of 20 and 40% w/w of biomass content grouped in the clusters A and B, respectively. Within the cluster C the fuel blends of 60 and 80% w/w of biomass content were included. The dendrogram in Fig. 4b enabled to group the parameters in three classes: class A: parameters nos. 1, 2, 3, 5, 6 and 9 (describing the contents of SiO2, Al2O3, Fe2O3, MgO, Na2O and TiO2 in a fuel blend), class B: parameters nos. 11e16 (describing the increase in the total gas, ngp, and hydrogen, nH2, volume at 700, 800 and 900 C), and class C: parameters nos. 4, 7, 8 and 10 (describing the contents of CaO, K2O, SO3 and P2O5 in a fuel blend).
3.5. Synergy effects study with application of HCA supplemented with a colour data map Supplementing the HCA with a colour map of the data enabled to trace the variations in metal oxides content in a sample and the synergy effects observed in steam co-gasification at 700, 800 and 900 C (see Fig. 5). The fuel blends of 20 and 40% w/w of biomass content differ from the ones of 60 and 80% w/w of biomass in terms of Al2O3, Fe2O3, MgO, Na2O and TiO2 concentration in a sample (parameters nos. 2, 3, 5, 6 and 9), and to a lesser extent, in terms of SiO2 and K2O content in a sample (parameters nos. 1 and 7). The fuel blends of 20% w/w of biomass content are characterized by the highest values of Al2O3, Fe2O3 and TiO2 content in a sample (parameters nos. 2, 3 and 9) and high content of MgO and Na2O in a sample (parameters nos. 5 and 6) in comparison with the remaining samples tested. However, no significant increase in the total gas, ngp, and hydrogen, nH2, volume was observed in co-gasification of these samples at 700, 800 and 900 C in comparison with other samples. A significant increase in the total gas, ngp, and hydrogen, nH2, volume is, however, observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature applied. The difference in the values of the increase in the total gas and hydrogen volume may be attributed primarily to the content of Al2O3, Fe2O3, Na2O, K2O and TiO2 in a sample (parameters nos. 2, 3, 6, 7 and 9). The highest increase in the total gas, ngp, and hydrogen, nH2, volume was observed in the co-gasification of the fuel blends of 40% w/w of HT, SP and SH biomass content. Based on the colour data map it can be concluded that these samples differ from the remaining fuel blends of 40% w/w of biomass content in terms of higher K2O content (parameter no 7). This demonstrates the significant impact of a certain amount of K2O on the composition and volume of gas produced in the process of co-gasification. The above findings are in line with the reports on catalytic effect of alkali and alkali earth metal compounds [26e30], especially potassium salts [31e35], on the gasification process. The cluster C groups the fuel blends of 60 and 80% w/w of biomass content. These fuel blends differ from the remaining samples mainly by lower values of Al2O3, Fe2O3, TiO2 and Na2O content in a sample (except for the fuel blend of 60% w/w of SP biomass content e object no 23) and MgO (except for the fuel blends of 60 and 80% w/w of AG biomass content e objects nos. 7 and 8). Within the cluster C, three following subgroups may be
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distinguished, which differentiate the fuel blends of 60 and 80% w/ w of biomass content: subgroup C1 composed of the fuel blends of SV and MXG biomass content (objects nos. 3, 4, 19 and 20), subgroup C2a including the fuel blends of AG and SP biomass content (objects nos. 7, 8, 23 and 24), and subgroup C2b composed of the fuel blend of HT and SH biomass content (objects nos. 11, 12, 15 and 16). The fuel blends of the subgroup C1 are characterized by lower increase in the total gas, ngp, and hydrogen, nH2, volume at each process temperature tested and lower values of metal oxides content in a sample (parameters nos. 11e16) than the remaining samples. The specificity of the fuel blends included in the subgroup C2a results from the highest content of SiO2 (parameter no 1) and high concentrations of K2O, SO3 and P2O5 in a sample (parameters nos. 7, 8 and 10) in comparison with the remaining samples tested. In the case of the subgroup C2b, the highest values of CaO, K2O, SO3 and P2O5 content in a sample (parameters nos. 4, 7, 8, and 10) were observed among the all samples tested. However, no significant influence of high concentrations of these metal oxides on the synergy effects discussed (parameters nos. 11e16) was observed. 3.6. Summary To sum up, the analysis of the physical and chemical parameters of the fuel blends as well as the parameters of the steam cogasification process with an application of the PCA enabled to observe the variations between the fuel blends of 20% and 40% w/w of biomass content, and of 60 and 80% w/w of biomass content, respectively. The first group was characterized by higher values of ash content in a sample, heat of combustion, calorific value, the total sulphur, carbon, nitrogen, fixed carbon, Al2O3, Fe2O3, MgO, Na2O, SO3 and TiO2 content in a sample, higher volume of hydrogen, carbon monoxide and carbon dioxide generated at 700, 800 and 900 C, higher values of the total gas volume, and lower content of volatiles, hydrogen and oxygen in a sample in comparison with the fuel blends included in the second group. The fuel blends of 40% w/w of SH, HT and SP biomass content were also characterized by the largest increase in the total gas and hydrogen volume generated at 700, 800 and 900 C, and the lowest value of t50 at 700 and 900 C, among all the samples tested. The ineffective data compression in the PCA analysis was overcome with an application of the HCA method, enabling to track the dissimilarities between fuel blends in the parameters space and between the measured parameters within the studied fuel blends space. A division of samples tested into two groups, including the fuel blends of 20 and 40% w/w of biomass content and 60 and 80% w/w of biomass content, was confirmed based on the HCA results. The highest value of the total gas and hydrogen volume increase in the process of steam co-gasification in comparison with the values reported for coal and biomass gasification was observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature applied. The synergy effect observed in the co-gasification process was associated with the catalytic effects of biomass ash e derived metal oxides present in a sample. Further differentiation between the fuel blends of 20 and 40% w/w of biomass content was also made based on the HCA results. It was found that the fuel blends of 20% w/w of biomass content were characterized by the highest values of Al2O3, Fe2O3 and TiO2 content in a sample and high content of MgO and Na2O in a sample, in comparison with the remaining fuel blends. However, no significant increase in the total gas and hydrogen volume for these
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ski / Energy 78 (2014) 814e825 N. Howaniec, A. Smolin
Fig. 5. Dendrograms for fuel blends in the space of selected parameters (see Table 3) and for variables in the space of objects, with a colour map showing the values of the variables. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
samples was reported in comparison with the results reported for the remaining samples. The significant increase in the total gas and hydrogen volume in the process of steam co-gasification was observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature tested. The results indicate therefore, that the synergy effect observed may be related to the impact of biomass-derived fuel blends components: Al2O3, Fe2O3, Na2O, K2O and TiO2 on the co-gasification process. The highest increase in the total gas and hydrogen volume was observed in the process of co-gasification of the fuel blends of 40% w/w of HT, SP and SH biomass, characterized by the highest content of K2O among all the samples of 40% w/w of biomass content, irrespective of the process temperature applied. This proves that there is an impact of a certain amount of K2O on the synergy effects observed in the cogasification process, and that the biomass compounds of catalytic properties may be considered as alternative to expensive catalysts applied in the process. 4. Conclusions The highest increase in the total gas and hydrogen volume in the process of steam co-gasification was observed for the fuel blends of 40% w/w of biomass content, irrespective of the process temperature applied. Based on the HCA results, it was associated with the
catalytic effects of biomass ash e derived metal oxides. Further differentiation between the fuel blends of 20 and 40% w/w of biomass content proved that the most significant synergy effects were observed for the fuel blends of HT, SP and SH biomass, of the highest K2O content among all the 40% w/w biomass content fuel samples.
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