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Biomass proximate analysis using thermogravimetry
Accepted Manuscript Biomass Proximate Analysis using Thermogravimetry Roberto García, Consuelo Pizarro, Antonio G. Lavín, Julio L. Bueno PII: DOI: Reference:
S0960-8524(13)00601-9 http://dx.doi.org/10.1016/j.biortech.2013.03.197 BITE 11657
To appear in:
Bioresource Technology
Received Date: Revised Date: Accepted Date:
24 July 2012 27 March 2013 29 March 2013
Please cite this article as: García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., Biomass Proximate Analysis using Thermogravimetry, Bioresource Technology (2013), doi: http://dx.doi.org/10.1016/j.biortech.2013.03.197
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Biomass Proximate Analysis using Thermogravimetry Roberto García, Consuelo Pizarro(*), Antonio G. Lavín, Julio L. Bueno Department of Chemical Engineering and Environmental Technology, Faculty of Chemistry, University of Oviedo. Julián Clavería 8, 33006, Oviedo, Spain
ABSTRACT This work proposes a 25 minutes-last thermogravimetric method as a tool to determine biomass sample’s proximate analysis data (moisture, ash, volatile matter and fixed carbon contents) just by direct measure of weight changes on each sample’s TG chart. Compared with international standards commonly used to that aim, TG is a faster and easier to develop technique. Obtained results were satisfactory, with AEE under 6% for moisture and volatile matter, close to 10% for fixed carbon determination and AAD of 1.6 points for ash content.
Key words Biomass, proximate analysis, thermogravimetry
* Representing Author: Tel +0034 985 103438; Fax +0034 985 103434 E-mail address: [email protected] (C. Pizarro) E-mail addresses: [email protected] (Roberto García), [email protected] (Consuelo Pizarro), [email protected] (Antonio G. Lavín), [email protected] (Julio L. Bueno)
1. Introduction As widely reported, biomass constitutes an important feedstock in the current world energetic scenario (McKendry, 2002; Muthuraman et al., 2010; Jorquera et al., 2010). It presents some environmental advantages, such as gaseous CO2 emissions neutrality (Gil et al., 2010; Munir et al., 2009), or low NOx and SO2 emissions (Li et al., 2009; Qian et al., 2011). In addition to this, it has social advantages too, providing a source of wealth in rural areas, avoiding its depopulation (Bahng et al., 2009). Finally it is cheap and easy to produce. In that way, biomass is an autonomous resource, which partly avoids dependence on fossil fuels, which are produced in only a limited number of countries. In addition to this, the low price of raw materials and the development of biomassconsuming energy systems have made them economically competitive with traditional fossil fuels. Because of that, biomass appears as an important role-playing fuel in several national and international policies (Gaska and Wandrasz, 2008; Rosendahl et al., 2007), such as the European Union White Papers on energy saving, or the PER (Renewable Energy Plan) in Spain (Lapuerta et al., 2004). As can be seen, the future of biomass energy conversion appears to be quite optimistic, but its energetic use also presents some problems, the most important one being related to its own nature, even though it is a highly heterogeneous fuel, with plenty of different origins, from a huge variety of industrial wastes to wood transformation industryresidues or energy crops. Taking this into account, biomass characterization is required, to reliably predict its behaviour as a fuel. When considering biomass thermal conversion, proximate analysis is one of the most important characterization methods. This consists in determining moisture, ash, volatile matter and fixed carbon contents of the raw biofuel. These values are essential ascertain moisture, volatile matter and fixed
carbon affect both on the combustion behaviour and the plant design. In that way, high moisture values decrease the combustion yield, while high volatile matter/fixed carbon ratios are related with the fuel’s reactivity. On the other hand, ash deeply influences the transport, handling and management costs of the process. It is also influential in corrosion and slag formation. Traditionally, these measures are developed following different national and international normative, such as ASTM E-871 for moisture, ASTM E-830, D-1102 or UNE-EN 14775 for ash or ASTM E-872 and ASTM E-1755 for volatile matter determination (Demirbas, 2004; Khalil et al., 2008). Fixed carbon is usually determined by difference. All those methods are time-consuming and tedious (Karatepe and Kücükbayrak, 1993), and the success of the operation is highly influenced by the operator’s skills. In that way, a fast, simple, reliable and highly accurate method for routine tests, would be desirable. Since all the above mentioned standards basically establish heating a sample under different specific conditions (Mayoral et al., 2001), and a weight difference determination, then a thermobalance and a conveniently adapted thermogravimetric study appears as an effective tool (Warne, 1991), resulting in both time-saving, from several hours to just a few minutes in each experiment (Sadek and Herrell, 1984) and sample quantity reduction, as it requires matter weight in the range of milligrams (Beamish, 1994). As is well known, thermal analysis can be defined as a group of methods for measuring the property of a substance when subjected to a controlled temperature program. It is a highly developed technique with many different uses when applied to biomass. Some authors have used it to study the thermal behaviour of biomass and fuel blends in both oxidative and inactive atmospheres (Ghetti et al., 1996; Varol et al., 2010; Wilson et al., 2010). Others used this technique as a tool to thermally characterize fuels and ashes, by studying its melting behaviour or structural changes (Biswas et al., 2011; Miranda et al.,
2011, Ross et al., 2008) while others used TG as a tool to develop kinetic modelization (Ramajo-Escalera et al., 2006; Wilson et al., 2011). However, but not so many works have been developed for biomass proximate analysis, whilst, on the other hand, thermogravimetry is often used in that way for coals (Ottaway, 1982; Slaghuis and Raijmakers, 2004). In this context, the aim of this work is to determinate if TG analysis can be used as an effective tool to approach ultimate analysis data of biomass fuels and propose a method, based on modified-coal methods to reliably and accurately obtain these data.
2. Materials and methods. Firstly, thirteen biomass samples were grinded and sieved to 500 µm to guarantee its homogeneity in a proximate analysis routine by using ASTM standards E 871, E 1755 and E872; for moisture, ash and volatile matter respectively. In addition to this, fixed carbon content was calculated by difference using the balance:
% FC = 100 − (% Ash + %VM )
[1]
where %FC, %Ash and %VM respectively mean the mass percentages of fixed carbon, ash and volatile matter of the raw sample. All these procedures were detailed and referenced in our previous works (García et al., 2012). After this routine every sample was tested using a Perkin Elmer STA 6000 thermobalance, using 10 and 20 mg of sample, and 40 ml/min flow of flue gas, for both nitrogen and air. Samples were selected all around Spain trying to track every possible biomass origin, commercial fuels, agri-food industry wastes, forest wastes, energy crops and cereals (Fernández et al., 2012) with the aim of obtaining a general method, suitable for a wide range of biomass fuels, with different characteristics and compositions.
The bibliographical review search for methods developed for coals showed two basic groups, some simpler ones, which consist in a chosen continuous temperature ramp path between room temperature and the final set-point (TR1 and SP1, as detailed in table 1). In this group we may include works proposed by Mayoral et al., 2001(MAY3), Sadek and Herrell, 1984) (SDK1) or (Lapuerta et al., 2004)(LAP). On the other hand, there are some other proceedings consisting in continuous heating (TR1, 2 or 3 depending on the number of this steps) combined with intermediate dwellings (DT1, 2 and 3) to reach different intermediate or final set-point (SP1, 2 or 3). In this group we can include some other Mayoral proposed methods (MAY1, MAY2 and MAY4 ), Ottaway–(OTT)– (Ottaway, 1982), Sadek and Herrel (SDK2), Karatepe and Kücükbayrak, 1993(KAR), Warne (WAR) (Warne, 1991), and Beamish (BEA) (Beamish, 1994). Table 1 summarizes the conditions required in each of these works, with each temperature ramp (TR), intermediate and final setpoints (SP) and dwelling times (DT) required when each set-point is reached. Most of them are conducted in inactive atmosphere (using argon, helium or nitrogen depending on the author) to measure moisture, volatile matter and fixed carbon and with a final combustion time in oxidative environment (oxygen or air), when set-point is reached, to measure ashes. On the other hand, Lapuerta’s work is completely carried out in oxidative atmosphere. Some of these works explain how relevant data must be obtained from Thermogravimetry. TG diagrams commonly present a number of slopes, each kinetics showing different phase changes. A first, normally, small one represents moisture release due to drying, occuring at a temperature under 150ºC (Zheng and Kozinski, 2000). In the range between 200 and 900 ºC, a huge mass loss is seen. In the context of different phenomena that can be observed. Thus, between 200 and 600 ºC the biggest mass loss occurs due to the release of CO2 and CH4, these gases coming from hemicelluloses (190-320 ºC), cellulose (280-400 ºC) and
lignin (320-450 ºC) decomposition (Strezov, 2004), and a later chemically bonded CO2 and chemically formed water release (450-600 ºC). Finally, from 600 to proximately 900 ºC mass loss rate decreases, due to the evolution of carbon-containing species (COx, CxHy and tars) and char oxidation until constant weight is reached (Haykiri-Açma, 2003). Five methods originally developed, and described in the literature, for coals and cokes proximate analysis determination, were tested. In this work once observed which of them presents the best results, they were slightly adapted to biomass samples, aiming to reduce the experimental time without impoverishing the obtained results. The tested methods were LAP, MAY1, SDK and KAR. In addition to this a variation of OTT, called OTT* consisting in a second dwelling of five minutes at 550ºC, was used. Obtained results are compared with proximate analaysis data determined using international standards, provided in Table 2 (García et al., 2012), experimental errors are calculated and average absolute error values shown in Figure 1. The error criteria used are the average experimental error (AEE) and the average bias error (ABE) commonly used by several authors in this field (Ahmaruzzaman, 2008; Majumder et al., 2008). In addition to this, the average absolute deviation (AAD) is also used. Those criteria are defined as follows: [2]
[3] [4] As can be seen in figure 1, the KAR method presents the best results for both moisture and volatile matter determination, and so, because of this will be considered as a base
method to develop future variations, trying to improve accuracy in both of this parameters and ash and fixed carbon contents.
3. Results and discussion
Once KAR’s method has been chosen and carried out as the base one, some changes are developed therein, aiming to obtain a more suitable method for biomass proximate analysis data determination. In that way, biomass is reportedly far more reactive than coal, and so, faster heating ramps can be used without losing complete conversion during combustion. In that way, some new methods are tested, proving that the best results were found when beginning with a heating ramp of 50ºC/min, from room temperature until obtaining an isothermal 120ºC for 3 minutes, then a new 100ºC/min heating ramp is programmed until 950ºC.When this point is reached, a cooling process with -100ºC/min ramp starts until reaching 450ºC. Until this set-point, the process is developed using nitrogen, to guarantee a non oxidative environment, but, when 450ºC is reached, flue gas is changed for air. Then, a new 100ºC/min heating ramp begins until 800ºC, which provides better results than previous methods, and is isothermally maintained for 3 minutes, when the program is finished, totalling 25 minutes per experiment, enabling two experiments per hour, including cooling and stabilization of the experimental equipment, which entails a great time saving compared to several hours taken for the moisture and ash determination. This method is also more convenient than ASTM standard, and so, volatile matter determination implies working with a furnace at 950ºC, involving physical risk for the operator when introducing and withdrawing samples. This proposed method requires direct measure of moisture, volatile matter, fixed carbon and ash by difference from slope to slope. Volatile matter
and fixed carbon values are thus obtained by applying Beamish’s correction which means adding values obtained with the next formulae to the measured value. [5] [6] where VM, FC, VMTG and FCTG are, respectively, the obtained volatile matter and fixed carbon and those measured in TG, and M and A, moisture and ash content read on the TG profile. These corrections are proven to slightly improve the results. Finally, results obtained for each of the samples are detailed in Table 3, and the average values are compared (named as NEW MET) with the coal-developed methods in Figure 1. As can be seen, moisture and volatile matter experimental errors are satisfactory, with AEE under 6 %, resulting in AAD of 0.5 and 4.4 points, respectively. On the other hand, fixed carbon and especially ash results highly improve the values obtained using coal-developed methods, offering values close to 10 and 50 %, respectively. Taking into account that fixed carbon determination is obtained empirically by difference, with the entailing precision and accuracy limits, an 11% average error and 2.0 average absolute deviation points, can be considered as quite acceptable result. Regarding ash determination, this involves a problem already referred to by other authors (Mayoral et al., 2001). As can be seen in Table 3, there exists a big difference in measured EE depending on the considered sample. In that way, beetroot pellets or wheat grain present accurate results, with an experimental error close to 5%, while the same error in some other samples such as hazelnut shell or both brands of wood pellets approaches 90 %. As can be observed with the absolute deviation, those values are not high at all; in fact the AAD is just over that of the moisture, presenting a 1.6 % value. However, as expected, ash values are really low in biomass, mainly in woody samples. Low absolute
errors imply really high deviations, in relative bases, like AEE, may be due to the formation of highly specific weight oxides when the oxidative environment is reached. 4. Conclusions
This work proposes a suitable method to determine proximate analysis data of biomass. Obtained results, with average experimental errors under 6 % for moisture and volatile matter and close to 10 % for fixed carbon greatly improve coal-developed methods, for the same tested samples. Ash determination entails average absolute deviation of 1.6 points, but the low expected values imply higher experimental errors. Other important advantages are time saving and simplicity, as this method requires 25 minutes to obtain all four data sets while only moisture or ash determination take several hours each, using the standard normative. ACKNOWLEDGMENTS
PSE-ARFRISOL, Ref. PS-120000-2005-1, is a science-technology project qualified as Strategic by 2004-07 Spanish National Research Plan, Development and Diffusion, cofinanced by European Regional Development Funds and the Spanish Ministry of Science and Education. We wish to acknowledge all members of the PSE-ARFRISOL partnership for their cooperation. We would also like to thank a number of companies such as Pellets Asturias, Factor Verde, Molygrasa, Dibiosur, Enfosur, Acciona, Nutral Arrocerías Dorado, CarsanBio, Parque Verde, Vinos Viñadecanes, Cooperativa Agrícola de Cangas del Narcea, Vino de la Tierra de Cangas, Gebio, Bioenergía Aragonesa, Cafés el Gallego, Biomasas Herrero, Ecowarm and Carbones Lamuño for their generous collaboration in supplying most of the required samples. References Sorted alphabetically
Ahmaruzzaman, M., [34] 2008. Proximate analyses and predicting HHV of chars obtained from cocracking of petroleum vacuum residue with coal, plastics and biomass. Bioresour. Technol. 99, 5043–5050. Bahng, M.K., Mukarakate, C., Robichaud, D.J., Nimlos, M.R., [8] 2009. Current technologies for analysis of biomass thermo-chemical processing: A review. Anal Chim Acta. 651, 117-138. Beamish, B.B., [18] 1994. Proximate analysis of New Zealand and Australian coals by thermogravimetry. New Zealand J. Geol. Geoph. 37, 387-392. Biswas, A.K., Umeki, K., Yang, W., Blasiak, W., [22] 2011. Change of pyrolysis characteristics and structure of woody biomass due to steam explosion pretreatment. Fuel Process. Technol. 92, 1849–1854. Demirbas, A., [12] 2004. Combustion characteristics of different biomass fuels. Prog. Energy Comb. Sci. 30, 219-230. Fernández, R.G., García, C.P., Lavín, A.G., Bueno, J.L., [30] 2012. Study of main combustion characteristics for biomass fuels used in boilers. Fuel Process. Technol. 103, 16–26. García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., [29] 2012. Characterization of Spanish biomass wastes for energy use. Bioresour. Technol. 103, 249-258. Gaska, K., Wandrasz, A.J., [9] 2008. Mathematical modeling of biomass fuels formation process. Waste manag. 28, 973-985. Ghetti, P., Ricca, L., Angelini, L., [19] 1996. Thermal analysis of biomass and corresponding pyrolysis products. Fuel. 75(5), 565–573. Gil, M.V., Oulego, P., Casal, M.D., Pevida, C., Pis, J.J., Rubiera, F., [4] 2010. Mechanical durability and combustion characteristics of pellets from biomass blends. Bioresour. Technol. 101, 8859-8867.
Haykiri-Açma, H., [33] 2003. Combustion characteristics of different biomass materials. Energy Convers. Manag. 44, 155-162. Jorquera, O., Kiperstok, A., Sales, E.A., Embiruçu, M., Ghirardi, M.L., [3] 2010. Comparative energy life-cycle analyses of microalgae biomass production in open ponds and photobioreactors. Bioresour. Technol. 101, 1406-1413. Karatepe, N., Kücükbayrak, S., [14] 1993. Proximate analysis of Turkish lignites by thermogravometry. Thermochim. Acta. 213,147-150. Khalil, R.A., Meszaros, E., Grønli, M.G., Várhegyi, G., Mohai, I., Marosvolgyi, B., Hustad, J.E., [13] 2008. Thermal analysis of energy crops Part I: The applicability of a macro-thermobalance for biomass studies. J. Anal. Appl. Pyrolysis. 81, 52–59. Lapuerta, M., Hernández, J.J., Rodríguez, J., [11] 2004. Kinetics of devolatilisation of forestry wastes from thermogravimetric analysis. Biomass Bioenerg. 27, 385 – 391 Li, Z., Zhao, W., Li, R., Wang, Z., Li, Y., Zhao, G., [6] 2009. Combustion characteristics and NO formation for biomass blends in a 35-ton-per-hour travelling grate utility boiler. Bioresour. Technol. 100, 2278-2283. Majumder, A.K., Jain, R., Banerjee, P., Barnwal, J.P., [35] 2008. Development of a new proximate analysis-based correlation to predict calorific value of coal. Fuel. 87, 3077–3081. Mayoral, M.C., Izquierdo, M.T., Andrés, J.M., Rubio, B., [15] 2001.Different approaches to proximate analysis by thermogravimetry analysis. Thermochim. Acta 370, 91-97. McKendry, P., [1] 2002. Energy production from biomass (Part 1): overview of biomass. Bioresour. Technol. 83, 37-46.
Miranda, M.T., Arranz, J.I., Román, S., Rojas, S., Montero, I., López, M., Cruz, J.A., [23] 2011. Characterization of grape pomace and Pyrenean oak pellets. Fuel Process. Technol. 92, 278–283. Munir, S., Daood, S.S., Nimmo, W., Cunliffe, A.M., Bibbs, B.M., [5] 2009. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour. Technol. 100,1413-1418. Muthuraman, M., Namioka, T., Yoshikawa, K., [2] 2010. A comparison of cocombustion characteristics of coal with wood and hydrothermally treated municipal solid waste. Bioresour. Technol. 101, 2477-2482. Ottaway, M., [27] 1982.Use of thermogravimetry for proximate analysis of coals and cokes. Fuel. 61, 713-716. Qian, F.P., Chyang, C.S., Huang, K.S., Tso, J., [7] 2011. Combustion and NO emission of high nitrogen content biomass in a pilot-scale vortexing fluidized bed combustor. Bioresour. Technol. 102, 1892-1898. Ramajo-Escalera, B., Espina, A., García, J.R., Sosa-Arnao, J.H., Nebra, S.A., [25] 2006. Model-free kinetics applied to sugarcane bagasse combustion. Thermochim. Acta. 448, 111–116. Ross, A.B., Jones, J.M., Kubacki, M.L., Bridgeman, T., [24] 2008. Classification of macroalgae as fuel and its thermochemical behaviour. Bioresour. Technol. 99, 6494– 6504 Rosendahl, L.A., Yin, C., Kaer, S.K., Friborg, K., Overgaard, P., [10] 2007. Physical characterization of biomass fuels prepared for suspension firing in utility boilers. Biomass Bioenerg. 31, 318-325. Sadek, F.S., Herrell, A.Y. [17] 1984. Methods of proximate analysis by thermogravimetry. Thermochim Acta. 81, 297-303.
Slaghuis, J.H., Raijmakers, N., [28] 2004. The use of thermogravimetry in establishing the Fischer tar of a series of South African coal types. Fuel. 83, 533–536. Strezov, V., Moghtaderi, B., Lucas, J.A., [32] 2004. Computational calorimetric investigation of the reactions during thermal conversion of wood biomass Biomass. Bioenerg. 27, 459-465. Varol, M., Atimtay, A.T., Bay, B., Olgun, H., [20] 2010. Investigation of cocombustion characteristics of low quality lignite coals and biomass with thermogravimetry analysis. Thermochim. Acta. 510,195-201. Warne, S.S.J., [16] 1991. Proximate analysis of coal, oil shale, low quality fossil fuels and related materials by thermogravimetry. Trends in Anal. Chem. 10(6), 195199. Wilson, L., John, G.R., Mhilu, C.F., Yang, W., Blasiak, W., [21] 2010. Coffee husks gasification using high temperature air/steam agent. Fuel Process. Technol. 91,1330– 1337. Wilson, L., Yang, W., Blasiak, W., John, G.R., Mhilu, C.F., [26] 2011. Thermal characterization of tropical biomass feedstocks. Energy Conserv. Manag. 52,191-198. Zheng, G., Kozinski, J.A., [31] 2000. Thermal events occurring during the combustion of biomass residue. Fuel. 79,181-192. Sorted numerically
[1] McKendry, P., 2002. Energy production from biomass (Part 1): overview of biomass. Bioresour. Technol. 83, 37-46. [2] Muthuraman, M., Namioka, T., Yoshikawa, K., 2010. A comparison of cocombustion characteristics of coal with wood and hydrothermally treated municipal solid waste. Bioresour. Technol. 101, 2477-2482.
[3] Jorquera, O., Kiperstok, A., Sales, E.A., Embiruçu, M., Ghirardi, M.L., 2010. Comparative energy life-cycle analyses of microalgae biomass production in open ponds and photobioreactors. Bioresour. Technol. 101, 1406-1413. [4] Gil, M.V., Oulego, P., Casal, M.D., Pevida, C., Pis, J.J., Rubiera, F., 2010. Mechanical durability and combustion characteristics of pellets from biomass blends. Bioresour. Technol. 101, 8859-8867. [5] Munir, S., Daood, S.S., Nimmo, W., Cunliffe, A.M., Bibbs, B.M., 2009. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour. Technol. 100,1413-1418. [6] Li, Z., Zhao, W., Li, R., Wang, Z., Li, Y., Zhao, G., 2009. Combustion characteristics and NO formation for biomass blends in a 35-ton-per-hour travelling grate utility boiler. Bioresour. Technol. 100, 2278-2283. [7] Qian, F.P., Chyang, C.S., Huang, K.S., Tso, J., 2011. Combustion and NO emission of high nitrogen content biomass in a pilot-scale vortexing fluidized bed combustor. Bioresour. Technol. 102, 1892-1898. [8] Bahng, M.K., Mukarakate, C., Robichaud, D.J., Nimlos, M.R., 2009. Current technologies for analysis of biomass thermo-chemical processing: A review. Anal Chim Acta. 651, 117-138. [9] Gaska, K., Wandrasz, A.J., 2008. Mathematical modeling of biomass fuels formation process. Waste manag. 28, 973-985. [10] Rosendahl, L.A., Yin, C., Kaer, S.K., Friborg, K., Overgaard, P., 2007. Physical characterization of biomass fuels prepared for suspension firing in utility boilers. Biomass Bioenerg. 31, 318-325. [11] Lapuerta, M., Hernández, J.J., Rodríguez, J., 2004. Kinetics of devolatilisation of forestry wastes from thermogravimetric analysis. Biomass Bioenerg. 27, 385 – 391
[12] Demirbas, A., 2004. Combustion characteristics of different biomass fuels. Prog. Energy Comb. Sci. 30, 219-230. [13] Khalil, R.A., Meszaros, E., Grønli, M.G., Várhegyi, G., Mohai, I., Marosvolgyi, B., Hustad, J.E., 2008. Thermal analysis of energy crops Part I: The applicability of a macro-thermobalance for biomass studies. J. Anal. Appl. Pyrolysis. 81, 52–59. [14] Karatepe, N., Kücükbayrak, S., 1993. Proximate analysis of Turkish lignites by thermogravometry. Thermochim. Acta. 213,147-150. [15] Mayoral, M.C., Izquierdo, M.T., Andrés, J.M., Rubio, B., 2001.Different approaches to proximate analysis by thermogravimetry analysis. Thermochim. Acta 370, 91-97. [16] Warne, S.S.J., 1991. Proximate analysis of coal, oil shale, low quality fossil fuels and related materials by thermogravimetry. Trends in Anal. Chem. 10(6), 195199. [17] Sadek, F.S., Herrell, A.Y. 1984. Methods of proximate analysis by thermogravimetry. Thermochim Acta. 81, 297-303. [18] Beamish, B.B., 1994. Proximate analysis of New Zealand and Australian coals by thermogravimetry. New Zealand J. Geol. Geoph. 37, 387-392. [19] Ghetti, P., Ricca, L., Angelini, L., 1996. Thermal analysis of biomass and corresponding pyrolysis products. Fuel. 75(5), 565–573. [20] Varol, M., Atimtay, A.T., Bay, B., Olgun, H., 2010. Investigation of cocombustion characteristics of low quality lignite coals and biomass with thermogravimetry analysis. Thermochim. Acta. 510,195-201.
[21] Wilson, L., John, G.R., Mhilu, C.F., Yang, W., Blasiak, W., 2010. Coffee husks gasification using high temperature air/steam agent. Fuel Process. Technol. 91,1330– 1337. [22] Biswas, A.K., Umeki, K., Yang, W., Blasiak, W., 2011. Change of pyrolysis characteristics and structure of woody biomass due to steam explosion pretreatment. Fuel Process. Technol. 92, 1849–1854. [23] Miranda, M.T., Arranz, J.I., Román, S., Rojas, S., Montero, I., López, M., Cruz, J.A., 2011. Characterization of grape pomace and Pyrenean oak pellets. Fuel Process. Technol. 92, 278–283. [24] Ross, A.B., Jones, J.M., Kubacki, M.L., Bridgeman, T., 2008. Classification of macroalgae as fuel and its thermochemical behaviour. Bioresour. Technol. 99, 6494– 6504 [25] Ramajo-Escalera, B., Espina, A., García, J.R., Sosa-Arnao, J.H., Nebra, S.A., 2006. Model-free kinetics applied to sugarcane bagasse combustion. Thermochim. Acta. 448, 111–116. [26] Wilson, L., Yang, W., Blasiak, W., John, G.R., Mhilu, C.F., 2011. Thermal characterization of tropical biomass feedstocks. Energy Conserv. Manag. 52,191-198. [27] Ottaway, M., 1982.Use of thermogravimetry for proximate analysis of coals and cokes. Fuel. 61, 713-716. [28] Slaghuis, J.H., Raijmakers, N., 2004. The use of thermogravimetry in establishing the Fischer tar of a series of South African coal types. Fuel. 83, 533–536. [29] García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., 2012. Characterization of Spanish biomass wastes for energy use. Bioresour. Technol. 103, 249-258.
[30] Fernández, R.G., García, C.P., Lavín, A.G., Bueno, J.L., 2012. Study of main combustion characteristics for biomass fuels used in boilers. Fuel Process. Technol. 103, 16–26. [31] Zheng, G., Kozinski, J.A., 2000. Thermal events occurring during the combustion of biomass residue. Fuel. 79,181-192. [32] Strezov, V., Moghtaderi, B., Lucas, J.A., 2004. Computational calorimetric investigation of the reactions during thermal conversion of wood biomass Biomass. Bioenerg. 27, 459-465. [33] Haykiri-Açma, H., 2003. Combustion characteristics of different biomass materials. Energy Convers. Manag. 44, 155-162. [34] Ahmaruzzaman, M., 2008. Proximate analyses and predicting HHV of chars obtained from cocracking of petroleum vacuum residue with coal, plastics and biomass. Bioresour. Technol. 99, 5043–5050. [35] Majumder, A.K., Jain, R., Banerjee, P., Barnwal, J.P., 2008. Development of a new proximate analysis-based correlation to predict calorific value of coal. Fuel. 87, 3077–3081.
Figure 1 – Average experimental errors observed for the tested methods.
METHOD MAY 1 MAY 2 MAY 3 MAY 4 OTT SDK 1 SDK 2 KAR WAR BEA LAP
INACTIVE ATMOSPHERE TR1 SP1 DT1 TR2 SP2 DT2 20 105 5 20 900 20 900 5 -25 450 80 1020 10 20 105 3 80 850 250 110 1-5 250 900 1-5 100 1000 3 100 135 2 100 1000 3 20 110 30 40 950 7 100 110 2 100 950 3 50 110 5 50 950 7 TR1 SP1 DT1 TR2 SP2 DT2 -
OXIDATIVE ATMOSPHERE CT TR3 SP3 DT3 20 20 700 1 3 3 -20 750 5 3 42 CT TR1 SP1 DT1 18-108 10-60 1100 -
Table 1 – Summary of the studied analysis methods developed for coals and methods assayed for biomass. Where: TR 1, 2 and 3 are the first, second and third temperature ramps respectively, measured in ºC/min. SP is the set-point for each temperature ramp, measured in ºC and DT is the dwelling time after each ramp, measured in minutes. CT is the combustion time in oxidative atmosphere when set-point is reached.
Sample Apple tree leaves Beetroot pellets Chestnut tree leaves Hazelnut shell Miscanthus Nectarine stone Peach stone Pine and pine apple leaf pellets Pistachio shell Soya grain Wheat grain Wood pellets 1 Wood pellets 2
%M 9.3 12.5 8.2 8.74 7.53 8.2 8.55 8.25 8.75 10.4 10.3 7.96 7.53
%A 12 9 4.9 2.2 9.6 1.1 0.5 3.2 1.3 4.8 2.8 1.3 0.66
% VM 71.9 76 72.41 77 79 76 75.6 74.5 82.5 77 80 82 84
% FC 16.1 15 22.69 20.8 11.4 22.9 23.9 22.3 16.2 18.2 17.2 16.7 15.34
Table 2 – Data obtained for moisture (M), ash (A), volatile matter (VM) and fixed carbon (FC) content of biomass samples using ASTM normative, measured in mass percentage.
Apple tree leaves Beetroot pellets Chestnut tree leaves Hazelnut shell Miscanthus Nectarine stone Peach stone Pine and pine apple leaf pellets Pistachio shell Soya grain Wheat grain Wood pellets 1 Wood pellets 2 Average values
M 13.3 12.1 13.0 14.1 11.8 18.6 19.5 13.0 14.7 19.1 17.0 14.1 13.4
MV 8.3 11.0 8.1 8.5 7.9 7.9 7.9 8.8 8.2 9.4 10.2 7.8 8.0
MOISTURE EE BE 11.0 -11.0 11.8 -11.8 1.4 -1.4 2.5 -2.5 5.5 5.5 4.1 -4.1 7.4 -7.4 6.8 6.8 6.1 -6.1 9.3 -9.3 0.7 -0.7 2.5 -2.5 5.7 5.7 5.8 -3.0
AD 1.0 1.5 0.1 0.2 0.4 0.3 0.6 0.6 0.5 1.0 0.1 0.2 0.4 0.5
MV 6.45 8.43 2.90 0.15 6.37 0.52 0.73 1.81 0.53 2.78 2.95 0.21 0.08
ASH EE BE 46.2 -46.2 6.3 -6.3 40.9 -40.9 93.4 -93.4 33.7 -33.7 53.1 -53.1 46.8 46.8 43.3 -43.3 59.1 -59.1 42.0 -42.0 5.3 5.3 84.0 -84.0 88.3 -88.3 49.4 -41.4
AD 5.5 0.6 2.0 2.1 3.2 0.6 0.2 1.4 0.8 2.0 0.1 1.1 0.6 1.6
VOLATILE MATTER MV EE BE AD 67.3 6.4 -6.4 4.6 62.5 17.8 -17.8 13.5 70.8 2.2 -2.2 1.6 73.5 4.5 -4.5 3.5 69.6 11.8 -11.8 9.4 80.7 6.2 6.2 4.7 81.8 8.1 8.1 6.2 70.5 5.4 -5.4 4.0 79.7 3.4 -3.4 2.8 76.6 0.5 -0.5 0.4 77.6 3.0 -3.0 2.4 79.8 2.7 -2.7 2.2 81.9 2.5 -2.5 2.1 5.7 -3.5 4.4
MV 16.6 10.7 20.2 23.3 13.5 21.1 20.4 21.3 17.5 18.1 13.0 18.4 15.4
FIXED CARBON EE BE 3.1 3.1 28.8 -28.8 10.8 -10.8 11.8 11.8 18.6 18.6 7.7 -7.7 14.7 -14.7 4.6 -4.6 7.8 7.8 0.7 -0.7 24.3 -24.3 10.1 10.1 0.4 0.4 11.0 -3.1
AD 0.5 4.3 2.5 2.5 2.1 1.8 3.5 1.0 1.3 0.1 4.2 1.7 0.1 2.0
Table 3 – Values obtained for each of the chosen samples. Where M is the mass of sample (mg), Mv the measured value (%), EE, BE and AD are respectively experimental error (%), the BIAS error and the absolute deviation for each individual sample. The average values are presented in the last row.
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A method to determinate proximate analysis data in biomass by TG is proposed Coal developed methods are compared and improved for biomass use. Satisfactory results are obtained, main on moisture and volatile matter values.
S0960-8524(13)00601-9 http://dx.doi.org/10.1016/j.biortech.2013.03.197 BITE 11657
To appear in:
Bioresource Technology
Received Date: Revised Date: Accepted Date:
24 July 2012 27 March 2013 29 March 2013
Please cite this article as: García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., Biomass Proximate Analysis using Thermogravimetry, Bioresource Technology (2013), doi: http://dx.doi.org/10.1016/j.biortech.2013.03.197
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Biomass Proximate Analysis using Thermogravimetry Roberto García, Consuelo Pizarro(*), Antonio G. Lavín, Julio L. Bueno Department of Chemical Engineering and Environmental Technology, Faculty of Chemistry, University of Oviedo. Julián Clavería 8, 33006, Oviedo, Spain
ABSTRACT This work proposes a 25 minutes-last thermogravimetric method as a tool to determine biomass sample’s proximate analysis data (moisture, ash, volatile matter and fixed carbon contents) just by direct measure of weight changes on each sample’s TG chart. Compared with international standards commonly used to that aim, TG is a faster and easier to develop technique. Obtained results were satisfactory, with AEE under 6% for moisture and volatile matter, close to 10% for fixed carbon determination and AAD of 1.6 points for ash content.
Key words Biomass, proximate analysis, thermogravimetry
* Representing Author: Tel +0034 985 103438; Fax +0034 985 103434 E-mail address: [email protected] (C. Pizarro) E-mail addresses: [email protected] (Roberto García), [email protected] (Consuelo Pizarro), [email protected] (Antonio G. Lavín), [email protected] (Julio L. Bueno)
1. Introduction As widely reported, biomass constitutes an important feedstock in the current world energetic scenario (McKendry, 2002; Muthuraman et al., 2010; Jorquera et al., 2010). It presents some environmental advantages, such as gaseous CO2 emissions neutrality (Gil et al., 2010; Munir et al., 2009), or low NOx and SO2 emissions (Li et al., 2009; Qian et al., 2011). In addition to this, it has social advantages too, providing a source of wealth in rural areas, avoiding its depopulation (Bahng et al., 2009). Finally it is cheap and easy to produce. In that way, biomass is an autonomous resource, which partly avoids dependence on fossil fuels, which are produced in only a limited number of countries. In addition to this, the low price of raw materials and the development of biomassconsuming energy systems have made them economically competitive with traditional fossil fuels. Because of that, biomass appears as an important role-playing fuel in several national and international policies (Gaska and Wandrasz, 2008; Rosendahl et al., 2007), such as the European Union White Papers on energy saving, or the PER (Renewable Energy Plan) in Spain (Lapuerta et al., 2004). As can be seen, the future of biomass energy conversion appears to be quite optimistic, but its energetic use also presents some problems, the most important one being related to its own nature, even though it is a highly heterogeneous fuel, with plenty of different origins, from a huge variety of industrial wastes to wood transformation industryresidues or energy crops. Taking this into account, biomass characterization is required, to reliably predict its behaviour as a fuel. When considering biomass thermal conversion, proximate analysis is one of the most important characterization methods. This consists in determining moisture, ash, volatile matter and fixed carbon contents of the raw biofuel. These values are essential ascertain moisture, volatile matter and fixed
carbon affect both on the combustion behaviour and the plant design. In that way, high moisture values decrease the combustion yield, while high volatile matter/fixed carbon ratios are related with the fuel’s reactivity. On the other hand, ash deeply influences the transport, handling and management costs of the process. It is also influential in corrosion and slag formation. Traditionally, these measures are developed following different national and international normative, such as ASTM E-871 for moisture, ASTM E-830, D-1102 or UNE-EN 14775 for ash or ASTM E-872 and ASTM E-1755 for volatile matter determination (Demirbas, 2004; Khalil et al., 2008). Fixed carbon is usually determined by difference. All those methods are time-consuming and tedious (Karatepe and Kücükbayrak, 1993), and the success of the operation is highly influenced by the operator’s skills. In that way, a fast, simple, reliable and highly accurate method for routine tests, would be desirable. Since all the above mentioned standards basically establish heating a sample under different specific conditions (Mayoral et al., 2001), and a weight difference determination, then a thermobalance and a conveniently adapted thermogravimetric study appears as an effective tool (Warne, 1991), resulting in both time-saving, from several hours to just a few minutes in each experiment (Sadek and Herrell, 1984) and sample quantity reduction, as it requires matter weight in the range of milligrams (Beamish, 1994). As is well known, thermal analysis can be defined as a group of methods for measuring the property of a substance when subjected to a controlled temperature program. It is a highly developed technique with many different uses when applied to biomass. Some authors have used it to study the thermal behaviour of biomass and fuel blends in both oxidative and inactive atmospheres (Ghetti et al., 1996; Varol et al., 2010; Wilson et al., 2010). Others used this technique as a tool to thermally characterize fuels and ashes, by studying its melting behaviour or structural changes (Biswas et al., 2011; Miranda et al.,
2011, Ross et al., 2008) while others used TG as a tool to develop kinetic modelization (Ramajo-Escalera et al., 2006; Wilson et al., 2011). However, but not so many works have been developed for biomass proximate analysis, whilst, on the other hand, thermogravimetry is often used in that way for coals (Ottaway, 1982; Slaghuis and Raijmakers, 2004). In this context, the aim of this work is to determinate if TG analysis can be used as an effective tool to approach ultimate analysis data of biomass fuels and propose a method, based on modified-coal methods to reliably and accurately obtain these data.
2. Materials and methods. Firstly, thirteen biomass samples were grinded and sieved to 500 µm to guarantee its homogeneity in a proximate analysis routine by using ASTM standards E 871, E 1755 and E872; for moisture, ash and volatile matter respectively. In addition to this, fixed carbon content was calculated by difference using the balance:
% FC = 100 − (% Ash + %VM )
[1]
where %FC, %Ash and %VM respectively mean the mass percentages of fixed carbon, ash and volatile matter of the raw sample. All these procedures were detailed and referenced in our previous works (García et al., 2012). After this routine every sample was tested using a Perkin Elmer STA 6000 thermobalance, using 10 and 20 mg of sample, and 40 ml/min flow of flue gas, for both nitrogen and air. Samples were selected all around Spain trying to track every possible biomass origin, commercial fuels, agri-food industry wastes, forest wastes, energy crops and cereals (Fernández et al., 2012) with the aim of obtaining a general method, suitable for a wide range of biomass fuels, with different characteristics and compositions.
The bibliographical review search for methods developed for coals showed two basic groups, some simpler ones, which consist in a chosen continuous temperature ramp path between room temperature and the final set-point (TR1 and SP1, as detailed in table 1). In this group we may include works proposed by Mayoral et al., 2001(MAY3), Sadek and Herrell, 1984) (SDK1) or (Lapuerta et al., 2004)(LAP). On the other hand, there are some other proceedings consisting in continuous heating (TR1, 2 or 3 depending on the number of this steps) combined with intermediate dwellings (DT1, 2 and 3) to reach different intermediate or final set-point (SP1, 2 or 3). In this group we can include some other Mayoral proposed methods (MAY1, MAY2 and MAY4 ), Ottaway–(OTT)– (Ottaway, 1982), Sadek and Herrel (SDK2), Karatepe and Kücükbayrak, 1993(KAR), Warne (WAR) (Warne, 1991), and Beamish (BEA) (Beamish, 1994). Table 1 summarizes the conditions required in each of these works, with each temperature ramp (TR), intermediate and final setpoints (SP) and dwelling times (DT) required when each set-point is reached. Most of them are conducted in inactive atmosphere (using argon, helium or nitrogen depending on the author) to measure moisture, volatile matter and fixed carbon and with a final combustion time in oxidative environment (oxygen or air), when set-point is reached, to measure ashes. On the other hand, Lapuerta’s work is completely carried out in oxidative atmosphere. Some of these works explain how relevant data must be obtained from Thermogravimetry. TG diagrams commonly present a number of slopes, each kinetics showing different phase changes. A first, normally, small one represents moisture release due to drying, occuring at a temperature under 150ºC (Zheng and Kozinski, 2000). In the range between 200 and 900 ºC, a huge mass loss is seen. In the context of different phenomena that can be observed. Thus, between 200 and 600 ºC the biggest mass loss occurs due to the release of CO2 and CH4, these gases coming from hemicelluloses (190-320 ºC), cellulose (280-400 ºC) and
lignin (320-450 ºC) decomposition (Strezov, 2004), and a later chemically bonded CO2 and chemically formed water release (450-600 ºC). Finally, from 600 to proximately 900 ºC mass loss rate decreases, due to the evolution of carbon-containing species (COx, CxHy and tars) and char oxidation until constant weight is reached (Haykiri-Açma, 2003). Five methods originally developed, and described in the literature, for coals and cokes proximate analysis determination, were tested. In this work once observed which of them presents the best results, they were slightly adapted to biomass samples, aiming to reduce the experimental time without impoverishing the obtained results. The tested methods were LAP, MAY1, SDK and KAR. In addition to this a variation of OTT, called OTT* consisting in a second dwelling of five minutes at 550ºC, was used. Obtained results are compared with proximate analaysis data determined using international standards, provided in Table 2 (García et al., 2012), experimental errors are calculated and average absolute error values shown in Figure 1. The error criteria used are the average experimental error (AEE) and the average bias error (ABE) commonly used by several authors in this field (Ahmaruzzaman, 2008; Majumder et al., 2008). In addition to this, the average absolute deviation (AAD) is also used. Those criteria are defined as follows: [2]
[3] [4] As can be seen in figure 1, the KAR method presents the best results for both moisture and volatile matter determination, and so, because of this will be considered as a base
method to develop future variations, trying to improve accuracy in both of this parameters and ash and fixed carbon contents.
3. Results and discussion
Once KAR’s method has been chosen and carried out as the base one, some changes are developed therein, aiming to obtain a more suitable method for biomass proximate analysis data determination. In that way, biomass is reportedly far more reactive than coal, and so, faster heating ramps can be used without losing complete conversion during combustion. In that way, some new methods are tested, proving that the best results were found when beginning with a heating ramp of 50ºC/min, from room temperature until obtaining an isothermal 120ºC for 3 minutes, then a new 100ºC/min heating ramp is programmed until 950ºC.When this point is reached, a cooling process with -100ºC/min ramp starts until reaching 450ºC. Until this set-point, the process is developed using nitrogen, to guarantee a non oxidative environment, but, when 450ºC is reached, flue gas is changed for air. Then, a new 100ºC/min heating ramp begins until 800ºC, which provides better results than previous methods, and is isothermally maintained for 3 minutes, when the program is finished, totalling 25 minutes per experiment, enabling two experiments per hour, including cooling and stabilization of the experimental equipment, which entails a great time saving compared to several hours taken for the moisture and ash determination. This method is also more convenient than ASTM standard, and so, volatile matter determination implies working with a furnace at 950ºC, involving physical risk for the operator when introducing and withdrawing samples. This proposed method requires direct measure of moisture, volatile matter, fixed carbon and ash by difference from slope to slope. Volatile matter
and fixed carbon values are thus obtained by applying Beamish’s correction which means adding values obtained with the next formulae to the measured value. [5] [6] where VM, FC, VMTG and FCTG are, respectively, the obtained volatile matter and fixed carbon and those measured in TG, and M and A, moisture and ash content read on the TG profile. These corrections are proven to slightly improve the results. Finally, results obtained for each of the samples are detailed in Table 3, and the average values are compared (named as NEW MET) with the coal-developed methods in Figure 1. As can be seen, moisture and volatile matter experimental errors are satisfactory, with AEE under 6 %, resulting in AAD of 0.5 and 4.4 points, respectively. On the other hand, fixed carbon and especially ash results highly improve the values obtained using coal-developed methods, offering values close to 10 and 50 %, respectively. Taking into account that fixed carbon determination is obtained empirically by difference, with the entailing precision and accuracy limits, an 11% average error and 2.0 average absolute deviation points, can be considered as quite acceptable result. Regarding ash determination, this involves a problem already referred to by other authors (Mayoral et al., 2001). As can be seen in Table 3, there exists a big difference in measured EE depending on the considered sample. In that way, beetroot pellets or wheat grain present accurate results, with an experimental error close to 5%, while the same error in some other samples such as hazelnut shell or both brands of wood pellets approaches 90 %. As can be observed with the absolute deviation, those values are not high at all; in fact the AAD is just over that of the moisture, presenting a 1.6 % value. However, as expected, ash values are really low in biomass, mainly in woody samples. Low absolute
errors imply really high deviations, in relative bases, like AEE, may be due to the formation of highly specific weight oxides when the oxidative environment is reached. 4. Conclusions
This work proposes a suitable method to determine proximate analysis data of biomass. Obtained results, with average experimental errors under 6 % for moisture and volatile matter and close to 10 % for fixed carbon greatly improve coal-developed methods, for the same tested samples. Ash determination entails average absolute deviation of 1.6 points, but the low expected values imply higher experimental errors. Other important advantages are time saving and simplicity, as this method requires 25 minutes to obtain all four data sets while only moisture or ash determination take several hours each, using the standard normative. ACKNOWLEDGMENTS
PSE-ARFRISOL, Ref. PS-120000-2005-1, is a science-technology project qualified as Strategic by 2004-07 Spanish National Research Plan, Development and Diffusion, cofinanced by European Regional Development Funds and the Spanish Ministry of Science and Education. We wish to acknowledge all members of the PSE-ARFRISOL partnership for their cooperation. We would also like to thank a number of companies such as Pellets Asturias, Factor Verde, Molygrasa, Dibiosur, Enfosur, Acciona, Nutral Arrocerías Dorado, CarsanBio, Parque Verde, Vinos Viñadecanes, Cooperativa Agrícola de Cangas del Narcea, Vino de la Tierra de Cangas, Gebio, Bioenergía Aragonesa, Cafés el Gallego, Biomasas Herrero, Ecowarm and Carbones Lamuño for their generous collaboration in supplying most of the required samples. References Sorted alphabetically
Ahmaruzzaman, M., [34] 2008. Proximate analyses and predicting HHV of chars obtained from cocracking of petroleum vacuum residue with coal, plastics and biomass. Bioresour. Technol. 99, 5043–5050. Bahng, M.K., Mukarakate, C., Robichaud, D.J., Nimlos, M.R., [8] 2009. Current technologies for analysis of biomass thermo-chemical processing: A review. Anal Chim Acta. 651, 117-138. Beamish, B.B., [18] 1994. Proximate analysis of New Zealand and Australian coals by thermogravimetry. New Zealand J. Geol. Geoph. 37, 387-392. Biswas, A.K., Umeki, K., Yang, W., Blasiak, W., [22] 2011. Change of pyrolysis characteristics and structure of woody biomass due to steam explosion pretreatment. Fuel Process. Technol. 92, 1849–1854. Demirbas, A., [12] 2004. Combustion characteristics of different biomass fuels. Prog. Energy Comb. Sci. 30, 219-230. Fernández, R.G., García, C.P., Lavín, A.G., Bueno, J.L., [30] 2012. Study of main combustion characteristics for biomass fuels used in boilers. Fuel Process. Technol. 103, 16–26. García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., [29] 2012. Characterization of Spanish biomass wastes for energy use. Bioresour. Technol. 103, 249-258. Gaska, K., Wandrasz, A.J., [9] 2008. Mathematical modeling of biomass fuels formation process. Waste manag. 28, 973-985. Ghetti, P., Ricca, L., Angelini, L., [19] 1996. Thermal analysis of biomass and corresponding pyrolysis products. Fuel. 75(5), 565–573. Gil, M.V., Oulego, P., Casal, M.D., Pevida, C., Pis, J.J., Rubiera, F., [4] 2010. Mechanical durability and combustion characteristics of pellets from biomass blends. Bioresour. Technol. 101, 8859-8867.
Haykiri-Açma, H., [33] 2003. Combustion characteristics of different biomass materials. Energy Convers. Manag. 44, 155-162. Jorquera, O., Kiperstok, A., Sales, E.A., Embiruçu, M., Ghirardi, M.L., [3] 2010. Comparative energy life-cycle analyses of microalgae biomass production in open ponds and photobioreactors. Bioresour. Technol. 101, 1406-1413. Karatepe, N., Kücükbayrak, S., [14] 1993. Proximate analysis of Turkish lignites by thermogravometry. Thermochim. Acta. 213,147-150. Khalil, R.A., Meszaros, E., Grønli, M.G., Várhegyi, G., Mohai, I., Marosvolgyi, B., Hustad, J.E., [13] 2008. Thermal analysis of energy crops Part I: The applicability of a macro-thermobalance for biomass studies. J. Anal. Appl. Pyrolysis. 81, 52–59. Lapuerta, M., Hernández, J.J., Rodríguez, J., [11] 2004. Kinetics of devolatilisation of forestry wastes from thermogravimetric analysis. Biomass Bioenerg. 27, 385 – 391 Li, Z., Zhao, W., Li, R., Wang, Z., Li, Y., Zhao, G., [6] 2009. Combustion characteristics and NO formation for biomass blends in a 35-ton-per-hour travelling grate utility boiler. Bioresour. Technol. 100, 2278-2283. Majumder, A.K., Jain, R., Banerjee, P., Barnwal, J.P., [35] 2008. Development of a new proximate analysis-based correlation to predict calorific value of coal. Fuel. 87, 3077–3081. Mayoral, M.C., Izquierdo, M.T., Andrés, J.M., Rubio, B., [15] 2001.Different approaches to proximate analysis by thermogravimetry analysis. Thermochim. Acta 370, 91-97. McKendry, P., [1] 2002. Energy production from biomass (Part 1): overview of biomass. Bioresour. Technol. 83, 37-46.
Miranda, M.T., Arranz, J.I., Román, S., Rojas, S., Montero, I., López, M., Cruz, J.A., [23] 2011. Characterization of grape pomace and Pyrenean oak pellets. Fuel Process. Technol. 92, 278–283. Munir, S., Daood, S.S., Nimmo, W., Cunliffe, A.M., Bibbs, B.M., [5] 2009. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour. Technol. 100,1413-1418. Muthuraman, M., Namioka, T., Yoshikawa, K., [2] 2010. A comparison of cocombustion characteristics of coal with wood and hydrothermally treated municipal solid waste. Bioresour. Technol. 101, 2477-2482. Ottaway, M., [27] 1982.Use of thermogravimetry for proximate analysis of coals and cokes. Fuel. 61, 713-716. Qian, F.P., Chyang, C.S., Huang, K.S., Tso, J., [7] 2011. Combustion and NO emission of high nitrogen content biomass in a pilot-scale vortexing fluidized bed combustor. Bioresour. Technol. 102, 1892-1898. Ramajo-Escalera, B., Espina, A., García, J.R., Sosa-Arnao, J.H., Nebra, S.A., [25] 2006. Model-free kinetics applied to sugarcane bagasse combustion. Thermochim. Acta. 448, 111–116. Ross, A.B., Jones, J.M., Kubacki, M.L., Bridgeman, T., [24] 2008. Classification of macroalgae as fuel and its thermochemical behaviour. Bioresour. Technol. 99, 6494– 6504 Rosendahl, L.A., Yin, C., Kaer, S.K., Friborg, K., Overgaard, P., [10] 2007. Physical characterization of biomass fuels prepared for suspension firing in utility boilers. Biomass Bioenerg. 31, 318-325. Sadek, F.S., Herrell, A.Y. [17] 1984. Methods of proximate analysis by thermogravimetry. Thermochim Acta. 81, 297-303.
Slaghuis, J.H., Raijmakers, N., [28] 2004. The use of thermogravimetry in establishing the Fischer tar of a series of South African coal types. Fuel. 83, 533–536. Strezov, V., Moghtaderi, B., Lucas, J.A., [32] 2004. Computational calorimetric investigation of the reactions during thermal conversion of wood biomass Biomass. Bioenerg. 27, 459-465. Varol, M., Atimtay, A.T., Bay, B., Olgun, H., [20] 2010. Investigation of cocombustion characteristics of low quality lignite coals and biomass with thermogravimetry analysis. Thermochim. Acta. 510,195-201. Warne, S.S.J., [16] 1991. Proximate analysis of coal, oil shale, low quality fossil fuels and related materials by thermogravimetry. Trends in Anal. Chem. 10(6), 195199. Wilson, L., John, G.R., Mhilu, C.F., Yang, W., Blasiak, W., [21] 2010. Coffee husks gasification using high temperature air/steam agent. Fuel Process. Technol. 91,1330– 1337. Wilson, L., Yang, W., Blasiak, W., John, G.R., Mhilu, C.F., [26] 2011. Thermal characterization of tropical biomass feedstocks. Energy Conserv. Manag. 52,191-198. Zheng, G., Kozinski, J.A., [31] 2000. Thermal events occurring during the combustion of biomass residue. Fuel. 79,181-192. Sorted numerically
[1] McKendry, P., 2002. Energy production from biomass (Part 1): overview of biomass. Bioresour. Technol. 83, 37-46. [2] Muthuraman, M., Namioka, T., Yoshikawa, K., 2010. A comparison of cocombustion characteristics of coal with wood and hydrothermally treated municipal solid waste. Bioresour. Technol. 101, 2477-2482.
[3] Jorquera, O., Kiperstok, A., Sales, E.A., Embiruçu, M., Ghirardi, M.L., 2010. Comparative energy life-cycle analyses of microalgae biomass production in open ponds and photobioreactors. Bioresour. Technol. 101, 1406-1413. [4] Gil, M.V., Oulego, P., Casal, M.D., Pevida, C., Pis, J.J., Rubiera, F., 2010. Mechanical durability and combustion characteristics of pellets from biomass blends. Bioresour. Technol. 101, 8859-8867. [5] Munir, S., Daood, S.S., Nimmo, W., Cunliffe, A.M., Bibbs, B.M., 2009. Thermal analysis and devolatilization kinetics of cotton stalk, sugar cane bagasse and shea meal under nitrogen and air atmospheres. Bioresour. Technol. 100,1413-1418. [6] Li, Z., Zhao, W., Li, R., Wang, Z., Li, Y., Zhao, G., 2009. Combustion characteristics and NO formation for biomass blends in a 35-ton-per-hour travelling grate utility boiler. Bioresour. Technol. 100, 2278-2283. [7] Qian, F.P., Chyang, C.S., Huang, K.S., Tso, J., 2011. Combustion and NO emission of high nitrogen content biomass in a pilot-scale vortexing fluidized bed combustor. Bioresour. Technol. 102, 1892-1898. [8] Bahng, M.K., Mukarakate, C., Robichaud, D.J., Nimlos, M.R., 2009. Current technologies for analysis of biomass thermo-chemical processing: A review. Anal Chim Acta. 651, 117-138. [9] Gaska, K., Wandrasz, A.J., 2008. Mathematical modeling of biomass fuels formation process. Waste manag. 28, 973-985. [10] Rosendahl, L.A., Yin, C., Kaer, S.K., Friborg, K., Overgaard, P., 2007. Physical characterization of biomass fuels prepared for suspension firing in utility boilers. Biomass Bioenerg. 31, 318-325. [11] Lapuerta, M., Hernández, J.J., Rodríguez, J., 2004. Kinetics of devolatilisation of forestry wastes from thermogravimetric analysis. Biomass Bioenerg. 27, 385 – 391
[12] Demirbas, A., 2004. Combustion characteristics of different biomass fuels. Prog. Energy Comb. Sci. 30, 219-230. [13] Khalil, R.A., Meszaros, E., Grønli, M.G., Várhegyi, G., Mohai, I., Marosvolgyi, B., Hustad, J.E., 2008. Thermal analysis of energy crops Part I: The applicability of a macro-thermobalance for biomass studies. J. Anal. Appl. Pyrolysis. 81, 52–59. [14] Karatepe, N., Kücükbayrak, S., 1993. Proximate analysis of Turkish lignites by thermogravometry. Thermochim. Acta. 213,147-150. [15] Mayoral, M.C., Izquierdo, M.T., Andrés, J.M., Rubio, B., 2001.Different approaches to proximate analysis by thermogravimetry analysis. Thermochim. Acta 370, 91-97. [16] Warne, S.S.J., 1991. Proximate analysis of coal, oil shale, low quality fossil fuels and related materials by thermogravimetry. Trends in Anal. Chem. 10(6), 195199. [17] Sadek, F.S., Herrell, A.Y. 1984. Methods of proximate analysis by thermogravimetry. Thermochim Acta. 81, 297-303. [18] Beamish, B.B., 1994. Proximate analysis of New Zealand and Australian coals by thermogravimetry. New Zealand J. Geol. Geoph. 37, 387-392. [19] Ghetti, P., Ricca, L., Angelini, L., 1996. Thermal analysis of biomass and corresponding pyrolysis products. Fuel. 75(5), 565–573. [20] Varol, M., Atimtay, A.T., Bay, B., Olgun, H., 2010. Investigation of cocombustion characteristics of low quality lignite coals and biomass with thermogravimetry analysis. Thermochim. Acta. 510,195-201.
[21] Wilson, L., John, G.R., Mhilu, C.F., Yang, W., Blasiak, W., 2010. Coffee husks gasification using high temperature air/steam agent. Fuel Process. Technol. 91,1330– 1337. [22] Biswas, A.K., Umeki, K., Yang, W., Blasiak, W., 2011. Change of pyrolysis characteristics and structure of woody biomass due to steam explosion pretreatment. Fuel Process. Technol. 92, 1849–1854. [23] Miranda, M.T., Arranz, J.I., Román, S., Rojas, S., Montero, I., López, M., Cruz, J.A., 2011. Characterization of grape pomace and Pyrenean oak pellets. Fuel Process. Technol. 92, 278–283. [24] Ross, A.B., Jones, J.M., Kubacki, M.L., Bridgeman, T., 2008. Classification of macroalgae as fuel and its thermochemical behaviour. Bioresour. Technol. 99, 6494– 6504 [25] Ramajo-Escalera, B., Espina, A., García, J.R., Sosa-Arnao, J.H., Nebra, S.A., 2006. Model-free kinetics applied to sugarcane bagasse combustion. Thermochim. Acta. 448, 111–116. [26] Wilson, L., Yang, W., Blasiak, W., John, G.R., Mhilu, C.F., 2011. Thermal characterization of tropical biomass feedstocks. Energy Conserv. Manag. 52,191-198. [27] Ottaway, M., 1982.Use of thermogravimetry for proximate analysis of coals and cokes. Fuel. 61, 713-716. [28] Slaghuis, J.H., Raijmakers, N., 2004. The use of thermogravimetry in establishing the Fischer tar of a series of South African coal types. Fuel. 83, 533–536. [29] García, R., Pizarro, C., Lavín, A.G., Bueno, J.L., 2012. Characterization of Spanish biomass wastes for energy use. Bioresour. Technol. 103, 249-258.
[30] Fernández, R.G., García, C.P., Lavín, A.G., Bueno, J.L., 2012. Study of main combustion characteristics for biomass fuels used in boilers. Fuel Process. Technol. 103, 16–26. [31] Zheng, G., Kozinski, J.A., 2000. Thermal events occurring during the combustion of biomass residue. Fuel. 79,181-192. [32] Strezov, V., Moghtaderi, B., Lucas, J.A., 2004. Computational calorimetric investigation of the reactions during thermal conversion of wood biomass Biomass. Bioenerg. 27, 459-465. [33] Haykiri-Açma, H., 2003. Combustion characteristics of different biomass materials. Energy Convers. Manag. 44, 155-162. [34] Ahmaruzzaman, M., 2008. Proximate analyses and predicting HHV of chars obtained from cocracking of petroleum vacuum residue with coal, plastics and biomass. Bioresour. Technol. 99, 5043–5050. [35] Majumder, A.K., Jain, R., Banerjee, P., Barnwal, J.P., 2008. Development of a new proximate analysis-based correlation to predict calorific value of coal. Fuel. 87, 3077–3081.
Figure 1 – Average experimental errors observed for the tested methods.
METHOD MAY 1 MAY 2 MAY 3 MAY 4 OTT SDK 1 SDK 2 KAR WAR BEA LAP
INACTIVE ATMOSPHERE TR1 SP1 DT1 TR2 SP2 DT2 20 105 5 20 900 20 900 5 -25 450 80 1020 10 20 105 3 80 850 250 110 1-5 250 900 1-5 100 1000 3 100 135 2 100 1000 3 20 110 30 40 950 7 100 110 2 100 950 3 50 110 5 50 950 7 TR1 SP1 DT1 TR2 SP2 DT2 -
OXIDATIVE ATMOSPHERE CT TR3 SP3 DT3 20 20 700 1 3 3 -20 750 5 3 42 CT TR1 SP1 DT1 18-108 10-60 1100 -
Table 1 – Summary of the studied analysis methods developed for coals and methods assayed for biomass. Where: TR 1, 2 and 3 are the first, second and third temperature ramps respectively, measured in ºC/min. SP is the set-point for each temperature ramp, measured in ºC and DT is the dwelling time after each ramp, measured in minutes. CT is the combustion time in oxidative atmosphere when set-point is reached.
Sample Apple tree leaves Beetroot pellets Chestnut tree leaves Hazelnut shell Miscanthus Nectarine stone Peach stone Pine and pine apple leaf pellets Pistachio shell Soya grain Wheat grain Wood pellets 1 Wood pellets 2
%M 9.3 12.5 8.2 8.74 7.53 8.2 8.55 8.25 8.75 10.4 10.3 7.96 7.53
%A 12 9 4.9 2.2 9.6 1.1 0.5 3.2 1.3 4.8 2.8 1.3 0.66
% VM 71.9 76 72.41 77 79 76 75.6 74.5 82.5 77 80 82 84
% FC 16.1 15 22.69 20.8 11.4 22.9 23.9 22.3 16.2 18.2 17.2 16.7 15.34
Table 2 – Data obtained for moisture (M), ash (A), volatile matter (VM) and fixed carbon (FC) content of biomass samples using ASTM normative, measured in mass percentage.
Apple tree leaves Beetroot pellets Chestnut tree leaves Hazelnut shell Miscanthus Nectarine stone Peach stone Pine and pine apple leaf pellets Pistachio shell Soya grain Wheat grain Wood pellets 1 Wood pellets 2 Average values
M 13.3 12.1 13.0 14.1 11.8 18.6 19.5 13.0 14.7 19.1 17.0 14.1 13.4
MV 8.3 11.0 8.1 8.5 7.9 7.9 7.9 8.8 8.2 9.4 10.2 7.8 8.0
MOISTURE EE BE 11.0 -11.0 11.8 -11.8 1.4 -1.4 2.5 -2.5 5.5 5.5 4.1 -4.1 7.4 -7.4 6.8 6.8 6.1 -6.1 9.3 -9.3 0.7 -0.7 2.5 -2.5 5.7 5.7 5.8 -3.0
AD 1.0 1.5 0.1 0.2 0.4 0.3 0.6 0.6 0.5 1.0 0.1 0.2 0.4 0.5
MV 6.45 8.43 2.90 0.15 6.37 0.52 0.73 1.81 0.53 2.78 2.95 0.21 0.08
ASH EE BE 46.2 -46.2 6.3 -6.3 40.9 -40.9 93.4 -93.4 33.7 -33.7 53.1 -53.1 46.8 46.8 43.3 -43.3 59.1 -59.1 42.0 -42.0 5.3 5.3 84.0 -84.0 88.3 -88.3 49.4 -41.4
AD 5.5 0.6 2.0 2.1 3.2 0.6 0.2 1.4 0.8 2.0 0.1 1.1 0.6 1.6
VOLATILE MATTER MV EE BE AD 67.3 6.4 -6.4 4.6 62.5 17.8 -17.8 13.5 70.8 2.2 -2.2 1.6 73.5 4.5 -4.5 3.5 69.6 11.8 -11.8 9.4 80.7 6.2 6.2 4.7 81.8 8.1 8.1 6.2 70.5 5.4 -5.4 4.0 79.7 3.4 -3.4 2.8 76.6 0.5 -0.5 0.4 77.6 3.0 -3.0 2.4 79.8 2.7 -2.7 2.2 81.9 2.5 -2.5 2.1 5.7 -3.5 4.4
MV 16.6 10.7 20.2 23.3 13.5 21.1 20.4 21.3 17.5 18.1 13.0 18.4 15.4
FIXED CARBON EE BE 3.1 3.1 28.8 -28.8 10.8 -10.8 11.8 11.8 18.6 18.6 7.7 -7.7 14.7 -14.7 4.6 -4.6 7.8 7.8 0.7 -0.7 24.3 -24.3 10.1 10.1 0.4 0.4 11.0 -3.1
AD 0.5 4.3 2.5 2.5 2.1 1.8 3.5 1.0 1.3 0.1 4.2 1.7 0.1 2.0
Table 3 – Values obtained for each of the chosen samples. Where M is the mass of sample (mg), Mv the measured value (%), EE, BE and AD are respectively experimental error (%), the BIAS error and the absolute deviation for each individual sample. The average values are presented in the last row.
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A method to determinate proximate analysis data in biomass by TG is proposed Coal developed methods are compared and improved for biomass use. Satisfactory results are obtained, main on moisture and volatile matter values.