Energy 109 (2016) 430e435 Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy How inorganic elements of biomass influence char steam gasification kinetics line Hognon a, Capucine Dupont a, *, Sylvain Jacob b, Khalil Ould Marrakchy a, Ce Maguelone Grateau a, Françoise Labalette c, Denilson Da Silva Perez b a b c CEA, LITEN, Laboratory of Preparation of Bioresources, 17 rue des Martyrs, 38054, Grenoble cedex 09, France GIE-Arvalis ONIDOL, Paris, France FCBA, Saint Martin d’H eres, France a r t i c l e i n f o a b s t r a c t Article history: Received 14 November 2015 Received in revised form 12 April 2016 Accepted 22 April 2016 A study was performed to elucidate the influence of biomass type on char steam gasification kinetics Isothermal experiments were carried out in a thermobalance in chemical regime on nineteen biomass chars produced under the same conditions The reactivity obtained varied of a factor of more than thirty This large difference appeared to be correlated with the biomass inorganic elements In particular, the catalytic effect of potassium (K) as well as the inhibiting effect of silicon (Si) and phosphorus (P) were highlighted Three different types of rate evolution versus conversion could be observed depending on the ratio K/(Si þ P) and were correlated with the ash surface composition after gasification Conversion could be satisfactorily predicted versus time through simple models taking into account the influence of inorganic elements and thus further useable in simulation of industrial gasifiers fed by various biomass types © 2016 Elsevier Ltd All rights reserved Keywords: Biomass Steam gasification Char Inorganic elements Kinetics Introduction Due to the energy context, there has been for several years an increasing interest worldwide for heat, power and biofuels production from unused biomass through the advanced process of gasification [7] Since the reaction of char steam gasification is the limiting phenomenon under typical gasification conditions, it controls conversion and the knowledge of its kinetics is therefore crucial to design and control gasifiers, in particular fixed beds, and thus to succeed in process industrial implementation Due to the limited availability of biomass, gasifiers will have to cope with various feedstocks, of different species, coming from different places and hence of variable nature Literature studies have shown that the kinetics of char steam gasification could be very different according to biomass, with a factor of more than twenty for chars prepared in an identical way [11] As stated in a previous paper [4], a simple calculation based on the Arrhenius law shows that even a difference of only a factor of four between two biomasses implies to change the operating * Corresponding author Tel.: þ33 38 78 02 05 E-mail address: capucine_dupont@hotmail.fr (C Dupont) http://dx.doi.org/10.1016/j.energy.2016.04.094 0360-5442/© 2016 Elsevier Ltd All rights reserved reactor temperature of 100 C to achieve the same level of conversion This significant impact on process control highlights the importance of studying the intrinsic kinetics of steam gasification of chars from various biomasses When series of chars are prepared and gasified under identical conditions, the differences of reactivity may only be attributed to differences of morphological structure and of inorganic elements content As highlighted in Di Blasi's review [3], surface area, and consequently morphological structure, seems to be less influential on steam gasification reactivity than content in inorganic elements, and particularly soluble minerals This latter parameter seems therefore to be the most important parameter to consider for understanding the differences of steam gasification behaviour of chars from different biomasses In recent years there have been several studies dealing with the influence of biomass inorganic elements on char steam gasification The majority of these studies was focused on woody biomass, even if some tests were carried out on agricultural biomass by Zhang et al [17] or on microalgae by Hognon et al [6] These studies emphasized the catalytic effect of alkaline (sodium Na, potassium K) and alkaline earth metallic (calcium Ca, magnesium Mg) species [9,10,17,18], on the contrary to silicon Si [4,14,15] and phosphorus P [6] However, the effect of inorganic elements was mostly addressed in a qualitative way Indeed, studies were generally C Dupont et al / Energy 109 (2016) 430e435 Nomenclature k Intrinsic kinetic parameter sÀ1 ki Kinetic constant sÀ1 mi Char mass before gasification kg mf Char mass after gasification kg PH2 O Steam partial pressure Pa r Gasification rate %.minÀ1 or %.sÀ1 rinteg Average reactivity %.minÀ1 SEM-EDXScanning Electron Microscopy coupled with Electronic Dispersive X-ray SRF/SRC Short Rotation Forestry/Coppice t Time s T Temperature K TGA Thermo Gravimetric Analysis wmf% Moisture-free mass percent X conversion based on the comparison of a few different biomass samples, which prevents to draw significant correlations from results Few attempts of quantitative empirical approaches have been made up to now on the influence of inorganic elements on biomass steam gasification Zhang et al [17] added two parameters in the random-pore model [1], as previously made by Struis et al (2002) The originality lay in the correlation of these parameters with the content in K of the twelve biomasses used for the development of the model However, the correlation was not explicitly given and the model performance as well as its physical meaning are therefore difficult to assess In a previous work from our team [4], a model was developed on twenty-one woody biomasses by adding a linear function of ratio K/Si in the shrinking-core model The model obtained gave correct estimation of the average conversion rate under the conditions explored and showed the catalytic effect of K and inhibiting effect of Si, but this approach did not describe possible differences of conversion profiles versus time and the associated phenomena More recently, it has been proposed in a study on five lignocellulosic and algae feedstock [6] that conversion profile would be correlated with ratio K/(Si þ P) Simple zeroth order and first order kinetic models were proposed to fit the two behaviours observed, but the associated parameters could not be linked with inorganic elements due to the limited samples number These different conversion profiles led authors to consider that gasification occurs through parallel reactions This principle was applied to two biomass feedstock e acid-washed or not e by Kajita et al [9] One reaction was said to be non catalytic e following first-order kinetic law e and one to be catalytic e following zeroth order kinetic law e with K as the main catalytic species However, no correlation was proposed to describe this catalytic influence of K Moreover, Si was not measured, which prevented to evaluate the impact of this element Similarly, Umeki et al performed experiments on nine biomasses and made an attempt to model the evolution of catalytic effect of inorganic elements versus conversion during gasification under various conditions through parallel reactions [15] As in Refs [6], the influence of Si on conversion profile was highlighted Nevertheless, according to the authors, model results are promising when CO2 is the gasifying agent but more work is required to catch the trends associated to steam gasification Based on this background, the present work aimed at: characterizing in thermobalance the kinetic behaviour of a large set of biomass types during char steam gasification 431 then using the results to elucidate the role of biomass type on steam gasification kinetics and more specifically correlating the conversion profile observed with inorganic elements contained in biomass finally deriving models i) able to predict conversion versus time from inorganic elements composition and ii) simple enough to be useable in process applications Materials and methods 2.1 Biomass samples Biomass samples were selected among species potentially useable as feedstock in gasification processes [5] They were classified as follows: Wood: beech, angelim, faveira, maçaranduba, mixture Scot pine þ spruce SRF/SRC (Short Rotation Forestry/Coppice): SRF of poplar and eucalyptus, SRC of poplar (numbered and 2) Agricultural biomasses: alfalfa, barley straw, miscanthus, switchgrass, tall fescue, triticale, two wheat straws (numbered and 2) Two samples of microalgae tested in Ref [6] were also used, as potential feedstock for gasification in third generation biorefinery: Chlamydomonas reinhardtii and Arthrospira platensis (Spirulina platensis) Sampling was carried out following standard CENT/TS 14780 The samples properties were measured following the standards on solid biofuels The main results are given in Table It has to be kept in mind that these values should be taken with caution due to the hardly-avoidable uncertainty of properties measurement of heterogeneous biomass resources [8] In agreement with previous studies on biomass characterization [2,5,16], ash amount was found to increase from wood samples, with values between 0.4 and 1.9 wmf%, then to SRF/SRC and perennial crops (miscanthus, switchgrass) samples, with values between 1.1 and 4.6 wmf%, and finally to the other agricultural biomasses samples, with values between 6.8 and 14.7 wmf% Ash content from microalgae was found to be similar to this last family As expected, the dominating inorganic elements were observed to be different among biomasses: Most woody biomasses, including SRC/SRF, as well as alfalfa, mainly contain Ca and K and small amounts of Si; Perennial crops, straws and the woody biomass faveira contain large/very large amounts of Si, then significant amounts of K and Ca; Fescue mainly contains K, and also significant amounts of Si The two microalgae tested are rich in K and Ca while poor in Si like most woody biomasses, but also contain large amounts of Na, Mg and P 2.2 Experimental procedure Most samples were received in the form of millimetre-particles To ensure good representativeness, 30 g of each of these samples was sampled following the standard CENT/TS 14780 and then pyrolysed under N2 atmosphere in a low heating rate furnace (a few C.minÀ1) It was kept at the final temperature of 450 C during h Then the produced char, which counted for about 25%w of the initial biomass, was ground with a mortar and sieved below 50 mm Char was assumed to be ground homogeneously enough to prevent 432 C Dupont et al / Energy 109 (2016) 430e435 Table Ash content and main inorganic elements content in biomass samples Species Si Na K Ca Mg P Al wmf% Wood SRC/SRF Agricultural biomasses Microalgae wmf% Standard used Internal method EN 15290 EN 15290 EN 15290 EN 15290 EN 15290 EN 15290 NF EN 14775 Angelim Beech Faveira Maçaranduba Pine þ spruce SRC False acacia SRC Poplar_1 SRC Poplar_2 SRF Eucalyptus SRF Poplar Alfalfa Barley straw Miscanthus Switchgrass Tall fescue Triticale Wheat straw_1 Wheat straw_2 Chlamydomonas Spiruline 0.0070 0.0115 0.4430 0.0040 0.0120 0.0193 0.0694 0.0326 0.0240 0.1483 0.0510 10.1724 0.4169 0.6589 0.6012 1.1691 1.4000 7.7652 0.0108 0.0000 0.0000 0.0004 0.0000 0.0000 0.0030 0.0000 0.0000 0.0000 0.0300 0.0070 0.0289 0.0147 0.0000 0.0000 0.1510 0.0000 0.0040 0.0108 0.2323 0.6821 0.0210 0.0910 0.0990 0.0990 0.0750 0.2160 0.4680 0.3825 0.1600 0.2700 2.5695 0.7791 0.1670 0.4720 1.9090 0.9250 1.3000 0.8100 0.4335 1.3823 0.1070 0.2516 0.1260 0.1260 0.0670 0.2010 1.1070 1.0167 0.3000 1.3000 0.9694 1.4259 0.1420 0.3140 0.0309 0.2130 0.4300 1.1880 0.2826 0.1830 0.0440 0.0475 0.0550 0.0550 0.0210 0.0230 0.0730 0.0842 0.0350 0.0740 0.1123 0.1176 0.0490 0.0980 0.1610 0.0740 0.0850 0.0972 0.6792 0.2387 0,0010 0.0075 0.0060 0.0060 0.0070 0.0380 0.1130 0.1204 0.0110 0.0570 0.2997 0.2058 0.0160 0.0700 0.1770 0.1270 0.1400 0.2268 2.6842 0.9687 0.002 0.001 0.002 0.002 0.012 0.001 0.005 0.010 0.003 0.074 0.030 0.515 0.003 0.006 0.009 0.020 0.013 0.076 0.004 0.004 1.6 0.6 1.9 0.5 0.4 1.8 2.9 3.6 1.1 4.6 8.0 14.7 3.3 3.4 6.8 8.5 7.4 10.8 10.6 8.8 any segregation when sieving Char gasification with steam took place in a TGA (Thermo Gravimetric Analysis) device operating at atmospheric pressure (SETARAM Setsys coupled with steam generator Wetsys) mg of sample was placed in the crucible of the thermobalance This crucible was a cylinder of 2.5 mm height and mm diameter The sample was heated at a rate of 24 C minÀ1 to the gasification temperature under a N2 gas flow of 0,05 L minÀ1 to end pyrolysis After the gasification temperature was reached and no mass loss was observed, the gas was switched to a mixture of H2O/N2 (20 vol%) with the same total flow rate and gasification occurred Microalgae samples were supplied in too small amounts to follow this procedure Thus for these samples, the first stage of the procedure was modified: 18 mg of sample ground below 200 mm were put in the TGA crucible and then directly pyrolysed in TGA at 450 C during h (as gasification results were shown to be the same for pyrolysis duration of or h) The remaining product followed the same procedure as the other samples: it was heated under N2 up to 800 C until no mass low was observed and then steam was injected for gasification Tests were performed on one reference sample to check that this modified procedure did not change the gasification behaviour Based on the measurements of mass loss versus time, the gasification rate r could be derived and expressed by the variation of the conversion X versus time: r¼ dX dt mi À mðtÞ mi À mf Z rinteg ¼ tX tX rðtÞdt À XðtÞ tX2 À tX1 (3) Preliminary experiments and calculations have shown that under the operating conditions (T ¼ 800 C; particle size (family 1) For biomass samples with ratio K/(Si þ P) > 1, a constant rate is observed until about conversion of 80% This trend corresponds to a constant concentration in active sites and can thus be described by a constant function of surface equal to Conversion is then simply given by: X ¼ k1 t (5) where X is conversion, t time and k1 a kinetic constant The law used is of zeroth order relatively to reacting solid This is characteristic of a catalytic reaction, whose kinetics is a function of catalyst concentration [9] This is the case when the catalyst is highly dispersed, as K seems to be according to SEM-EDX observations The rate is mainly determined by the reactions between catalyst and carbon Under the conditions explored, the steam gasification kinetics of these biomasses rich in potassium in comparison with silicon and phosphorus would thus be determined by the concentration of some inorganic elements in char This seems to be confirmed by the positive correlation observed by the kinetic parameter k1 and the potassium concentration (see Fig 4) Such result implies that it may be possible to predict steam gasification kinetics of biomasses rich in potassium in comparison with silicon and phosphorus simply through the knowledge of their potassium content As shown in Fig with the example of beech, conversion can be described in a very good way by this simple kinetic model up to about 80% of conversion However, above this value, the description of the increase of rate observed experimentally would require the addition of a complex function In a process viewpoint, this is not necessary since a satisfactory approximation of the time required for char conversion versus the concentration of inorganic elements in biomass is obtained from this simple model, with less than 15% of difference Fig Comparison at 800 C between experimental results and model predictions for an example of biomass with K/(Si þ P) above (beech); K/(Si þ P) lower than (miscanthus) and K/(Si þ P) slightly lower than (wheat straw) 4.2 Biomasses with K/(Si þ P) < (family and family 3) For biomass samples with ratio K/(Si þ P) < 1, the decrease of rate observed along conversion can be associated to a decrease in active sites concentration and thus be described by the following function of surface (Equation (6)) This function of surface corresponds to that of the volumetric model, of first order, already used by previous researchers to describe gasification reaction [12] FðXÞ ¼ À X (6) Conversion is then expressed by: X ¼ À expðÀk2 tÞ (7) where X is conversion, t time and k2 a kinetic constant As can be seen in Fig 5, the agreement is very good between experimental results and model predictions on an example of biomass from family 2, i.e miscanthus Thus the model chosen enables to describe the trend observed experimentally As mentioned above, the biomass samples whose ratio K/(Si þ P) is significantly lower than have very similar kinetics despite C Dupont et al / Energy 109 (2016) 430e435 different contents in inorganic elements This would imply that for these biomasses, the gasification mechanism would not be catalytic, due to inactivation of potassium by silicon or phosphorus In a process viewpoint, such result has major consequences: it implies that one kinetic law with one value of the kinetic constant k2 is sufficient to describe the conversion of all these feedstocks As can be seen in Fig 5, the agreement between experimental results and model predictions on the example of biomass from family 3, i.e on wheat straw, is not as correct as on the other families This observation seems logical as these samples have ratio K/(Si þ P) close to and therefore an intermediate behaviour between family and family Thus, the model of order 1, which is suitable with family 2, catches the decreasing trend observed at reaction end for family 3whilethe zeroth order model, which is suitable with family 1, can be at least as efficient to describe the global trend along reaction and to estimate conversion time Conclusion The steam gasification experiments carried out in thermobalance on various biomass chars have confirmed the clear influence of feedstock on reaction kinetics Major differences could be observed both in terms of average reactivity and conversion profile versus time, in relation with the inorganic species contained in biomass In particular, potassium seems to act as a catalyst, while silicon and phosphorus would inhibit reaction by capturing potassium More precisely, when potassium is contained in biomass in sufficient amount not to be significantly captured by silicon or phosphorus, the reaction seems to occur following a catalytic mechanism and the kinetics can be determined by potassium amount On the contrary, in biomasses rich in silicon or phosphorus, gasification seems to occur following a non-catalytic mechanism In a process viewpoint, these results highlight the importance of the knowledge of the ratio K/(Si þ P) to predict steam gasification kinetics of biomass chars, with several situations: When biomasses are rich in potassium in comparison with silicon and phosphorus, a law of zeroth order enables to give a satisfactory approximation of time conversion, with a kinetic parameter correlated with potassium content When biomasses are very rich in silicon or phosphorus in comparison with potassium, they seem to follow the same kinetics, which is correctly described by a first order law Lastly, when the content of potassium is close to those of silicon and phosphorus, the situation seems intermediate, and time conversion appears to be satisfactorily estimated both by a zeroth order law and a first order law 435 Acknowledgements The authors gratefully acknowledge the financial assistance of the French National Research Agency in the frame of the AMAZON project performed under contract ANR 08-BIOE-010 Appendix A Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.energy.2016.04.094 References [1] Bhatia SK, Perlmutter DD A random pore model for fluid-solid reactions: I 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