Considering the comprehensive structural composition of biomass, which is related to its origin and growth conditions, the multicomponent kinetic modelling approach was developed in order to cover all issues related to composition analysis [26]. Commonly the pyrolysis process has been simulated by a scheme consisting of several parallel reactions [27]. The exact number of considered reactions during kinetic modelling depends of type, origin and overall structure of the tested biomass sample [28]. According to already presented methodology the multicomponent kinetic modelling was applied for apricot kernel shell pyrolysis in order to identify the constitutive Pseudo-Components (PSCs) which could be of interest during the conversion process of the tested biomass samples [29].

The brief summary of the complete methodology used in this paper is shown in Figure 1, and is presented in detail in the literature [26].

Figure 1.

Methodology flowchart used in this study [pseudo-component (PS), number of runs (P)] [26]

Application of the selected methodology together with peak to peak analysis approach could also provide calculation of kinetic parameters of each biomass PSC as well as pseudo-component fraction (X_i) at considered heating rate, which could be used for composition analysis of the selected material [30].

The simultaneous Thermogravimetric Analysis (TGA) – Derivative Thermogravimetry (DTG) curves in an argon atmosphere at heating rates of β = 5, 10, 15 and 20 °C min^-1 are shown on Figure 2a-d.

At all observed heating rates, after the mass loss (TG-%) up to a temperature of approximately 120 °C which can be attributed to removing of moisture (in the range of 3.00-3.41% and this amount of mass loss was recorded due to loss of water present in the sample and external water bound by surface tension), the non-isothermal decomposition of apricot kernel shell occurs through four successive processes accompanied by designated mass losses in Figure 2a-d. Then in the temperature range from 120 °C to approximately 150 °C, the shells do not undergo any transformations that change their mass.

Figure 2.

Simultaneous TGA-DTG curves in an argon atmosphere at β = 5, 10, 15 and 20 °C min^-1 for apricot kernel shell pyrolysis (a-d)

Above the indicated temperature (150 °C), the thermal stability of apricot kernel shells gradually decreases, and decomposition takes place (Figure 2a-d). The decomposition begins with extracting the volatile products that respectively accompanied by mass reduction. The total mass loss completes at about 850 °C, with residual mass loss in the range of 24.25-24.93% considering all heating rates. Since the apricot kernel shells represent the lignocellulosic material, the devolatilization process includes the decomposition reactions of the basic structural components of lignocellulosic biomass. The first two stages include decomposition of structural polymers, mainly hemicelluloses and cellulose [10] in the temperature range of 250-360 °C with about 56.28% in the largest mass loss of the sample (Figure 2a-d). The third stage can be attributed to the slow decomposition of aromatic polymer (lignin), which decomposes in the widest temperature range [35]. In a general manner, in accordance with results reported by Gasparovič et al. [36], the thermal decomposition of lignocellulosic material takes place in three main stages: evaporation of moisture and light hydrocarbons, active and passive pyrolysis. The decomposition of hemicelluloses and cellulose takes place through active pyrolysis in the temperature ranges of 160-305 °C and 305-370 °C, with temperatures at the maximum rate of mass losses as T_p1 = 285.7 °C and T_p2 = 355.9 °C (considering the values attached to β = 10 °C min^-1, where “1” and “2” are attributed to hemicelluloses and cellulose, respectively) (Figure 2). The lignin decomposes in both stages, in active and passive pyrolysis zones, in the range of 160-900 °C, without a clearly expressed characteristic DTG peak (Figure 2a-d). The pyrolyzable fraction of this pseudo-component starts its devolatilization at the lowest temperature, but it is decomposed in a wide temperature range. The lignin decomposition is hindered by decomposition of hemicelluloses and cellulose, excepting at high temperature (especially in the temperature range of 380-480 °C, Figure 2a-d). These issues are clearly evidence for existence of multi-step process, which is typical for behaviour of complex reactions, involving the multiple, parallel, and consecutive processes during the decomposition of lignocellulosic biomass under inert atmosphere [37]. The effect of increase in heating rate can be observed in the DTG curves of studied biomass sample, during pyrolysis process. In actual case, the increase resulted in an enlargement of the curves resulting in the shifts in the size of the DTG curves, and changes in the main temperature profile characteristics for apricot kernel shell: Tonset, Tp (inflection) and Tend (Figure 1).

It can be seen that pyrolyzable fraction of hemicelluloses has much lower thermal stability than celluloses pyrolyzable fraction. In addition, the char fractions decompose at the higher temperatures (above 500 °C), with the hemicelluloses non-pyrolyzable fraction being the one that is burnt at lower temperatures, followed by the cellulose char. Lignin char was decomposed at the highest temperature and its yield may be very high. However, in this case, the analysis is especially difficult due to the formation of complex phenolic species during lignin decomposition. This behaviour has been previously reported by other authors [38]. In addition, the heating rate affects the rate of volatile evolution from biomass sample. It can be observed from DTG curves (Figure 2) that higher heating rate above 300 °C promotes rapid evolution of volatiles. The molecular disruption is extremely fast and volatile fragments are released so rapidly that successive adjustments and the equilibrium leading to further primary reactions that yield char have less opportunity to take place. As the heat rate increases, the amount of volatiles released increases.

The DTA curves of apricot kernel shell pyrolysis at the various heating rates (β_j, j = 1, 2, 3, 4) and at one (selected) specific heating rate value (β = 10 °C min^-1) are shown in Figure 3a-b.

According to Figure 3a-b, the initial drop over 40 °C to 120 °C (Figure 3b) is due to moisture evaporation, which is an endothermic reaction.

Figure 3.

The DTA curves of apricot kernel shell pyrolysis at the various heating rates (a) and the DTA curve at one selected heating rate value (10 °C min^-1) (b)

In the second phase, in the temperature range of 250-390/400 °C (Figure 3a-b), a broad exothermic DTA peak can be observed at all heating rates (where the largest mass loss of the sample occurs) and this can be attributed to the thermal decomposition of hemicelluloses and cellulose. The next exothermic peak can be observed in the temperature range of 580-680 °C, where its strict position depends on the applied heating rate (Figure 3a-b). This exothermic peak corresponds to the oxidation (by the oxygen from biomass material) of the complex, aromatic structure of the lignin. So, in the indicated temperature range, the decomposition and evaporation of pyrolyzable matter from lignin can be expected.

At the temperature of approximately 850 °C, the char was produced from each pyrolysis process monitoring, and produced char generally contains two entities. These entities are fixed carbon and ash. The summation of the fixed carbon and ash contents obtained for the apricot kernel shells (Table 1) is in very good agreement with the char yield that was obtained from every TG measurement (Figure 2a-d) (~ 16.45%). The values of char yield obtained at various operating temperatures and residence times can be obtained from the relation: Char yield $[\%]=[(m_f^{*})/(m_i)]\times 100$, where $m_f^{*}$ is the mass at desired temperature and time, while m_i is the mass at time t = 0 (min). Figure 4 shows the char yield of apricot kernel shell pyrolysis obtained at the different operating temperatures and different residence times (t₁, t₂, ..., t₈) at the heating rate of β = 10 °C min^-1.

Figure 4.

The char yield of apricot kernel shell pyrolysis obtained at the different operating temperatures and different residence times at the heating rate of 10 °C min^-1

From obtained results, a very high yield of the char was obtained at lowest operating temperature and at shortest residence time [250 °C and t₁ = 21 min. (~ 95.55%)], where with rise in the operating temperature and the prolongation of residence time, the char yields exponentially decrease and reach a minimum value at 600 °C for t₈ = 56 min. (~ 27.83%). High char yields allow greater net CO₂ benefits, but lower electrical output from slow pyrolysis. It can be observed from Figure 4 that a high char yield was obtained in the decomposition stage which occurs between 200 and 350 °C and can be related to the depolymerization reactions during cellulose decomposition. These reactions give rise to the oligomers and anhydrous sugars (levoglucosan, cellobiosan, cellotriosan, glucose, etc.). Above 350 °C, the random fragmentation of glucosidic bonds in cellulose, hemicellulose and their oligomers may generate volatile compounds with higher oxygen content, leaving a carbonaceous residue – the char. At temperatures above 400 °C, carbon monoxide (CO) and CO₂ are released by the depolymerization reactions from lignin-rich aromatic carbonaceous matrix.

The global rate of biomass conversion depends on the chosen heating rate and, since the slow pyrolysis uses low heating rates, the whole process results in heat transfer rate limited biomass conversion. Although, the low heat transfer rates are not so critical for the process considering the long residence times used in the slow pyrolysis [39].

So, in the context of the actual discussion, it can be supposed that the tested biomass may contains a high lignin content which can produce high char yields, given the stability of lignin to the thermal decomposition, once it is preferentially converted to the solid char, instead of the liquids or the vapors. This assumption can also be validated from the shape appearance of the DTG curves in Figure 2a-d.

The two distinct mass loss rate peaks in DTG curves (shown in Figure 1) of apricot kernel shell pyrolysis significantly differentiated with other biomasses which show only one DTG peak (such as pine wood [40], bamboo [41], or corn stover [42]) or accompanied with extra “shoulder” peak, as in the case of wheat straw [43]. This might be caused by the higher content of lignin in the apricot kernel shells. Thus, the high levels of lignin in tested biomass can make the thermal decomposition of apricot kernel shell much slower than other biomasses, and lead to the mass loss of hemicelluloses and cellulose being separated more clearly (Figure 2).

Furthermore, the operating temperature represents the most important factor controlling the properties of resulting char (Figure 4). So, the yield of product decreases as operating temperature increases, as a result of greater thermal decomposition of organic constituents, while high yields obtained at lower operating temperatures are due to partially pyrolyzed biomass (Figure 4).

Namely, it happens due to more severe pyrolysis conditions which lead to an impact on energy distribution between three products of pyrolysis process, such as, gas, liquids, and solid. Namely, with an increasing of operating temperature, there is a tendency for the chemical energy to stay in liquids and gases, rather than in solids. According to Novak et al. [44], the decrease in the char yield with an increase in operating temperature is due to the dehydration of hydroxyl groups and thermal decomposition of cellulose and lignin structures with a great yield reduction which occurs above 400 °C (Figure 4). Therefore, the char obtained from lower operating temperatures, are characterized by higher volatile matter content with an easy decomposable material constituent [45], and, therefore, the amount of volatile matter in the char is supposed to be the highest with lowest operating temperature used in experiments [46].

Based on the estimated results for thermal conversion of apricot kernel shells, in order to obtain a high char yield, low temperature and as low heating rates as possible with long gas residence times are required.

To evaluate the variation of the apparent activation energy on the conversion degree during the major pyrolysis process, the forty-one levels of conversion α, varying from α = 0.10 (10%) to α = 0.90 (90%) with an increment of Δα = 0.02 (2%) employed at different heating rates (5, 10, 15 and 20 °C min^-1) were used in the calculations.

Based on the applied isoconversional methods of the FR, KAS and OFW, in order to make a more accurate judgments about suitability and quality of fitting between given isoconversional methods, student’s test and D-W statistics were used in error analysis. Table 2 shows overall results of statistical analysis of regression lines obtained from FR, OFW, and KAS isoconversional methods, which are applied in kinetic analysis of pyrolysis.

D-W statistics tests the null hypothesis that the residuals from an ordinary least-squares regression are not auto-correlated against alternative that the residuals follow an AR(1) process [47]. From obtained D-W factor for FR method, the D-W factor towards 4.000 that indicates negative auto-correlation, while for OFW and KAS methods, the D-W factor is undefined (NaN) (Table 2).

In the case of FR method, the D-W factor suggests on the successive error terms which are negatively correlated. In regressions, this can imply an underestimation of the level of statistical significance. It can be noted that in a manner of a rule of thumb is that test statistic values in the range of 1.500 to 2.5000 are relatively normal. So, based on the established D-W factor for FR method, which is outside of this range, the results obtained from FR’s should be taken with great care [an indication associated with this fact is also a poor value for R² (R² = 0.98740) (Table 2)]. However, the D-W value close to zero, indicates that autocorrelation exists and supports the p-value tests, with high sum of residual squares.

On the other hand, the other two methods (OFW and KAS methods) are characterized by overall super-values of R² (1.00000), with no sum of residual squares and mean residuals. From a statistical point of view, this means that the results obtained by applying these methods are exactly the same. This also indicates that there is no spread at all in obtained results data, and also, it can be extracted only one (single) value with a high probability which would constitute the mean value from a whole data sample. Distribution of the E_a values in a function of α, estimated by the FR, KAS and OFW methods for apricot kernel shell pyrolysis was presented in Figure 5a-c.

Effective activation energy estimated from the KAS and OFW methods showed excellent agreement with each other, which was confirmed by the results given in Table 2. However, there are some differences in the obtained E_a values calculated from the KAS and OFW methods, with those calculated from the FR method.

This can be clearly seen from the calculated mean values of effective activation energy, which are as follows: E_a,mean^FR [kJ mol^-1] = 229.68, E_a,mean^KAS [kJ mol^-1] = 221.42, and E_a,mean^OFW [kj mol^-1] = 220.02, respectively. It can be seen that E_a,mean^FR ≠ E_a,mean^KAS ≈ E_a,mean^OFW, so that actual difference in E_a,mean values between differential and integral isoconversional methods suggests on the presence of reaction complexity of the process under study, which can also be seen from the shape of E_a-α dependency (Figure 5). However, consistency of results from both integral methods, and measured TGA curves from multi-heating rate scanning, validated accuracy and reliability of estimated effective activation energy.

Figure 5.

Variation of E_a with respect to conversion degree (α) estimated by the FR, KAS and OFW, respectively, for the apricot kernel shell pyrolysis (a-c)

The value of E_a was affected by several factors, such as different kinetic models, heating rate, species of the biomass, the particle size, and different types of the used TGA (modulated, quasistatic, etc.). Thus, the obtained effective activation energy of apricot kernel shell pyrolysis was only valid for this kind of the experimental parameters described in sub-sections 2.1, and 2.3.

It can be observed from Figure 6 that certain fluctuations of E_a-α dependence exist, and this revealed that apricot kernel shell pyrolysis was a complex process [48], which may include parallel, competitive and consecutive reaction pathways [49].

The first process area, from α = 0.10 to α = 0.40 (Figure 5b and 5c), the E_a value increases from 168.0 to 235.0 kJ mol^-1 for the KAS method, and from 168.0 to 232.0 kJ mol^-1 for the OFW method, as conversion degree increased, and this could be mainly attributed to hemicelluloses decomposition. In the initial stage, the decomposition started easily on weakly linked sites inherent to the polymeric lineal chain of the hemicelluloses, which led to the lower effective activation energy. After the weaker bonds broke, the random scission on the lineal chain can be expected, which may cause the increase in E_a. Meanwhile, it can be expected that there is an interaction between hemicelluloses and lignin [50], where the obtained value of E_a in the actual stage is much higher than the value for the single component of xylan (represents the hemicelluloses) usually analyzed in pyrolysis process (87.65 kJ mol^-1 and 69.39 kJ mol^-1) [51].

The second process area, which encompass 0.40 < α < 0.80, shows that there are some stabilized values in E_a, observing all three isoconversional methods (Figure 5a and Figure 5c), with average values of $E_{\text{a}}^{\text{avg}}(\text{KAS})=240.0\text{kJ}{\text{mol}}^{-1}$ and $E_{\text{a}}^{\text{avg}}(\text{OFW})=237.0\text{kJ}{\text{mol}}^{-1}$. The indicated conversion range falls in the experimental temperature range of ΔT = 310-360 °C, and this area was just located between two mass loss rate peaks in DTG curves (Figure 2a and Figure 2d). Since that we assumed a presence of high lignin content in the tested biomass, this issue may leads to a tighter cross-link of three pseudo-components in the apricot kernel shell. On the other hand, for cellulose decomposition in this reaction stage, the cellulose initially pyrolyzed to active cellulose according to Briodo-Shafizadeh extended model, and this can lead to reducing of the degree of polymerization and the length of molecule chain. The active cellulose represents the intermediate product before further pyrolysis. This fact was validated from a certain increase of E_a value during transition from the first to the second reaction stage. In considered α range (0.40 < α < 0.80), the active cellulose continues to decompose with approximately constant effective activation energy. This decomposition process may eventually split into two parallel and competitive reaction pathways, one which produces char favored at lower temperatures, and the other that produces tar and gas occurring prior to higher temperatures [52]. The obtained value of E_a for this stage is higher than the single (or pure) component of cellulose (119.21 kJ mol^-1 [50] and 150-175 kJ mol^-1 [53]).

Figure 6.

The experimental and model pyrolysis rate curves for 4 pseudo-components model (PSC-1, PSC-2, PSC-3 and PSC-4) at different heating rates (a-d)

In the final process area, for α > 0.80 (Figure 5), the significant variation in E_a value with decreased trends can be observed. In this area, the E_a value drops at 158.0 kJ mol^-1 (for KAS method) and at 161.8 kJ mol^-1 (for OFW method), and actual behavior can be related to lignin decomposition. Since that lignin is mainly composed of three kinds of benzene-propane, which was heavily cross-linked, these facts cause it to have a very high thermal stability. In the later stage of pyrolysis process, within the high temperature zone, part of three-dimensional network structure broke, which requires a much higher E_a values. A large amount of solid product with lower reaction activities may be formed in this process area, which can lead to increased E_a’s. This fact explains why the obtained E_a value in an actual stage (see above) is much higher than E_a value for the pure alkali lignin (70.68 kJ mol^-1 [50]).

It can be noted that the identified variation of E_a with α in the conversion degrees range of 0.10 ≤ α ≤ 0.80 was typical for biomass feedstock with a high content of lignin with application of KAS and OFW methods, as is the case with apricot kernel shells (the apricot kernel shell lignocellulosic content reported by Demirbas [54]: cellulose = 22.4%, hemicelluloses = 20.8%, and lignin = 51.4%). Consequently, the interaction of biomass pseudo-components with various contents and structures may leads to distribution variations in E_a values.

Using the algorithm described in the methodology section, the simulation was performed between 3 and 5 pseudo-components, but the best fit quality results show the data related to four pseudo-components model. Also, the proposed kinetic model includes composition analysis with already presented methodology [30].

Simulations for four pseudo-components (designated by PSC-1, PSC-2, PSC-3 and PSC-4) with corresponding model fit parameters at different heating rates, together with overall model values are presented in Table 3 and Figure 6a-d. Optimization strategy includes the use of Akaike Information Criterion (AIC) for statistical identification of the number of distinct processes and this criterion has already been included in the algorithm described in sub-section 3.1. Among all pseudo-component models (with three, four and five ‒ components), the four pseudo-component model showed the lowest values of statistical parameters and modeling quality parameters, in the case of the best assessment of model accuracy. Therefore, the lowest Quality of Fit (QOF) values and extremely low values of AIC for maximum number of pseudo-components which were used without disturbing the fit quality at all heating rates were obtained for 4-pseudo-component model (Table 3).

In accordance with necessary and sufficient conditions for the minimum number of processes taking place during pyrolysis, the AIC values (Table 3) clearly show that (excluding dehydration process) there are four unique processes which occur in the decomposition of apricot kernel shell, and these correspond to cellulose (PSC-1), hemicelluloses (PSC-2), lignin (PSC-3) decompositions, as well as the char combustion (PSC-4) (oxidation) process, respectively.

Within active pyrolysis region, the decomposition of PSC-1 and PSC-2 occurs (cellulose and hemicelluloses) in the temperature range of 150-400 °C (Figure 6a-d). On the other hand, the decomposition of PSC-3 which corresponds to lignin takes place in both, active and passive pyrolysis regions (Figure 6a-d), indicating a very broad decomposition temperature range. In addition, PSC-4 processing occurs only in the passive pyrolysis region (Figure 6a-d).

Considering results presented in Table 3, it can be observed that contribution φ of PSC-1, PSC-2 and PSC-3 in the overall apricot kernel shell pyrolysis at all heating rates is approximately identical, while the contribution of PSC-4 is significantly lower. Generally, in biomass lignocellulose breakdown, the identified PSC-1, PSC-2 and PSC-3 pseudo-components which correspond to the cellulose, hemicelluloses and lignin, respectively, are within active pyrolysis and passive pyrolysis regions.

Observing different heating rates, and taking the average values of the apparent activation energy for the model, the E_a follows an order: PSC-3 (pseudo-lignin) (55.2 kJ mol^-1) < PSC-2 (pseudo-hemicelluloses) (119.9 kJ mol^-1) < PSC-1 (pseudo-cellulose) (123.1 kJ mol^-1). Since that E_a represents the minimum energy required to start a reaction, the lowest value of E_a for PSC-3 (pseudo-lignin) indicates that this pseudo-component decomposes easier than the other two pseudo-components. It should be noted that thermal decomposition of lignin is generally influenced by the heat and mass transfer processes, which significantly affect the apparent activation energy and pre-exponential factor values. The obtained values of E_a for PSC-3 that correspond to lignin decomposition (Table 3, with an average value of 55.2 kJ mol^-1) and for average reaction order of n = 1.19 (Table 3) is typical for isolated lignin decomposition [55] (through the first-order or close to the first-order reactions, with an E_a varying from 54.3 kJ mol^-1 to 81.2 kJ mol^-1 covering a very broad temperature range of 200-1,167 °C, with pre-exponential factor A varying from × 10^0.3 to × 10³ min^-1 [56]). The PSC-4 pseudo-component instantly arises from PSC-3 decomposition reaction, since that lignin may decompose through two reaction pathways one of which occurs with the lower E_a (liberating H₂O, CO₂, CO and char producing), while others occur with the higher E_a (producing monomers). The cleavage of the functional groups gives low molecular weight products, while the complete re-arrangement of the lignin molecule backbone at higher temperatures leads to 30-50 wt% of char and to the release of volatile products. The cleavage of the aryl-ether linkages results in the formation of highly reactive and unstable free radicals that may further react through re-arrangement, electron abstraction or radical-radical interactions, to form products with increased stability. The PSC-4 accounts for the enriched product with char compound that obeys oxidation reaction, which characterizes with elevated average E_a value (59.6 kJ mol^-1, Table 3) and with higher average reaction order (n = 1.22, Table 3). This reaction takes place at much higher temperature zone of the apricot kernel shell pyrolysis, where char oxidation may occur when oxygen reaches the char particle surface during devolatilization stage [57]. This can especially be expressed for the biomass feedstock that contains a high oxygen concentration in its composition, as is the case for apricot kernel shells (Table 1). The char oxidation occurs at high temperatures and was followed by highly exothermic effect (typically for oxidative surface reactions) where for apricot kernel shell pyrolysis it can be expected about/or above 600 °C (Figure 2a-d). This process is characterized with a slow rate (Figure 6a-d), so, the completion of char oxidation requires more residence time, than dehydration, and devolatilization processes. This process is also strongly governed by the particle sizes of the used biomass, and the rate is higher for a smaller particles, and similar to devolatilization, the elevated temperature can significantly accelerates the actual process [58].

For PSC-1 and PSC-2 decompositions related to cellulose and hemicelluloses decomposition reactions, the obtained apparent activation energies (Table 3) are lower than those for pure cellulose (between 140.00 and 240.23 kJ mol^-1) [59] and pure hemicelluloses (xylan) (179.84 kJ mol^-1) [60]. This difference may be due to the temperature difference of various reaction stages, which can vary from one to another biomass feedstock used in pyrolysis experiments. Also, the lignocellulose content may affect this behavior, since that lignin could have an impact on the required energy to decompose cellulose and hemicelluloses. These variations can also be seen from the obtained FWHM’s attached to corresponding decomposition rate curves of individual pseudo-components (Table 3), which also shows significant changes in its values. From results presented in Table 3, it can be seen that in both cases, for PSC-1 and PSC-2 decompositions, non-integer reaction orders were obtained. These values are characteristic for the description of random scission mechanisms, where the non-integer reaction orders indicate that the processes are complex and comprise several elementary reaction stages. So, this behavior is not unusual considering that both, cellulose and hemicelluloses degradation includes a series of decomposition reaction pathways which cannot be described by the simple integer reaction-order kinetics, because the complicated reactions related to entire apricot kernel shell pyrolysis. On the other hand, in all considered cases, it can be observed that the kinetic parameters (E_a and A) calculated by the Janković et al. [30], are in good agreement with those derived from the model (Table 3). On the other hand, the pseudo-component fraction X related to each component varies slightly with the change of the heating rate (Table 3), and theoretically calculated pseudo-component fraction values are arranged in the following order: PSC-4 < PSC-3 < PSC-2 < PSC-1. These fractions follow the total area values under the each rate decomposition curve attached to individual PSC (Table 3). This behavior can indicate on contribution of individual components to the overall TGA profile for apricot kernel shell pyrolysis (Figure 2a-d). Namely, from these results, it can be seen that the contribution of cellulose dominates, which is confirmed by the results established through the MS analysis (Figure 4).

The deconvolution of apricot kernel shell through the STA using the multicomponent model, expresses the devolatilization kinetics of tested biomass feedstock, as the individual components. The total E_a of individual reactions should be correlated with devolatilization E_a values obtained from isoconversional (“model-free”) approach. Considering all heating rates, the total E_a value related to three main pseudo-components of biomass (pseudo-cellulose, pseudo-hemicelluloses and pseudo-lignin) is equal to $E_a^{°}=298.12\text{kJ}{\text{mol}}^{-1}$ which is slightly higher than mean values of effective activation energy, obtained by “model-free” methods. However, the observed difference is still within acceptable limit.