Experimental and Kinetic Study of Biochar in N-Absorption Reaction of Chemical Looping Ammonia Generation

Abstract

The study conducted isothermal tests for biochar-based N-absorption reaction in Chemical Looping Ammonia Generation to investigate the factors affecting biochar conversion, the kinetic model, and the reaction mechanism. The results show that the N₂ gas flows had little effect on biochar conversion. Raising the reaction temperature and the molar ratio of α-Al₂O₃ to C enhanced the conversion of biochar. When the N₂ flow rate was set to 200 mL/min, the reaction temperature to 1600 °C, and the α-Al₂O₃/C molar ratio to 3:3, the biochar conversion reached its peak at 95.45%. After evaluating several kinetic models, the D₁ diffusion model was found to provide the closest match to the biochar conversion. The activation energy decreased from 241.91 kJ/mol at a 1:3 α-Al₂O₃/C molar ratio to 146.77 kJ/mol at a 3:3 ratio with an increasing α-Al₂O₃/C molar ratio. The biochar’s high specific surface area and abundant pore structure facilitated a rapid reaction between carbon and oxygen on the carbon surface. Additionally, the diffusion of oxygen produced during the decomposition of α-Al₂O₃ became the limiting factor in the N-absorption reaction.

1. Introduction

Produced annually in quantities of 176 million tons, ammonia is one of the key synthetic chemicals in contemporary industry [1]. It is widely used in fertilizer synthesis and the chemical, food, and pharmaceutical industries [2,3]. Ammonia is an efficient hydrogen carrier, with 17.6% hydrogen content, a volumetric hydrogen-carrying efficiency 1.5 times greater than hydrogen, easy liquefaction at −33 °C, and ease of transport [4,5]. Meanwhile, ammonia is also a potential clean fuel due to the non-polluting production of N₂ and H₂O during complete combustion, with no carbon emissions [6,7]. It is foreseeable that the demand for NH₃ will continue to increase due to increasing population, greater industrial demand, and new uses as an energy carrier.

The Haber–Bosch process produces 90% ammonia from high-purity N₂ and H₂ [8]. The stability of the N≡N bond in N₂ leads to challenging reaction conditions, with temperatures of 350~550 °C and pressures of 10~30 MPa required, along with an Fe-based catalyst [9]. Furthermore, the Haber–Bosch process utilizes hydrogen sourced from fossil fuels and requires stringent reaction conditions to synthesize ammonia. These factors contribute to several major drawbacks, including significant carbon dioxide emissions (ranging between 1.9 and 16.7 tons of CO₂ per ton of NH₃ produced), considerable energy usage (28 to 166 gigajoules per ton of NH₃, which represents approximately 1–2% of the global annual energy output), and relatively low reaction efficiency (10–15%) [9,10]. Therefore, a new ammonia preparation method is needed to cope with the greater demand in the future from the view of environmental protection and the economy.

Galvez et al. introduced the Chemical Looping Ammonia Generation (CLAG) method, which employs aluminum-based nitrogen carriers. This process uses carbon and water as primary inputs and incorporates Al₂O₃/AlN as nitrogen carriers to produce ammonia through alternating N-absorption (R1) and N-desorption (R2) reactions [11]. It has advantages in terms of environmental protection and economy: it reacts at atmospheric pressure, does not require a catalyst, gets rid of the relatively expensive H₂ as a raw material, and has good recyclability and zero CO₂ emissions, thus attracting much attention [12,13,14].

N-absorption reaction:

$${\mathrm{Al}}_2{\mathrm{O}}_3{+3\mathrm{C}+\mathrm{N}}_2\to 2\mathrm{AlN}+3\mathrm{CO} \mathsf{\Delta }{H^0}_{25℃}=708{.1 \mathrm{kJ} \mathrm{mol}}^{-1}$$

(R1)

N-desorption reaction:

$${2\mathrm{AlN}+3\mathrm{H}}_2\mathrm{O}\to {\mathrm{Al}}_2{\mathrm{O}}_3{+2\mathrm{NH}}_3 \mathsf{\Delta }{H^0}_{25℃}=-274{.1 \mathrm{kJ} \mathrm{mol}}^{-1}$$

(R2)

As a consumable in the CLAG, carbon is completely converted to CO in the N-absorption reaction and can be collected and utilized, e.g., to produce methanol [14]. Thus, the added value of carbon is increased. The reactivity and reaction mechanisms of different carbon sources in the N-absorption reaction are variable. Forslund et al. investigated the mechanism of the N-absorption reaction with carbon black and concluded that Al₂O₃ was first converted to Al₂O(g) and then reacted with N₂ to form AlN [15,16]. In contrast, Lefort et al. indicated that Al₂O₃ undergoes an initial conversion into Al(g) and O₂. Subsequently, the produced O₂ reacts with C to generate CO, while Al(g) engages with N₂ to produce AlN, as experimentally proved [17]. Calvez et al. investigated carbon black and petcoke comparatively [18]. They fitted the reaction curves with diffusion and shrinking core models and concluded with carbon black as a diffusion model and petcoke as a shrinking core model. Zhang et al. investigated the carbon black and graphite reactivity and believed that the poor reactivity of graphite was due to the low degree of disorder [19]. Feng et al. utilized coal coke through pyrolysis in the CLAG process and evaluated how different pyrolysis conditions influence its reactivity [20]. Since the presence of SiO₂ within coal hinders the N-absorption reaction [21], it is necessary to use hydrogen fluoride and hydrochloric acid to get rid of SiO₂ in the process of coke preparation from coal.

Biomass has much lower SiO₂ than coal [22,23] and can be used in biochar preparation without removing SiO₂, making it a promising carbon source for applications in CLAG. Furthermore, biomass contains less sulfur than coal and offers benefits such as widespread availability, renewability, and environmental friendliness compared to traditional fossil fuel carbon sources [24,25]. Produced through slow pyrolysis, biochar possesses a more extensive specific surface area and a more developed pore structure compared to petcoke and coal coke, resulting in enhanced reactivity [26,27]. In our previous research, we successfully pyrolyzed biomass into biochar for use in the N-absorption reaction of CLAG. We determined the pyrolysis parameters for producing highly reactive biochar: under a CO₂ atmosphere, with a heating rate of 10 °C/min, a pyrolysis temperature of 700 °C, and a resident time of 30 min [28]. However, when evaluating the reactivity of biochar, the reaction conditions for the N-absorption reaction were fixed. To optimize the reaction conditions for N-absorption reaction, it is essential to investigate how these conditions affect the conversion rate of biochar. Additionally, to quantitatively describe the reaction process of biochar-based N-absorption reaction, kinetic studies are also required. However, comprehensive studies in this area remain limited.

The primary goal of this research was to examine the kinetics of biochar in CLAG’s N-absorption reaction. Because carbon acts as a consumable material, any portion that remains unreacted during N-absorption must undergo a carbon removal step (via water vapor) before proceeding to the N-desorption reaction [18,29], carbon conversion has to be increased from the economic point of view. Furthermore, appropriately enhancing the conversion rate of carbon can boost the amount of nitrogen carrier per unit of carbon processed, thereby increasing overall efficiency [30]. Therefore, the effects of the gas flow rate, reaction temperature, and Al₂O₃/C molar ratio on carbon conversion were first investigated. Then, the kinetic model of the biomass was established. Finally, the mechanism of N-absorption reaction based on biochar was investigated, which provided theoretical guidance for practical production.

2. Experiments

2.1. Feedstock

As α-Al₂O₃ is formed during the N-desorption reaction, alumina remains in the CLAG cycle in its α-phase [12,31]. The nitrogen carrier was chosen to be α-Al₂O₃ (99.9% purity, with a particle size of 10 μm from Ningbo Beigaer New Materials Corporation (Ningbo, Zhejiang, China)) in this study. The biomass was selected as birch sawdust, which is common in Northeast China. Birch sawdust underwent pyrolysis in a CO₂ atmosphere, heating at 10 °C/min to reach 700 °C, with a residence time of 30 min [28]. The biochar was processed by grinding in a mortar and subsequently sieved to obtain particles no larger than 75 μm.

Table 1. Ultimate analysis and pore structure of biochar.

Ultimate Analysis (d.b.)	wt%	Pore Structure
C	96.5	SBET (m2/g)	341.0305
H	1.2	Smic (m2/g)	308.4343
O (by difference)	1.84	Sext (m2/g)	32.5962
N	0.45	Dave (nm)	1.8016
S	0.01	Vmic (cm3/g)	0.1213
		Vtotal (cm3/g)	0.1536
		Vmic/Vtotal	0.7897

shows the ultimate analysis (Optima 4300DV, Perkin Elmer Corporation (Waltham, MA, USA)) and pore structure (TriStar II 3020, Micromeritics Instrument Corporation (Norcross, GA, USA)) of the biochar. According to Table 1, the biochar contained a significant carbon level and a well-developed pore structure.

2.2. Experiment Process

Figure 1 illustrates the experimental setup for the biochar-based N-absorption reaction. A constant amount of biochar (0.72 g) was maintained, while the quantity of α-Al₂O₃ was adjusted to achieve varying α-Al₂O₃/C molar ratios. To ensure uniformity, biochar and α-Al₂O₃ were thoroughly pulverized using a mortar. The homogenized mixture was then transferred to a graphite crucible and placed inside a tube furnace for heating. During the initial heating phase, Ar gas was introduced at a 200 mL/min flow rate. Once the desired temperature was attained, the gas supply was switched to N₂ to initiate the reaction. Upon completion, the atmosphere was reverted to Ar, and the temperature was gradually decreased. The entire process was conducted for a duration of three hours.

Biochar conversion was utilized to quantify the extent of the reaction. The biochar conversion rate was calculated from the amount of CO produced as follows:

$$X_{\mathrm{c}}={\displaystyle \frac{12\times {\displaystyle \int {\phi }_{\mathrm{co}}Qdt}}{22.4\times 1000\times m_{\mathrm{c}}}}\times 100\%$$

(1)

where Q stands for the volume flow rate of the gas, ml/min; ${\phi }_{\mathrm{C}\mathrm{O}}$ refers to the CO volume fraction in the produced gas, measured by a gas analyzer, %; and m_c is the mass of biochar introduced before the reaction, g.

2.3. Kinetic Calculation

Table 2. Kinetic models used for the N-absorption reaction based on biochar.

Code	Reaction Model	Differential f(x)	Integral F(x)
Am	Nucleus production model	$m\left(1-x\right){\left[-\ln \left(1-x\right)\right]}^{{\displaystyle \frac{m-1}{m}}}$	${\left[-\ln \left(1-x\right)\right]}^{{\displaystyle \frac{1}{m}}}$
A1	m = 1	1 − x	−ln(1 − x)
A2	m = 2	2(1 − x)[−ln(1 − x)]1/2	[−ln(1 − x)]1/2
A3	m = 3	3(1 − x)[−ln(1 − x)]2/3	[−ln(1 − x)]1/3
A4	m = 4	4(1 − x)[−ln(1 − x)]1/4	[−ln(1 − x)]1/4
Rm	Shrinking core model	$m{\left(1-x\right)}^{{\displaystyle \frac{m-1}{m}}}$	$1-{\left(1-x\right)}^{{\displaystyle \frac{1}{m}}}$
R1/2	m = 1/2	(1/2)(1 − x)−1	1 − (1 − x)2
R1/3	m = 1/3	(1/3)(1 − x)−2	1 − (1 − x)3
R1/4	m = 1/4	(1/4)(1 − x)−3	1 − (1 − x)4
R2	m = 2	2(1 − x)1/2	1 − (1 − x)1/2
R3	m = 3	3(1 − x)2/3	1 − (1 − x)1/3
Dm	Dimensional diffusion model
D1	Dimensional diffusion	1/2x−1	x2
D2	Two-dimensional diffusion	[−ln(1 − x)]−1	x + (1 − x)ln(1 − x)
D3	Three-dimensional diffusion	(3/2)(1 − x)2/3[1 − (1 − x)1/3]−1	[1 − (1 − x)1/3]2
D4	Three-dimensional diffusion	(3/2)[(1 − x)−1/3 − 1]−1	1 − 2/3x − (1 − x)2/3
D5	3-D (Anti-Jander)	(3/2)(1 + x)2/3[(1 + x)1/3 − 1]−1	[(1 + x)1/3 − 1]2
D6	3-D (ZLT)	(3/2)(1 − x)4/3[(1 − x)−1/3 − 1]−1	[(1 − x)−1/3 − 1]2
D7	3-D (Jander)	6(1 − x)2/3[1 − (1 − x)1/3]1/2	[1 − (1 − x)1/3]1/2
D8	2-D (Jander)	(1 − x)1/2[1 − (1 − x)1/2]−1	[1 − (1 − x)1/2]2
Cn	Phase boundary reaction	${\left(1-x\right)}^n$	${\displaystyle \frac{1-{\left(1-x\right)}^{1-n}}{1-n}}$
C1	Reaction order: n = 2	(1 − x)2	(1 − x)−1 − 1
C2	Reaction order: n = 3/2	2(1 − x)3/2	(1 − x)−1/2 − 1

shows the kinetic models used for the N-absorption reaction based on biochar. These models provide insights into the mechanisms governing the reaction process [32,33]. The nucleus production model describes reactions where new reaction sites (nuclei) form and grow on the surface of reactants, often applicable to solid-state transformations. The shrinking core model assumes a reaction progresses inward from the outer surface of a solid particle, with an unreacted core shrinking over time, frequently used in heterogeneous reactions. In the dimensional diffusion model, the reaction rate is dictated by the diffusion process of reactants or products within the system, which is relevant when reactants must traverse a medium (e.g., gas or liquid phase) to reach the reaction interface. The phase boundary reaction model assumes that the reaction occurs at the interface between two phases, with the rate determined by the movement or transformation of this boundary. These models describe different mechanisms governing solid-state or gas–solid reactions.

The biochar reaction rate was characterized by the reaction rate coefficient and the differential form of the mechanism function (seen in Equation (3)). Then, through Equation (3), it can be deduced that the mechanism function integral form was proportional to time at a certain temperature. Thus, in comparing the model fits in Table 2, the kinetic model for biochar’s N-absorption reaction can be identified.

$$x={\displaystyle \frac{X\mathrm{c}}{100}}$$

(2)

$$r={\displaystyle \frac{dx}{dt}}=k(T)\cdot f(x)$$

(3)

$$F(x)=k(T)\cdot t$$

(4)

where x denotes the proportion of biochar that has been consumed, r is the reaction rate, t represents the time during the reaction, k(T) is the reaction rate constant, T refers to the temperature of the reaction, and f(x) and F(x) correspond to the differential and integral forms of the mechanism, respectively.

The relationship between the activation energy and the reaction rate coefficient was determined with Arrhenius’ equation (Equation (5)). ln(k(T)) and 1/T were linearly related, so the frequency factor and activation energy can be calculated by linear regression analysis.

$$\ln (k(T))=-{\displaystyle \frac{E_{\mathrm{a}}}{RT}}+\ln (A)$$

(5)

where A is the frequency factor, R is the universal molar gas constant, and E_a is the activation energy.

3. Results and Discussion

3.1. Aspects Influencing Biochar Conversion Efficiency

3.1.1. Gas Flow Rate

Figure 2 illustrates how gas flow rates influence biochar conversion at various α-Al₂O₃/C molar ratios. Regardless of the α-Al₂O₃/C molar ratios, the influence of the gas flow rate on biochar conversion followed the same trend. As the flow rate of N₂ gas increased, the conversion of biochar declined. This is because the time that gas molecules continued interacting with the solid reactants was shortened at higher flow rates, leading to a reduced reaction degree [15]. When the N₂ gas flow rose from 200 to 500 mL/min, the reduction in biochar conversion was minimal, peaking at 2.34%. This suggests that the influence of N₂ flow on biochar conversion was insignificant. In the kinetic study in the next section, the gas flow rate selected was 200 mL/min.

3.1.2. Reaction Temperature

Figure 3 illustrates how reaction temperature influences biochar conversion across various α-Al₂O₃/C molar ratios. At varying α-Al₂O₃/C molar ratios, biochar conversion rose as the temperature increased. This occurred because the N-absorption reaction (R1) was an endothermic process (ΔH > 0), so increasing temperature was favorable for the reaction. The biochar conversion reached 95.45% at a reaction temperature of 1600 °C and an α-Al₂O₃/C molar ratio of 3:3.

3.1.3. α-Al₂O₃/C Molar Ratio

Figure 4 illustrates how the α-Al₂O₃/C molar ratio impacts biochar conversion under varying reaction temperatures. When the α-Al₂O₃/C molar ratio was adjusted from 1:3 to 3:3, the biochar conversion rate increased, indicating that a higher ratio effectively enhances the conversion process. Additionally, α-Al₂O₃ addition supported the N-absorption reaction. Figure 5 illustrates how the α-Al₂O₃/C molar ratio influences α-Al₂O₃ conversion under various reaction temperatures. As the α-Al₂O₃/C molar ratio increased, the α-Al₂O₃ conversion decreased and leveled off. Furthermore, as the α-Al₂O₃/C molar ratio shifted from 1:3 to 3:3, the reduction in α-Al₂O₃ conversion became more pronounced with higher reaction temperatures. As the α-Al₂O₃/C molar ratio increased, biochar conversion increased, while α-Al₂O₃ conversion decreased, making the impact of this ratio on N-absorption reaction efficiency unclear. To assess the efficiency of biochar in CLAG, the turnover number (TON) was defined [30]. TON represents the ratio of moles of Al atoms converted to moles of C atoms consumed during the cycle, as shown below:

$$TON={\displaystyle \frac{\Delta n\mathrm{Al}}{\Delta n_{\mathrm{C}}}}$$

(6)

where Δn_C corresponds to the moles of C atoms involved in the CLAG cycle (the initial moles of C in the N-absorption reaction); Δn_Al represents the moles of Al atoms transformed from Al₂O₃ to AlN, respectively. Figure 6 illustrates how the α-Al₂O₃/C molar ratio influences TON at various reaction temperatures. As the Al₂O₃/C molar ratio increased, TON also rose, indicating that optimizing the ratio enhances biochar utilization efficiency in CLAG.

3.2. Kinetic Modeling of Biochar-Based N-Absorption Reaction

3.2.1. Kinetic Model of Biochar-Based N-Absorption Reaction

As shown in Equation (4), when the initial conditions (α-Al₂O₃/C molar ratio and reaction temperature) are kept constant, the integral expression of the mechanism function (F(x)) shows a linear dependence on reaction time (t). To analyze this relationship, we applied the least squares method to linearly fit the conversion rates corresponding to F(x) and t at different molar ratios of α-Al₂O₃/C and reaction temperatures (data points in Figure 7, Figure 8 and Figure 9). The fitting results are represented as straight lines in Figure 7, Figure 8 and Figure 9.

Given the difficulty of visually assessing the compatibility of different kinetic models with the data, R² values were adopted as a measure of how well the models match the experimental results, as defined by

$$R^2=1-[{\displaystyle \frac{{\displaystyle \sum _{i=1}^n{(Y_i-\overline{Y_i})}^2}}{{\displaystyle \sum _{i=1}^n{Y}_i^2-{({\displaystyle \sum _{i=1}^n{Y}_i})}^2/n}}}]$$

(7)

where n is the data point number, Y_i denotes experimental values, and $\overline{Y_i}$ denotes the calculated values, respectively. Figure 7, Figure 8 and Figure 9 show the fits of different kinetic models for α-Al₂O₃/C molar ratios of 1:3, 2:3, and 3:3, respectively. We applied a color mapping method, where models with the highest R² values are represented with increasingly red colors. The averaged R² values over the whole temperature for the diffusion model (D₁) were highest when the α-Al₂O₃/C molar ratios were 1:3, 2:3, and 3:3. Thus, the D₁ diffusion model was selected as the kinetic model. Figure 10 shows the conversion curves for biochar-based N-absorption reaction and calculated conversion with the selected model at different temperatures and α-Al₂O₃/C molar ratios. The conversion curves predicted by the D₁ diffusion model align closely with the experimental results

3.2.2. The Activation Energy and the Reaction Rate Coefficient

Figure 11 illustrates the plots of the biochar-based N-absorption reaction derived from the D₁ diffusion model at various α-Al₂O₃/C molar ratios using the Arrhenius equation.

Table 3. Arrhenius kinetic parameters for the biochar-based N-absorption reaction at different α-Al₂O₃/C molar ratios.

α-Al2O3/C Molar Ratio	Intercept	Slope	Ea (kJ/mol)	k0 (min−1)	R2
1:3	9.31	−29,096.66	241.91	11,061.43	0.99754
2:3	5.45	−20,850.46	173.35	233.58	0.97167
3:3	4.23	−17,653.34	146.77	68.49	0.98175

shows the Arrhenius kinetic parameters corresponding to Figure 11. When the α-Al₂O₃/C molar ratio was constant, ln k(T) and 1/(T + 273.15) were strongly linearly correlated (R² value close to 1), which also illustrates that the D₁ diffusion model could well describe the N-absorption reaction based on the biochar. The α-Al₂O₃/C molar ratio obviously influenced the Arrhenius plot. As the α-Al₂O₃/C molar ratio increased, E_a decreased, indicating that α-Al₂O₃ promoted the N-absorption reaction.

Table 4. Kinetic equations of the biochar-based N-absorption reaction at different α-Al₂O₃/C molar ratios.

α-Al2O3/C Molar Ratio	Kinetic Equations of the Biochar-Based N-Absorption Reaction
1:3	$dx/dt=5530.71\times \exp (-241.91/RT)x^{-1}$
2:3	$dx/dt=116.79\times \exp (-173.35/RT)x^{-1}$
3:3	$dx/dt=73.39\times \exp (-68.49/RT)x^{-1}$

illustrates the kinetic equations for various α-Al₂O₃/C molar ratios obtained by combining the kinetic parameters of Table 3 and the selected D₁ kinetic model.

3.3. Interpretation of the Kinetic Model

The N-absorption reaction of biochar followed the diffusion model (D₁); however, the N-absorption reaction of petcoke [18] and coal coke [20] followed the shrinking core model (R₃). The reason for this was the difference in the reaction mechanism of the different carbon sources. According to the theory of Lefort et al. [17], the N-absorption reaction was carried out according to the following reactions:

$${\mathrm{Al}}_2{\mathrm{O}}_3=2\mathrm{Al}\left(\mathrm{g}\right)+{\displaystyle \frac{3}{2}}{\mathrm{O}}_2$$

(8)

$$3\mathrm{C}+{\displaystyle \frac{3}{2}}{\mathrm{O}}_2\to 3\mathrm{CO}$$

(9)

$${2\mathrm{Al}\left(\mathrm{g}\right)+\mathrm{N}}_2=2\mathrm{AlN}$$

(10)

Figure 12 shows a schematic of the reaction mechanism of the biochar-based N-absorption reaction. Initially, Al₂O₃ was heated to decompose into Al(g) and O₂. The O₂ molecules then diffused to the C surface, leading to the formation of CO, while Al(g) reacted with N₂ to produce AlN. The reaction between C and O₂ proceeded slowly, potentially resulting in the formation of CO₂ as an intermediate product. The reaction of Al(g) and N₂ to form AlN was relatively fast [17]. Thus, two steps governed the N-absorption reaction rate: the O₂ diffusion from the vapor interface near the Al₂O₃ spherical shell toward the C spherical shell and the O₂ reaction with the C at the intersection between the vapor interface and the C spherical shell. The reaction rate between C and O₂ was related to the surface structure of C [26,34]. A high specific surface area and microporosity enhance the surface reaction rate of C [35,36]. The specific surface area of biochar was 341.03 m²/g, and the microporosity was 0.79 (Table 1). The specific surface area of petcoke was 6.2 m²/g [18], and that of coal coke was 7.61 m²/g [20]. Both had a poor pore structure. When C (biochar) had an abundant pore structure and high specific surface area, the reaction rate between C and O₂ was faster, and the diffusion of O₂ controlled the N-absorption reaction rate, so the kinetic model was a diffusion model. Furthermore, when the specific surface area of C (petcoke and coal coke) was small and the pore structure was poor, the reaction rate between C and O₂ was slower, and it was a rate-controlling step for the N-absorption reaction, so the kinetic model was a shrinking core model.

In conclusion, different carbon surface structures affected the surface reaction rate of C, thus resulting in different reaction mechanisms. The results were manifested in different reaction kinetic models. This mechanism also explains well why the addition of α-Al₂O₃ can promote the C conversion: due to the increase in the amount of α-Al₂O₃, which led to the increase in O₂(g) in the reaction system, O₂(g) diffused more easily into the intersection and reacted with C, thus promoting the reaction.

4. Conclusions

We comprehensively examined how reaction conditions influence the biochar-based N-absorption reaction. We demonstrated that the influence of the gas flow rate on biochar conversion was negligible, while increasing the reaction temperature and the α-Al₂O₃/C molar ratio significantly enhanced carbon conversion. Among the tested kinetic models, the D1 diffusion model was found to best describe the N-absorption reaction. Moreover, raising the α-Al₂O₃/C molar ratio significantly lowered the reaction’s activation energy. The diffusion of O₂, derived from the decomposition of α-Al₂O₃, was the rate-controlling step of the reaction.

This research enhances the use of biochar in Chemical Looping Ammonia Generation. The derived kinetic model provides a solid foundation for further chemical process simulations. It is important to note that this study focused on deriving the kinetic model based on experimental data, without delving into the microscopic molecular dynamics of the reaction. Future research could focus on studying the molecular-level structural and energy changes during the reaction process using density functional theory.