Comparative analysis of CFD modeling and process
Simulation for pyro-gasification of biomass

Diakaridia Sangare, Verónica Belandria, Stéphane Bostyn, Mario

Moscosa-Santillan

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Diakaridia Sangare, Verónica Belandria, Stéphane Bostyn, Mario Moscosa-Santillan. Comparative analysis of CFD modeling and process Simulation for pyro-gasification of biomass. Bioresources, 2025, 20 (2), pp.2844-2870. 10.15376/biores.20.2.2844-2870. hal-05182942

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# Comparative Analysis of CFD Modeling and Process Simulation for Pyro-Gasification of Biomass

Diakaridia Sangaré ID a,b,c,\* Verónica Belandria, c,d Stéphane Bostyn, c,d and Mario Moscosa-Santillan ID b,\*

\* Corresponding authors: diakaridia.sangare@cirad.fr, mario.moscosa@uaslp.mx

DOI: 10.15376/biores.20.2.2844-2870

## GRAPHICAL ABSTRACT

<figure><img src="image_2.png" /></figure>
# Comparative Analysis of CFD Modeling and Process Simulation for Pyro-Gasification of Biomass

Diakaridia Sangaré ID, a,b,c,\* Verónica Belandria, c,d Stéphane Bostyn, c,d and
Mario Moscosa-Santillan ID b,\*

A comparative analysis of Computational Fluid Dynamics (CFD) modeling and process simulation (SIM) was carried out to evaluate biomass pyro-gasification, using experimental data from agave bagasse (AB) as a case study. Experimental data were obtained *via* thermogravimetric analysis (TGA) under varying conditions, including non-isothermal (700 to 1000 °C) and isothermal (900 °C and 950 °C) gasification at different air-to-biomass ratios (ABR). CFD modeling in COMSOL Multiphysics focused on micro-scale mass and heat transfer phenomena, while Aspen Plus simulations provided macro-scale process insights. Results showed that SIM closely matched experimental product yields under isothermal conditions (maximum deviation: 4.23 wt.%), while CFD excelled in predicting gas composition under non-isothermal conditions (e.g., H₂ deviation: 3.29 vol.%). Sensitivity analysis showed how temperature and ABR are critical factors that influence the product yield and gas composition, highlighting the strengths of each modeling approach. These findings underline the potential of integrating CFD and SIM approaches for improving the accuracy of biomass conversion modeling, paving the way for optimized process designs and scalable industrial applications.

DOI: 10.15376/biores.20.2.2844-2870

Keywords: Biomass; Pyrolysis; Gasification; CFD Modeling; Process simulation; Sensitivity analysis

Contact information: a: CIRAD, UPR BioWooEB, 73 rue Jean-François Breton, F-34398 Montpellier, France; b: Facultad de Ciencias Químicas Universidad Autónoma de San Luis Potosí Av. Dr. Nava # 6, Zona Universitaria San Luis Potosí, S.L.P., CP: 78210, México; c: Institut de Combustion, Aérothermique, Réactivité, et Environnement (ICARE)-CNRS UPR3021, 1C avenue de la recherche scientifique 45071 Orléans Cedex 2, France; d: Université d'Orléans, Institut Universitaire de Technologie, 16 rue d'Issoudun BP16724 45067 Orléans Cedex 2, France; \* Corresponding authors: diakaridia.sangare@cirad.fr, mario.moscosa@uaslp.mx

## INTRODUCTION

The rising global energy demand and environmental concerns associated with fossil fuel use have intensified the search for sustainable energy alternatives (Shahbaz et al. 2017; Okoro et al. 2020; Ajorloo et al. 2022). Biomass, as a renewable energy source, offers a significant advantage due to its potential to reduce net CO₂ emissions and its abundance across diverse regions (Ajorloo et al. 2022). However, using biomass as an energy source requires efficient conversion processes. In this field, thermochemical methods, such as pyrolysis, gasification, and combustion, are the most widely used technologies (Nsafu et al. 2013; Liu et al. 2017). Among thermochemical conversion processes, pyrolysis and gasification are efficient methods to convert biomass into valuable products such as synthesis gas, biochar, and bio-oil. Gasification is particularly
versatile, enabling the production of synthesis gas (syngas) as a precursor for energy generation and chemical synthesis (Nsaful et al. 2013; Liu et al. 2017). The syngas can be used as a source of energy (electricity, hydrogen) and in the manufacture of high-value-added products in the chemical industry (Basu 2018; Sangaré et al. 2022).

Pyrolysis is the initial stage of gasification, and it plays a critical role in determining product distribution, including non-condensable gases (NCG), solid carbon (biochar), and condensable vapor (bio-oil). Factors such as temperature, heating rate, and the air-to-biomass ratio (ABR) strongly influence the yield of the products. In addition, bio-oils, including tars, reach their maximum yield between 400 and 700 °C, before undergoing thermal cracking into lighter gases at temperatures above 700 °C (Lin et al. 2016; Sangaré et al. 2022). Although the increased temperature and heating rates generally favor gas production, they also complicate process optimization by favoring tar formation, a major operational challenge due to its tendency to condense and obstruct pipelines (Devi et al. 2005). In addition, these complex mixtures, which include polycyclic aromatic hydrocarbons (PAHs), require advanced mitigation techniques, including thermal and catalytic cracking (Li and Suzuki 2009; Yan et al. 2014; Zeng et al. 2020).

To solve these challenges, the pyro-gasification process offers a promising solution. This process consists of two main stages: an initial pyrolysis stage, where biomass is thermally decomposed to produce biochar, non-condensable gases, and condensable vapor (including tar), followed by partial oxidation of condensable vapor and char. During the partial oxidation stage, the biochar and tars generated during pyrolysis are converted into syngas through thermal oxidation processes (Okoro et al. 2020; Sangaré et al. 2024a).

Although gasification and pyro-gasification are similar processes, the distribution of products can vary depending on the operating conditions. In both cases, product distribution is strongly influenced by heating rate, gasifying agent, and temperature. Numerous studies have been conducted on the gasification of charcoal using various gasifying agents, including air (Wang et al. 2008; Zhang et al. 2015; Zhang et al. 2017), steam (Moghadam et al. 2014; Jayaraman and Gökalp 2015; Elorf et al. 2019), oxygen (Niu et al. 2014), carbon dioxide (Gao et al. 2016b), supercritical water (Guo et al. 2015), and so forth. Generally, gasification with oxygen, steam, carbon dioxide, or supercritical water yields syngas with higher heating values (HHV) than air gasification. However, air gasification remains the most studied and applied method due to the low cost of air, the reaction process's simplicity, and the reactor's straightforward design (Gao et al. 2016a; Okoro et al. 2017).

Despite the potential of pyro-gasification, product optimization and tar formation challenges remain significant, as these depend heavily on operating parameters. While experimental studies provide valuable data, their interpretation is often complicated by the difficulty of extrapolating laboratory-scale results to industrial applications. In this context, modeling and simulation have become essential tools. Various modeling approaches have been used to study biomass thermochemical conversion, including thermodynamic equilibrium, kinetic, and Computational Fluid Dynamics (CFD) models.

Thermodynamic equilibrium models are effective for establishing theoretical limits, offering insights into maximum efficiency and outputs under ideal conditions. However, their reliance on simplifying assumptions restricts their applicability to non-equilibrium scenarios, such as low-temperature oxidation or systems with complex reaction kinetics (Beheshti et al. 2015; Patra and Sheth 2015; Gmehling et al. 2019;
Safarian *et al.* 2019). Kinetic models, on the other hand, provide detailed insights into reaction mechanisms and rates by incorporating dynamic chemical interactions (Upreti 2017; Gmehling *et al.* 2019; Ascher *et al.* 2022). However, these models often fall short in addressing spatial and temporal variations within reactors.

Modeling and simulation approaches have thus become indispensable for navigating these complexities and bridging the gap between laboratory findings and industrial-scale applications. CFD excels in resolving spatially and temporally detailed heat and mass transfer phenomena, while process simulation (SIM) offers a macroscopic perspective, integrating equilibrium and kinetic assumptions (Vyazovkin *et al.* 2014; Van Hoecke *et al.* 2023; Sangaré *et al.* 2024b). Despite extensive research on these individual approaches, comparative studies evaluating their predictive accuracies under varying gasification conditions remain scarce.

This study aims to bridge this gap by conducting a comparative analysis of CFD modeling using COMSOL Multiphysics and SIM using Aspen Plus for the pyro-gasification of agave bagasse, a by-product of mezcal production. The objective is also to highlight their respective strengths and limitations in predicting pyro-gasification behavior. Experimental data obtained through thermogravimetric analysis (TGA) under isothermal and non-isothermal conditions were used to validate and evaluate model performance. The specific objectives were (1) to assess the accuracy of CFD and SIM in predicting product yields (biochar, bio-oil, and non-condensable gases) and gas compositions; (2) to identify the operational conditions where each method performs optimally; (3) to provide insights into integrating CFD and SIM for improved modeling frameworks. By addressing these objectives, the study contributes to the development of more accurate and scalable modeling strategies for biomass pyro-gasification, facilitating process optimization and reducing the environmental footprint of bioenergy production.

# EXPERIMENTAL

## Feedstock Properties and Description

This study focused on agave bagasse (AB), a by-product of mezcal production. The AB was collected from the "Santa Teresa" distillery in San Luis Potosí, Mexico. It was initially air-dried at ambient temperature (28 to 35 °C) for approximately one week to reduce moisture content, followed by drying at 105 °C for 24 h to achieve a constant mass. The dried material was then milled to a particle size between 0.1 and 1 mm, ensuring uniformity for thermochemical conversion experiments. A char was produced from AB through pyrolysis at 700 °C, using a heating rate of 20 °C/min under argon atmosphere until the final temperature was reached. The properties of this char, along with the physicochemical characteristics of raw AB, are presented in Table 1, and a detailed description of this feedstock is available in previous publications (Sangaré *et al.* 2024a,b).
## Experimental Setup and Analytical Methods

The experiments were conducted using a Netzsch STA 449 F3 Jupiter thermal analyzer, coupled with an SRA SOLIA 490 µ-GC, equipped with three thermal conductivity detectors (TCD) to allow comprehensive gas quantitation and analysis. The experimental setup and analytical methodology used were described in a previous study (Sangaré et al. 2024a). For each experiment, around 150 ± 2 mg of biomass; was placed in a crucible. The biomass samples were heated from room temperature to 700 °C at 40 °C/min, with argon as the carrier gas at a flow rate of 20 mL/min. For char oxidation experiments, air was introduced at 3 mL/min from 700 to 1000 °C under non-isothermal conditions to evaluate all four biomass types. Additionally, separate isothermal gasification experiments were conducted at 900 and 950 °C with an airflow rate of 4 mL/min, focusing exclusively on AB to investigate its behavior under these specific conditions.

## CFD Modeling Strategy

### Reactor geometry

In this CFD simulation, the reactor geometry is shown in Fig. 1. The reactor consists of an alumina (Al₂O₃) crucible integrated into a thermogravimetric analyzer, with a total volume of 5 mL. The geometric models were created using COMSOL Multiphysics®6.2. Based on the reactor geometry characteristics and to reduce computational time, a 2D axisymmetric model was employed, adjusted to correspond to the dimensions of the reactors used experimentally to simulate the entire pyro-gasification process. The mesh independence study was conducted in the previous publication (Sangaré et al. 2024b), and the final refined mesh used in this work is presented in Fig. 1.
<table><caption>**Table 1.** Physicochemical Properties and Compositions of AB</caption><thead><tr><th></th><th>AB</th><th>Char<br>700 °C</th><th>Char<br>800 °C</th><th>Char<br>900 °C</th><th>Char<br>1000 °C</th></tr></thead><tbody><tr><td colspan="6"><strong>Proximate Analysis (wt%)</strong></td></tr><tr><td>Moisture content<br>(MC)</td><td>4.81±1.21</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Volatile matter (VM)</td><td>77.91±1.42</td><td>23.30 ± 1.58</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Fixed carbon (FC)<sup>a</sup></td><td>18.76±1.61</td><td>65.30 ± 2.18</td><td>85.61 ± 3.50</td><td>87.92 ±<br>3.20</td><td>86.60 ± 1.38</td></tr><tr><td>Ash</td><td>3.34±0.43</td><td>12.90 ± 0.84</td><td>14.39 ± 2.19</td><td>12.08 ±<br>3.20</td><td>13.40 ± 1.38</td></tr><tr><td colspan="6"><strong>Ultimate Analysis (wt%)</strong></td></tr><tr><td>Carbon</td><td>45.14 ± 0.21</td><td>76.75 ± 0.06</td><td>77.40 ± 1.58</td><td>80.31 ±<br>2.32</td><td>79.40 ± 1.88</td></tr><tr><td>Hydrogen</td><td>5.50 ± 0.23</td><td>1.90 ± 0.00</td><td>1.41 ± 0.01</td><td>0.94 ± 0.00</td><td>0.75 ± 0.01</td></tr><tr><td>Nitrogen</td><td>0.31±0.10</td><td>0.61 ± 0.06</td><td>0.86 ± 0.07</td><td>0.88 ± 0.04</td><td>0.98 ± 0.04</td></tr><tr><td>Sulfur</td><td>nd.</td><td>nd.</td><td>nd.</td><td>nd.</td><td>nd.</td></tr><tr><td>Oxygen<sup>a</sup></td><td>45.71±0.22</td><td>7.84 ± 0.12</td><td>7.20 ± 1.52</td><td>7.78 ± 2.28</td><td>5.10 ± 1.90</td></tr><tr><td colspan="6"><strong>Chemical Analysis (wt%) *</strong></td></tr><tr><td>Extractives<br><sup>d</sup> (wt.)</td><td>TGL<br>TANN</td><td>1.10<br>0.40</td><td></td><td></td><td></td></tr><tr><td rowspan="3">Lignin<sup>d</sup><br>(wt.%)</td><td>LIGC</td><td>5.01</td><td></td><td></td><td></td></tr><tr><td>LIGH</td><td>8.12</td><td></td><td></td><td></td></tr><tr><td>LIGO</td><td>1.12</td><td></td><td></td><td></td></tr><tr><td>Hemicellulose (wt.%)</td><td colspan="5">43.81</td></tr><tr><td>Cellulose<sup>a</sup> (wt.%)</td><td colspan="5">40.74</td></tr><tr><td><strong>HHV(MJ/kg)</strong></td><td colspan="5"><strong>16.5±0.3</strong></td></tr><tr><td>Bulk density<sup>b</sup> (kg/m<sup>3</sup>)</td><td colspan="5">673.4</td></tr><tr><td>Actual density<sup>c</sup><br>(kg/m<sup>3</sup>)</td><td colspan="5">1559</td></tr><tr><td>Porosity (-)</td><td colspan="5">0.57</td></tr><tr><td>Average particle size<br>(mm)</td><td colspan="5">0.5</td></tr><tr><td>Thermal conductivity (W/m-K)</td><td colspan="5">k<sub>b</sub> = 0.035 + 1.73 × 10<sup>-4</sup>T<br>k<sub>char</sub> = 0.08-3×10<sup>-4</sup>T</td></tr><tr><td>Heat capacity (J/kg·K)</td><td colspan="5">C<sub>pchar</sub> = 1003.2 + 2.09 T<br>C<sub>pb</sub> = 103.1 + 3.87T</td></tr><tr><td colspan="6"><strong>Standard analysis methods</strong></td></tr><tr><td>Moisture content (MC)</td><td colspan="5">EN 14774-3: 2009: E</td></tr><tr><td>Volatile matter (VM)</td><td colspan="5">EN 15148: 2009 E</td></tr><tr><td>Ash content</td><td colspan="5">EN 14775: 2009: E</td></tr><tr><td>Fixed carbon (FC)</td><td colspan="5">FC = 100 - [MC + VM + Ash]</td></tr><tr><td>Proximate analysis</td><td colspan="5">CEN/TS 15104:2005</td></tr><tr><td>Higher heating values (HHV)</td><td colspan="5">ASTM D5865-12</td></tr><tr><td>Bulk density</td><td colspan="5">ASTM E873-82</td></tr><tr><td colspan="6">nd: not detected (&lt;0.1%); <sup>a</sup>:by difference; <sup>b</sup>:bulk density was estimated considering that the porosity is the space between the biomass particles; <sup>c</sup>:actual density estimated from biomass chemical composition, <sup>d</sup>: methodology described by Debiagi et al. (2015) and <sup>e</sup>:Procedures described by Li et al. (2004).</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Fig. 1. The reactor geometry: a) CFD domain and b) 2D-axisymmetric meshed</figcaption></figure>

CFD Numerical Model

CFD modeling was carried out using COMSOL Multiphysics® 6.2. The numerical modeling included drying, devolatilization, and secondary reactions, such as tar cracking and the subsequent char oxidation and homogeneous reactions in fluid phases. Due to its characteristics, the system was considered a porous medium system. To describe the flow in the porous medium, Darcy's law was used in combination with Brinkman's equations, which allowed the incorporation of the velocity and pressure interaction in the porous medium associated with the solid phase. Equation (1) describes fluid flow in a porous medium.

$$ \frac{1}{\varepsilon_p} \rho \left( \frac{\partial u}{\partial t} + (u \cdot \nabla) \frac{u}{\varepsilon_p} \right) = -\nabla p + \nabla \cdot \left( \frac{1}{\varepsilon_p} \left( \mu(\nabla u + (\nabla u)^\tau) - \frac{2}{3}\mu(\nabla \cdot u)I \right) \right) - \left( \frac{\mu}{\kappa} + \frac{Q_m}{\varepsilon_p^2} \right) u + F \quad (1) $$

where u is velocity (m/s), μ is fluid viscosity (Pa·s), ρ is fluid density (kg/m³), εp is porosity (-), p is pressure (Pa), κ is permeability (m²), and F represents external forces (kg/(m²·s²)). Additionally, I and τ denote the identity matrix and the transpose operator, respectively. Furthermore, Qm is the mass source (kg/(m³·s)). Equation 1 is always solved together with the continuity Eq. 2.

$$ \frac{\partial(\varepsilon_p \rho)}{\partial t} + \nabla \cdot (\varepsilon_p \rho u) = Q_m \quad (2) $$

There is no mass source term, as (Qm=0). In porous media, the κ controls the
directional movement of flow, establishing a linear relationship between pressure drop (p) and the flow velocity field (u).

$$-\nabla p = \mu \frac{u}{k} \qquad (3)$$

Darcy flow can be characterized by Kozeny-Carman equation, which estimates the permeability of a porous medium based on the average particle diameter ($d_p$) and porosity ($\epsilon_p$) (Bear and Braester 1972).

$$\kappa = \frac{d_p^2 \epsilon_p^3}{180 (1 - \epsilon_p)^2} \qquad (4)$$

The Brinkman Equation (Eq. 1), combined with the continuity Eq. 2 and the species transport Eq. 5 in porous media, provides a comprehensive framework for modeling biomass reactions,

$$\frac{\partial(\phi_i c_i)}{\partial t} + \frac{\partial(\rho c_{p,i})}{\partial t} + u \cdot \nabla c_i = D_{e,i} \cdot \nabla^2 c_i + R_i \qquad (5)$$

where $c_i$ represents the concentration of species i (mol/m³) in the fluid phase, $t$ is time (s), $c_{p,i}$ denotes the quantity adsorbed into biomass bed (mol/kg of the solid), $\phi_i$ is volume fraction of fluid (-), $\rho$ is the liquid's bulk density (kg/m³), $D_{e,i}$ is the diffusion coefficient (m²/s), and $R_i$ describes the reaction rate model (mol/m³·s). The value of $R_i$ is given by the following equation,

$$R_i = C_i k_i e^{\frac{-E_i}{RT}} \qquad (6)$$

where $k_i$ is the pre-exponential factor (1/s), $E_i$ is the activation energy (J/mol), $R$ is the gas constant (J/mol·K), and $T$ is the temperature (K).

The value of $D_{e,i}$ was obtained from literature correlations (Guizani et al. 2015; Mermoud et al. 2006), and it is expressed as follows,

$$D_{e,i} = \frac{\epsilon_p}{\gamma} \left( \frac{1}{\frac{1}{D_{Knudsen,i}} + \frac{1}{D_{AB,i}}} \right) \qquad (7)$$

$$D_{AB,i} = 1.67 \times 10^{-5} \left(\frac{T}{298}\right)^{1.75} \qquad (8)$$

$$D_{Knudsen,i} = 0.97 \left(\frac{d_p}{2}\right) \sqrt{\frac{T}{M_i}} \qquad (9)$$

where $D_{AB,i}$ represents the binary diffusivity (m²/s) of the single-phase diffusion coefficient for species A (reactive gases) as it migrates through a carrier gas B (Argon). The $D_{Knudsen,i}$ represents the Knudsen diffusion coefficient, which accounts for molecular transport in porous media. This occurs when the mean free path of gas molecules is comparable to or larger than the pore diameter, leading to more frequent molecule-wall collisions than intermolecular collisions (Guizani et al. 2015). $M_i$ is the molecular mass (g/mol). The tortuosity factor $\gamma$ accounts for the reduced diffusivity resulting from the impediments caused by particle grains, which hinder Brownian motion. For saturated media, the effective tortuosity can be determined using the Millington and Quirk model (1961),

$$\gamma = \epsilon_p^{-1/3} \qquad (10)$$
$$ \varepsilon_p = 1 - \frac{\rho_b}{\rho_r} \qquad (11) $$

The heat transfer equation is given as follows,

$$ \boldsymbol{\varepsilon}_p \rho C_{p,f} \frac{\partial T}{\partial t} + \rho C_{p,f} \boldsymbol{u} \cdot \nabla T + \nabla \cdot \boldsymbol{\varepsilon}_p k_f \nabla T = Q \qquad (12) $$

$$ \phi_s \rho_s C_{p,s} \frac{\partial T}{\partial t} - \nabla \cdot \phi_s k_s \nabla T = Q \qquad (13) $$

where $C_{pf}$ and $C_{ps}$ represent the heat capacities (J/kg-K) of fluid and solid, respectively, $k_f$ and $k_s$ are the thermal conductivities (W/m-K) of fluid and solid, and $Q$ denotes an external heat source (W/m³). This CFD modeling did not include heat generated by pressure changes due to viscous dissipation.

## Chemical Kinetics Applied to CFD Modeling of Pyrolysis

The biomass pyrolysis process begins with moisture evaporation during drying, followed by devolatilization, which produces volatile products, including non-condensable and condensable gases, bio-oil, and biochar containing fixed carbon solids and ash. Secondary reactions, such as char and tar cracking, follow. The multi-step kinetic mechanism scheme described in a previous study (Sangaré et al. 2024b) was used to model this process.

### Reaction kinetics of char oxidations

After the biomass was pyrolyzed, the resulting char was oxidized by injecting a gasifying agent. Char oxidation involves both heterogeneous and homogeneous reactions. The heterogeneous reaction of char with the surrounding gaseous species such as $O_2$, $CO_2$, and $H_2O$ is a complex process involving specific interactions such as char-$O_2$, char-$CO_2$, and char-$H_2O$ reactions (Nguyen et al. 2018; Fatehi et al. 2019; Yang et al. 2022). The kinetic details are summarized in Table 2.

In R-1, the parameter Z is defined from Eq. 14.

$$ z = \frac{c_{CO}}{c_{CO_2}} = 2.5 \times 10^3 e^{-\frac{6249}{T}} \qquad (14) $$

## Process Simulation Strategy

In gasification modeling using Aspen Plus®, two primary approaches can be applied: one that assumes the complete equilibrium of the system and another that applies a semi-detailed kinetic mechanism (Beheshti et al. 2015). A semi-detailed kinetic method was employed because the system may not strictly achieve equilibrium under the simulation conditions evaluated in this study. This approach allows for estimating the product yield and gas composition during the pyrolysis stage, followed by the different kinetics of oxidation reactions.

### Thermodynamic model and physical properties

Process simulations were conducted using Aspen Plus® and the Redlich-Kwong-Soave (RK-SOAVE) thermodynamic model to calculate thermodynamic properties. The RK-SOAVE model is recommended for mixtures containing hydrocarbons and light gases and is suitable for estimating the thermodynamic properties of certain solids, including carbon (François et al. 2013). This model was employed to predict the physical properties
of both conventional mixed and non-conventional components. For non-conventional components, such as biomass, char, and ash, the enthalpy and density were calculated using the HCOALGEN and DCOALIGT models, respectively. The HCOALGEN model in Aspen Plus® requires three key attributes: proximate analysis (PROXANAL), ultimate analysis (ULTANAL), and sulfur analysis (SULFANAL). Ultimate analysis gives biomass or char mass composition in C, H, N, S, O, and ash. Proximate analysis reports the contents of MC, FC, VM, and ash. Finally, sulfur analysis divides the sulfur into pyritic and sulfate

<table><caption>**Table 2.** Reaction Rates for Homogeneous and Heterogeneous Reactions Involved in Char Oxidation</caption><thead><tr><th>#</th><th>Gasification Reaction</th><th>Reaction Rate (mol/m<sup>3</sup>·s))</th><th>Reference</th></tr></thead><tbody><tr><td colspan="4" style="text-align:center; font-weight:bold;">Heterogeneous reaction</td></tr><tr><td>R- 1</td><td>C + <sup>Z + 2</sup>&frasl;<sub>2Z + 2</sub>O<sub>2</sub><br/>→ <sup>Z</sup>&frasl;<sub>Z + 1</sub>CO + <sup>1</sup>&frasl;<sub>Z + 1</sub>CO<sub>2</sub></td><td><sup>P<sub>O2</sub></sup>&frasl;<sub>4.0 × 10<sup>5</sup> (1 + <sup>1</sup>&frasl;<sub>ε<sub>b</sub><sup>2.5</sup>(<sup>1</sup>&frasl;<sub>Y</sub> - 1)) d<sub>p</sub>)</sub> T<sup>0.75</sup></td><td>(Al-Zareer et al. 2016; He et al. 2013; Wen and Chaung 1979)</td></tr><tr><td>R- 2</td><td>C + H<sub>2</sub>O → CO + H<sub>2</sub></td><td>930e<sup>-22647/T</sup> [c<sub>c</sub> × (P<sub>H2O</sub> - (3.23 × 10<sup>7</sup> (P<sub>H2</sub> · P<sub>CO</sub> / (e<sup>-16330/T</sup>)))])</td><td>(He et al. 2013; Wen and Chaung 1979)</td></tr><tr><td>R- 3</td><td>C + CO<sub>2</sub> → 2CO</td><td>930e<sup>-22647/T</sup> [c<sub>c</sub> × (P<sub>CO2</sub> - (1.22 × 10<sup>9</sup> (P<sub>CO</sub><sup>2</sup> / (e<sup>-20280/T</sup>)))])</td><td>(He et al. 2013)</td></tr><tr><td>R- 4</td><td>C + 2H<sub>2</sub> → CH<sub>4</sub></td><td>8.35 × 10<sup>-4</sup>e<sup>-8078/T</sup> [c<sub>c</sub> × (P<sub>H2</sub> - (P<sub>H2</sub><sup>*</sup> - (6.80 × 10<sup>5</sup> (P<sub>CH4</sub> / (e<sup>-10100/T</sup>)))<sup>0.5</sup>))]</td><td>(Al-Zareer et al. 2016; He et al. 2013)</td></tr><tr><td colspan="4" style="text-align:center; font-weight:bold;">Homogeneous reaction</td></tr><tr><td>R- 5</td><td>H<sub>2</sub> + 0.50<sub>2</sub> → H<sub>2</sub>O</td><td>8.83 × 10<sup>5</sup>e<sup>-11997/T</sup>[c<sub>H2</sub> × c<sub>O2</sub>]</td><td>(Al-Zareer et al. 2016)</td></tr></tbody></table>
<table><tr><td>R- 6</td><td>CH<sub>4</sub> + 2O<sub>2</sub> → CO<sub>2</sub> + 2H<sub>2</sub>O</td><td>3.55 × 10<sup>11</sup>e<sup>-11997/T</sup>[c<sub>CH4</sub> × c<sub>O2</sub>]</td><td>(Al-Zareer et al. 2016; Xie et al. 2012)</td></tr><tr><td>R- 7</td><td>CO + 0.5O<sub>2</sub> → CO<sub>2</sub></td><td>6.36 × 10<sup>3</sup>e<sup>-11997/T</sup>[c<sub>CO2</sub> × c<sub>CO</sub>]</td><td>(Al-Zareer et al. 2016)</td></tr><tr><td rowspan="2">R- 8</td><td rowspan="2">CO + H<sub>2</sub>O ↔ CO<sub>2</sub> + H<sub>2</sub></td><td>1: 2.78 × 10<sup>3</sup>e<sup>-1510/T</sup></td><td rowspan="2">(Rabea et al. 2022)</td></tr><tr><td>2: 9.59 × 10<sup>4</sup>e<sup>-5609/T</sup></td></tr><tr><td rowspan="2">R- 9</td><td rowspan="2">CH<sub>4</sub> + H<sub>2</sub>O ↔ CO + 3H<sub>2</sub></td><td>1: 6.09 × 10<sup>14</sup>e<sup>-30908/T</sup></td><td rowspan="2">(Rabea et al. 2022)</td></tr><tr><td>2: 3.12 × 10<sup>2</sup>e<sup>-3608/T</sup></td></tr></table>

where c<sub>C</sub>, c<sub>CH4</sub> and c<sub>H2</sub> are concentrations of C, CH4 and H2. P<sub>H2O</sub>, P<sub>CO</sub>, P<sub>CO2</sub>, P<sub>H2</sub> and P<sub>CH4</sub> are partial pressure of components (atm). x<sub>CO</sub>, x<sub>CO2</sub>, and x<sub>H2</sub> are mol fractions of components.

### Reaction kinetic of pyrolysis in the process simulation

The drying and devolatilization of biomass in the process simulation using Aspen Plus® can be modeled from the pyrolysis yields (Beheshti et al. 2015). In this approach, empirical correlations derived from experimental data are used. The pyrolysis reactions are presented below:

$$ \text{Dry biomass} \rightarrow a\text{Char} + bH_2 + c\text{CO} + d\text{CO}_2 + e\text{CH}_4 + fH_2O_{(g)} + gC_xH_yO_z + hN_2 \quad (15) $$

The volatile matter produced includes CO, H<sub>2</sub>, H<sub>2</sub>O, CO<sub>2</sub>, CH<sub>4</sub>, N<sub>2</sub>, and C<sub>x</sub>H<sub>y</sub>O<sub>z</sub>, where C<sub>x</sub>H<sub>y</sub>O<sub>z</sub> represents condensable gases and tar as an empirical chemical compound. Two main approaches are commonly used to characterize pyrolysis products: experimental methods and theoretical models, such as functional group models (Hobbs et al. 1992). The experimental approach was used in this study due to its simplicity and the availability of experimental data. The H<sub>2</sub>O production was estimated based on the cellulose, hemicellulose, and lignin content, as analyzed by Stefanidis et al. (2014). The experimental data between 700 and 1000 °C yields were used to determine the coefficients of the Eq 15. In addition, the elemental compositions (carbon, hydrogen, and oxygen) of dry biomass and char under these conditions were used to balance the pyrolysis equation, allowing the determination of the empirical formula for char and C<sub>x</sub>H<sub>y</sub>O<sub>z</sub> values. Table 3 presents the product yield balances obtained from the pyrolysis of AB at different temperatures.

<table><caption>**Table 3. Product Mass Yields from Pyrolysis of AB at Different Temperatures**</caption><thead><tr><th rowspan="2">Component</th><th colspan="5">Product Mass Yield (wt%)</th></tr><tr><th>700 °C</th><th>800 °C</th><th>900 °C</th><th>950 °C</th><th>1000 °C</th></tr></thead><tbody><tr><td>CHAR</td><td>26.59</td><td>26.11</td><td>24.02</td><td>23.34</td><td>23.38</td></tr><tr><td>CO</td><td>3.64</td><td>5.28</td><td>7.22</td><td>8.27</td><td>8.46</td></tr><tr><td>H<sub>2</sub></td><td>0.35</td><td>0.31</td><td>0.66</td><td>0.64</td><td>0.75</td></tr><tr><td>CO<sub>2</sub></td><td>8.21</td><td>9.45</td><td>11.10</td><td>11.14</td><td>11.96</td></tr><tr><td>H<sub>2</sub>O</td><td>24.00</td><td>24.00</td><td>24.00</td><td>24.00</td><td>24.00</td></tr><tr><td>H<sub>2</sub>S</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td><td>0.00</td></tr><tr><td>N<sub>2</sub></td><td>0.40</td><td>0.40</td><td>0.40</td><td>0.40</td><td>0.40</td></tr><tr><td>CH<sub>4</sub></td><td>0.88</td><td>1.03</td><td>1.03</td><td>0.98</td><td>0.98</td></tr><tr><td>C<sub>x</sub>H<sub>y</sub>O<sub>z</sub></td><td>35.94</td><td>33.42</td><td>31.57</td><td>31.19</td><td>30.07</td></tr></tbody></table>
## Process description and assumptions

The pyro-gasification process involves three main stages: drying, devolatilization (pyrolysis), and char oxidation. The stages were modeled in sequence. The flowchart modeled in Aspen Plus® is shown in Fig. 2. Briefly, the pyrolysis of biomasses was simulated with a RYield block (PYROLYS). This block decomposes the dry biomass into CO, H₂, CO₂, H₂O, N₂, CH₄, CₓHᵧOz, and char. The yield of each component is specified according to the experimental data. After pyrolysis, the products flow into the SEP-2 block to separate the pyrolysis gas and solid (char). The separator is configured as a split fraction separator, allowing the separation of gas-phase and solid-phase products based on their physical state. The gases from SEP-2 pass into a gas mixer (M-GAS). The char enters an RStoic model (CHAR-DEC) to decompose into its elemental components (C, H, O, N, and ash), according to the ultimate analysis of the char. For non-isothermal gasification, these elemental compounds pass through a series of plug flow reactor (PFRs) gasifiers operating at different temperatures, from 700 to 1000 °C, increasing in 50 °C for each reactor (RHETE-1 to RHETE-7) for heterogeneous reactions and (RHOMO-1 to RHOMO-7) for homogeneous reactions. For isothermal gasification, a stirred tank reactor (RCSTR) was used, with a residence time of 30 min. This reactor replaced the series of Plug Flow Reactors (PFRs) under isothermal conditions, ensuring the implementation of both homogeneous and heterogeneous reactions, as originally considered in the non-isothermal configuration. The details of each equipment are presented in Table 4.

<table><caption>**Table 4.** Details of Each Block and its Conditions</caption><thead><tr><td>Block</td><td>Model</td><td>Function and Conditions</td></tr></thead><tbody><tr><td>DRYING</td><td>RYield</td><td>Simulate biomass drying based on the value of the moisture content in the proximate analysis of biomass. The biomass with initial mass flow of 1500 kg/h.</td></tr><tr><td>PYROLYS</td><td>RYield</td><td>Simulate biomass pyrolysis based on the results data of pyrolysis experiment of biomasses</td></tr><tr><td>SEP-1 to SEP-3</td><td>SEP</td><td>The separator is configured as a split fraction separator, allowing the separation of gas-phase and solid-phase products based on their physical state.</td></tr><tr><td>CHAR-DEC</td><td>RStoic</td><td>Decompose char into its elemental components (C, H, O, N, and ash), according to the ultimate analysis</td></tr><tr><td>AIR-SEP</td><td>FSplit</td><td>Stream splitter. This separator was used to distribute the airflow equally to each reactor due to the non-isothermal condition of the process</td></tr><tr><td>MIX-1 to MIX-7</td><td>Mixer</td><td>This mixer combines the 'char' from the previous reactor with the airflow before entering the next reactor. It operates under the temperature and pressure conditions of the next reactor.</td></tr><tr><td>RHETE-1 to RHETE-7</td><td>RPlug</td><td>Simulation of heterogeneous gasification reactions (R1 to R4) was conducted using seven reactors, each operating at a different temperature ranging from 700 to 1000 °C, with increments of 50 °C per reactor. This approach was chosen to account for the non-isothermal nature of the gasification process.</td></tr><tr><td>SE-1 to SE-7</td><td>SSplit</td><td>Substream splitter (cyclone), this system allows the separation of solids, specifically char, from the reaction gases.</td></tr><tr><td>RHOMO-1 to RHOMO-7</td><td>RPlug</td><td>Simulation of homogeneous gasification reactions (R5 to R9) was conducted under the same conditions as the heterogeneous gasification, differing only in the reactions involved.</td></tr><tr><td>M-GAS</td><td>Mixer</td><td>Mix the product gas: Combines pyrolysis and gasification product gases along with the water vapor generated during drying.</td></tr></tbody></table>
In process modeling, several technical assumptions were considered:

* The process operates in a steady state.

* The reactor is assumed to be adiabatic.

* Non-condensable gases are limited to H₂, CH₄, CO, and CO₂.

* Char composition is modeled based on its ultimate carbon, hydrogen, nitrogen, oxygen, and ash content analysis.

* The gasifier system is considered a perfectly mixed environment.

* The operation is conducted at atmospheric pressure, with pressure drops in equipment neglected.

* Biomass drying and devolatilization (pyrolysis) are assumed to occur instantaneously at 700 °C for non-isothermal gasification and at 900 and 950 °C for isothermal gasification.

* NOx, HCN, and NH₃ concentrations were not taken into account; all biomass nitrogen is assumed to be inert (N₂).

* Pyrolysis product yields serve as initial conditions in the gasification step.
<figure><img src="image_4.png" /><figcaption>Fig. 2. Simulation model flowchart in Aspen Plus®</figcaption></figure>
# RESULTS AND DISCUSSION

## Comparison of CFD Modeling and Process Simulation with Experimental Data

This section presents a comparative analysis of CFD modeling and process simulation results with experimental data for gasification processes. The analysis begins with non-isothermal gasification outcomes, followed by an examination of isothermal gasification results. The experimental data were replicated with a maximum error of 7% across all cases.

### *Non-isothermal gasification results*

The results of non-isothermal gasification, derived from experimental analysis using TGA, were compared with predictions from CFD and SIM simulations. This comparative analysis aimed to assess the accuracy and predictive capabilities of the models across a range of ABR values (0.187 to 1). Experimental data served as a reference, emphasizing the influence of ABR on the yields of non-isothermal gasification products, including biochar, bio-oil, and non-condensable gases (NCG or biogas), within the temperature range of 700 to 1000 °C. The ABR in this study plays a role analogous to the equivalence ratio (ER), as commonly discussed in gasification studies (Beheshti et al. 2015; Torres et al. 2019), governing the oxygen availability relative to biomass and influencing the oxidation-reduction dynamics of the process. Figure 3 compares the overall product yields as a function of ABR variation.

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Comparison of experimental and simulation results for the main products yields of non-isothermal gasification at ABR values: a) biochar, b) NCG, and c) Bio-oil</figcaption></figure>

Both CFD and SIM exhibit deviations from experimental results, with the magnitude of these discrepancies varying based on the ABR. Figure 3a shows the comparison of biochar yield as a function of ABR. At low ABR values, such as 0.187, the SIM aligns closely with the experimental results, showing a minimal error of approximately 0.23 wt.%, whereas CFD slightly underestimates the biochar yield. However, as ABR increases, SIM overestimates the biochar yield, particularly at ABR = 0.375, where the error reaches 3.08 wt.%. In contrast, CFD consistently underestimates biochar yield across the entire ABR range, with the largest deviation observed at ABR = 0.375, reaching 3.55 wt.%. Both experimental and simulation results indicate that increasing ABR reduces biochar content, consistent with findings from non-isothermal gasification studies, where higher ABR values enhance oxidation intensity, reducing biochar yield (Torres et al. 2019).

The comparison of bio-oil yields indicates that the CFD model generally underestimates bio-oil yield at lower ABR values but aligns more closely with experimental data as ABR increases, as shown in Fig. 3b. For an ABR of 0.375, the prediction error of the CFD model was
1.04 wt.%, representing a better prediction under intermediate conditions. In contrast, the SIM model tends to overestimate the bio-oil yield at higher ABR value, with a notable maximum error of 5.87 wt.% at 0.25. This disparity highlights differences in how the models capture secondary reactions and product distributions.

The NCG yields (Fig. 3c) reveal distinct predictive behaviors for the two models across varying ABR values. At low ABR (e.g., 0.187), the CFD model significantly overestimates NCG yields, while the SIM model underestimates them. This contrast reflects differences in how the models handle reaction dynamics under oxidation conditions. As ABR increases, both models improve their agreement with experimental results. At ABR=0.375, the SIM model closely matches the experimental data, with an error of 1.44 wt.%, whereas the CFD model slightly overestimates yields, with an error of 2.52 wt.%. At the highest ABR (e.g., 1.0), the CFD model achieves near-perfect accuracy with an error of 1.77 wt.%, while the SIM model slightly underestimates NCG yields, with an error of 2.13 wt.%.

The analysis of individual non-condensable gases (H₂, CH₄, CO, and CO₂) shows discrepancies between experimental data and model predictions across all ABR values, as shown in Fig. 4.

<figure><img src="image_6.png" /><figcaption>**Fig. 4.** Comparison of experimental and simulation results for the NCG composition of non-isothermal gasification</figcaption></figure>

At low ABR (e.g., 0.187), experimental results reveal higher H₂ and CO ratios, reflecting the dominance of partial oxidation and water-gas reactions. The CFD model accurately captures these trends, with an error of 1.05 wt.% for CO, but underestimates CH₄. In contrast, the SIM model exhibits larger deviations for H₂ and CO, with errors exceeding 7.67 wt.% in some cases. As ABR increases, reaction pathways shift, resulting in higher CO₂ production and reduced CO
and CH₄ ratios. Experimental data at ABR=0.375 shows a significant increase in CO₂, which the CFD model predicts well, with errors below 3.62 wt.%. However, the SIM model underestimates CO₂ at higher ABR values, overestimates CO, and consistently underpredicts H₂. Both models struggle with CH₄ predictions, consistently underestimating its composition, though the CFD model demonstrates slightly better alignment than the SIM model.

Under non-isothermal conditions (700 to 1000 °C) and varying ABR (0.187 to 1), both models demonstrate significant predictive capabilities but show specific limitations. The SIM model exhibits greater accuracy in predicting global products, such as biochar, bio-oil, and NCG (gases), which can be attributed to its continuous-system approach and ability to capture general trends. However, certain discrepancies in the prediction of non-condensable gas compositions, such as H₂, CH₄, CO, and CO₂, indicate that the model would require refinements in its kinetic description and aspects of thermodynamic models to better represent the phase equilibria under non-isothermal conditions studied.

In contrast, the CFD model provides more accurate predictions of the non-condensable gas composition, better representing the thermochemical dynamics of the reactor, including reaction kinetics and heat transfer phenomena. However, discrepancies in the overall product yields were observed, suggesting the need for further refinement in modeling under these conditions.

The comparative analysis showed the complementary strengths of both models. While the SIM model better predicts overall product trends, the CFD model better predicts gas composition. This suggests that a hybrid modeling approach, which takes advantage of the strengths of both frameworks, could significantly improve the accuracy of predictions for non-isothermal gasification processes.

Isothermal Gasification Results

Figure 5 presents the comparison between experimental data and predicted values. The results reveal that both CFD and SIM consistently overestimate biochar yields compared to experimental data. At 900 °C, the CFD model overpredicts by 7.24 wt.%, decreasing slightly to 6.04 wt.% at 950 °C. Conversely, SIM exhibits more moderate overestimations, with differences of 3.6 w.% at 900 °C and 2.51 wt.% at 950 °C.

<figure><img src="image_7.png" /><figcaption>**Fig. 5.** Comparison of experimental and simulation results for the main products yields of isothermal gasification</figcaption></figure>

Both models overestimate the experimental results for bio-oil. At 900 °C, the CFD model predicts a yield of 36.67 wt.%, exceeding the experimental value by 10.09 wt.%. This discrepancy increases to 11.59 wt.% at 950 °C. In contrast, SIM achieves better accuracy with deviations of 1.89 wt.% and 1.72 wt.% at 900 °C and 950 °C, respectively.

In the case of NCG, both models underestimate experimental yields. The CFD
significantly underpredicts with differences exceeding 17 wt.% for both temperatures. While still underestimating, SIM exhibits smaller deviations of 5.49 wt.% at 900 °C and 4.24 wt.% at 950 °C. Overall, the models overestimate biochar and bio-oil yields while significantly underestimating NCG. SIM aligns more closely with experimental data across all product types, whereas CFD exhibits more significant deviations, particularly in gas-phase reactions. Both models perform slightly better at higher temperatures (950 °C) but require further refinement for improved accuracy. Figure 6 shows that the analysis of gas compositions reveals distinct differences between experimental data and the predictions of CFD and SIM models at 900 and 950 °C.

<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Comparison of experimental and simulation results for NCG composition of isothermal gasification</figcaption></figure>

At 900 °C, both models follow similar trends, slightly overestimating H₂, CH₄, and CO while underestimating CO₂. For H₂, the CFD model shows a deviation of 3.71 vol.%, whereas SIM demonstrates more accurate predictions with a smaller difference of 0.7 vol.%. Both models demonstrated minimal differences for CH₄, with CFD deviating by 0.66 vol.% and SIM by 0.32 vol.%. For CO, CFD overestimates by 4.85 vol.%, while SIM shows a smaller difference of 3.24 vol.%. For CO₂, both models underestimate its content, with CFD showing a difference of 9.22 vol.% compared to 4.26 vol.% for SIM.

At 950 °C, the CFD model slightly overestimates H₂ content, with a deviation of 2.55 vol.%, while SIM achieves greater accuracy, showing a difference of only 0.52 vol.%. For CH₄, both models agree better with the experimental data, with differences of less than 0.8 vol.%. For CO, CFD underestimates the content by 2.98 vol.%, whereas SIM shows a smaller difference of 0.82 vol.%. Finally, for CO₂, both models achieve highly accurate predictions, with differences below 0.5 vol.%. Overall, SIM demonstrates better alignment with experimental data, particularly for H₂ and CO at 950 °C, while CFD exhibits greater variability, especially at 900 °C, where it overestimates H₂ and CO. Both models' persistent underestimation of CO₂ suggests a need for refining the kinetic dynamics under gasification conditions.
Although both models underestimate certain yields (biochar and bio-oil) under non-isothermal gasification conditions, SIM generally agrees better with experimental data for overall product yields. However, the CFD model excels in predicting gas compositions, reflecting its ability to capture the dynamics of specific reactions, particularly under non-equilibrium conditions, where temperature gradients may exist in the reactor. Under isothermal gasification conditions, where the system approaches thermal equilibrium, the SIM model accurately predicts overall product yields and gas compositions, consistent with its thermodynamic equilibrium-based assumptions.

## Sensitivity Analysis of Isothermal Gasification Processes

The sensitivity analysis of isothermal gasification was conducted using CFD modeling and process simulations to evaluate the impact of the key parameters: (i) the isothermal gasification temperature, from 700 to 1500 °C; and (ii) the ABR values between 1 and 2.5. These factors were analyzed to determine their influence on the overall products yields in the process and the composition of the produced.

### *Effect of ABR and temperature on overall products yields*

The yields of the main products —biochar, bio-oil, and NCG— are significantly influenced by temperature and ABR, as illustrated in Figs. 7 through 9. Biochar, as shown in Fig. 7, decreases progressively with increasing temperature. This reduction is attributed to the activation of heterogeneous reactions in the solid phase, particularly carbon oxidation (R-1) (Al-Zareer *et al.* 2016; He *et al.* 2013; Wen and Chaung 1979) and the Boudouard reaction (R-3) (He *et al.* 2013). These endothermic reactions are favored at higher temperatures. Incomplete carbon conversion results in higher biochar yields at lower temperatures and ABR values. However, oxygen availability promotes oxidation as the ABR increases, accelerating biochar conversion into gases. In this context, CFD and SIM simulations exhibit similar trends, though CFD predicts higher biochar yields and a more gradual decline than SIM. This emphasizes the influence of modeling approaches on product distribution.

<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Effect of ABR and temperature on biochar yield: SIM and CFD comparison</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 8.** Effect of ABR and temperature on bio-oil yield: SIM and CFD comparison</figcaption></figure>

Bio-oil, primarily comprising condensable gas products and water vapor, is influenced by temperature and ABR, as shown in Fig. 8. During pyrolysis, biomass undergoes thermal decomposition, releasing vapors that condense into bio-oil. As the temperature increases, these vapors undergo secondary reactions, such as steam reforming and thermal cracking, promoting the formation of non-condensable gases (Sangaré *et al.* 2024b; Zhu *et al.* 2018). Additionally, during gasification, increased ABR enhances biochar oxidation through oxygen supply, further decreasing bio-oil production. The CFD and SIM models exhibit similar trends, but CFD predicts a more gradual decrease in bio-oil yield, whereas SIM shows an abrupt decline, indicating distinct modeling behaviors under similar conditions.

<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Effect of ABR and temperature on NCG yield: SIM and CFD comparison</figcaption></figure>
The yields of NCG, including H₂, CH₄, CO, and CO₂, increase significantly with the increasing temperature and ABR, as shown in Fig. 9. This is due to the dominant role of heterogeneous reactions (R-1 to R-4) (He et al. 2013; Al-Zareer et al. 2016). At low ABR values and high temperatures, limited oxidation favors the production of combustible gases such as H₂ and CO. However, as ABR increases, homogeneous oxidation reactions become more prominent, leading to a higher proportion of CO₂ (Jangsawang et al. 2015), increased overall yields, and reduced proportions of combustible gases. The CFD and SIM models exhibit similar trends, but their distributions differ: the SIM model predicts higher and more uniform NCG yields with increasing ABR, while the CFD model shows lower yields under similar conditions.

Overall, the observed differences between CFD and SIM predictions highlight the influence of model assumptions on phase interactions and reaction dynamics. CFD offers a detailed perspective by explicitly accounting for heterogeneous and homogeneous phase reactions, while the SIM equilibrium-based approach results in a more uniform phase distribution.

*Effect of ABR and temperature on gas composition*

The effects of ABR and temperature on gas composition are presented in Figs. 10 through 13. As depicted in Fig. 10, the volumetric fraction of H₂ consistently increases with temperature in both models, reflecting the endothermic nature of heterogeneous gasification reaction (R-2).

<figure><img src="image_12.png" /><figcaption>**Fig. 10.** Effect of ABR and temperature on H₂ composition: SIM and CFD comparison</figcaption></figure>

This reaction predominantly occurs on the solid surface and is facilitated by increased thermal energy, resulting in greater H₂ production. Under low ABR conditions, where oxygen availability is limited, H₂ reaches its maximum concentration as homogeneous oxidation reactions, such as R-5, are constrained. Conversely, at higher ABR values, H₂ is consumed in oxidation reactions to produce water vapor, reducing its concentration. This phenomenon, as described by Kumar and Paul (2020), underscores the sensitivity of these endothermic reactions to temperature changes and their influence on gas composition during gasification process. Both heterogeneous and homogeneous reactions contribute to H₂ production, yielding similar trends across the two models.

The evolution of H₂ is directly linked to the behavior of CH₄, as shown in Fig. 11. At low temperatures, CH₄ formation is dominated by the heterogeneous methanation reaction (R-
4), where solid carbon reacts with H₂ in an exothermic. However, as the temperature increases, CH₄ undergoes decomposition via steam reforming (R- 9), an endothermic reaction that generates H₂ and CO. Notably, most of the CH₄ in the gas mixture originates from pyrolysis. In both models, CH₄ concentrations decrease with increasing ABR, as oxygen promotes oxidation, which constrains CH₄ formation. Furthermore, high temperatures lead to a slight reduction in CH₄, particularly in the SIM results, due to enhanced gas-phase reaction reforming reaction (R- 9). However, the CFD model predicts higher CH₄ concentrations than the SIM results, especially at high temperatures and low ABR values.

<figure><img src="image_13.png" /><figcaption>**Fig. 11.** Effect of ABR and temperature on CH₄ composition: SIM and CFD comparison</figcaption></figure>

The behavior of CO, shown in Fig. 12, highlights the important role of heterogeneous
reactions such as the partial oxidation of solid carbon (R- 1), which produces CO and CO₂. The
Z coefficient in Eq. 14 regulates this reaction, increasing with temperature and favoring CO
production at low ABR values. As the temperature increases, the Boudouard reaction (R- 3) and
the steam reaction (R- 2) dominate, converting CO₂ and water vapor into CO and H₂. These
heterogeneous reactions are key drivers of CO formation (Kumar and Paul 2020). However, at
high ABR values, homogeneous oxidation reactions (R- 7) consume CO at high ABR values,
reducing its concentration. While the CFD model better captures localized surface interactions,
the SIM results align well with theoretical expectations, particularly at high temperatures and
ABR values, which is likely due to its representation of the homogeneous CO oxidation reaction
(R- 7).

CO formation is predominantly driven by heterogeneous reactions in the solid phase, whereas CO₂ production is mainly associated with homogeneous reactions, such as CO oxidation (R- 7), CH₄ combustion (R- 6), and the Water-Gas Shift reaction (R- 8) at lower temperatures (Jangsawang *et al.* 2015). Figure 13 illustrates the interactions of ABR and temperature on CO₂ composition.
<figure><img src="image_14.png" /><figcaption>Fig. 12. Effect of ABR and temperature on CO composition: SIM and CFD comparison</figcaption></figure>

<figure><img src="image_15.png" /><figcaption>Fig. 13. Effect of ABR and temperature on CO₂ composition: SIM and CFD comparison</figcaption></figure>

Theoretically, CO₂ production is favored at low temperatures and high ABR due to the increased availability of oxygen, promoting oxidation reactions. However, CO₂ is consumed at higher temperatures via the Boudouard reaction (R-3), resulting in its progressive decrease in high-temperature, low ABR conditions. Figure 13 also reveals differences between CFD and SIM predictions. The CFD model shows higher values for CO₂ concentrations compared to the SIM results, particularly at higher ABR and temperatures. This discrepancy may be linked to homogeneous reactions. Ajorloo et al. (2022) emphasize the significance of homogeneous reactions in determining CO₂ distribution. The CFD model explicitly separates heterogeneous reactions in the solid phase from homogeneous reactions in the gas phase, whereas the SIM model assumes a uniform distribution of solids and gases throughout the reactor. These differences in phase representation may lead to variations in how phase interactions and reaction dynamics are modeled.
In summary, the formation of H₂ and CH₄ primarily arises from heterogeneous reactions, with both models showing similar trends. In contrast, while CO is mainly generated through heterogeneous reactions, it is subsequently consumed during homogeneous reactions to form CO₂. The discrepancies in CO and CO₂ trends between the models likely stem from differences in the treatment of homogeneous reactions, particularly CO oxidation, and the representation of phase interactions in each modeling approach.

## CONCLUSIONS

1. This study has provided a detailed comparative analysis of computational fluid dynamics (CFD) and process simulation (SIM) approaches for modeling biomass pyro-gasification, supported by experimental data from agave bagasse. The findings emphasize the complementary strengths of these methodologies. Indeed, concerning modeling performance and applicability, CFD proved superior for non-isothermal conditions (700 to 1000 °C), accurately capturing heat and mass transfer dynamics and localized reactions, which are critical for understanding reactor behavior under temperature gradients, while SIM demonstrated better alignment with experimental yields under isothermal conditions (900 to 950 °C), where equilibrium-based models provide reliable predictions.

2. Sensitivity analysis showed the influence of temperature and ABR on overall product yield distributions and non-condensable gas compositions, demonstrating how these parameters can be used to optimize syngas production. Furthermore, the results underline the importance of selecting modeling methods based on process conditions.

3. For instance, CFD is highly suitable for reactors with thermal gradients, while SIM offers reliable performance in systems operating at near-equilibrium conditions. A hybrid modeling approach combining the detailed insights of CFD with the scalability of SIM could facilitate the development of more efficient biomass conversion technologies.

4. By leveraging the complementary capabilities of CFD and SIM, researchers and industry practitioners can address critical challenges in biomass pyro-gasification, accelerating the development of sustainable and efficient energy solutions.

## ACKNOWLEDGMENTS

The authors express their gratitude to CONACYT (Consejo Nacional de Ciencia y Tecnología de México) for awarding scholarship No. 659624, which provided essential support for this research. We also acknowledge CIRAD-BioWooEB teams (France) for their technical support and the loan of equipment used in the analyses. Additionally, we thank ICARE-CNRS (France) for providing laboratory equipment and technical assistance, as well as the Région Centre-Val de Loire for financial support through the FITe federation.

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Article submitted January 4, 2025; Peer review completed: February 1, 2025; Revised version received: February 7, 2025; Accepted: February 9, 2025; Published: February 21, 2025. DOI: 10.15376/biores.20.2.2844-2870