Next Article in Journal
Process Design and Kinetic-Based Simulation of a Coupled Biomass Gasification and Chemical Looping Ammonia Generation System
Previous Article in Journal
Performance Impact of the Nano-Colloidal Aphron-Based Drilling Fluids on Rheological and Filtration Properties
Previous Article in Special Issue
Experimental Study on Microscopic Distribution Characteristics of Water-Sealed Gas in Pore–Fracture Carbonate Gas Reservoirs
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Processes
Volume 14
Issue 4
10.3390/pr14040589
processes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Optimization of Burnout Air Parameters in a Large-Scale Biomass Grate Boiler: A CFD Study with Engineering Validation

by
Yan Liang
1,
Shidan Chi
1,
Anxin Chen
1,
Yong Luo
1,
Xiaoxu Zhang
2,
Changqun Dong
2,
Shuying Zhang
1,
Weixi Chen
3 and
Yan Gao
2,*
1
Shandong Electric Power Engineering Consulting Institute Co., Ltd., Jinan 250061, China
2
Jinan Key Laboratory of High-Efficiency Hydrogen Production and Storage & Hydrogen Energy Utilization in Low-Carbon Buildings, School of Thermal Engineering, Shandong Jianzhu University, Jinan 250101, China
3
School of Nuclear Science, Energy and Power Engineering, Shandong University, Jinan 250013, China
*
Author to whom correspondence should be addressed.
Processes 2026, 14(4), 589; https://doi.org/10.3390/pr14040589
Submission received: 2 October 2025 / Revised: 18 January 2026 / Accepted: 5 February 2026 / Published: 8 February 2026
(This article belongs to the Special Issue Recent Developments on Waste-to-Energy Conversion: Biomass, Waste Gasification, Pyrolysis and Combustion Processes)
Browse Figures
Versions Notes

Abstract

Under the context of carbon neutrality, optimizing biomass boiler efficiency is crucial. This study employed Computational Fluid Dynamics (CFD) to investigate the impact of a dedicated burnout air system on combustion in a 130 t/h biomass grate boiler. A high-fidelity model was established and validated against field measurements, with relative errors within 7%. The research systematically analyzed the effects of burnout air parameters, including outlet velocity, pipe diameter, and injection angle. Results showed that implementing the burnout air significantly enhanced combustion efficiency. Increasing the outlet velocity effectively elevated the oxygen concentration and expanded its distribution in the rear grate section, which intensified the burnout of unburned carbon particles. The char burnout ratio was remarkably improved from 76.5% (baseline) to a maximum of 87.8% under optimized conditions, representing a 14.8% relative improvement. A larger pipe diameter also improved oxygen availability and flue gas temperature, enhancing turbulent mixing. In contrast, variations in the injection angle demonstrated minimal effects. The rational adjustment of the burnout air velocity and pipe diameter is key to optimizing boiler efficiency, although these parameters require careful balancing to mitigate potential slagging risks associated with excessively high furnace outlet temperature. This work provides novel, industrially validated insights into the optimization of a dedicated burnout air system for large-scale biomass grate boilers, highlighting a critical balance between burnout enhancement and slagging suppression.

1. Introduction

Against the backdrop of global efforts to address climate change and transition towards sustainable development, deep transformation of the energy structure centered on the “Carbon Neutrality” goal has become a common strategic choice for nations worldwide [1,2]. This ambitious goal necessitates a fundamental reshaping of energy production and consumption patterns, progressively reducing reliance on fossil fuels, and vigorously developing clean, low-carbon renewable energy technologies [3,4]. Amidst the challenges to grid stability posed by the large-scale integration of intermittent energy sources like wind and solar power, biomass energy occupies an increasingly strategic position in the global energy landscape due to its unique multiple advantages [5,6].
Biomass energy derived from organic materials, such as plants and algae, essentially stores chemical energy fixed from solar energy via photosynthesis. Its core advantage lies in the “Carbon-Neutral” cycle characteristic: biomass absorbs carbon dioxide (CO2) from the atmosphere during growth and releases it back upon combustion, theoretically resulting in no net increase in greenhouse gas emissions [7,8]. Biomass resources are vast, widely distributed and possess significant potential to serve as baseload power, including crop straw, forestry residues, energy crops and organic municipal waste [9]. Compared to variable sources like wind and solar, biomass energy is storable and dispatchable, capable of providing stable and reliable electrical and thermal output, playing an irreplaceable role in ensuring energy system security and mitigating fluctuations from new energy sources [10].
Among various biomass utilization technologies, direct combustion for power generation is currently the most technologically mature, widely applied, and scaled-up core pathway [11]. Grate-fired combustion technology, particularly reciprocating grate boilers, has become the mainstream choice for global industrial boilers and small-to-medium-sized biomass power plants, owing to its excellent adaptability to fuel type, size, and moisture content, as well as relatively simple fuel pre-treatment requirements [12,13]. The inherent complexity and variability of biomass fuels pose severe challenges to the stable and efficient operation of grate-fired boilers, such as chemical composition, moisture content and ash characteristics significantly influenced by season and region [14,15]. Due to suboptimal combustion organization and insufficient mixing of air and fuel, boilers commonly face issues like relatively low combustion efficiency, high carbon content in bottom ash, and difficulties in controlling pollutant emissions (especially nitrogen oxides, NOx). This not only wastes energy but also undermines the economic viability and environmental benefits of biomass power generation [16]. In-depth analysis of the biomass grate-firing process, precise regulation of combustion conditions, and achieving its clean and efficient utilization have become critical scientific and engineering problems urgently requiring solutions in this field [17,18].
The combustion process within a grate-fired boiler is a complex, multi-stage, multi-physics coupled process occurring on a fixed or moving grate. Fuel on the grate sequentially undergoes stages of preheating and drying, devolatilization and volatile combustion, char combustion, and burnout. To accommodate the different oxygen demands of these stages, combustion air is typically supplied in stages: primary air supplied beneath the grate and secondary air (or Over-Fire Air, OFA) supplied into the upper furnace. Primary air primarily supports fuel drying, pyrolysis, and char combustion, while also controlling bed temperature. Secondary air aims to mix thoroughly with volatiles and combustible gases (e.g., CO, H2) from the primary combustion stage, achieving gas-phase combustion, ensuring complete fuel burnout, and suppressing pollutant formation.
Despite the clear principles of grate-firing technology, challenges in practical operation remain severe [19,20]. Grate-fired boilers can “accommodate” different types of biomass fuels, but drastic fluctuations in fuel properties, such as the sudden feeding of high-moisture straw, can severely disrupt bed temperature and combustion rates, leading to deteriorated combustion conditions or even flame extinction [21]. Dynamically optimizing the air distribution strategy based on real-time fuel characteristics to maintain stable combustion represents a core operational challenge. Incomplete combustion is the primary cause of low efficiency, mainly manifested in two ways: unburned carbon particles in the fly ash carried away by flue gas, and high carbon content in the bottom ash slag due to unburned char. This typically results from uneven penetration of primary air through the fuel bed, insufficient mixing of secondary air with combustible gases, or inadequate residence time of gases in high-temperature zones. Biomass fuels contain nitrogen and sulfur, leading to the formation of nitrogen oxides (NOx) and sulfur oxides (SOx) during combustion [22]. The mechanisms of NOx formation are complex, involving both Fuel-NOx derived from nitrogen in the fuel and Thermal-NOx generated from the oxidation of atmospheric nitrogen at high temperature [23]. Precise air staging is the mainstream technique for NOx control. It involves creating a fuel-rich, reducing atmosphere in the lower furnace to suppress NOx formation, followed by the introduction of secondary air to complete combustion.
Confronting these challenges, the traditional trial-and-error optimization approach based on field tests is not only costly and time-consuming but also high-risk and limited in providing comprehensive insights into the complex combustion mechanisms inside the furnace. With the rapid advancement of computational power and the continuous improvement of Computational Fluid Dynamics (CFD) theory, CFD-based numerical simulation has become a powerful tool for studying and optimizing boiler combustion processes [24]. CFD can construct a virtual 3D model identical to the actual boiler, accurately simulating complex physico-chemical processes such as gas–solid two-phase flow, heat and mass transfer, chemical reactions, and even pollutant formation by solving a series of partial differential equations for mass, momentum, energy, and species conservation, thereby enabling visual analysis and quantitative prediction of combustion conditions [25].
In recent years, researchers worldwide have conducted extensive in-depth studies on biomass grate-fired boilers using CFD technology, yielding fruitful results [26,27]. Regarding air distribution strategy and secondary air system optimization, some studies analyzed the combustion process of mixed biomass pellets in a boiler, focusing particularly on the impact of the primary-to-secondary air ratio on the combustion process, emission characteristics, and thermal balance. Results indicated that a 60/40 (primary/secondary) air distribution ratio led to more complete combustion and improved boiler efficiency [28]. Other sensitivity analyses on primary and secondary air showed that changing the primary-to-secondary air ratio from 79/21 to 40/60 reduced the average CO mass fraction at the furnace outlet by over 50%. The average furnace temperature increased to a maximum of 1323.15 K, enhancing combustion efficiency. Results also indicated that shifting char combustion towards the rear of the grate caused temperature imbalances near the boiler walls, emphasizing the importance of grate-specific conversion curves. These findings highlight the importance of optimized air distribution [13].
Regarding advancements in fuel bed combustion modeling, the combustion of the solid fuel bed on the grate is the boundary condition and foundation of the entire process, and its accurate simulation is crucial. To overcome the limitations of traditional CFD models that simplify the bed as a porous medium or fixed heat/species source, advanced models have been developed. The study used an Eulerian–Lagrangian approach to resolve the intense combustion in both the grate and fuel bed within a single framework [12]. The results showed uneven distribution of key parameters across the grate width, poorer airflow and combustion conditions near the water-cooled walls compared to the furnace center. This led to significant delays in volatile release and char oxidation near the walls, ultimately causing notable differences in temperature and species distributions. At 3.3 m from the fuel inlet, the peak CO concentration at the center was 12.6%, while near the water-cooled wall, the peak occurred around 3.5 m with a concentration of 8.6%. It provided a method for calculating fly ash particles using size grouping and non-spherical particles. The study, based on a 3D Eulerian–Lagrangian model, analyzed the effect of combustion pressure (P) on gas–solid flow and reaction characteristics [29]. The results demonstrated that the model accurately predicted flow structure, temperature, and concentrations of CO2, CO, O2, NO, N2O, and SO2. Increased pressure created favorable gas–solid flow and chemical reaction conditions, leading not only to better temperature distribution and higher CO2 concentration in the flue gas but also reduced pollutant emissions. The study advanced understanding by showing that increased pressure, without altering local gas velocity but increasing heat input, constructed favorable conditions.
In terms of pollutant formation mechanisms and control, CFD simulation has also shown great potential in predicting and controlling NOx emissions [30]. By coupling detailed NOx formation and reduction chemical mechanisms into CFD models, researchers can quantitatively analyze the impact of different air staging strategies (e.g., vertical placement height and flow rate of secondary air) on NOx emissions. Studies confirm that positioning secondary air slightly above the main combustion zone while maintaining an appropriate overall excess air ratio can establish a stable reducing atmosphere in the lower furnace, maximizing the conversion of fuel nitrogen to harmless N2, thereby achieving NOx reduction at the source.
Although the aforementioned studies provide valuable insights into combustion optimization in biomass grate-fired boilers from perspectives like primary-to-secondary air ratio, secondary air configuration, and velocity, most focus on the front and middle sections of the combustion process. For large-scale biomass boilers, especially when burning high-volatility, low-density straw-based fuels, a significant amount of char particles undergo final combustion in the rear section (burnout zone) of the grate. The combustion efficiency in this stage directly determines the final carbon content in the bottom ash and is key to improving the overall thermal efficiency of the boiler [31].
The systematic and quantitative research on the air distribution method specifically for the rear grate zone, the dedicated “Burnout Air” system and its impact on the overall combustion process, particularly its application in large-scale industrial straw-fired boilers, remains relatively scarce [32]. Key questions, such as whether the design and regulation of burnout air can effectively enhance char burnout in the rear section and reduce slagging loss, and how it might conversely affect the temperature field, species concentration field in the upper furnace, and pollutant formation, await in-depth investigation [33].
This paper takes a typical 130 t/h large-scale straw-fired grate boiler as the specific research object. Distinguished from previous CFD studies that primarily focused on primary-to-secondary air ratios or general secondary air configurations, this work specifically targets the dedicated ‘burnout air’ system at the rear grate section, a critical yet under investigated component for large-scale boilers burning high-volatility biomass. The advanced CFD numerical simulation technology to construct a high-fidelity, full-scale three-dimensional gas–solid two-phase combustion model is employed. The core of the research focuses on systematically evaluating the effects of key burnout air parameters (outlet velocity, pipe diameter, and injection angle) on the flow field and combustion characteristics in the furnace rear section. By comparatively analyzing key performance indicators, such as the char burnout ratio, flue gas temperature distribution and species concentration fields (e.g., O2, CO and CO2), this study aims to quantitatively reveal the mechanisms through which rear-stage burnout air enhances combustion completeness. The findings are expected to provide direct scientific guidance for optimizing the operation of similar large-scale biomass boilers and offer solid theoretical support for the design of refined air distribution strategies, thereby contributing to improved efficiency and reduced carbon emissions in biomass power generation.

2. Materials and Methods

2.1. Physical Model of the Grate-Fired Boiler and Geometric Modeling

The study object is a 130 t/h biomass grate boiler. The main furnace geometry is illustrated in Figure 1a. The combustion chamber has a cross-section of 6.480 m (width, X-direction) × 9.2 m (depth, Y-direction) and a total height of 23.9 m from the grate surface to the drum centerline. The traveling grate is 8.5 m long. The combustion air supply system of the boiler employed a staged air distribution approach. Primary air was supplied from the windbox located beneath the grate, entering and penetrating the fuel bed through air holes on the grate segments. The secondary air system was arranged on the front and rear walls within the furnace throat region. The secondary air nozzles on the front wall were arranged in three vertical levels (upper, middle, lower), with their precise locations illustrated in Figure 1. To monitor the combustion temperature distribution within the furnace, four temperature measurement points were installed along the height of the side walls.
The staged combustion air system comprises the following: (1) Primary Air: Supplied uniformly from the windbox beneath the grate. (2) Secondary Air: Introduced through nozzles on the front and rear walls. On the front wall, three levels of secondary air nozzles are located at heights of 2.5 m (lower), 3.5 m (middle), and 4.5 m (upper) above the grate. Each level contains 4 nozzles with an inner diameter of 150 mm. On the rear wall, one level of secondary air nozzles is located at 3.0 m above the grate, consisting of 4 nozzles (inner diameter 140 mm). (3) Burnout Air: The focus of this study, two burnout air nozzles were added on the rear wall, positioned 0.5 m above the grate and 1.5 m from the side walls. The pipe inner diameter was varied as a parameter (76, 89, 108 mm). The injection angle was defined relative to the horizontal plane.
The boiler was designed to combust a blend of biomass fuels. Under normal operation, the primary fuel is processing residues of Chinese fir (Cunninghamia lanceolata), mixed with auxiliary fuels like pine bark, sawdust, and bamboo to achieve a consistent feed. For this simulation, a representative, time-averaged fuel blend based on plant records was used, and its proximate and ultimate analyses are presented in Table 1. The Chinese fir residues, which constitute the major fraction, typically exhibit high volatile matter (>70% dry basis) and a low ash softening temperature (often below 1200 °C), characteristics common to many herbaceous and agricultural biomass fuels. The blending of auxiliary fuels primarily adjusts the overall moisture and calorific value towards design specifications. The main design and operational parameters of the boiler were summarized in Table 2.
Based on the actual structural dimensions of the boiler, a three-dimensional physical model of the furnace was constructed. Prior to numerical simulation, the entire computational domain was discretized using a hybrid structured grid. To ensure computational accuracy, local grid refinement was applied to critical regions, including the secondary air nozzles on the front and rear walls and the newly added burnout air nozzles at the rear. A grid independence study was conducted to verify the insensitivity of the results to grid resolution. The mesh quality was assessed using standard metrics. The average cell skewness was 0.18, and the minimum orthogonal quality was 0.25, indicating a good-quality grid suitable for the simulation. A systematic grid independence study was conducted using three mesh sizes: coarse (2.10 million cells), medium (2.99 million cells), and fine (4.10 million cells). The solution was considered grid-independent when further refinement resulted in negligible changes in key global parameters. For the medium grid, the differences in the area-averaged gas temperature and O2 concentration at the furnace outlet plane, compared to the fine grid, were less than 0.8% and 0.5%, respectively. The velocity flow field on the central cross-section also showed no significant visual differences between the medium and fine grids. The medium grid (2.99 million cells) was deemed sufficient for achieving a balance between computational accuracy and cost.

2.2. Numerical Method and Sub-Model Description

2.2.1. Numerical Method

The numerical simulations were conducted using the commercial CFD software ANSYS Fluent (Version 2022 R1). For its robustness in handling incompressible and weakly compressible reacting flows, the pressure-based coupled solver was employed. All transport equations were discretized using a second-order upwind scheme [34,35]. The convergence of the iterative solution was judged based on dual criteria: the scaled residuals for all governing equations (continuity, momentum, energy, species, k, ε) had to decrease below 1 × 10−4, with a stricter requirement of 10−6 for the energy equation; the key physical quantities had to stabilize, exhibiting variations in less than 1% over the final 500 iterations, specifically the area-weighted average gas temperature and O2 concentration at the furnace outlet. These combined criteria ensured that a steady-state and physically consistent solution was attained for each simulation case.
The finite volume method within the commercial CFD software ANSYS Fluent was employed to solve the governing equations for mass, momentum, energy, and species conservation. The pressure-velocity coupling was handled using the SIMPLE algorithm, and second-order upwind discretization schemes were applied for all convective terms to enhance accuracy [36,37].

2.2.2. Combustion and Reaction Models

The Reynolds-Averaged Navier–Stokes (RANS) approach was employed [38,39]. Turbulence was modeled using the standard k-ε model with standard wall functions. The model constants were: C1ε = 1.44, C2ε = 1.92, Cμ = 0.09, turbulent Prandtl number σk = 1.0, and σε = 1.3. This model was selected for its robustness, computational efficiency, and proven reliability in simulating fully developed turbulent flows in large-scale boiler furnaces, where accurate prediction of mean flow and mixing is the primary concern.
Gas-phase combustion of the released volatiles (simplified as CH4) was simulated using the finite-rate/eddy-dissipation model [21,40]. The net reaction rate was taken as the minimum of the Arrhenius kinetic rate and the turbulent mixing rate. The global two-step mechanism was:
CH4 + 1.5O2 → CO + 2H2O
CO + 0.5O2 ↔ CO2
The Arrhenius parameters for these reactions were adopted from the default values in the ANSYS Fluent database for methane-air combustion.
The solid fuel bed on the grate was modeled as a fixed porous zone with a thickness of 0.5 m. Instead of resolving individual fuel particles, the bed was assigned a porosity of 0.4 and a viscous resistance coefficient of 1.0 × 108  kg/(m3·s) to approximate the pressure drop across the dense fuel layer. The source terms for mass, species, and energy from the bed processes (drying, devolatilization, and char combustion) were imposed as constant fluxes on the top surface of this porous zone. The longitudinal distribution profile of these sources (e.g., volatile release rate peaked in the mid-grate section) was derived from plant heat balance data and characteristic conversion profiles for reciprocating grates, ensuring the correct total input and spatial trend entered the furnace chamber.
Turbulent flow within the furnace was modeled using the standard k-ε model with standard wall functions, which provides a robust and computationally efficient solution for the fully developed turbulent flow typical of large boiler furnaces. Gas-phase combustion was simulated using the finite-rate/eddy-dissipation model. The reaction rates were controlled by the slower of the Arrhenius kinetic rate and the turbulent mixing rate. A global two-step reaction mechanism for methane oxidation was employed to represent the volatile combustion, as volatiles are the dominant gaseous fuel in biomass combustion.
Radiative heat transfer was modeled using the P1 radiation model. The absorption coefficient of the flue gas was calculated using the weighted-sum-of-gray-gases model (WSGGM) [41,42]. The P1 model was selected for its good accuracy and relatively low computational cost in geometries with high optical thickness, such as boiler furnaces containing absorbing/emitting gases and particles. The primary focus of this study was on combustion efficiency and flow dynamics influenced by burnout air. The detailed kinetic models for NOx and polycyclic aromatic hydrocarbons (PAHs) formation were not activated in the current simulations.

2.3. Assumption and Boundaries

The finite volume method was used for discretization. The pressure-velocity coupling was handled using the SIMPLE algorithm. Second-order discretization schemes were applied for all calculated physical quantities, and standard wall functions were used for solid wall treatment. Other model assumptions and boundary condition settings are as follows:
(1)
For the baseline air distribution case (Case 0): The primary air ratio was 30%. The secondary air velocities for the upper, middle, and lower levels on the front wall were 32 m/s, 50 m/s, and 36 m/s, respectively.
(2)
The secondary air velocity on the rear wall was 57 m/s.
(3)
Boundary definitions: The flow inlet was defined at the primary air outlet from beneath the grate.
(4)
Velocity inlets were specified for all secondary air outlets, and a pressure outlet was defined at the furnace outlet.
(5)
The total air flow rate of the air distribution system remained constant.
(6)
The burnout air was extracted from the total secondary air allocation. The maximum allowable ratio for the burnout air was limited to 7.3% of the total air supply. This limit was determined based on the existing boiler design constraints, ensuring that the flow rates to the conventional secondary air nozzles on the front and rear walls remained above their minimum required velocities (as per original design specifications) to maintain their essential functions of gas mixing and combustion stabilization. The case with this maximum ratio (Case C60) was included in the simulation matrix to evaluate the upper bound of performance enhancement and potential risks.
All simulations were conducted to replicate the boiler’s rated operating condition, which served as the basis for field validation. The total air flow rate was calculated from the design fuel feed rate (35.93 t/h) and the design excess air ratio of 1.3. All air streams (primary air, secondary air on the front and rear walls, and burnout air) were defined as velocity-inlet boundaries. The velocity magnitude for each air stream was determined based on its assigned volumetric flow rate (according to the distribution in Table 3) and the total physical flow area of its nozzles. A uniform velocity profile was specified at each inlet, which is a standard simplification in large-scale boiler simulations, based on the premise that upstream ducting is designed to provide well-distributed flow. The turbulence at the inlets was defined as having a medium turbulence intensity of 5%. All combustion air streams were preheated to 473 K. The furnace outlet was defined as a pressure-outlet boundary at atmospheric pressure (0 Pa gauge). All other walls were treated as no-slip, stationary walls using standard wall functions. A constant heat flux was applied to the walls according to the boiler’s design heat absorption profile.
To reduce the carbon content in the bottom ash and improve boiler combustion efficiency, a burnout air distribution system was added at the rear section of the grate. The air distribution system described herein consists of the primary air system, secondary air system, and the tail-end burnout air system. The burnout air is extracted from the total secondary air allocation. Under the constraint of a constant total air flow rate, and with a fixed number of burnout air ducts, its outlet velocity and diameter determine its proportion of the total air flow. The variations in these parameters affect the flow field inside the furnace and the motion characteristics of unburned carbon particles in the rear grate section. This study conducted comparative simulations between the baseline air distribution system and systems with different burnout air ratios, as detailed in Table 3.

3. Results and Discussion

The following results were obtained using the numerical models and setup described in detail in Section 2.2. The analysis focuses on the impact of burnout air parameters on key combustion indicators, including in-furnace species concentration, temperature fields, and the overall char burnout ratio.

3.1. Model Reliability Validation

The simulation results of the biomass grate-fired boiler under the initial operating condition were compared with field measurement data for validation. The validation relied on temperature data from four fixed thermocouples (P1–P4) installed at different heights along the boiler side walls, as well as O2 concentration measured at the rear of the grate. These measurement points were selected because they are standard monitoring locations in the industrial boiler, providing reliable and representative data on the spatial temperature distribution and combustion atmosphere. The specific coordinates of the temperature points are listed in Table 4, and their schematic locations are shown in Figure 1b. The in-furnace temperature was measured using type-K shielded thermocouple with an estimated uncertainty of ±1.5 K. The O2 concentration at the rear of the grate was obtained from KM9106 Comprehensive Flue Gas Analyzer (Kane, Welwyn Garden City, UK, uncertainty: ±0.1 vol.%). The carbon content in the bottom ash was determined via laboratory loss-on-ignition tests according to standard procedures.
The simulated values for temperature near the side walls, oxygen concentration at the rear of the grate, and carbon content in the bottom ash showed good agreement with the measured data, with relative errors generally controlled within 7%, as detailed in Table 4. For instance, at location (5.00, 23.00), the measured temperature was 1134.45 K, while the simulated result was 1171.38 K, resulting in a relative deviation of 3.26%. At location (2.76, 12.20), the measured temperature was 1082.95 K, compared to a simulated value of 1136.25 K (deviation of 6.58%). For location (4.12, 7.40), the measured temperature was 1187.25 K versus a simulated value of 1156.97 K (deviation of 3.31%). At location (8.12, 6.80), the measured temperature was 1052.35 K, and the simulated value was 1065.49 K (deviation of 1.69%). The measured oxygen concentration at the furnace rear was 11.67%, and the simulated value was 12.25% (deviation of 4.97%). These error ranges fell within acceptable engineering tolerances, adequately demonstrating that the established model accurately predicted the in-furnace combustion state and the flue gas characteristics at the furnace outlet, thus providing a reliable basis for subsequent optimization analysis.
Field measurements indicated a high carbon content in the bottom ash (approximately 25–30% as received) under the baseline condition, which directly corresponds to the simulated low char burnout ratio of 76.5% and confirms significant incomplete combustion heat loss. This was potentially attributable to insufficient fuel residence time on the grate or uneven local oxygen concentration distribution, leading to the discharge of unburned carbon particles with the slag and consequently reducing the overall combustion efficiency. This phenomenon underscored the necessity of optimizing air distribution parameters to enhance combustion completeness and reduce energy waste.

3.2. Results Influenced by Multiple Factors

Under the condition of a constant total system air flow, the effect of increasing the velocity of the burnout air, added with a specific pipe diameter, on the oxygen content inside the furnace is shown in Figure 2. As the burnout air velocity increased, both the oxygen concentration and its distribution range in the rear furnace section significantly expanded. The higher outlet velocity leads to greater penetration of the burnout air jet into the furnace cross-flow, creating a stronger recirculation zone that draws hot flue gases back towards the grate. This enhanced mixing and transport of oxygen and heat intensified the combustion of unburned carbon particles. This indicated that increasing the burnout air flow effectively enhanced the oxygen concentration in this region, thereby intensifying the combustion process. Specifically, the higher velocity promoted the mixing of air with unburned fuel, enhanced turbulent diffusion effects, allowing oxygen to penetrate the fuel bed more easily and promoting the late-stage combustion of carbon particles.
The influence of velocity variation under different pipe diameters on the oxygen content and flue gas temperature at the furnace outlet is shown in Figure 3. Differences in the oxygen content carried by burnout air at different outlet velocities caused corresponding changes in the oxygen concentration at the end of the grate. As the burnout air outlet velocity increased, the oxygen concentration in the rear grate region increased, and the flue gas temperature consequently rose. This was primarily because the oxidation reaction of unburned carbon particles intensified in the high-oxygen environment, releasing more heat and thereby elevating the flue gas temperature. Under the same pipe diameter condition, the flue gas temperature at the rear gradually increased with the increasing burnout air velocity, and the temperature trend was consistent across different pipe diameters, further confirming the positive impact of velocity on the combustion process. Quantitatively, a strong positive correlation was observed among the burnout air parameters, the resulting furnace conditions, and combustion completeness. Across all simulated cases with varying velocity and diameter, the increase in area-averaged O2 concentration at the furnace rear showed a near-linear relationship (R2 > 0.9) with the rise in local flue gas temperature. Both of these parameters were strongly correlated with the improvement in the char burnout ratio, which increased from a baseline of 76.5% up to 87.8%. This tripartite relationship underscores that enhancing oxygen availability and mixing in the burnout zone directly elevates local temperatures through intensified combustion, which in turn drives more complete char oxidation. The increase in furnace outlet temperature, under the premise of constant total air flow, indirectly indicated improved combustion conditions within the furnace and enhanced fuel combustion efficiency, which helped reduce incomplete combustion heat loss and improve the overall thermal efficiency of the boiler. The excessively high velocities could lead to localized overheating or excessive flow disturbance, potentially causing slagging or erosion issues. The optimal velocity range needed careful consideration in practice.
The influence of the burnout air pipe diameter on the oxygen content inside the furnace is shown in Figure 4. Analysis revealed that the overall impact of pipe diameter on the oxygen concentration distribution was relatively minor. The oxygen concentration was higher at both ends of the grate and close to zero in the central region, reflecting the zonal characteristics of combustion within the furnace: direct injection of burnout air led to higher oxygen concentrations at the ends, while the central region likely experienced local oxygen deficiency due to concentrated fuel combustion consuming large amounts of oxygen. As the pipe diameter increased, the oxygen concentration at the rear of the grate showed some improvement, and the turbulence intensity inside the furnace enhanced. This was mainly because a larger pipe diameter could deliver more air volume at the same velocity, enhancing the penetration and mixing capability of the airflow through the fuel bed, thus favoring the combustion of particles on the grate.
The effect of the burnout air pipe diameter on the flue gas temperature is shown in Figure 5. The flue gas temperature increased with the increasing pipe diameter. When the burnout air outlet velocity remained constant, increasing the pipe diameter raised the oxygen concentration at the rear of the grate and the flue gas temperature, optimizing the in-furnace combustion process. For example, a larger pipe diameter might promote more uniform oxygen distribution by increasing the air flow area, reducing regions of local incomplete combustion. However, vigilance was required, as excessively high furnace outlet flue gas temperature could easily lead to wall slagging, potentially affecting the long-term operational safety and maintenance costs of the boiler. In practical application, the boiler operating conditions needed comprehensive consideration based on the aforementioned data to select appropriate burnout air parameters, aiming to improve combustion efficiency while avoiding potential problems.
To directly address the improvement in combustion efficiency, the char burnout ratio, which was the primary indicator of solid fuel completeness, was calculated for each case. As detailed in Table 3, the baseline case (Case 0) exhibited a burnout ratio of 76.5%. Implementing burnout air improved this ratio, with values ranging from approximately 83.2% (Case A30) to a maximum of 87.8% (Case C60). This corresponds to a relative increase in combustion efficiency of up to 14.8%. Analysis of the parametric variations reveals that increasing the outlet velocity has the most pronounced effect on enhancing the burnout ratio within the tested range. For a fixed pipe diameter (76 mm), increasing velocity from 30 to 60 m/s (A30 to A60) accounted for the majority of the improvement. Increasing the pipe diameter at a fixed velocity (60 m/s, from A60 to C60) provided a further, though comparatively smaller, gain. This identifies outlet velocity as the most significant parameter for optimizing combustion efficiency via the burnout air system.
The influence of different injection angles on the oxygen content inside the furnace is shown in Figure 6. The tested angle range (10° to 15° upward from horizontal) was selected based on the physical constraints of the rear furnace wall and the need to avoid direct impingement of the high-velocity jet onto the opposite wall. Under this operating condition, the oxygen concentration distribution was characterized by high levels at both ends of the grate and low levels in the middle. Changes in the burnout air injection angle had a minimal effect on the oxygen concentration in the rear grate region. This was likely because, under constant total air flow and velocity conditions, changes in angle produced limited alterations to the flow path and mixing effectiveness, failing to significantly change the oxygen distribution pattern.
The influence of the injection angle on the flue gas temperature and in-furnace oxygen concentration is summarized in Table 5. As the angle of the burnout air pipe changed from 10° to 15°, neither the flue gas temperature nor the oxygen concentration at the furnace outlet changed significantly. The oxygen concentration remained between 14.85% and 15.55%, and the flue gas temperature fluctuated within the range of 1161.40 K to 1224.65 K, with a variation amplitude of approximately 5%. This indicated that changing the burnout air pipe angle resulted in minimal changes to the oxygen concentration and the degree of flow field disturbance at the furnace rear, having a limited impact on the in-furnace combustion process. This might stem from the angle adjustment failing to effectively alter the aerodynamic characteristics of the core combustion zone, or the angle having lower sensitivity compared to other parameters such as velocity and pipe diameter. When optimizing boiler operation, priority might be given to adjusting the velocity and pipe diameter rather than the angle.
To provide contextual, qualitative insight into the real boiler operation, Figure 7 presents photographs of the combustion flame taken through inspection ports on the side wall during stable operation under conditions corresponding to the baseline simulation. The visible intense flames in the rear grate section (both left and right sides) corroborate the practical significance of this zone, where incomplete combustion of char particles occurs. While not a quantitative validation of turbulent structure, these images visually confirm the presence of active combustion in the region targeted for optimization by the burnout air system, aligning with the simulated prediction of high temperature and ongoing reactions in this area.
By adjusting the velocity and pipe diameter of the tail-end burnout air, the oxygen concentration and temperature distribution inside the furnace could be effectively optimized, thereby enhancing combustion efficiency and reducing incomplete combustion heat loss. In contrast, angle adjustment proved less effective. This might provide important guidance for boiler design and operation, suggesting that focusing on the optimization of velocity and pipe diameter could be more cost-effective when resources are limited. These findings emphasized the importance of comprehensively balancing various parameters in biomass boiler operation to achieve efficient, stable, and environmentally friendly combustion processes, while also providing direction for future research, such as further exploring multi-parameter interactions or introducing advanced control strategies to maximize boiler performance. While the total thermal input remained constant across all cases, defined by the fixed fuel feed rate and lower heating value, the spatial distribution of combustion changed significantly. This localized increase in energy release is consistent with the observed rise in local flue gas temperature and oxygen consumption, providing a direct thermodynamic measure of the burnout air’s effectiveness in enhancing combustion in the target region.

3.3. Implications for Pollutant Emissions

While the primary focus of this study was on improving combustion efficiency, the optimization of burnout air parameters also has significant implications for pollutant emissions. The intensified combustion and elevated temperature in the rear grate section, as evidenced by the increased flue gas temperature and oxygen consumption, are conducive to the complete oxidation of CO, thereby potentially reducing CO emissions. The localized high-temperature zones resulting from enhanced char burnout could potentially promote the formation of thermal NOx. Although a detailed NOx reaction mechanism was not activated in the current simulations, this trade-off warrants attention in future work. An optimal burnout air strategy should therefore strike a balance between maximizing carbon burnout and minimizing NOx formation, possibly through further staging or temperature modulation.

3.4. Practical Application and Load Fluctuation

It is important to contextualize the findings within practical boiler operation. This study was conducted under the rated design condition of the boiler with a constant total air supply. This approach was essential to isolate and quantify the individual effects of burnout air parameters (velocity, diameter and angle) on combustion characteristics. In actual operation, the boiler load fluctuates in response to grid demand, necessitating adjustments in both the total air flow and the distribution ratio between primary and secondary air (including burnout air). While the optimal absolute values of burnout air velocity and flow rate identified here might shift under part-load conditions, the fundamental trends and mechanisms revealed are expected to remain valid, such as the strong positive correlation of velocity and diameter with rear-grate oxygen availability and burnout efficiency and the weak sensitivity to injection angle. The validated CFD model developed in this work can serve as a tool for future investigations into optimal coordinated control strategies across a wider range of operating loads, ensuring both high efficiency and low emissions under variable conditions.

3.5. Generalized Parameters and Future Work

While this study identified velocity and pipe diameter as dominant parameters within the tested configuration, the momentum flux ratio (J) of the burnout air jet to the cross-flow in the rear furnace is a key dimensionless parameter governing jet penetration and mixing, indicating significant penetration capability which explains the effective oxygen delivery. The limited sensitivity to injection angle (10–15°) suggests that within this ‘window’, the variation in the normal momentum component relative to the cross-flow is insufficient to drastically alter the jet trajectory and mixing pattern in this specific geometry.
Future work should expand the parametric space to include a wider range of injection angles (including downward angles), different nozzle elevations and spacing arrangements, and the potential use of swirl to enhance mixing. Systematic studies correlating J, Reynolds number, and the stoichiometry in the burnout zone with overall combustion efficiency and pollutant emissions would yield more generalizable design guidelines for burnout air systems across different boiler scales and designs. The impact of burnout air optimization on the burnout of entrained fly ash particles could be investigated to provide a more complete picture of combustion efficiency. Systematically incorporating variations in fuel composition (moisture, volatile matter, ash content) into the parametric study could facilitate a comprehensive multivariate analysis. This could establish quantitative relationships between key controllable parameters (e.g., burnout air settings), intrinsic fuel properties, and critical performance indicators like O2 concentration, temperature distribution, and carbon-in-ash content.

4. Conclusions

This work systematically investigates the neglected burnout air system at the rear grate of an industrial boiler. It provides three key contributions: a concurrent parametric analysis of velocity, pipe diameter, and injection angle; industrially validated guidance identifying velocity (30–60 m/s) and pipe diameter (76–108 mm) as the main controls for char burnout (up to 87.8%), with low sensitivity to angle (10–15°); and a critical link between burnout enhancement and slagging suppression, balancing efficiency against furnace outlet temperature. A mathematical model with high accuracy was established based on actual operational parameters: fuel consumption rate of 35.93 t/h, grate speed of 7.5 m/h, excess air ratio of 1.3, and primary-to-secondary air ratio of 3:7. Validation against field measurements showed relative errors for temperature at critical furnace locations and carbon content in bottom ash remained within 7%, confirming the model’s reliability and providing a solid foundation for subsequent optimization analysis.
The results quantitatively demonstrated that implementing tail-end burnout air significantly improved combustion efficiency, quantified by an increase in the char burnout ratio from 76.5% to a maximum of 87.8%. Parametric sensitivity analysis identified the outlet velocity as the most influential factor, with pipe diameter providing secondary benefits. Injection angle (within 10–15°) showed negligible impact. A larger pipe diameter provided additional benefits, further elevating oxygen availability and turbulence intensity. These improvements directly indicate a substantial reduction in unburned carbon loss. The larger pipe diameters additionally enhanced oxygen concentration and flue gas temperature in the rear section while intensifying gas flow turbulence, thereby optimizing the combustion process.
Selecting optimal burnout air parameters required balancing multiple factors. Although increased velocity and pipe diameter improved combustion, excessively high flue gas temperature at the furnace outlet risked wall slagging, threatening operational safety. Practical engineering design should avoid extreme values for these parameters, instead requiring comprehensive consideration of furnace outlet temperature characteristics combined with biomass ash slagging tendencies. Varying the burnout air injection angle between 10° and 15°, a typical practical range, produced minimal effects, indicating low sensitivity within this constrained window. This can be interpreted through the jet momentum flux ratio (J), where changes in the angle within this range did not substantially alter the effective penetration and mixing dynamics under the given cross-flow conditions.
Achieving economical, stable, and environmentally friendly boiler operation required balancing burnout enhancement against slagging suppression. The rational adjustment of burnout air velocity and pipe diameter effectively enhanced boiler combustion efficiency, thereby providing a technical pathway towards more stable, efficient, and environmentally friendly boiler operation. The key to practical implementation lies in balancing the enhancement of char burnout against the suppression of potential slagging risks caused by elevated furnace outlet temperature. A detailed techno-economic assessment (considering fan power, retrofit costs, and payback period) falls beyond the scope of this parametric study but would be a logical next step, utilizing the efficiency gains quantified here as primary input. While this study focused on the char burnout on the grate (reflected in bottom ash), the validated model provides a foundation for future work to investigate the impact of air distribution strategies on the formation and burnout of fly ash particles.

Author Contributions

Conceptualization, Y.L. (Yan Liang) and S.C.; methodology, S.C.; software, S.C. and W.C.; validation, Y.L. (Yan Liang), Y.L. (Yong Luo) and W.C.; formal analysis, X.Z. and C.D.; investigation, A.C. and S.Z.; resources, Y.G.; data curation, S.C. and W.C.; writing—original draft preparation, Y.L. (Yan Liang) and S.C.; writing—review and editing, W.C. and Y.G.; visualization, Y.L. (Yan Liang); supervision, Y.G.; project administration, Y.G.; funding acquisition, Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Shandong Provincial Science and Technology-based Small and Medium-sized Enterprises Innovation Capability Enhancement Project (2023TSGC0206); Shandong Enterprise Technology Innovation Project Plan (2024637010001092); Shandong Province Technology Innovation Project Plan (202360001105, 202350101441); “Chunhui Plan” Cooperative Scientific Research Project of the Ministry of Education of China (HZKY20220498); Shandong Province Housing and Urban-Rural Construction Science and Technology Project (2022-K7-11, 2021-K8-10). The APC was funded by Shandong Enterprise Technology Innovation Project Plan (2024637010001092).

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

Authors Yan Liang, Shidan Chi, Anxin Chen, Yong Luo and Shuying Zhang was employed by the Shandong Electric Power Engineering Consulting Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

  1. Pastore, L.M.; de Santoli, L. 100% renewable energy Italy: A vision to achieve full energy system decarbonisation by 2050. Energy 2025, 317, 134749. [Google Scholar] [CrossRef]
  2. Williams, J.H.; Jones, R.A.; Haley, B.; Kwok, G.; Hargreaves, J.; Farbes, J.; Torn, M.S. Carbon-Neutral Pathways for the United States. AGU Adv. 2021, 2, e2020AV000284. [Google Scholar] [CrossRef]
  3. Zhang, R.S.; Hanaoka, T. Cross-cutting scenarios and strategies for designing decarbonization pathways in the transport sector toward carbon neutrality. Nat. Commun. 2022, 13, 3629. [Google Scholar] [CrossRef] [PubMed]
  4. Yu, X.; Gao, Y.; Zhou, G.; Chi, S.; Wang, C.; Tan, C.; Burkitbayev, A.; Yan, J.; Zhao, X. Steam Stripping-Coupled Cycle for Thermodynamic Optimization and Carbon Cycle Intensification in CO/CO2 Mixed-Gas Hydrogenation Methanol Synthesis. ACS Omega 2025, 10, 40477–40491. [Google Scholar] [CrossRef] [PubMed]
  5. Saleh, H.M.; Hassan, A.I. Green Conversion of Carbon Dioxide and Sustainable Fuel Synthesis. Fire 2023, 6, 128. [Google Scholar] [CrossRef]
  6. Lu, B.; Gao, Y.; Tan, C.; Zhou, G.; Zhang, B.; Xu, L.; Huang, L.; Li, G.; Yan, M. Multi-parameter optimization and operational adaptability analysis of a static mixer with bionic conch-inspired natural gas-hydrogen blending inlet structure. Int. J. Hydrogen Energy 2025, 170, 151279. [Google Scholar] [CrossRef]
  7. Osman, A.I.; Farghali, M.; Ihara, I.; Elgarahy, A.M.; Ayyad, A.; Mehta, N.; Ng, K.H.; El-Monaem, E.M.A.; Eltaweil, A.S.; Hosny, M.; et al. Materials, fuels, upgrading, economy, and life cycle assessment of the pyrolysis of algal and lignocellulosic biomass: A review. Environ. Chem. Lett. 2023, 21, 1419–1476. [Google Scholar] [CrossRef]
  8. Guo, J.; Gao, Y.; Cao, X.; Li, L.; Yu, X.; Chi, S.; Liu, H.; Tian, G.; Zhao, X. Ni/N co-doped NH2-MIL-88(Fe) derived porous carbon as an efficient electrocatalyst for methanol and water co-electrolysis. Renew. Energy 2025, 244, 122661122661. [Google Scholar] [CrossRef]
  9. Gabrielli, P.; Gazzani, M.; Mazzotti, M. The Role of Carbon Capture and Utilization, Carbon Capture and Storage, and Biomass to Enable a Net-Zero-CO2 Emissions Chemical Industry. Ind. Eng. Chem. Res. 2020, 59, 7033–7045. [Google Scholar] [CrossRef]
  10. Mansy, A.E.; Daniel, S.; Monguen, C.K.F.; Wang, H.; Osman, A.I.; Tian, Z.Y. Catalytic production of aviation jet biofuels from biomass: A review. Environ. Chem. Lett. 2025, 23, 419–461. [Google Scholar] [CrossRef]
  11. Alobaid, F.; Almohammed, N.; Farid, M.M.; May, J.; Rossger, P.; Richter, A.; Epple, B. Progress in CFD Simulations of Fluidized Beds for Chemical and Energy Process Engineering. Prog. Energy Combust. Sci. 2022, 91, 100930. [Google Scholar] [CrossRef]
  12. Su, X.Q.; Chen, X.K.; Fang, Q.Y.; Ma, L.; Tan, P.; Zhang, C.; Chen, G.; Yin, C.G. An integrated model for flexible simulation of biomass combustion in a travelling grate-fired boiler. Energy 2024, 307, 132605. [Google Scholar] [CrossRef]
  13. Silva, J.P.; Teixeira, S.; Teixeira, J.C. Development of a CFD Model to Study the Fundamental Phenomena Associated with Biomass Combustion in a Grate-Fired Boiler. Processes 2025, 13, 2617. [Google Scholar] [CrossRef]
  14. Su, X.Q.; Fang, Q.Y.; Ma, L.; Zhang, C.; Chen, G.; Yin, C.E.; Yang, W.M. Efficient and low-NOr combustion in a grate-fired boiler by feeding biomass non-uniformly along grate width: An integrated modeling study with experimental validation. Energy 2024, 312, 133583. [Google Scholar] [CrossRef]
  15. Su, X.Q.; Fang, Q.Y.; Ma, L.; Yin, C.E.; Chen, X.K.; Zhang, C.; Tan, P.; Chen, G. Mathematical modeling of a 30 MW biomass-fired grate boiler: A reliable baseline model taking fuel-bed structure into account. Energy 2024, 288, 129861. [Google Scholar] [CrossRef]
  16. Nascimento, R.F.; Avila, M.F.; Taranto, O.P.; Kurozawa, L.E. Agglomeration in fluidized bed: Bibliometric analysis, a review, and future perspectives. Powder Technol. 2022, 406, 117597. [Google Scholar] [CrossRef]
  17. Li, H.; Li, M.; Wang, H.; Tan, M.J.; Zhang, G.X.; Huang, Z.L.; Yuan, X.Z. A review on migration and transformation of nitrogen during sewage sludge thermochemical treatment: Focusing on pyrolysis, gasification and combustion. Fuel Process. Technol. 2023, 240, 107562. [Google Scholar] [CrossRef]
  18. Kuba, M.; Skoglund, N.; Öhman, M.; Hofbauer, H. A review on bed material particle layer formation and its positive influence on the performance of thermo-chemical biomass conversion in fluidized beds. Fuel 2021, 291, 120214. [Google Scholar] [CrossRef]
  19. Jafarsalehi, M.; Mashayekh, M.; Miranzadeh, M.B.; Mirzaei, N.; Ebrahimi, M. Primary measures for cleaner biomass combustion to reduce NOx: A narrative review on solid biomass fuels, NOx sources, small-scale boilers, axillary equipment, fuel management and fuel quality improvement. Fuel 2025, 396, 134891. [Google Scholar] [CrossRef]
  20. Chanphavong, L.; Zhang, J.Y.; Veksha, A.; Lisak, G. Numerical investigation on Characteristic and performance of biomass gasification in a Two-Stage gasifier. Appl. Therm. Eng. 2025, 274, 126826. [Google Scholar] [CrossRef]
  21. Su, X.Q.; Ma, L.; Fang, Q.Y.; Yin, C.G.; Zhuang, H.Y.; Qiao, Y.; Zhang, C.; Chen, G. Optimizing biomass combustion in a 130 t/h grate boiler: Assessing gas-phase reaction models and primary air distribution strategies. Appl. Therm. Eng. 2024, 238, 122043. [Google Scholar] [CrossRef]
  22. Chen, H.Y.; Shan, R.; Zhao, F.X.; Gu, J.; Zhang, Y.Y.; Yuan, H.R.; Chen, Y. A review on the NOx precursors release during biomass pyrolysis. Chem. Eng. J. 2023, 451, 138979. [Google Scholar] [CrossRef]
  23. Tian, J.; Wang, L.; Xiong, Y.; Wang, Y.Q.; Yin, W.; Tian, G.H.; Wang, Z.Y.; Cheng, Y.; Ji, S.B. Enhancing combustion efficiency and reducing nitrogen oxide emissions from ammonia combustion: A comprehensive review. Process Saf. Environ. Prot. 2024, 183, 514–543. [Google Scholar] [CrossRef]
  24. Zhu, D.; Wang, Q.H.; Zhang, Z.J.; Xie, G.L.; Luo, Z.Y. Kinetics simulation study of biomass partial gasification for producer gas and biochar co-production in the fluidized bed. Energy 2025, 318, 134919. [Google Scholar] [CrossRef]
  25. Steiner, M.; Scharler, R.; Hochenauer, C.; Buchmayr, M. A combined primary and secondary DeNOx concept to achieve ultra-low NOx emissions in small-scale multi-fuel biomass grate furnaces. Fuel 2025, 400, 135794. [Google Scholar] [CrossRef]
  26. Yu, Y.; Xu, L.; Niu, Y.Q. Synergistic reduction of PM and NOx in different preheating co-firing modes of coal and biomass. J. Energy Inst. 2025, 121, 102151. [Google Scholar] [CrossRef]
  27. Tabrak, P.; Unsomsri, N.; Manchit, P.; Tawkaew, S.; Wiriyasart, S.; Kaewluan, S. Performance analysis of cross-draft biomass gasifier and synthesis gas burner as heat source for small ceramic kilns. Case Stud. Therm. Eng. 2025, 71, 106191. [Google Scholar] [CrossRef]
  28. Zlateva, P.; Terziev, A.; Krumov, K.; Murzova, M.; Mileva, N. Research on the Combustion of Mixed Biomass Pellets in a Domestic Boiler. Fuels 2025, 6, 40. [Google Scholar] [CrossRef]
  29. Liu, Q.W.; Zhong, W.Q.; Yu, A.B. Study on the gas-solid flow and reaction characteristics of oxy-fuel co-firing of coal and biomass in a pressurized fluidized bed by 3D Eulerian-Lagrangian modelling. Powder Technol. 2025, 456, 120808. [Google Scholar] [CrossRef]
  30. Liu, J.W.; Zhou, H. Numerical Simulation Study on Low-NOx Combustion of Pulverized Coal Swirl Burner with Flue Gas Recirculation. Combust. Sci. Technol. 2025, 1–28. [Google Scholar] [CrossRef]
  31. Wang, L.M.; Tang, C.L.; Zhu, T.; Fang, F.; Ning, X.; Che, D.F. Experimental Investigation on Combustion and NOx Formation Characteristics of Low-Ash-Melting-Point Coal in Cyclone Furnace. ACS Omega 2022, 7, 26537–26548. [Google Scholar] [CrossRef]
  32. Lertanan, T.; Podjanasatja, V.; Chuchan, D.; Sukjai, Y. Investigating the Characteristics of Pulverized Coal Combustion Using Ansys Fluent: A CFD Study of a 300 kW Swirl Burner. J. Combust. 2024, 2024, 3128387. [Google Scholar] [CrossRef]
  33. Zhou, H.Q.; Zhang, R.Z.; Wang, L.Z.; Luo, Y.H. Comprehensive Assessment and Optimization of a Middle-Arch Dual-Channel Municipal Solid Waste Incinerator Using Numerical Simulation Methods. ACS Omega 2024, 9, 42010–42026. [Google Scholar] [CrossRef] [PubMed]
  34. Mor-Yossef, Y. Improved convergence characteristics of two-equation turbulence models on unstructured grids. Comput. Fluids 2021, 230, 105127. [Google Scholar] [CrossRef]
  35. Yadav, A.S.; Shukla, O.P.; Sharma, A.; Khan, I.A. CFD analysis of heat transfer performance of ribbed solar air heater. Mater. Today: Proc. 2022, 62, 1413–1419. [Google Scholar] [CrossRef]
  36. Serra, N.; Semiao, V. ESIMPLE, a new pressure-velocity coupling algorithm for built-environment CFD simulations. Build. Environ. 2021, 204, 108170. [Google Scholar] [CrossRef]
  37. Le Ba, T.; Gróf, G.; Odhiambo, V.O.; Wongwises, S.; Szilágyi, I.M. A CFD Study on Heat Transfer Performance of SiO2-TiO2 Nanofluids under Turbulent Flow. Nanomaterials 2022, 12, 299. [Google Scholar] [CrossRef]
  38. Eivazi, H.; Tahani, M.; Schlatter, P.; Vinuesa, R. Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations. Phys. Fluids 2022, 34, 075117. [Google Scholar] [CrossRef]
  39. Duraisamy, K. Perspectives on machine learning-augmented Reynolds-averaged and large eddy simulation models of turbulence. Phys. Rev. Fluids 2021, 6, 050504. [Google Scholar] [CrossRef]
  40. Lewandowski, M.T.; Parente, A.; Pozorski, J. Generalised Eddy Dissipation Concept for MILD combustion regime at low local Reynolds and Damkohler numbers. Part 1: Model framework development. Fuel 2020, 278, 117743. [Google Scholar] [CrossRef]
  41. Losacker, J.; Garcia, A.M.; Schmitz, N.; Wuppermann, C. Full-spectrum k-distribution weighted sum of gray gases model for air and oxyfuel combustion of hydrogen-hydrocarbon blends at atmospheric pressure. Therm. Sci. Eng. Prog. 2025, 61, 103514. [Google Scholar] [CrossRef]
  42. An, R.; Zhang, X.B. Evaluation of weighted-sum-of-gray-gases models and radiation characteristics analysis for gas-ash particle mixture in ash deposition. Appl. Therm. Eng. 2025, 267, 125820. [Google Scholar] [CrossRef]
Processes 14 00589 g001
Figure 1. 3D modeling analysis of grate furnace and locations of validation points: (a) Geometric modeling; (b) Model grid division and schematic locations of the four temperature measurement points (P1, P2, P3, P4) on the side wall used for model validation; (c) A systematic grid independence study using three mesh sizes: coarse (2.10 million cells), medium (2.99 million cells), and fine (4.10 million cells).
Figure 1. 3D modeling analysis of grate furnace and locations of validation points: (a) Geometric modeling; (b) Model grid division and schematic locations of the four temperature measurement points (P1, P2, P3, P4) on the side wall used for model validation; (c) A systematic grid independence study using three mesh sizes: coarse (2.10 million cells), medium (2.99 million cells), and fine (4.10 million cells).
Processes 14 00589 g001
Processes 14 00589 g002
Figure 2. O2 concentration distribution on the central vertical plane of the furnace for different burnout air outlet velocities (Case A30, A40, A50, A60).
Figure 2. O2 concentration distribution on the central vertical plane of the furnace for different burnout air outlet velocities (Case A30, A40, A50, A60).
Processes 14 00589 g002
Processes 14 00589 g003
Figure 3. Effect of burnout air outlet velocity on key furnace outlet parameters (Cases A30–A60: 76 mm; B30–B60: 89 mm; C30–C60: 108 mm): (a) Area-averaged O2 concentration at the furnace outlet plane; (b) Area-averaged flue gas temperature at the furnace outlet plane.
Figure 3. Effect of burnout air outlet velocity on key furnace outlet parameters (Cases A30–A60: 76 mm; B30–B60: 89 mm; C30–C60: 108 mm): (a) Area-averaged O2 concentration at the furnace outlet plane; (b) Area-averaged flue gas temperature at the furnace outlet plane.
Processes 14 00589 g003
Processes 14 00589 g004
Figure 4. Contours of O2 molar concentration (vol.%) on the central vertical plane of the furnace for different burnout air pipe diameters at a fixed outlet velocity of 60 m/s (Cases A60, B60, C60).
Figure 4. Contours of O2 molar concentration (vol.%) on the central vertical plane of the furnace for different burnout air pipe diameters at a fixed outlet velocity of 60 m/s (Cases A60, B60, C60).
Processes 14 00589 g004
Processes 14 00589 g005
Figure 5. Flue gas temperature on the central vertical plane of the furnace for different burnout air pipe diameters at the changing outlet velocity.
Figure 5. Flue gas temperature on the central vertical plane of the furnace for different burnout air pipe diameters at the changing outlet velocity.
Processes 14 00589 g005
Processes 14 00589 g006aProcesses 14 00589 g006b
Figure 6. O2 molar concentration (vol.%) on the central vertical plane of the furnace for different burnout air injection angles (10° to 15°) at a fixed velocity (60 m/s) and pipe diameter (108 mm, Case C60).
Figure 6. O2 molar concentration (vol.%) on the central vertical plane of the furnace for different burnout air injection angles (10° to 15°) at a fixed velocity (60 m/s) and pipe diameter (108 mm, Case C60).
Processes 14 00589 g006aProcesses 14 00589 g006b
Processes 14 00589 g007
Figure 7. Field photographs of combustion flames inside the actual boiler furnace during operation under baseline conditions (corresponding to Case 0): (a) Flame observed at the left side of the rear grate section; (b) Flame observed at the right side.
Figure 7. Field photographs of combustion flames inside the actual boiler furnace during operation under baseline conditions (corresponding to Case 0): (a) Flame observed at the left side of the rear grate section; (b) Flame observed at the right side.
Processes 14 00589 g007
Table 1. The proximate and ultimate analysis.
Table 1. The proximate and ultimate analysis.
Ultimate Analysis (%)Proximate Analysis (%)Q (kJ/kg)
CHONSMVFCA/
36.256.1628.970.380.1324.8554.3613.567.2310,332
Note: C, H, O, N, S: mass fraction of carbon, hydrogen, oxygen, nitrogen, and sulfur, respectively. M, V, FC, A: mass fraction of moisture, volatile matter, fixed carbon, and ash, respectively. Q: Lower Heating Value (LHV).
Table 2. The main design parameters of grate boiler.
Table 2. The main design parameters of grate boiler.
Design ParametersValue
Main steam flow (t/h) 130
Main steam pressure (MPa)9.2
Main steam temperature (°C)540
Feed water temperature (°C)220
Primary air temperature (°C)200
Secondary air temperature (°C)200
Ratio of the primary and secondary air3:7
Table 3. Air distribution condition of boiler cold flow field.
Table 3. Air distribution condition of boiler cold flow field.
Numbers 1Primary Air (%)Lower Primary Air on Front Wall (%)Middle Primary Air on Front Wall (%)Upper
Primary Air on Front Wall (%)		Primary Air on Back Wall (%)Tail Burnout Air on Grate (%)Outlet Velocity of Grate Burnout Air
(m/s)
0308251423——
A30 2308251421.351.6530
A40308251420.82.240
A50308251420.252.7550
A60308251419.73.360
B60308231420560
C60308221319.77.360
1 A/B/C refers to the air distribution system with tail burnout air and the pipe diameter of which is 76 mm/89 mm/108 mm. 2 A30 is defined as trail burnout air pipe diameter is 76 mm and outlet velocity is 30 m/s.
Table 4. Comparison between the simulation results and the field test data of the biomass-fired grate boiler.
Table 4. Comparison between the simulation results and the field test data of the biomass-fired grate boiler.
ItemPosition
(5.00, 23.00)		Position
(2.76, 12.20)		Position
(4.12, 7.40)		Position
(8.12, 6.80)		O2 Concentration
(%)
Test (K)1134.451082.951187.251052.3511.67
Simulation (K)1171.381136.251156.971065.4912.25
Deviation (%)3.266.583.311.694.97
Table 5. O2 content and flue gas temperature in different tail burnout air pipe angles.
Table 5. O2 content and flue gas temperature in different tail burnout air pipe angles.
Angle
(°)		O2 Concentration
(%)		Flue Gas Temperature
(K)
1015.551209.00
1115.241175.76
1214.851161.40
1315.071197.82
1415.421187.15
1515.341224.65
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Liang, Y.; Chi, S.; Chen, A.; Luo, Y.; Zhang, X.; Dong, C.; Zhang, S.; Chen, W.; Gao, Y. Optimization of Burnout Air Parameters in a Large-Scale Biomass Grate Boiler: A CFD Study with Engineering Validation. Processes 2026, 14, 589. https://doi.org/10.3390/pr14040589

AMA Style

Liang Y, Chi S, Chen A, Luo Y, Zhang X, Dong C, Zhang S, Chen W, Gao Y. Optimization of Burnout Air Parameters in a Large-Scale Biomass Grate Boiler: A CFD Study with Engineering Validation. Processes. 2026; 14(4):589. https://doi.org/10.3390/pr14040589

Chicago/Turabian Style

Liang, Yan, Shidan Chi, Anxin Chen, Yong Luo, Xiaoxu Zhang, Changqun Dong, Shuying Zhang, Weixi Chen, and Yan Gao. 2026. "Optimization of Burnout Air Parameters in a Large-Scale Biomass Grate Boiler: A CFD Study with Engineering Validation" Processes 14, no. 4: 589. https://doi.org/10.3390/pr14040589

APA Style

Liang, Y., Chi, S., Chen, A., Luo, Y., Zhang, X., Dong, C., Zhang, S., Chen, W., & Gao, Y. (2026). Optimization of Burnout Air Parameters in a Large-Scale Biomass Grate Boiler: A CFD Study with Engineering Validation. Processes, 14(4), 589. https://doi.org/10.3390/pr14040589

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop