Numerical Simulation Study on the Stable Combustion of a 660 MW Supercritical Unit Boiler at Ultra-Low Load

Abstract

To investigate the safe, stable, and economically viable operation of a boiler under ultra-low-load conditions during the deep peaking process of coal-fired units, a numerical simulation study was conducted on a 660 MW front- and rear-wall hedge cyclone burner boiler. The current research on low load conditions is limited to achieving stable combustion by adjusting the operating parameters, and few effective boiler operating parameter predictions are given for very low-load conditions, i.e., below 20%. Various burner operation modes under ultra-low load conditions were analyzed using computational fluid dynamics (CFDs) methods; this operation was successfully tested with six types of pulverized coal combustion in this paper, and fitting models for outlet flue gas temperature and NO_x emissions were derived based on the combustion characteristics of different types of pulverized coal. The results indicate that under 20% ultra-low-load conditions, the use of lower burners leads to a uniform temperature distribution within the furnace, achieving a minimum NO_x emission of 112 ppm and a flue gas temperature of 743 K. Coal type 3, with the highest carbon content and a calorific value of 22,440 kJ/kg, has the highest average section temperature of 1435.76 K. In contrast, coal type 1 has a higher nitrogen content, with a maximum cross-sectional average NO_x concentration of 865.90 ppm and an exit NO_x emission concentration of 800 ppm. The overall lower NO_x emissions of coal type 3 are primarily attributed to its reduced nitrogen content and increased oxygen content, which enhance pulverized coal combustion and suppress NO_x formation. The fitting models accurately capture the influence of pulverized coal composition on outlet flue gas temperature and NO_x emissions. This control strategy can be extended to the stable combustion of many kinds of coal. For validation, the fitting error bar for the predicted outlet flue gas temperature based on the elemental composition of coal type 6 was 8.09%, whereas the fitting error bar for the outlet NO_x emissions was only 1.45%.

1. Introduction

According to the energy production data released by the National Bureau of Statistics of China on 17 May 2024, the power generation of the regulated industry reached 690.1 billion kilowatt-hours in April. Of this total, thermal power generation amounted to 457.88 billion kilowatt-hours, accounting for 66.35 percent of the national power generation [1]. In comparison, hydroelectric power contributed 12.1 percent, while wind power accounted for 11.7 percent of the total energy generation. The inherent volatility, randomness, and various influencing factors associated with wind and solar power generation make it challenging to alter the dominant role of coal-fired power in the short term [2].

The State Council recently issued the 2024–2025 Energy Conservation and Carbon Reduction Action Programme, which focuses on controlling fossil energy consumption, strengthening the management of carbon emission intensity, and implementing specific actions for energy conservation and carbon reduction across various fields and industries. This initiative aims to establish a solid foundation for achieving the goals of carbon peaking and carbon neutrality. To align with these targets, coal-fired boilers will facilitate the large-scale integration of renewable energy during deep peak regulation [3].

The objective of this study is to investigate critical technologies for the deep peaking of coal-fired units, specifically emphasizing 20% deep peaking technology for very low loads. This holds significant practical implications for enhancing the peaking depth of the units and ensuring their safe, stable, and economical operation. First, the continuous integration of clean energy into the power grid is elevating the demand for grid peaking. Furthermore, achieving deep peaking is consistent with current and future development trends in coal power. Second, comprehensive research will facilitate the assessment of the unit’s safety performance, thereby determining the extent to which it can be fully utilized. Third, in the context of a year-on-year reduction in the generation load and operating hours of thermal power units, the implementation of this strategy will enhance enterprise profitability and contribute to reducing ultra-low pollutant emissions.

Units engaged in deep peaking operations experience significant challenges due to the increasing rate, frequency, and depth of load changes. These fluctuations cause both the main and auxiliary engines to progressively deviate from their optimal operating states, leading to a range of issues. Consequently, concerns arise regarding safety, economic efficiency, environmental protection, and stable operation, all of which are exacerbated by heightened coal consumption [4]. As the unit load decreases, the thermal intensity of the boiler chamber and combustion stability diminish, potentially resulting in flameouts [5]. Additionally, the unit’s ability to adapt to changing operating conditions weakens, and minor load disturbances can cause boiler fires or unit shutdowns, with high tube-wall temperatures due to thermal deviations being a significant concern [6]. However, most of the studies on this topic (see

Table 1. Summary of the literature on boiler combustion.

Reference	Approach	Contributions
Hu et al. [7]	Simulation	Numerical simulation of steady combustion under 25% BMCR conditions was carried out, and the results showed that the temperature distribution of the two burners in operation was more homogeneous.
Wang et al. [8]	Simulation	The effect of burner swirl angle on steady combustion was investigated, and it was concluded that the optimum swirl angle was 15–30°.
Tian et al. [5]	Experiment	Pulverized coal preheating combustion technology was developed, realizing the efficient and low-NOx combustion of pulverized coal under ultra-low-load working conditions.
Ma et al. [9]	Simulation	Numerical simulation of W-type boiler at 45% low-load conditions was carried out, and they found that operating two side burners and reducing the amount of spent gas is conducive to improving the combustion stability.
Li et al. [10]	Simulation	A study of steady combustion at 33% low-load conditions was carried out, along with the operation of multi-story burners to maintain furnace temperature and reduce NOx at all times.
Hong et al. [11]	Experiment	The performance of CFB boilers at low loads was evaluated, and it was found that adjusting the primary air flow and air distribution, etc., can improve operational stability.
Wang et al. [12]	Simulation	Numerical simulation of steady combustion in a boiler with a minimum power of 200 MW was carried out, adjusting the air excess coefficient and adjusting the burner output power to achieve steady combustion.
Ma et al. [13]	Simulation	Stable combustion in low-load boilers was studied, and it was concluded that reducing oxygen improves combustion stability.
Li et al. [14]	Simulation and experiment	The combustion characteristics and NOx emission of a 330 MW coal-fired boiler were investigated after modification, and the results showed that the modification was effective and the NOx emission reduction was significant, and the simulation was consistent with the experimental results.
Tong et al. [15]	Simulation	Stable combustion at a 20 percent ultra-low load was achieved when the oxygen volume fraction in the operating condition was 12–13 percent.
Wei et al. [16]	Simulation	Numerical simulation to study the combustion effect of the boiler of a 300 MW unit was carried out, and an air excess coefficient of 1.25 produced the best combustion effect.
Gürel et al. [17]	Simulation and experiment	This study explores the combustion characteristics of different types of lignite in a circulating fluidized bed boiler (CFBB), with a focus on the impact of bed material sphericity on combustion, energy production, and emissions.
Zeleke et al. [18]	Experiment	The transient characteristics of diesel and castor biodiesel blended fuel in a small boiler were studied experimentally with the aim of optimizing combustion efficiency.
Alekseenko et al. [19]	Simulation	The co-combustion process of coal–water slurry (CWS) and pulverized coal fuel (PCF) in an E500 pilot boiler when using a staged afterburning scheme was investigated based on numerical simulation.
Coskun et al. [20]	Simulation and experiment	This study focuses on experimental and numerical investigations of methane–air mixture combustion using a porous burner made of metal fiber material.
Erne et al. [21]	Simulation	The effective diffusion due to dispersive flow in porous media is critical for accurately capturing the reaction zone, thus preventing an overprediction of flame temperature and NO concentration.
Kuznetsov et al. [22]	Simulation	Numerical studies of the processes of oxy-fuel combustion of pulverized coal (PC) in an industrial BKZ 500–140–1 boiler (with a capacity of about 400 MW) were carried out.
Mazur et al. [23]	Simulation and experiment	A unique approach and algorithm for the classification of wastes (solid ash) and the identification of dangerous compounds that may originate from waste co-combustion were created and presented in this work.

) have focused on adjusting the parameters of the front- and rear-wall boilers to achieve steady combustion at lower loads and have not provided accurate predictions for the steady combustion of different coal types at low loads. In a recent study, Hu et al. [7] conducted a numerical simulation study on a 600 MW hedge boiler operating under 25% Minimum Boiler Continuous Rating (MBCR) conditions, finding that the temperature distribution was more uniform when the two-layer burner was operated, achieving stable combustion. Wang et al. [8] performed a comparative analysis of the cyclone angle of the lower cyclone in the pulverized coal combustion chamber of a 660 MW boiler under different operating conditions, concluding that the optimal cyclone angle should be 15–30°. Tian et al. [5] developed pulverized coal preheating and combustion technology, enabling efficient, low-NO_x (nitrogen oxides produced during combustion, especially when the fuel is burned at high temperatures; the nitrogen in the pulverized coal reacts with oxygen to form NO_x, mainly NO and NO₂) combustion of pulverized coal under ultra-low-load conditions (boiler ultra-low-load conditions mean that the boiler operates below its rated load, and the boiler’s output is usually only about 20 to 30 percent of its rated output). Ma et al. [9] conducted numerical simulations of a 660 MW supercritical W-flame boiler at minimum operating conditions of 45%, finding that starting the two side burners at low-load conditions and reducing exhaust air intake improved combustion stability. However, stable combustion could not be achieved at 35% operating conditions, resulting in flameouts. Li et al. [10] studied the temperature field due to pulverized coal combustion at 33% of the maximum continuous rated load, discovering that maintaining a relatively high temperature in the furnace by operating continuous multi-story burner nozzles reduced NO_x emissions. Hong et al. [11] evaluated the performance of CFB (circulating fluidized bed) boilers operating at low loads and improved their operational stability by adjusting the primary air flow, air distribution, and cyclone separator efficiency. Wang et al. [12] conducted CFD simulations to investigate steady-state combustion options at low loads in various boilers by increasing the air excess coefficient and adjusting the burner output. In their study of low-load conditions, Ma et al. [13] suggested that oxygen reduction can improve combustion stability while reducing NO_x emissions. A smaller number of researchers have tested pulverized coal boilers. Li et al. [14] performed an investigation on a retrofitted low-volatile coal-fired 330 MW boiler. Tong et al. [15] achieved stable combustion at a 20% ultra-low load by maintaining a reasonable aerodynamic field with an oxygen volume fraction of 12–13%. Wei [16] determined the optimal excess air coefficient to be 1.25 for a 300 MW boiler across various loads using numerical simulations. To achieve optimal combustion, the burnout air rate should increase as the load decreases, with a burnout air rate of 28% observed at a 30% load. Gürel et al. [17] explored the combustion characteristics of different types of lignite in a circulating fluidized bed boiler (CFBB). Zeleke et al. [18] conducted an experimental study on the transient characteristics of a mixture of diesel and castor biodiesel in a small boiler. Alekseenko et al. [19] studied the co-combustion process of coal–water slurry and pulverized coal fuel in an E500 pilot boiler with a graded force-adding scheme by numerical simulation. Coskun et al. [20] focused on experimental and numerical investigations of the combustion of a methane–air mixture using a porous burner made of metal fiber material. Erne et al. [21] found that the effective diffusion of dispersion flows in porous media is essential for the accurate capturing of the reaction region. Kuznetsov et al. [22] studied the processes of the oxy-fuel combustion of pulverized coal (PC) in an industrial boiler. Mazur et al. [23] created and proposed unique methods and algorithms for the waste (solid ash) classification and identification of hazardous compounds that may come from waste co-combustion.

Investigating the peaking capacity of coal-fired units under deep peaking conditions is crucial for ensuring safe, stable, and economically viable operations. To achieve this objective, it is essential to implement targeted operational assessments and control optimizations. Although experimental methodologies for examining combustion processes in laboratory settings may appear intuitive, they are accompanied by significant limitations, including extensive lead times for experimental setup and execution, as well as considerable resource consumption and associated costs. Consequently, the primary research methodology has increasingly shifted toward computer modeling, which is further enhanced by simulations using ANSYS Fluent 2022 R1 software to obtain more accurate numerical solutions. This transition highlights the necessity for innovative approaches to effectively address the challenges posed by deep peaking conditions in coal-fired power generation.

This study simulated stable combustion and reduced NO_x emissions in a 660 MW boiler operating at extremely low-load conditions of 20%. The boiler’s burner and air supply management system consists of a cyclone burner (the working principle is to make the pulverized coal gas stream or hot air form a rotating jet by means of a cyclone generator, thus generating a high-temperature flue gas reflux zone conducive to ignition and a strong mixing effect during the combustion process) that operates in conjunction with wall and combustion air. In the simulation, various parameters are adjusted to optimize performance under varying operating conditions. Specifically, the amount of pulverized coal is adjusted to achieve ultra-low-load conditions. Additionally, adjustments are made to the velocity and temperature of primary and secondary air, as well as that of wall and combustion air. The cyclone angle of the burner is also varied to enhance combustion efficiency. Qualitative and quantitative analyses focused on the temperature field of the boiler cross-section, the average temperature along the height direction, the NO_x concentration, the oxygen mass fraction, and the NO_x concentration at the outlet under various operating conditions, with the aim of achieving stable combustion and reducing emissions. Concurrently, several measures are proposed to ensure stable combustion under very low-load conditions and to reduce NO_x emissions at the outlet. These measures provide a reliable, realistic, and practical technical solution for the commissioning of the boiler and for potential future modifications.

2. Model Creation and Simulation Condition Classification

2.1. Boiler Overview

The 660 MW boiler examined in this study was designed and manufactured by DongFang Boiler Co., Ltd.( The manufacturer of the equipment is Dongfang Electric Group Dongfang Boiler Co., LTD. The country and city of production is Nanchang City, Jiangxi Province, China), with the model designation DG2060/26.15-II2. The hearth height is 71.2 m, the width is 22.16 m, and the depth is 15.5 m, as illustrated in Figure 1. Each layer contains 6 burners arranged in three layers on the front and rear walls of the boiler, resulting in a total of 36 burners. The furnace is equipped with a total of 36 cyclone pulverized coal burners arranged in three layers on the front and rear walls, with each layer comprising 6 burners. Six medium-speed coal mills (ZGM113G) supply wind–dust mixtures to the same layer of 6 pulverized coal burners. Following modifications, the boiler burner was upgraded to a new type of cyclone wear-resistant ceramic pulverized coal burner (HBS-LNSB III), with the structural sketch of the center-fed cyclone pulverized coal burner illustrated in Figure 2. Two wall wind burners are added between each layer of the burners and the front and rear walls at the wind exit, resulting in a total of sixteen across 4 layers. Above the burners on the front and rear walls, there are two layers of air outlets, amounting to a total of twenty-eight.

2.2. Meshing

The simulation model maintains a 1:1 ratio with the boiler geometry, as illustrated in Figure 3a, which presents the overall mesh delineation of the boiler. The mesh was generated using the ANSYS meshing tool ICEM. Given that this study focuses on combustion within the boiler and the associated pollutant generation and considering the complexity of the burner structure, which has minimal influence on the fine structure, it is essential to implement appropriate mesh refinement in both the burner and the inlet area, as illustrated in Figure 3b. Drawing on prior experience, a suitable simplification of the burner structure was achieved by reducing the heat exchanger tubes, including the superheater and reheater, to sub-mesh features. A specific heat exchanger tube structure was modeled; however, this component was simplified to a dispersed block using ICEM chunking [24], as depicted in Figure 3c. This configuration facilitates heat transfer between the tube bundle and the fluid while preserving the primary fluid flow. In the burner nozzle and primary combustion area, the mesh is refined to enhance calculation accuracy, whereas in the cold ash hopper region at both the top and bottom of the boiler, the mesh count can be reduced due to minimal variations in parameters.

2.3. Selection and Application of Physical Models in CFDs

The combustion of pulverized coal in the boiler involves complex physicochemical reactions, encompassing the flow of pulverized coal particles, the distribution of the flow field within the boiler chamber, the temperature distribution, and various modes of heat transfer, thereby complicating the accurate determination of particle trajectories in the furnace chamber. Furthermore, due to the stochastic nature of turbulent movement in the boiler, the probability density function (PDF) is employed to qualitatively analyze the combustion process. This approach facilitates a comprehensive discussion of the intrinsic factors and principles influencing pulverized coal combustion in ultra-low-load boiler furnaces. During the CFD numerical simulation, the results presented are steady-state analyses; this is due to the fact that during the simulation process, the temperature values on a certain plane of the boiler are monitored to reach a stable value to determine the full combustion of pulverized coal.

Consequently, for the processes associated with turbulent motion, combustion, radiation, and NO_x generation, the following physical models are utilized in this study:

(1) Turbulence model

According to existing fluid dynamics theories, single-equation or double-equation turbulence models are typically used to solve turbulent flows. Since the combustor and combustion air inlet regions are circular jets and involve medium-intensity cyclonic flows with large pressure gradients [25], it is more appropriate to use the realizable k-ε turbulence model [26]. Since the distance from the wall in the gas flow process significantly affects the viscous force [27] and the Reynolds number also changes, the actual conditions of the inner wall of the furnace must be considered. The two sides of the range model are corrected using the “wall function” to address this situation. Equation (1) represents the general realizable k-ε equation [28], while Equations (2) and (3) depict the k and ε equations, respectively.

$${\displaystyle \frac{\partial }{\partial x}}(\rho U\phi )+{\displaystyle \frac{\partial }{\partial y}}(\rho V\phi )+{\displaystyle \frac{\partial }{\partial z}}(\rho W\phi )={\displaystyle \frac{\partial }{\partial x}}\left({\mathsf{\Gamma }}_{\phi }{\displaystyle \frac{\partial \phi }{\partial x}}\right)+{\displaystyle \frac{\partial }{\partial y}}\left({\mathsf{\Gamma }}_{\phi }{\displaystyle \frac{\partial \phi }{\partial y}}\right)+{\displaystyle \frac{\partial }{\partial z}}\left({\mathsf{\Gamma }}_{\phi }{\displaystyle \frac{\partial \phi }{\partial z}}\right)+S_{\phi }$$

(1)

$${\displaystyle \frac{\partial }{\partial t}}(\rho k)+{\displaystyle \frac{\partial }{\partial x_i}}\left(\rho k{u}_j\right)={\displaystyle \frac{\partial }{\partial x_i}}\left[\left(\mu +{\displaystyle \frac{{\mu }_t}{{\sigma }_k}}\right){\displaystyle \frac{\partial k}{\partial x_j}}\right]+G_k+G_b-\rho \epsilon -Y_M+S_k$$

(2)

$${\displaystyle \frac{\partial }{\partial t}}(\rho \epsilon )+{\displaystyle \frac{\partial }{\partial x_j}}\left(\rho \epsilon u_j\right)={\displaystyle \frac{\partial }{\partial x_j}}\left[\left(\mu +{\displaystyle \frac{{\mu }_t}{{\sigma }_{\epsilon }}}\right){\displaystyle \frac{\partial \epsilon }{\partial x_j}}\right]+\rho C_{1}S\epsilon -\rho C_2{\displaystyle \frac{{\epsilon }^2}{k+\sqrt{\upsilon \epsilon }}}+C_{1\epsilon }{\displaystyle \frac{\epsilon }{k}}{C}_{3\epsilon }{G}_b+S_{\epsilon }$$

(3)

where G_k is related to the turbulent energy, k, generated by the mean gradient to which it is subjected [29]; G_b is related to the turbulent energy, k, generated due to buoyancy forces; Y_M is the intensity of pulsation expansion in compressible turbulence; C_1ε, C_2ε, and C_3ε are constant; σ_k and σ_ε are the turbulent Prandtl numbers of k and ε, respectively; and S_k and S_ε are the turbulence source terms.

(2) Discrete phase model

The combustion of pulverized coal in a boiler involves interactions between solid particles and gas, necessitating the use of gas–solid two-phase flow theory. Splitting the solid particles from the fluid is a common method in current gas–solid two-phase flow theory. Therefore, the Euler–Lagrange model was used to analyze the motion of coal particles. Since the particle size of coal dust particles falls within a certain range, the Rosin–Rammler method was used to describe the particle size distribution. The minimum particle size is 1 μm, the maximum is 100 μm, and the average particle size is set to 10 μm.

(3) Gas phase combustion modeling

Gas-phase combustion involves more complex physicochemical changes. In the numerical simulation of boiler pulverized coal combustion, the non-premixed combustion/probability density distribution model, mainly through the optimized PDF, is often used. This model is based on a statistical description and involves solving the transport equation for its conserved scalar, i.e., the transport equation for the mixing fraction, often used in combustion simulations for pulverized coal. Thus, the time-averaged concentration is

$$\overline{Y_{\alpha }}={\displaystyle {\int }_0^1{Y}_{\alpha }(f)p(f)df}$$

(4)

The specific reaction process is

$$coal\stackrel{k_1}{\to }(1-Y_1)char+Y_1(volatiles)$$

(5)

$$coal\stackrel{k_2}{\to }(1-Y_2)char+Y_2(volatiles)$$

(6)

where k₁ and k₂ are reaction rate constants, calculated using the Arrhenius equation:

$$k_1=A_1\exp ({\displaystyle \frac{-E_1}{R{T}_{a\nu }}})$$

(7)

$$k_2=A_2\exp ({\displaystyle \frac{-E_2}{R{T}_{a\nu }}})$$

(8)

where $A_1=3.7\times {10}^5 {\mathrm{s}}^{-1}$, $E_1=7.366\times {10}^4 (\mathrm{J}/\mathrm{mol})$, $A_2=1.46\times {10}^{13} {\mathrm{s}}^{-1}$, $E_2=2.511\times {10}^5 (\mathrm{J}/\mathrm{mol})$.

(4) Volatilization analysis model

When a certain temperature is reached, the pulverized coal decomposes due to heat, and the volatiles gradually precipitate. This process is very complicated. The precipitation of volatiles was modeled using a two-step competitive reaction rate model [30], and the relevant equations are given below:

$$R_1=A_1{e}^{-\left(E_1/R{T}_p\right)}$$

(9)

$$R_2=A_2{e}^{-\left(E_2/R{T}_p\right)}$$

(10)

where R₁ and R₂ are competitive precipitation rate constants that control the rate of precipitation over different temperature ranges. These two rate constants together determine the total precipitation rate based on different weighted values:

$${\displaystyle \frac{m_{\nu }(t)}{(1-f_{w,0})m_{p,0}-m_{\alpha }}}={{\displaystyle \int }}_0^t({\alpha }_1{R}_1+{\alpha }_2{R}_2)\exp (-{{\displaystyle \int }}_0^t(R_1+R_2)dt)dt$$

(11)

where m_v(t) is the mass of volatile matter that has precipitated at time t; m_p,0 is the initial particle mass; α₁ and α₂ are the generation rate factors; and m_a is the ash content of the particles.

(5) Radiative heat transfer model

When pulverized coal is burned in a power-plant boiler, the high temperature inside the furnace makes radiant heat transfer the primary heat transfer method. The P-1 radiation model [31], which accounts for diffusion effects, is both more accurate and less computationally intensive for radiation calculations. Therefore, this study uses the P-1 radiation model to calculate radiative heat transfer in the boiler. The P-1 model equation is as follows:

$$q_r=-{\displaystyle \frac{1}{3(a+{\sigma }_s)-C{\sigma }_s}}\nabla G$$

(12)

where a is the absorption coefficient, σ_s is the scattering coefficient, G is the incident radiation, and C is the linear anisotropic phase function coefficient.

(6) Pulverized coal combustion model

The combustion of pulverized coal can be divided into several stages: the heating of pulverized coal, the evaporation and boiling of water, the analysis of volatile matter, and the combustion of coke. Given that the temperature of pulverized coal is set to the primary air temperature in the numerical simulation, which exceeds the evaporation and boiling temperatures of water, the water evaporation and coke precipitation processes are ignored, focusing solely on the precipitation of volatile matter and the combustion process of coke. Here, coke combustion was carried out using the diffusion dynamics-controlled combustion model [32], which offers a better simulation of the combustion reactions in the boiler. The diffusion rate constant in this model is

$$D_0=C_1{\displaystyle \frac{{[(T_p+T_{\infty })/2]}^{0.75}}{d_p}}$$

(13)

The kinetic reaction rate constant is

$$R=C_2{e}^{-(E/R{T}_p)}$$

(14)

The two are differently weighted to the rate of combustion of the coke:

$${\displaystyle \frac{d{m}_p}{dt}}=-\pi d_p^2{p}_{ox}{\displaystyle \frac{D_{0}R}{D_0+R}}$$

(15)

where p_ox is the partial pressure of the gas-phase oxidant around the particles.

(7) NO_x generation model

The pollutant NO_x is emitted during the combustion of pulverized coal. NO_x generation predominantly comprises thermal NO_x, fuel NO_x, and fast NO_x. In combustion processes in pulverized coal boilers, NO makes up approximately 90% or more of the nitrogen oxides in the furnace. Fast-type NO_x primarily arises from hydrocarbon fuel combustion. Therefore, this study excludes the consideration of fast-type NO_x generation and instead employs the Zeldovich model for thermal NO_x [33] and the DeSoete model for fuel-type NO_x [34].

2.4. Working Conditions

This study aims to achieve the stable combustion of pulverized coal under ultra-low-load conditions while minimizing NO_x emissions at the outlet. Consequently, the following operational conditions are established:

(1) Validation of meshes and physical models under rated operating conditions;

(2) Different operational layers for burners operating at a 20% ultra-low load are examined. (Numerical simulations are conducted under conditions involving the appropriate arrangement of the up-and-down oscillation angles for burnout wind and the cyclone angle for secondary wind outside the combustor, aiming to identify effective measures for achieving stable combustion at a 20% ultra-low load).

Thus,

Table 2. Coal composition.

Ingredient	Notation	Unit	Content
Carbon (received basis)	Car	%	54.2
Hydrogen (received base)	Har	%	3.47
Oxygen (received base)	Oar	%	3.41
Nitrogen (received basis)	Nar	%	0.96
Sulfur (received base)	Sar	%	1
Total moisture	Mar	%	8.1
Ash	Aar	%	28.86
Moisture (air-dry basis)	Mad	%	1.05
Volatile matter (ashless dry basis)	Vdaf	%	26.396
Inferior calorific value (received base)	QLHV	kJ/kg	21,040

gives the coal composition for the designed coal type, and

Table 3. Main parameters for 20% load.

Physical Quantity	Unit	20% Load
Primary air temperature	°C	80
Secondary air temperature	°C	276
Primary wind speed	m/s	27
Secondary air velocity	m/s	35
Amount of coal consumed	t/h	89.28
Wall-mounted wind angle	°	20
External secondary air cyclone angle	°	30

shows the main setting parameters for the 20% load.

2.5. Grid Independence Verification and Reliability Verification

(1) Grid Independence Verification

To verify the accuracy of the established mesh model, the irrelevance of different mesh numbers was first verified using a reasonably simplified mesh system. Four distinct grid configurations were established for the boiler, with capacities of 2.92 million, 5.84 million, and 7.24 million. The exit velocities (measured in meters per second) of the boiler were then recorded after each grid configuration was implemented. The recorded velocities were 20.231013 m/s, 20.54403 m/s, and 20.41087 m/s, respectively. The computed velocity deviation between the 2.92 million and 5.84 million grids is 1.5 percent, whereas the velocity deviation between the 5.84 million and 7.24 million grids is 0.65 percent. After a substantial reduction in this gap, it was concluded that the 5.84 million tetrahedral mesh met the criteria for irrelevance verification.

(2) Physical model reliability verification

A numerical simulation of the model was conducted using measurements of the rated operating conditions on a grid consisting of 5.84 million elements. The simulation results were then compared to experimental data from previous research on the same topic. The comparison included analyzing the highest and lowest temperatures, as well as the highest flow rates, observed in both the simulation and the experiments. The statistics for the rated condition are presented in

Table 4. Rated working condition parameters.

Physical Parameter	Units	BMCR
Superheated steam flow rate	t/h	2060
Superheater outlet steam pressure	MPa	26.15
Superheater outlet steam temperature	°C	605
Reheated steam flow rate	t/h	1659.1
Reheater inlet steam pressure	MPa	5.33
Reheater outlet steam pressure	MPa	5.14
Reheater inlet steam temperature	°C	362
Reheater outlet steam temperature	°C	603
Coal economiser inlet feedwater temperature	°C	297
Primary air temperature	°C	70
Secondary air temperature	°C	320
Primary wind speed	m/s	23
Secondary air velocity	m/s	37
Amount of coal consumed	t/h	254.66

, while the results of the statistical comparison can be found in

Table 5. Comparison of simulation and experimental data (experimental data from a power plant in China Boiler).

Projects	Simulation Data	Measurement Data	Deviation (%)
maximum temperature (K)	2040	2070	1.45
minimum temperature (K)	353	338	4.25
maximum velocity (m/s)	39.2	43.9	10.7

. The temperature in the furnace chamber is measured by a portable visible image temperature measurement system, and the flue gas flow rate is measured by an ultrasonic flow meter. The concentration of the flue gas at the boiler outlet was measured using a non-dispersive infrared absorption method. The comparison between the simulation results and the experimental data demonstrates that the grid and the selected physical model may accurately predict the actual combustion reactions and different heat transport properties in the furnace chamber to some degree.

3. Results and Discussion

3.1. Investigation of a 20% Ultra-Low-Load Burner Operation Scheme for Boilers

This study addresses issues related to unburned coal dust and improper combustion locations under 20% ultra-low-load conditions. This boiler is equipped with a total of 36 burners, which are distributed across three levels and positioned on both the front and rear walls. The investigation was conducted under the following conditions: condition 1 involved operating the lowest burner, condition 2 involved operating the middle burner, condition 3 involved operating the upper burner, condition 4 involved operating the lower and middle burners, condition 5 involved operating the lower and upper burners, and condition 6 involved operating the middle and upper burners. Additionally, the amount of pulverized coal [35] was reduced to 24.8 kg/s for all six conditions, mimicking the coal consumption for 20% ultra-low loading.

From the average temperature distribution along the furnace height given in Figure 4a, it is observed that the overall temperature variation trends for the six operating methods are similar. The temperature progressively increases with hearth height, reaching a maximum value of 1198 K at the upper burner, located at a height of 12.05 m. As the burnout wind and wall-mounted air at lower temperatures enter the furnace, along with heat absorption by the water-cooled wall, the temperature drops slightly between furnace heights of 20–35 m. Under working condition 1, the temperature reduces to 908.08 K at a height of 34.47 m. In the upper and middle sections of the furnace, residual unburnt coal dust undergoes secondary combustion upon fresh air supply. This process generates high-temperature flue gas, which transfers heat through convection and radiation with the superheater and reheater. As a result, the flue gas temperature decreases once again. The simulated combustion for nearly all working conditions reaches its lowest value at a height of 52.57 m, with working condition 1 reaching 904.75 K and working condition 6 reaching 943.98 K.

The analysis of NO_x concentration along the furnace height in Figure 4b shows that in working conditions 1–3, where one layer of burners is operated, the maximum NO_x concentration occurs around the height of the active burners, with the concentration reaching a peak value of 675.86 ppm at the lower burner height (12.05 m) in case 1. In working conditions 4–6, where two layers of burners are running, the maximum NO_x concentration increases with burner height. This increase is attributed to the high oxygen mass fraction at the burner level, as illustrated in Figure 5, where pulverized coal combustion first occurs. As shown in the cross-sectional temperature plot in Figure 6, at this time, the operating condition is at 20% ultra-low load; the temperature distribution is in the YZ plane distribution cloud, where YZ represents the height and length of the furnace chamber; and the specific cross-section, which is the width of the furnace chamber along the X-direction, of is 9.3 m. compared to operating conditions 2 and 3, operating condition 1 exhibits higher temperatures in the furnace chamber, which enhances heat absorption by the water-cooled wall. In simulated combustion for conditions 4–6, there is a significant discontinuity compared to conditions 1–3. Additionally, flame fullness is poor, the top furnace temperature is high, which may lead to overheating and potential tube rupture in the high-temperature heating surface. The NO_x distribution plot in Figure 7 shows that during single-burner operation, the NO_x concentration near the wall is smaller, which reduces the contact between acidic oxides and significantly reduces the corrosion and slagging of the boiler wall and water-cooled wall. However, as shown in the analysis of Figure 7d–f, under conditions where a two-layer burner is operational, the increased openness of the cyclone burners and the dispersed distribution of incoming coal reduce the contact between pulverized coal and oxygen. This results in incomplete combustion in the center of the furnace and elevated NO_x concentrations near the walls, thereby raising the overall NO_x concentration within the furnace. Furthermore, as the burner rotates more, the exposure time of pulverized coal to air decreases, leading to increased generation of NO_x. This trend is corroborated by the NO_x emission concentrations shown in Figure 8b, where a sequential increase from 743 ppm to 810 ppm is observed from working condition 1 to condition 6. Furthermore, as shown in Figure 8a, under single-burner operation, both the exit NO_x concentration and flue gas temperature are significantly lower compared to the other two groups of conditions. This suggests that under single-burner operation, the pulverized coal is fully burned and pollutant emissions are better controlled.

Therefore, when comparing the six conditions, operating the lower burner allows the pulverized coal to burn more completely and for a longer duration in the burner region of the furnace. As shown in Figure 4b, the highest NO_x concentration in working condition 1 occurs near the first burner. This is because the pulverized coal first enters the furnace and does not have enough time to fully combust, resulting in higher NO_x levels. As combustion progresses, NO_x levels decrease as pulverized coal undergoes complete combustion, as evidenced by the outlet NO_x concentration in Figure 8a, which is 112 ppm. In contrast, when operating the two-layer burners in cases 4, 5, and 6, the burners are closer to the upper furnace, leading to the incomplete combustion of pulverized coal and higher overall NO_x levels. The temperature distribution with the lower burner is more uniform, allowing the water-cooled wall to absorb more heat without localized overheating. Therefore, case 1 is well suited for the stable combustion of pulverized coal under super-low-load conditions.

3.2. The Impact of Coal Types on Stable Combustion

The variations in elemental composition, such as carbon (C), hydrogen (H), oxygen (O), nitrogen (N), and sulfur (S), along with differences in volatile matter, moisture, ash content, and heating value, significantly affect the ignition and stable combustion characteristics of power-plant boilers.

The analysis of the four coal types is presented in

Table 6. Quality of coal.

Ingredient	Notation	Unit	Coal Type 1	Coal Type 2	Coal Type 3	Coal Type 4
Carbon (received base)	Car	%	54.2	54	58.56	54.18
Hydrogen (received base)	Har	%	3.47	2.9	3.36	2.44
Oxygen (received base)	Oar	%	3.41	4.5	7.28	1.53
Nitrogen (received base)	Nar	%	0.96	1	0.79	0.86
Sulfur (received base)	Sar	%	1	1.6	0.63	1.84
Total moisture	Mar	%	8.1	8	9.61	6.6
Ash	Aar	%	28.86	28	19.77	32.55
Moisture (air-dry base)	Mad	%	1.05	1.8
Volatile matter (ashless dry basis)	Vdaf	%	26.396	22.188	32.314	14.36
Lower heating value	QLHV	kJ/kg	21,040	21,000	22,440	20,800

. Among these, the designed coal type 1 is used as the standard, as discussed above, and it was simulated under a 20% load, as shown in Table 3. Coal type 3 has the highest lower heating value (LHV) of the four coals, at 22,440 kJ/kg. As shown in Figure 9a, the cross-sectional average combustion temperature for this coal is also the highest, with the peak temperature reached at h = 12.05 m. Compared to other coals, the heat released during the combustion of coal type 3 is more concentrated and efficient. This characteristic is not only directly reflected in its relatively high LHV value but also indirectly reflected in the average cross-sectional combustion temperature, which reached 1435.76 K. In the temperature field distribution shown in Figure 9a, the peak temperature during coal type 3 combustion is significantly higher than that of the other coals, particularly compared to coal type 4, which exhibits the lowest temperature levels. Due to the fact that coal type 4 has the lowest hydrogen and oxygen content and a low carbon content, the average cross-section temperature during combustion is the lowest and peaks at 1084.30 K. This significant difference can be reasonably explained by the chemical composition, especially the carbon content. Coal type 3 has the highest carbon content, at 58.56%. Since carbon is the primary combustible element in coal, its content has a direct impact on the heating value. This can be verified through the average cross-sectional temperatures of coal type 1 and coal type 2. From Figure 9a, the distributions of the cross-sectional temperature profiles of these two types of pulverized coal are very close to each other, which is due to the fact that coal type 1 has a carbon content of 54.2%, while coal type 2 has a carbon content of 54%. Additionally, the relatively high hydrogen content (3.36%) in coal type 3 promotes more complete combustion, thus increasing the average cross-sectional temperature. Hydrogen’s heating value is very high, even exceeding that of carbon. This indicates that the elemental composition of coal strongly influences combustion temperature and plays a key role on optimizing combustion efficiency.

Based on the cross-sectional NO_x concentration distribution in Figure 9b, coal type 1 has the highest NO_x emission concentration, at 638.06 ppm, followed by coal types 4 and 2, while coal type 3 has the lowest NO_x concentration. From the elemental analysis in Table 6, it is clear that the lower NO_x emissions from coal type 3 are primarily due to its relatively low nitrogen content and high oxygen content of 7.28%. This higher oxygen content enhances the complete combustion of pulverized coal, thereby reducing fuel-based NO_x formation. During combustion, higher oxygen levels facilitate the complete release and combustion of volatile matter, thus limiting nitrogen conversion to NO_x at high temperatures. Although coal type 2 has slightly more nitrogen than coal types 1 and 4, its NO_x emissions are comparatively lower. This can be explained by the interaction between nitrogen chemistry dynamics and the combustion process. Despite containing more nitrogen, coal type 2 has the highest oxygen content among the three, which likely enhances nitrogen reduction reactions or inhibits NO_x formation pathways, resulting in lower actual NO_x emissions compared to coal types 1 and 4.

The temperature field in the cross-section shown in Figure 10 indicates that a substantial portion of coal type 1 combustion occurs near the top of the furnace, rather than centrally. It can be seen in Figure 11a that the No. 1 coal type, at the exit of the lower burner, generates more NO_x. This combustion pattern could lead to increased slagging near the top of the furnace. In contrast, coal types 2 and 3 demonstrate more complete combustion and greater hearth fullness than coal type 4. This is reflected in the NO_x emission concentrations at the outlet, where coal types 2 and 3 exhibit lower NO_x concentrations and moderate flue gas temperatures at the outlet. Therefore, it can be seen that the No. 3 coal type has better combustion characteristics, not only with the highest average cross-section combustion temperature but also with the lowest average cross-section NO_x emissions. In Figure 10c, the pulverized coal is ejected from the lower burner and reaches its maximum value in the middle of the chamber (20 m), where the flame is more concentrated and efficiently combusted, and also generates less NO_x, as seen in Figure 11c. This finding aligns with the statistical results for NO_x concentrations at the outlet shown in Figure 12, further confirming the complex influence of coal characteristics and combustion conditions on NO_x emissions.

Although we have extended our boiler operation mode to four different types of coal and it has been proven to be an efficient and stable strategy, its application to a wide range of available coal types remains unexplored. To effectively address this issue, we proposed a numerical model that predicts NO_x emission concentrations and cross-sectional temperature during combustion by fitting a polynomial functional relationship between key coal quality parameters, such as carbon, hydrogen, oxygen, nitrogen, sulfur content, volatile matter, moisture, ash content, and heating value. This model allows for the real-time optimization and adjustment of the combustion process, providing a foundation for efficiently predicting NO_x emissions and temperature in coal combustion boilers using any type of coal. Using mathematical data analysis and predictive modeling in MATLAB 2022b, the relationships were fitted into the following formulas for outlet flue gas temperature, T_out, and NO_x emission concentration, C_NOx:

$$\begin{array}{l}{T}_{out}=2.4062x_1^{0.1403}+0.0005x_2^{9.2758}+5.1887x_3^{-0.4688}\\ +2.0372x_4^{-0.2584}+1.9931x_5^{-0.1495}+16.6161x_6^{0.3790}\end{array}$$

(16)

x₁–x₆ correspond to the mass fractions of carbon (C), hydrogen (H), oxygen (O), moisture (M), and ash (A) and the lower heating value (LHV). Since not all coal components significantly impact combustion temperature, six key parameters were selected for the fitting formula after comparing multiple data sets. The power index of oxygen is −0.4688, indicating a decisive influence on the outlet temperature. The coefficient of carbon is higher, but its power index is lower, while the coefficient for the low heating value is the highest, at 16.6161, indicating its significant influence on the exit flue gas temperature. High moisture content and increased volatile matter during combustion can reduce the outlet temperature. Thus, this formula is applicable to the corresponding boiler load, and the coefficients and indices in the formula should be adjusted according to changes in the commissioning mode.

$$\begin{array}{l}{C}_{NOx}=0.5783x_1^{-1.0828}+2.2585x_2^{2.7616}-0.1752x_3^{-2.5993}+1.0659x_4^{1.0523}\\ -0.7187x_5^{-1.1970}-0.0023x_6^{4.7012}+1.0998x_7^{0.8100}+0.1073x_8^{0.7146}\end{array}$$

(17)

where x₁–x₈ denote the mass fractions of carbon (C), hydrogen (H), oxygen (O), nitrogen (N), sulfur (S), moisture (M), and ash (A) and the lower heating value (LHV). Nitrogen content positively influences NO_x emissions, with a coefficient of 1.0659 and an exponent of 1.0523. Nitrogen strongly affects NO_x emissions; higher nitrogen content leads to higher NO_x emissions, as fuel-bound nitrogen directly contributes to NO_x formation. The negative coefficient of oxygen content suggests that increasing oxygen significantly reduces NO_x emissions by promoting pulverized coal combustion, thereby lowering NO_x formation. In Equation (17), reducing carbon, nitrogen, and ash content can help minimize NO_x emissions, as higher levels of these factors increase NO_x production. Increasing oxygen and sulfur content, particularly oxygen, may inhibit NO_x production.

The fitting results for outlet temperature, shown in Figure 13, indicate minimal differences between the simulation results and the calculated results from the fitted formula for the four coal types, with a deviation below 3%. For coal type 4, the simulated and fitted values for the average outlet temperature show a mere 0.35% deviation, indicating that the formula effectively captures the behavior of different coal types under these conditions. The fitting results for NO_x emissions in Figure 14 demonstrate excellent accuracy, with the smallest deviation reaching 0.00838%. This suggests that the method can reliably predict both outlet temperature and NO_x emissions for these coal types.

To further validate the fitting model’s accuracy, two additional coal types were constructed, with their elemental compositions detailed in

Table 7. Quality of coal.

Ingredient	Notation	Unit	Coal Type 5	Coal Type 6
Carbon (received basis)	Car	%	56.77	58.6
Hydrogen (received base)	Har	%	3.8	3.36
Oxygen (received base)	Oar	%	3.58	6.1
Nitrogen (received basis)	Nar	%	1.134	0.7
Sulfur (received base)	Sar	%	1.066	0.64
Total moisture	Mar	%	7.9	8.5
Ash	Aar	%	25.75	22.1
Moisture (air-dry basis)	Mad	%	1.05	1.8
Volatile matter (ashless dry basis)	Vdaf	%	19.4	19.8
Inferior calorific value (received base)	QLHV	kJ/kg	20,940	22,500

. The simulation parameters, listed in Table 3, reveal that the overall trends in furnace cross-sectional average temperature and NO_x concentration (Figure 15) for these two coal types are similar to those of the previous four coal types. Initially, both the temperature and NO_x concentration increase with furnace height, peaking at the upper burner level, with the highest temperature reaching 1875.89 K and the maximum NO_x concentration reaching 959.46 ppm. The temperature contour plots (Figure 16) demonstrate efficient combustion performance, with good flame filling, indicating strong furnace adaptability under these operational conditions. As shown in the NO_x distribution contour plot (Figure 17), during the operation of the lowest burner, a significant amount of NO_x is generated in the furnace’s midsection, similar to the combustion characteristics of the previously tested coals.

As illustrated in Figure 18a, using Formula (16) to predict the flue gas outlet temperature for coal types 5 and 6 resulted in a significant deviation of 19.5%. The higher nitrogen content in coal type 5 was not directly accounted for in the current formula, which may explain the larger deviation. Nitrogen affects heat release and reaction rates during combustion, particularly in high-temperature environments, where NO_x formation can absorb heat and reduce the outlet temperature. Since coal type 6 contains less nitrogen, its effect on temperature is weaker, allowing the existing formula to more efficiently capture the combustion characteristics of such coal. This suggests that the current formula performs well for coal types with a lower nitrogen content. As shown in Figure 18b, the fitting model performed accurately for coal type 6, with a minimal deviation of 1.45%. This demonstrates that the model effectively captures NO_x emission characteristics for low-nitrogen coals, yielding strong fitting results. Although the deviation for coal type 5 was larger, it remained within a reasonable range.

In the previously proposed 20% ultra-low-load operation strategy, stable operation could be achieved using various types of pulverized coal. The relationship between the exit flue gas temperature and NO_x emissions as a function of coal composition was derived through a fitting equation based on the different coal elements. The results of the work in this study, on the one hand, explore the scientific aspects of pulverized coal combustion and, on the other hand, this operating scheme (the commissioning of the lower burner and adjustment of the angle of the corresponding cyclone burner to 30° and the angle of the wall air to 20°, the specific parameters of which are given in Table 3) will be used as an optimized scheme for new boilers and for the improvement of the existing equipment.

4. Conclusions

This work employs numerical simulations to investigate the temperature distribution, NO_x distribution, outlet NO_x concentration, and flue gas temperature in the boiler chamber under ultra-low-load conditions, focusing on a 660 MW supercritical hedge-type boiler with front- and rear-wall burners. The results of multiple simulations highlight several key findings:

Operating burners at different layers affects steady combustion characteristics at low loads. Activating the lower burner raises the temperature to 1609 K within the same section, achieving the highest average combustion temperature along the height direction. In the six operational scenarios, using the lower burner provides a more uniform temperature distribution in the boiler chamber, increasing heat absorption by the water-cooled wall and preventing localized high-temperature zones. NO_x emissions are reduced to 112 ppm, and the outlet flue gas temperature drops to 743 K, ensuring the stable combustion of the 20% ultra-low-load pulverized coal. The combustion characteristics of four coal types and their effects on the cross-sectional temperature and NO_x emissions were analyzed. Coal type 3, with the highest lower heating value (22440 kJ/kg) and a relatively high hydrogen content, released more efficient heat, resulting in the highest average cross-sectional temperature of 1435.76 K. In contrast, coal type 4, which contains a lower carbon content and status calorific value status, exhibits exhibited the lowest cross-section mean temperature, peaking at approximately 1084.30 K. In terms of NO_x emissions, coal type 1, with higher amounts of nitrogen, released the highest levels of NO_x, while coal type 3, with the lowest amount of nitrogen, released the lowest levels of NO_x. This was attributed to the smaller nitrogen and higher oxygen contents of coal type 3, which promoted complete combustion and reduced NO_x formation. The fitted model effectively captures key factors influencing NO_x emissions, including nitrogen content and combustion temperature. In the simulation validation, the NO_x emission fitting error bar for coal type 6 was only 1.45%, demonstrating excellent fitting performance. For the high-nitrogen-content large coal type, the deviation was 5.2%.

In conclusion, this study on stable combustion in a 660 MW supercritical unit boiler operating at a 20% ultra-low load demonstrates that activating the lower burner ensures stable combustion while effectively reducing the emissions of pollutants, particularly NO_x, at the outlet. Furthermore, by fitting polynomial functions based on coal composition, it becomes possible to accurately predict changes in NO_x emission concentrations and flue gas exit temperatures during the boiler combustion process.