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The Monte Carlo method (MCM) can be used to calculate the radiative heat flux and divergence with high accuracy, and the Distribution of Ratios of Energy Scattered by the medium Or Reflected by the boundary surface (DRESOR) method based on the MCM provides the radiation intensity with high directional resolution. However, the accuracy and efficiency of the DRESOR method for calculating the radiative heat flux and divergence have not been verified. Therefore, in this article, the DRESOR method is reformed to make it more suitable for calculating the radiative heat flux and divergence, and the DRESOR method and MCM are compared quantitatively. In the comparison, the influences of the Mean Optical Thickness per Element (MOTE), the surface and medium parameters, anisotropic scattering, and a nonuniform medium are considered. The results show that the figure of merit (FoM) of the reformed DRESOR method is 81.18 and 6.04 times that of the MCM for the surface element and space element, respectively when the MOTE is 0.17 for calculating the radiative heat flux and divergence. The larger the surface emissivity, and the smaller the scattering albedo, the better the performance of the DRESOR method than the MCM is. Meanwhile, the MOTE (mesh density), anisotropic scattering, and the nonuniform medium have similar influences on the DRESOR method and the MCM. [ABSTRACT FROM AUTHOR]

The Distribution of Ratios of Energy Scattered by the medium Or Reflected by the boundary surface (DRESOR) method based on the Monte Carlo method is an important new approach to solve the radiative transfer equation accurately and efficiently. This method has been applied extensively for the detection and analysis of the furnace flame temperature. However, the discrete method, the number of direct incident radiation directions (DIRDs), and the number of scattered or reflected energy beams (SREBs) lack quantitative references. Therefore, in this paper, the effects of different discrete methods, the number of DIRDs, and the number of SREBs on the radiative heat flux, divergence error, and computational efficiency of three-dimensional homogeneous absorbing, emitting, and scattering media are analyzed. The results show that the random discrete method of the DIRD for the space element substantially reduces the divergence error of the radiation heat flux. The uniform discrete method of the DIRD should be used for the surface element to minimize the error of the radiation heat flux error. In addition, the relative error of the DRESOR method is reduced to 0.004, and the figure of merit reaches the maximum when the number of DIRDs is about 1000, the number of SREB is 500, and the scattering albedo is less than 0.8. The fitted equations describing the relationship between the calculation errors and the number of SREBs for the surface and space elements are obtained for different scattering albedos. An increase in the number of DIRDs and SREBs does not significantly reduce the calculation error. The results provide a quantitative reference for determining the parameters of the DRESOR method in radiant heat analysis. [ABSTRACT FROM AUTHOR]

• An accurate and efficient formula for calculating scattering fraction was delivered. • A simplified tracking strategy of energy beam was proposed. The Distribution of Ratios of Energy Scattered by the medium Or Reflected by the boundary surface (DRESOR) method, one of the most effective methods to solve the radiation intensity problem with high directional resolution in radiation transfer analysis, suffers performance degradation as the medium scattering albedo becomes large. A scheme is put forward to subdivide the discrete energy beam step length, and take its infinitesimal limit to calculate the scattering/reflection fraction (DRESOR value) with high efficiency and precision. The preliminary simulation results show that when the scattering albedo is greater than 0.5, the optimized DRESOR method can greatly reduce the calculation time and improve the calculation accuracy. [ABSTRACT FROM AUTHOR]

A novel channel-selection convolutional neural network (CS-CNN) is proposed to predict NOx emission from coal-fired boilers under steady-state and transient load conditions. First, a new channel-selection convolutional layer (CS-CL) is presented to replace regular convolutional layer (RCL). The CS-CL evaluates the channel importance of the input variables, selects the Top-C important channels and releases the hyperparameters of the remaining low-importance channels, thus contributing to maximize the utilization of the parameter resources of the model. The advantages of using CS-CLs are the preservation of the great majority of manipulated variables involved in combustion control among the input variables and the prevention of the model overfitting problem due to the redundancy of input variables. Second, a sliding window-based preprocessing method is applied to the historical data of the boiler which is divided into four-dimensional (4D) tensors. Then, comparative tests are performed on long short-term memory (LSTM) model, baseline CNN and CS-CNN using the historical data of a 670 MW boiler. The results of tests showed that CS-CNN has higher prediction performance. Finally, in order to increase the interpretability of the deep learning black box model, this study analyzes the working mechanism of the CS-CNN through ablation analysis and visualization of model parameters. • Channel importance assessment of input variables by channel selection convolutional layer. • Data-driven model for complete burner manipulation variables. • Novel convolutional neural network for predicting NOx emission from coal-fired boilers. • Model visualization analysis of convolutional neural network for industrial time series data. [ABSTRACT FROM AUTHOR]

[Display omitted] • EffNet and ResNet excel in microalgae biomass quantitative monitoring. • Rhodophyta RGB trends are more suited for quantitative forecasts than Spirulina. • ResNet demonstrates greater generalization and robustness than EffNet. The effective monitoring of microalgae cultivation is crucial for optimizing their energy utilization efficiency. In this paper, a quantitative analysis method, using microalgae images based on two convolutional neural networks, EfficientNet (EFF) and residual network (RES), is proposed. Suspension samples prepared from two types of dried microalgae powders, Rhodophyta (RH) and Spirulina (SP), were used to mimic real microalgae cultivation settings. The method's prediction accuracy of the algae concentration ranges from 0.94 to 0.99. RH, with a distinctively pronounced red-green-blue value shift, achieves a higher prediction accuracy than SP. The prediction results of the two algorithms were significantly superior to those of a linear regression. Additionally, RES outperforms EFF in terms of its generalization ability and robustness, which is attributable to its distinct residual block architecture. The RES provides a viable approach for the image-based quantitative analysis. [ABSTRACT FROM AUTHOR]

This paper presents a method to invert the two-dimensional distribution of a temperature and volume concentration of soot particles from color images. By using numerical simulation, the temperature field and particle volume-concentration field of a non-uniform soot flame are simultaneously reconstructed using the wide-response spectrum of a color CCD camera without adding monochromatic filters. The influence of number of cameras, error of camera position angle, measurement noise and different reconstruction algorithms on measurement accuracy are analyzed. The numerical-simulation results demonstrate that camera-position angle errors play a crucial role in the reconstruction accuracy. In addition, increasing the number of cameras can improve the reconstruction result accuracy. Compared with the least squares algorithm, the Tikhonov-regularization algorithm has a stronger anti-noise ability and can resist 39 dB of noise. The conclusions obtained in this paper are helpful to guide following experimental studies. [ABSTRACT FROM AUTHOR]

In the field of micro and mesoscale combustion, the feature of flame-wall thermal coupling is of great significance because of its small scale nature. Thus, this work provides a comprehensive heat transfer analysis in cylindrical combustors from the perspective of numerical simulation. The combustor has a fixed length-to-diameter aspect ratio of 10, and the channel diameter is scaling up from 1 mm to 11 mm to explore the influence of chamber dimension on heat transfer and flame structure. The distribution of convective and radiative heat flux on inner surface, contribution of thermal radiation are given. Moreover, the role of radiation in flame structure is analyzed, and the convective and radiative heat losses are quantitatively analyzed. We find that radiative heat flux is smaller compared to convective heat flux, and the proportion of radiative heat flux becomes larger with an increasing diameter. Thermal radiation does not change the flame structure when the diameter is less than 3 mm. When the diameter is greater than 5 mm, thermal radiation changes the location of flame front. The heat loss becomes larger at a smaller diameter, and heat loss ratio can reach approximately 73.6% in the combustor with diameter of 1 mm. [Display omitted] • Comprehensive heat transfer analyses in micro combustors are revealed. • The contributions of thermal radiation in combustors with different scales are investigated. • The role of radiation in the flame structure is analyzed. • Heat losses in cylindrical combustors from microscale to mesoscale are quantified. [ABSTRACT FROM AUTHOR]

• A mesh in downstream region can suppress oscillation by 50% and raise frequency. • Incorporating a mesh leads to more system state variations from recurrence analysis. • Mesh number can affect system behaviours to a larger extent than mesh position. • Degree of mesh temperature fluctuation is related to both mesh number and position. • The mesh acts as a heat and flow disturbance source in the downstream region. Self-excited thermoacoustic oscillations are undesirable in most combustion systems due to their negative influence on combustion efficiency and structural vibration. Nonlinear thermoacoustic oscillations driven by combustion can be sensitive to changes in the heat and flow conditions. Therefore, a comprehensive study on self-excited thermoacoustic oscillations was conducted to investigate the effect of incorporating a woven mesh with varying mesh numbers and positions into the downstream region of a Rijke tube. This study determined system frequency and amplitude response and revealed dynamic properties of the system by applying recurrence analysis. To obtain temperature distributions, a thermal imaging system was employed, comprising a short wavelength infrared (SWIR) camera for the mesh and a long wavelength infrared (LWIR) camera for the tube wall. A high-speed schlieren imaging system was utilised for visualising heat and flow conditions at the tube end. The study demonstrated the effect of including a mesh in changing the oscillation eigenfrequency, suppressing the oscillation amplitude by up to 50 % and influencing the system dynamics. Higher mesh number demonstrated greater effectiveness. The captured thermal and schlieren images clearly evidenced the mesh's significant influence on the downstream heat and flow conditions. The insights gained from this work provide a potential control method of self-excited, nonlinear thermoacoustic oscillations. [ABSTRACT FROM AUTHOR]

The selective catalytic reduction (SCR) denitrification efficiency of coal-fired boilers is used to assess the clean production of power plants. Accurate predictions of NOx emissions at the SCR inlet can help improve the denitrification efficiency. However, the generation and distribution of NOx in a furnace involves complex combustion, thermodynamics, and fluid mechanics processes. The physical-based method for NOx emission models is complex and requires expensive computational resources. To address these issues, deep learning was employed to develop a prediction approach based on the random forest (RF) algorithm and lightweight convolutional neural network (CNN). To avoid curse of dimensionality, the random forest algorithm was utilized to select the importance of the candidate variables. The selected variables were in line with the boiler NOx generation and combustion control trends. The lightweight CNN was developed to build the model that predicts NOx emissions. The key was that a Cross-Channel Communication (C3)-Block was introduced into the lightweight CNN to resolve the issue in which there was no communication between feature maps in the same convolutional layer. This model was named C3–CNN. The predictive effectiveness of the C3–CNN-based model was assessed on the real historical dataset of a 600 MW down-fired boiler. The experimental results indicated that the proposed method reduced the model depth of the CNN (saved 3 convolutional layers, 1.5 times parameters, and 1 times FLOPs than 6-layer CNN) while also ensuring the model prediction accuracy (RMSE = 13.52704 ± 0.21036 mg/m3, MAE = 9.89313 ± 0.18288 mg/m3, R 2 = 0.93337 ± 0.00222), which was suitable for the online optimization of industrial pollutant control and may assist cleaner production. [Display omitted] • A novel regression approach based on convolutional neural networks (C3–CNN) is proposed. • C3–CNN can make NOx emission predicts for coal-fired boilers. • C3–CNN improves efficiency and accuracy of NOx emission predicts under steady-state and transient conditions. • C3–CNN's lightweight framework encourages cross-channel communication between feature maps of the same convolutional layer. • Random forest algorithm is utilized to prioritize the model's manipulated variables for high-dimensional industrial variables. [ABSTRACT FROM AUTHOR]

Driven by the demand for accurate intrinsic kinetics for the prediction of the combustion rate of purely fired Zhundong coal (ZDC) in a slag tapping boiler, the surface activation function (SAF) and intrinsic kinetic parameters of two types of rapid-pyrolysis ZDC chars were revealed via the isothermal and nonisothermal thermogravimetric analysis methods. The isothermal kinetic results show that the activation energy (E), reaction order and SAF of these two chars were the same, at 146 kJ/mol, 0.78 and X 0.44 (1 – X)−0.48, respectively. Therefore, the difference in the intrinsic reaction rate between these two chars was caused only by the different pre-exponential factors, which are mainly influenced by the specific surface area and catalytic elements in char ash. Nonisothermal kinetic parameters were obtained via the isothermal model-fitting method (ISOM), thereby using the isothermal SAF to fit the nonisothermal data at different heating rates (β s). The results indicate that the E values of the two chars were 148.95 ± 3.12 kJ/mol and 146.16 ± 1.66 kJ/mol, indicating that β has little influence on E. These measured values are consistent with the isothermal E values, so the ISOM is suitable for the determination of the nonisothermal kinetics of rapid-pyrolysis char combustion. • Intrinsic combustion kinetics of rapid-pyrolysis Zhundong coal chars were obtained. • Different chars have the same E , reaction order and surface activation function. • Catalytic elements have a positive effect on A but have little influence on E. • The heating rate has little influence on E of rapid-pyrolysis char combustion. • ISOM is suitable to describe the combustion kinetics of rapid-pyrolysis char. [ABSTRACT FROM AUTHOR]

• An experimental design was used to investigate the effects of different torrefaction conditions on the properties of biomass. • Two spectrometers were simultaneously used to capture high SNR signals of continuous spectrum and characteristic spectrum. • In-situ detection of the temperature and K release concentration of torrefied biomass pellet combustion was achieved. • Changes in combustion characteristics and K release characteristics of torrefied biomass were evaluated. The orthogonal experiment designed in this paper investigated the effect of changes in experimental parameters such as biomass species, torrefaction temperature, torrefaction time, torrefaction oxygen concentration on the physicochemical properties and combustion characteristics of the torrefaction products. Two optical fiber spectrometers were used to capture the spontaneous emission spectra of the combustion flame of compressed pellets of torrefied biomass, and the in-situ measurement of pellet combustion temperature and K release concentration at high heating rate was realized by multi-spectral analysis algorithm. The experimental results indicate that for biomass, torrefaction will lead to a decrease of volatile and moisture content and an increase of fixed carbon and ash content in components, a decrease of O and H and an increase of C content in elements, a certain loss of biomass quality, an increase of high heat value, and each indicator move closer to coal. The effects of various experimental parameters on the volatile content, mass yield, high heat value increase rate and energy yield in biomass torrefaction products have similar laws, i.e., biomass species / torrefaction oxygen concentration > torrefaction temperature > torrefaction time. The mild torrefaction product pellets have a shorter ignition delay, increased volatile combustion flame height and peak temperature, longer volatile and char combustion duration, but lower peak char combustion temperature, among which the selected torrefaction products of rice straw and pine wood showed an increase in peak temperature of 0.56 % (11 K) and 0.61 % (12 K) for the volatile stage respectively, and a decrease of and 1.29 % (20 K) and 1.29 % (21 K) for the char stage respectively. In addition, the peak K release concentration during combustion of the mild torrefaction product pellets was reduced, among which the peak K release concentrations during the volatile and char stage were reduced by 43.22 % (3.89 ppm) and 52.93 % (51.55 ppm) of the rice straw torrefaction product respectively, and by 73.14 % (1.28 ppm) and 62.08 % (15.01 ppm) for the pine wood torrefaction product respectively. Biomass torrefaction products have fuel properties closer to coal, less K released from the combustion process, which have the potential to become an alternative to coal. [ABSTRACT FROM AUTHOR]

A novel planar micro-combustor with four-corner entrances is proposed by considering high and uniform outer wall temperature distribution used in micro thermo-photovoltaic generator. For this, a three-dimensional numerical model with detailed chemical mechanism, transport, and heat transfer and a thermo-photovoltaic energy conversion model are developed to examine its performance. Results show that the proposed micro-combustor has excellent wall temperature uniformity because a uniform distribution of high-temperature zones of four individual flames is formed in the combustion chamber. Thermal performance and energy efficiency of the proposed micro-combustor are better than conventional micro-combustor at a low flow rate (m) and large equivalence ratio (φ). Additionally, the implemented plate-insertion strategy effectively overcome the deterioration of combustor performance under higher m or lower φ. Proposed micro-combustor with plates insertion has the largest wall temperature and energy efficiency, no matter what φ and m are adopted. Plates extend the fluid ravel path and produce some recirculation zones, which promotes heat transfer, preferential diffusion, and flame anchoring, while its pressure loss is larger. Of all the tested cases, the system efficiency reaches a maximum of 6.39%, corresponding to 12.1 W electricity output. This work provides effective combustor design and research insights to develop high-efficiency micro-power system. • A novel micro-combustor with four entrances is proposed for micro-TPV system. • Excellent performance of wall temperature uniformity is obtained. • Inserting plates boosts thermal performance and energy conversion efficiency. • A maximum system efficiency of 6.39% is achieved with 12.1 W electric output. [ABSTRACT FROM AUTHOR]

This paper proposes a hyperspectral imaging technique for capturing radiative images at a high temporal–spatial resolution, for the purpose of spectrally visualizing the instantaneous combustion behaviors of single coal particles. We initially use a hyperspectral imaging device to record the combustion processes of two types of single pulverized coal particles on a McKenna flat-flame burner. From the snapshot images captured by the device, we observe the combustion processes, ignition characteristics, flame morphologies, and sizes of the two single coal particles. The observations indicate that the two single coal particles undergo volatile release/combustion and char ignition/oxidation during the luminous combustion process; moreover, both particles exhibit homogeneous ignition characteristics under the experimental conditions. Furthermore, based on the spectral radiative intensity images obtained via the device, the soot temperature and volume fraction images of the two single coal particles in the envelope flame are obtained at different burning times. Thereafter, the influences of the combustion behaviors and soot formation mechanisms on the distribution rules of the soot temperature and concentration at different combustion periods are discussed. Finally, the temperature and apparent spectral emissivity values of the two single char particles during the char oxidation process are also calculated to analyze their combustion behaviors. The results show that the temperature changes within a relatively narrow range with respect to the burning time during the oxidation stage, whereas the spectral emissivity continues to increase with the oxidation of chars and subsequently decreases. [ABSTRACT FROM AUTHOR]