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Based on the low-carbon transition needs of coal-fired boilers, this study conducted industrial trials of direct biomass co-firing on a 620 t/h high-temperature, high-pressure circulating fluidized bed (CFB) boiler, gradually increasing the co-firing ratio. It used compressed biomass pellets, achieving stable 20 wt% (weight percent) operation. By analyzing boiler parameters and post-shutdown samples, the comprehensive impact of biomass co-firing on the boiler system was assessed. The results indicate that biomass pellets were blended with coal at the last conveyor belt section before the furnace, successfully ensuring operational continuity during co-firing. Further, co-firing biomass up rates of to 20 wt% do not significantly impact the fuel combustion efficiency (gaseous and solid phases) or boiler thermal efficiency and also have positive effects in reducing the bottom ash and SOx and NOx emissions and lowering the risk of low-temperature corrosion. The biomass co-firing slightly increases the combustion share in the dense phase zone and raises the bed temperature. The strong ash adhesion characteristics of the biomass were observed, which were overcome by increasing the ash blowing frequency. Under 20 wt% co-firing, the annual CO2 emissions reductions can reach 130,000 tons. This study provides technical references and practical experience for the engineering application of direct biomass co-firing in industrial-scale CFB boilers.

The supercritical circulating fluidized bed (CFB) boiler, which combines the advantages of CFB combustion with low cost emission control and supercritical steam cycle with high efficiency of coal energy, is believed to be the future of CFB combustion technology. It is also of greatest importance for low rank coal utilization in China. Different from the supercritical pulverized coal boiler that has been developed more than 50 years, the supercritical CFB boiler is still a new one which requires further investigation. Without any precedentor engineering reference, Chinese researchers have conducted fundamental research, development, design of the supercritical CFB boilers independently. The design theory and key technology for supercritical CFB boiler were proposed. Key components and novel structures were invented. The first 600 MWe supercritical CFB boiler and its auxiliaries were successfully developed and demonstrated in Baima Power Plant, Shenhua Group as well as the simulator, control technology, installation technology, commissioning technology, system integration and operation technology. Compared with the 460 MWe supercritical CFB in Poland, developed in the same period and the only other supercritical one of commercial running in the word beside Baima, the 600 MWe one in Baima has a better performance. Besides, supercritical CFB boilers of 350 MWe have been developed and widely commercialized in China. In this paper, the updated progress of 660 MWe ultra-supercritical CFB boilers under development is introduced.

Aiming at the problem that it is difficult to adjust the parameters of the controller in the circulating fluidized bed (CFB) boiler combustion system due to its multivariable and strong coupling, an improved linear active disturbance rejection controller (ILADRC) parameter tuning strategy based on the Lévy flight double chaotic sparrow search algorithm (LF-DCSSA) is proposed. The LF-DCSSA algorithm is used to tune the parameters of the ILADRC controller in the multivariable coupled combustion control system of the CFB boiler built by Simulink, so that its control effect can reach the best state. The step response simulation and perturbation simulation are carried out with the theoretically tuned PID and ILADRC. The simulation results show that LF-DCSSA-ILADRC has obvious advantages in the three indexes of time–domain response, such as adjustment time, overshoot, and ITAE, which is more efficient and accurate than that of the theoretical setting, providing a new strategy for the control of the CFB boiler combustion system.

Climate change poses a significant global challenge that necessitates concerted efforts toward carbon neutrality. Circulating fluidized bed (CFB) boilers have gained prominence in various industries due to their adaptability and reduced emissions. However, many current control systems rely heavily on manual operator intervention and lack advanced automation, which constrains the operational efficiency. This study addressed the need for dynamic models capable of monitoring and optimizing NOx emissions in CFB boilers, especially under fluctuating loads and strict regulatory standards. We introduced the TimesNet model, which utilizes fast Fourier transform (FFT) to extract key frequency components, transforming 1D time series data into 2D tensors for enhanced feature representation. The model employs Inception blocks for multi-scale feature extraction and incorporates residual connections with amplitude-weighted aggregation to mitigate catastrophic forgetting during training. The results indicated that TimesNet achieved R2 values of 0.98, 0.97, and 0.95 across training, validation, and testing datasets, respectively, surpassing conventional models with a reduced MAE of 1.63 mg/m3 and RMSE of 3.35 mg/m3. Additionally, it excelled in multi-step predictions and effectively managed long-term dependencies. In conclusion, TimesNet provides an innovative solution for the precise monitoring of NOx emissions in CFB boilers by enhancing predictive stability and robustness and addressing salient limitations in existing models to optimize combustion efficiency and regulatory compliance.

Furnace temperature and oxygen content are important parameters reflecting the combustion inside a circulating fluidized bed (CFB) boiler. Accurately predicting boiler output is a complex task due to the high noise and nonsmoothness of actual boiler input and output data. In this paper, a new hybrid convolutional neural network (CNN), bidirectional long short-term memory (biLSTM) network, and squeezing and excitation (SE) network prediction model is proposed to significantly improve the prediction accuracy of oxygen content and furnace temperature by combining the advantages of multiple deep learning networks. This network can extract spatiotemporal characteristics of input parameters such as coal feed to effectively predict boiler furnace temperature and oxygen content. CNNs can extract complex features such as dynamic and static nonlinearities between multiple variables affecting the furnace temperature and oxygen content, as well as high noise. The biLSTM network layer can efficiently handle the temporal information of irregular trends in modeling time series components; SE can extract the important information between channels through the feature relationships between channels for better overfeature extraction. The CNN-biLSTM-SE model can effectively solve the problem of nonlinear mapping complexity between inputs and outputs. Experiments show that the proposed CNN-biLSTM-SE model outperforms existing methods. The experimental results showed that the average MAPE errors for oxygen content prediction were CNN-biLSTM-SE (0.038), CNN-biLSTM with attention mechanism (AM) (0.043), CNN-biLSTM (0.051), CNN-LSTM (0.051), biLSTM (0.051), RNN (0.051), LSTM(0.0052), and CNN(0.0054). Extensive experiments in CFB boilers with oxygen content and furnace temperature show that the proposed CNN-biLSTM-SE model achieves better results in terms of goodness-of-fit, generalization ability and accuracy.

In order to solve the problem of low thermal efficiency of a 130 t/h industrial circulating fluidized bed boiler, a computational particle fluid dynamic approach was used in this work to study two-phase gas–solid flow, heat transfer, and combustion. The factors influencing coal particle size distributions, air distribution strategies, and operational loads are addressed. The results showed that particle distribution exhibits “core–annulus” flow with a dense-phase bottom region and dilute-phase upper zone. A higher primary air ratio (0.8–1.5) enhances axial gas velocity and bed temperature but reduces secondary air zone (2.5–5.8 m) temperature. A higher primary air ratio also decreases outlet O2 mole fraction and increases fly ash carbon content, with optimal thermal efficiency at a ratio of 1.0. In addition, as the coal PSD decreases and the load increases, the overall temperature of the furnace increases and the outlet O2 mole fraction decreases.

Circulating fluidized bed (CFB) boilers excel in low emissions and high efficiency, with bed temperature serving as a critical indicator of combustion stability, heat transfer efficiency, and pollutant reduction. This study proposes a novel framework for predicting bed temperature in CFB boilers under complex operating conditions. The framework begins by collecting historical operational data from a power plant Distributed Control System (DCS) database. Next, the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw signals into distinct modes. By analyzing the trade-offs of combining modes with different energy levels, data denoising and outlier reconstruction are achieved. Key features are then selected using Normalized Mutual Information (NMI), and the inflection point of NMI values is used to determine the number of variables included. Finally, an iTransformer-based model is developed to capture long-term dependencies in bed temperature dynamics. Results show that the CEEMDAN-NMI–iTransformer framework effectively adapts to diverse datasets and performs better in capturing spatiotemporal relationships and delivering superior single-step prediction accuracy, compared to Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and Transformer models. For multi-step predictions, the model achieves accurate forecasts within 6 min and maintains an R2 above 0.95 for 24 min predictions, demonstrating robust predictive performance and generalization.

  Field studies were conducted to study the emission and distribution characteristics of dioxins by elevating the chlorine concentration in feedstock in a circular fluidized bed boiler. The concentration and total equivalent quantity of polychlorinated dibenzo–p–dioxins and polychlorinated dibenzofurans (PCDD/Fs) in all flue gas, electrostatic ash, bag filter ash, and bottom ash samples under blank condition (i.e., feedstock was normal coal) and chlorine labeling condition (i.e., feedstock mixed with coal and chlorine-containing labeling agent) were analyzed. Results illustrated that the concentration of PCDD/Fs in all gaseous and ash samples increased with the addition of chlorine in feedstock, with the largest and least increment in dioxin concentration observed in electrostatic ash and flue gas. PCDDs were the predominate congeners in flue gas, accounted for 50.1–60.4% of the total PCDD/F concentration under chlorine labeling and blank conditions, while PCDD/F distribution changed from PCDD– to PCDF–predominate by increasing chlorine content in feedstock under all field test conditions: 46.6–92.9%, 34.0–76.1%, and 47.0–53.1% of PCDFs were distributed in electrostatic ash, bag filter ash, and bottom ash, respectively. Highly chlorinated PCDD/F congeners such as O 8 CDD/F and 1,2,3,4,6,7,8-H 7 CDD/F were the primary contributors to dioxin concentration in flue gas and bottom ash samples, whereas low-chlorinated 2,3,7,8-T 4 CDF and 1,2,3,7,8-P 5 CDF congeners became critically dominating in electrostatic and bag filter ash.

Mercury (Hg) emissions from coal-fired power plants are of increasing concern around the world. In this study, field tests were carried out to understand the Hg emission characteristics and its migration behaviors in a commercial CFB boiler unit with the electricity generation capacity of 25 MW. This boiler is equipped with one electrostatic precipitator (ESP) and two fabric filters (FFs) in series for removing particulates from the flue gas. The EPA 30B method was used for simultaneous flue gas Hg sampling at the inlet of the ESP and the outlet of the second FF. The Hg mass balance in the range of 104.07% to 112.87% was obtained throughout the CFB unit by measuring the Hg contents in the feed fuel, the fly ash and the bottom ash, as well as in the flue gas at the outlet of the particulate control device (PCD) system. More than 99% of Hg contained in the feed fuel was captured by the fly ash, whilst less than 1% of Hg was remained in the bottom ash or the flue gas after passing the PCD system. The gaseous Hg obviously migrated from the flue gas to the fly ash in the air pre-heater, where the flue gas temperature decreased from 250 °C at the inlet to 120 °C at the outlet. Other gaseous Hg migrated from the flue gas to the fly ash in the PCD system, as the Hg concentrations in the flue gas ranged from 3.14 to 4.14 μg/m3 at the inlet of the ESP and ranged from 0.30 to 0.36 μg/m3 at the outlet of the second FF. The average Hg contents in the fly ash samples collected from the ESP, the first FF and the second FF were 912.3, 1313.6 and 1464.9 ng/g, respectively, while the mean particle diameters of these fly ash samples tend to decrease along the flow pass in the PCD system. Compared to large fly ash particles, smaller fly ash particles exhibit higher Hg capture performance due to their high unburned carbon (UBC) content and large specific surface area. The migration of gaseous Hg from the flue gas to the fly ash downstream of the CFB boiler unit was easier than that downstream of the PC boiler unit due to high UBC content and specific surface area.

This study explores the potential of utilizing circulating fluidized bed boiler fly ash (CFBFA) in the production of composite gravels, with the aim of achieving performance comparable to natural gravel while promoting sustainability. CFBFA, activated by hydrated lime and gypsum, was investigated for its pozzolanic reaction and carbonation curing under simulated coal-fired power plant flue gas conditions (80 °C, 0.4 MPa, 15% CO2, 85% N2). The study focused on optimizing the ratios of gypsum and hydrated lime in CFBFA-based cementitious materials, with the goal of enhancing their mechanical properties and understanding the underlying hydration and carbonation mechanisms. X-ray diffraction (XRD) and scanning electron microscopy (SEM) were used to analyze the mineral composition and microstructure of the composite gravels. The results revealed that the optimal gypsum-to-hydrated lime ratio for CFBFA composite gravels is 2:1, achieving a compressive strength of 9.01 MPa after 28 days of carbonation curing. Carbonation curing accelerated hydration, improving the material’s strength, stability, and microstructure. Additionally, the production of CFBFA composite gravels demonstrated significant environmental benefits, reducing Cumulative Energy Demand (CED) by 86.52% and Global Warming Potential (GWP) by 87.81% compared to cement road base materials. This research underscores the potential of CFBFA as a sustainable construction material, with insights into improving its mechanical performance and expanding its large-scale use through carbonation curing with flue gas.