Coal-fired Power Plant Boiler Research Articles

Optimizing Two-Phase Flow Heat Transfer: DCS Hybrid Modeling and Automation in Coal-Fired Power Plant Boilers

Optimizing Two-Phase Flow Heat Transfer: DCS Hybrid Modeling and Automation in Coal-Fired Power Plant Boilers

Numerical and Experimental Investigation of the Decoupling Combustion Characteristics of a Burner with Flame Stabilizer

In order to integrate renewable electricity into the power grid, it is crucial for coal-fired power plant boilers to operate stably across a wide load range. Achieving steady combustion with low nitrogen oxide (NOx) emissions poses a significant challenge for boilers burning low-volatile coal in coal-fired power plants. This study focuses on developing a decoupling combustion technology for low-volatile coal-fired boilers operating at low loads. A three-dimensional numerical simulation is employed to analyze and optimize the geometrical parameters of a burner applied in a real 300 MW pulverized coal fired boiler. Detailed analysis of the burner’s decoupling combustion characteristics, including stable combustion ability and NOx reduction principles, is conducted. The results indicate that this burner showed three stages of coal/air separation, and the flame holder facilitates the stepwise spontaneous ignition and combustion of low-volatile coal. By extending the time between coal pyrolysis and carbon combustion, the burner enhances decoupling combustion and achieves low nitrogen oxide emissions. Based on optimization, a flat partition plate without inclination demonstrates excellent performance in terms of velocity vector field distribution, coal air flow rich/lean separation, combustion, and nitrogen oxide generation. Compared with the initial structural design, the average nitrogen oxide concentration at the outlet is reduced by 59%.

A hybrid prediction approach for enhancing heat transfer efficiency of coal-fired power plant boiler

Predicting future fouling status is a crucial but tough topic in the applications of energy conservation and pollution reduction at coal-fired power plants because of the significant influence that ash slag has on the heat transfer efficiency of boilers in coal-fired power plants. For the prediction of gray areas in heated areas, a hybrid system based on complementary ensemble empirical modal decomposition, gray models, and long short-term memory networks is presented. This is because the time series of ash pollution degrees is not linear and not smooth. Initially, using a complementary ensemble empirical modal decomposition, the original sequence after wavelet threshold denoising is divided into a number of subseries components. The projected values for the cleanliness factor were then generated by superimposing the predictions from the IMF and residual components. The experimental findings support the model’s precision and dependability and demonstrate that the CEEMD-GM-LSTM model does, in fact, perform very well in forecasting the ash situation in the heated zone.

Acoustic reconstruction of the vortex field in the nonuniform temperature field of a simulated furnace

The high-temperature thermal vortex flows generated by the tangential combustion of large-scale coal-fired power plant boilers represent a complex medium that causes acoustic ray bending. In this paper, nonlinear acoustic tomography is combined with particle swarm optimization as a means of reconstructing the two-dimensional thermal vortex field. The nonuniform and large-gradient temperature distribution is the main cause of acoustic ray bending. We establish curved paths of acoustic propagation that are closely related to the temperature distribution. Based on these curved paths, a set of radial basis functions combined with Tikhonov regularization is derived to achieve more accurate reconstruction of the temperature field. The nonuniform temperature field affects the reconstruction of the flow field information. Our model considers a variation in the angle between the direction vector of the acoustic curved paths and the horizontal and vertical components of the flow velocity, and incorporates a priori information and a six-parameter hypersurface into the flow field reconstruction scheme. Particle swarm optimization is applied to minimize the objective function and obtain the vortex velocity field. Numerical experiments show that the proposed mathematical model of acoustic ray bending improves the reconstruction accuracy of the temperature distribution and vortex flow field. The acoustic time-of-flight is experimentally measured through the generalized cross-correlation with different window functions. The temperature and vortex fields reconstructed based on the actual time-of-flight are compared with thermocouple and anemometer measurements to demonstrate the validity and robustness of the proposed reconstruction model.

Combustion optimization of a coal-fired power plant boiler using artificial intelligence neural networks

This study establishes detailed neural network modeling procedures for the combustion optimization of a power generation unit, equipped with a Selective Catalytic Reduction (SCR) system. In particular, artificial intelligence neural networks were applied due to its capability in treating large pools of data of great nonlinearity. The objective of this study was to reduce nitrogen oxide (NOx) emissions in the stack gas, lower the cost of emissions compliance and minimize the net unit heat rate at this power generation unit at full-load conditions. These procedures include building a database with real coal power plant operating data, modeling the database and using the genetic algorithm to optimize the operating conditions. The operation is optimized with respect to the steam temperature, selective catalyst reactor, and separated over-fire air conditions. Verifications of the results demonstrated that by carrying out the reported study, the pollutant concentrations of the boiler are significantly decreased and the catalyst layer is effectively preserved, while the operation can be maintained stable near the full-load condition. It was found that the average ammonia (NH3) flow to the SCR under optimized conditions was 229 kg/h, while the average NH3 flow under non-optimal operational conditions was 260 kg/h, resulting in a reduction in NH3 consumption of approximately 12 % from the non-optimized operational condition. In the meantime, the SCR inlet (boiler exit) NOx was reduced from 523 mg/Nm3 (non-optimized operational condition) to 444 mg/Nm3 (optimized operational condition), which represent about 15 % reduction. Based on the experience with SCR from manufactures and operators, the SCR catalyst life expectancy has a direct relationship with the SCR inlet NOx concentration and NH3 injection rate. The lower the NOx concentration at the SCR inlet and the less the NH3 injection rate, the longer of the SCR catalyst life will be. Thus, it is expected that the SCR catalyst layer replacement frequency could be reduced by 10 to 20 % over the normal catalyst replacement cycle, while meeting the same environmental NOx emissions limit. Additionally, a Delta Heat Rate was also calculated using best available data from the optimized operational conditions, including boiler load, boiler excess oxygen (O2), fly ash unburned carbon and attemperation flow. Results were compared with the average Delta Heat Rate calculated from baseline tests. It was found that the average Delta Heat Rate from baseline tests are −60 kJ/kWh, while the delta heat rate from the optimized operation condition hours is −118 kJ/kWh. This represents about 0.57 % improvement on unit heat rate from the baseline average.

Facility-Level Emissions and Synergistic Control of Energy-Related Air Pollutants and Carbon Dioxide in China.

Boilers involve ∼60% of primary energy consumption in China and emit more air pollutants and CO2 than any other infrastructures. Here, we established a nationwide, facility-level emission data set considering over 185,000 active boilers in China by fusing multiple data sources and jointly using various technical means. The emission uncertainties and spatial allocations were significantly improved. We found that coal-fired power plant boilers were not the most emission-intensive boilers with regard to SO2, NOx, PM, and mercury but emitted the highest CO2. However, biomass- and municipal waste-fired combustion, regarded as zero-carbon technologies, emitted a large fraction of SO2, NOx, and PM. Future biomass or municipal waste mixing in coal-fired power plant boilers can make full use of the advantages of zero-carbon fuel and the pollution control devices of coal-fired power plants. We identified small-size boilers, medium-size boilers using circulating fluidized bed boilers, and large-size boilers located in China's coal mine bases as the main high emitters. Future focuses on high-emitter control can substantially mitigate the emissions of SO2 by 66%, NOx by 49%, PM by 90%, mercury by 51%, and CO2 by 46% at the most. Our study sheds light on other countries wishing to reduce their energy-related emissions and thus the related impacts on humans, ecosystems, and climates.

Engineering And Economic Analysis of Sawdust Biomass Processing As A Co-Firing Fuel In Coal-Fired Power Plant Boiler Pulverized Coal Type

One type of alternative fuel that has the potential to be developed and can contribute significantly to the NRE mix is biomass. The significance of the energy mix is obtained from the use of the co-firing system at the Coal-Fired Power Plant (CFPP) by mixing coal fuel with biomass such as sawdust. Sawdust biomass as cofiring material for CFPP is very efficient because it has energy content and availability that is easy to manage. The aim of this research is to make technical and economic analysis of sawdust biomass as solid fuel for pulverized coal CFPP Co-firing type boiler. The research stages are mapping the potential of sawdust biomass to determine the availability of potential biomass around the location of the co-firing, analyze the technical side and specifications contained in sawdust biomass raw materials to determine the suitability or feasibility of pulverized coal type CFPP specifications, and analyze the economic feasibility of developing sawdust biomass processing technology to determine the cost of production of sawdust biomass so that later it will not have a technical and financial impact. The results of the economic analysis show that the sawdust biomass production business is feasible with NPV parameters > IDR 5,252,097,371, IRR=11.1% and payback period=7.7 years. Provided that if the PLTU Rembang buys biomass at a price of Rp. 862,730/Ton. Sensitivity analysis is used to determine the steps to optimize the adjustment of the ability to purchase biomass from CFPP Rembang where the reference limit for the highest reference price for biomass is IDR 782,006. Efforts that can be made to adjust the ability to purchase biomass from the CFPP Rembang include increasing the yield by 20% or reducing the price of wood by 20%.

A Hybrid Method for Prediction of Ash Fouling on Heat Transfer Surfaces

Soot blowing optimization is a key, but challenging question in the health management of coal-fired power plant boiler. The monitoring and prediction of ash fouling for heat transfer surfaces is an important way to solve this problem. This study provides a hybrid data-driven model based on advanced machine-learning techniques for ash fouling prediction. First, the cleanliness factor is utilized to represent the level of ash fouling, which is the original data from the distributed control system. The wavelet threshold denoising algorithm is employed as the data preprocessing approach. Based on the empirical mode decomposition (EMD), the denoised cleanliness factor data is decoupled into a series of intrinsic mode functions (IMFs) and a residual component. Second, the support vector regression (SVR) model is used to fit the residual, and the Gaussian process regression (GPR) model is applied to estimate the IMFs. The cleanliness factor data of ash accumulation on the heat transfer surface of diverse devices are deployed to appraise the performance of the proposed SVR + GPR model in comparison with the sole SVR, sole GPR, SVR + EDM and GPR + EDM models. The illustrative results prove that the hybrid SVR + GPR model is superior to other models and can obtain satisfactory effects both in one-step- and the multistep-ahead cleanliness factor predictions.

Experimental Investigation into Flue Gas Water and Waste Heat Recovery Using a Purge Gas Ceramic Membrane Condenser.

The direct discharge of wet saturated flue gas from a coal-fired power plant boiler causes a lot of water and waste heat loss. An inorganic ceramic membrane condenser recovers water and waste heat from the flue gas, which has great significance to improve energy utilization efficiency and reduce water consumption. However, the flue gas temperature is relatively low; thus, it is difficult to effectively utilize waste heat. In this paper, it is attempted to use the boiler secondary air as the cooling medium of the ceramic membrane condenser to realize the flue gas waste heat reuse. Based on the above ideas, a purge gas ceramic membrane condenser experimental platform was built for the water and waste heat recovery from the flue gas, and the water and waste heat recovery characteristics and the purge gas outlet parameters were discussed. Simultaneously, the heat transfer resistance and water recovery power consumption are also analyzed. The experimental results show that the water and waste heat recovery characteristics are enhanced with the purge gas flow increases. Increasing the flue gas temperature will increase the water recovery rate and heat recovery power. The ceramic membrane transmission efficiency is a key factor in restricting the actual water recovery efficiency. The purge gas absorbs the water and waste heat from the flue gas, the purge gas temperature and moisture content are significantly increased, and the purge gas relative humidity is also close to saturation. The Biot number of the ceramic membrane condenser is about 3.2 × 10–3 to 1.9 × 10–2; thus, the ceramic membrane tube wall thermal resistance can be neglected. There is a temperature difference between the flue gas and the purge gas, and the entropy production value of the ceramic membrane condenser increases with the flue gas temperature increases by the irreversible process.

Numerical simulation of the heat transfer characteristics of the second and third superheaters in a coal-fired thermal power plant boiler

Numerical simulation of the heat transfer characteristics of the second and third superheaters in a coal-fired thermal power plant boiler

Numerical simulation of the thermal radiation and convective heat transfer characteristics of the second superheater in a coal-fired thermal power plant boiler

Superheaters are critical components in a coal-fired power plant because they sustain the highest tube wall (metal) temperature point in the boiler. The pressurized steam flowing in superheater tubes is mainly heated via the thermal radiation and convection from the combustion gas in the radiant zone. To prevent the bursting of superheater tubes caused by the high-temperature creep, corrosion, and thermal fatigue for proper plant operation and maintenance, accurately predicting complex heat transfer characteristics, estimating the local temperature, and assessing the heat flux of the superheater are essential. In this study, a computational fluid dynamics model of the boiler and the superheater in a coal-fired power plant was developed using radiation and turbulence models. The local metal temperature and heat flux of the superheater were evaluated by calculating the heat exchange between the combustion gas and pressurized steam flows in the tubes. The calculated values of the steam outlet temperature accurately matched the values measured using the equipment at the plant, and thermal radiation was confirmed to be dominant in the boiler. The metal temperature and heat flux of the superheater at the outermost heat transfer tubes, which receive the maximum thermal radiation, were larger than those of the superheater at the inner tubes. Therefore, the outermost tubes were determined to be under the most severe thermal conditions despite having a higher mass flow rate of steam. Additionally, the heat fluxes on the center lines of superheater tube panels were higher due to the large gap between the adjacent heat transfer tubes at the bent part.

Errata: Numerical simulation of the thermal radiation and convective heat transfer characteristics of the second superheater in a coal-fired thermal power plant boiler [Mechanical Engineering Journal, Vol. 9, No. 4 (2022), 22-00040

Errata: Numerical simulation of the thermal radiation and convective heat transfer characteristics of the second superheater in a coal-fired thermal power plant boiler [Mechanical Engineering Journal, Vol. 9, No. 4 (2022), 22-00040

Heat Transfer Efficiency Prediction of Coal-Fired Power Plant Boiler Based on CEEMDAN-NAR Considering Ash Fouling

Ash fouling has been an important factor in reducing the heat transfer efficiency and safety of the coal-fired power plant boilers. Scientific and accurate prediction of ash fouling of heat transfer surfaces is the basis of formulating a reasonable soot blowing strategy to improve energy efficiency. This study presented a comprehensive approach of dynamic prediction of the ash fouling of heat transfer surfaces in coal-fired power plant boilers. At first, the cleanliness factor is used to reflect the fouling level of the heat transfer surfaces. Then, a dynamic model is proposed to predict ash deposits in the coal-fired boilers by combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and nonlinear autoregressive neural networks (NARNN). To construct a reasonable network model, the minimum information criterion and trial-and-error method are used to determine the delay orders and hidden layers. Finally, the experimental object is established on the 300 MV economizer clearness factor dataset of the power station, and the root mean square error and mean absolute percentage error of the proposed method are the smallest. In addition, the experimental results show that this multiscale prediction model is more competitive than the Elman model.

Control of Main Steam Pressure in Coal-Fired Power Plant Boilers by Fractional-Order Controller with Smith Predictor Structure for Delay Compensation

In a thermal plant, the combustion system of the coal-fired boiler is a nonlinear time-varying system, which leads to undesired responses, if traditional proportional-integral-derivative controllers are utilized. Internal process delay is an important characteristic of the steam pressure in the boiler of coal-fired power plants, which affects the closed-loop dynamics. To compensate for this significant delay and reach the desired performance, a novel control strategy based on a combination of an optimal fractional-order proportional-integral-derivative (OFOPID) controller with a Smith predictor (SP) structure is suggested in this paper that achieves the desired control performance required for the optimal operation of the boiler. The simulation results of a coal-fired power plant boiler with the power of 300 MW show that the performance is improved considerably by having lower overshoot and lower rise and settling times, as well as a lower mean square error during the operation of the power plant Boiler.

Emission Concentration and Characteristics of Particulate Matter and Water-Soluble Ions in Exhaust Gas of Typical Combustion Sources with Ultra-Low Emission

A self-developed direct condensation sampling system and monitoring method for total particulate matter (TPM) in ultra-low-emission and high-humidity exhaust gas were applied to the emission monitoring of particulate matter in flue gas from three typical combustion sources with ultra-low emissions in Beijing. The emission levels and composition characteristics of particulate matter and water-soluble ions in the exhaust gas of typical combustion sources with ultra-low emissions were analyzed and evaluated. The interaction and influencing factors of filterable particulate matter (FPM) and condensable particulate matter (CPM) and their water-soluble ions were explored. The results showed that the emission concentration of FPM in the exhaust gas of the coal-fired boiler with ultra-low emissions was between 1.04 mg·m-3 and 1.11 mg·m-3 in standard smoke oxygen content, and that of TPM was between 3.82 mg·m-3 and 8.69 mg·m-3, which all met the national ultra-low emission limit (10 mg·m-3). However, the TPM emission concentration of the coal-fired power plant exceeded the emission limit of Beijing (5 mg·m-3). The emission concentrations of CPM and its total water-soluble ions from the coal-fired heating boiler were 3.05 mg·m-3 and 1.30 mg·m-3, respectively, which were significantly lower than those of the coal-fired power plant, and were related to the higher load and flue gas temperature of the coal-fired power plant. Furthermore, the emission concentrations of CPM and its total water-soluble ions from the coal-fired power plant boiler were 2.2 to 2.4 times and 1.7 to 2.2 times greater than those of the coal-fired heating boiler, respectively. The emission concentrations of TPM and its total water-soluble ions from the gas power plant were 1.99 mg·m-3 and 1.44 mg·m-3, respectively, which were significantly lower than those from the coal-fired boiler. CPM was the main form of particulate matter in the exhaust gas of the combustion source. The contribution of CPM to TPM in the ultra-low-emission boiler flue gas increased significantly, and increased with the increase in the flue gas temperature, ranging from 72.6% to 88.1% for the coal-fired boiler and 93.1% for the gas power plant. Total water-soluble ions made up 66.1% to 94.2% of the CPM. The flue gas temperature had a significant impact on the existing forms, removal efficiencies, and emission concentrations of particulate matter and water-soluble ions. SO42- was the main characteristic water-soluble ion of particulate matter in the coal-fired boiler, and its emission concentration ranged from 0.98 mg·m-3 to 1.18 mg·m-3, accounting for 27.7% to 49.6% of the total water-soluble ion emissions, which originated from flue gas desulfurization. F- was another characteristic water-soluble ion of particulate matter in the coal-fired power plant, and its emission concentration ranged from 1.91 mg·m-3 to 2.32 mg·m-3, accounting for 54.4% to 56.1% of the total water-soluble ion emissions, which might have been related to the high F content of fuel coal. NH4+ was the main characteristic water-soluble ion of particulate matter in the gas power plant, and its emission concentration was 0.92 mg·m-3, accounting for 64.2% of the total water-soluble ion emissions, which originated from the escape of NH3 in the process of selective catalytic reduction. The emission concentration of NH4+ was significantly higher than that of the coal-fired boiler; this might have been related to the synergistic removal effect of the gas-fired power plant, which lacked other purification facilities.

Research on Optimization of Flow Field of Denitration System of 600MW Unit and Modification of Fine Ammonia Injection

Aiming at the uneven distribution of flow field in the denitration system of coal-fired power plant boilers, poor mixing of ammonia and nitrogen, increased ammonia escape, and easy blockage of the air preheater, a flow field optimization and transformation plan was proposed through numerical simulation and added in the flue of the denitration system. Equipped with baffles and static mixers, modified the ammonia injection pipeline in zones, implemented ammonia injection total control and grid balance automatic adjustment control. After the transformation of precise ammonia injection for denitrification, the NOx emission of the unit is stably controlled at 40 mg/Nm3, the steady-state deviation value and other indicators are better than the acceptance standard, and the air preheater differential pressure is effectively controlled.

Steady combustion test and numerical simulation analysis of a single direct-flow pulverized coal burner at low load

In this paper, the low load steady combustion test of a single burner was carried out on the 30MW thermal test bench of the State Key Laboratory of efficient and clean coal-fired power plant boiler. Combined with numerical simulation, the lower limit of the minimum stable combustion load of the burner was tried to find out, which provided the basic test data for the furnace combustion stability during the deep peak load regulation of the boiler. The experimental and numerical simulation results show that under 40% rated load, the outlet flame is stable and bright, and the negative pressure in the furnace is stable, which can realize the stable combustion of a single burner.

A Neural-Network-Based Method for Ash Fouling Prediction of Heat Transfer Surface in Coal-Fired Power Plant Boiler

Soot blowing optimization and health management of coal-fired power plant boiler has received increasing attention in recent years. The ash fouling monitoring and prediction are the basis for achieve this goal. Nowadays, with the development of neural network technology, the new data-driven methodologies are provided for ash fouling monitoring and prediction. This paper presents a comprehensive method based on neural-network for ash fouling prediction. Firstly, the health factor-clearness factor of the heated surface was established from the actual heat transfer coefficient and the theoretical heat transfer coefficient. Wavelet threshold denoising algorithm will be used as data preprocessing method. Secondly, use Ensemble Empirical Mode Decomposition (EEMD) to obtain a series of frequency stable parts. Finally, Encoder-Decoder based Attention (EDA) is used to predict the ash deposit on the heat transfer surface. The EDA model consists of an encoder, a decoder and an attention mechanism. The encoder and decoder are composed of Bidirectional Long Short-Term Memory (BI-LSTM) and Long Short-term Memory Network (LSTM) respectively. The function of the attention mechanism is that the output of each time step of the encoder is given a different degree of attention, and it is sent to the decoder as an attention vector after a weighted average operation. Ash accumulation data on the heat transfer surface of various devices are used to verify the effectiveness of the proposed hybrid model. In addition, the experimental results show that this method has better prediction accuracy than other variant models.

Numerical simulation on metal temperature and heat flux of the superheater in a coal-fired thermal power plant boiler

Numerical simulation on metal temperature and heat flux of the superheater in a coal-fired thermal power plant boiler

Combustion Optimization for Coal Fired Power Plant Boilers Based on Improved Distributed ELM and Distributed PSO

Increasing the combustion efficiency of power plant boilers and reducing pollutant emissions are important for energy conservation and environmental protection. The power plant boiler combustion process is a complex multi-input/multi-output system, with a high degree of nonlinearity and strong coupling characteristics. It is necessary to optimize the boiler combustion model by means of artificial intelligence methods. However, the traditional intelligent algorithms cannot deal effectively with the massive and high dimensional power station data. In this paper, a distributed combustion optimization method for boilers is proposed. The MapReduce programming framework is used to parallelize the proposed algorithm model and improve its ability to deal with big data. An improved distributed extreme learning machine is used to establish the combustion system model aiming at boiler combustion efficiency and NOx emission. The distributed particle swarm optimization algorithm based on MapReduce is used to optimize the input parameters of boiler combustion model, and weighted coefficient method is used to solve the multi-objective optimization problem (boiler combustion efficiency and NOx emissions). According to the experimental analysis, the results show that the method can optimize the boiler combustion efficiency and NOx emissions by combining different weight coefficients as needed.