The proposal of the "dual carbon" has brought profound changes to the development of China's energy and power industry. As the leading enterprises in China's power industry, the five major power generation groups should play a demonstration and leading role, analyze and evaluate their low-carbon transformation achievements have important significance for the power generation industry to achieve the "dual carbon" goal as scheduled. This article uses the entropy weight TOPSIS method to conduct empirical research and analysis on the indicator data of the five major power generation groups in China from 2017 to 2021. The empirical results show that from a vertical dimension, it is found that the low-carbon transformation of the five power generation groups has achieved good results in 2017 to 2021, and the low-carbon transformation performance of each power generation group has shown an increasing trend year by year. This indicates the implementation of policies related to structural adjustment, emission reduction, and re transformation of the five power generation groups, achieved positive and significant results; from a horizontal perspective, the rankings of the five major power generation groups have been constantly changing from 2017 to 2021, indicating that each power generation group has made varying degrees of progress in low-carbon transformation.

In order to study the feasibility and economic benefits of implementing carbon capture, utilization and storage (CCUS) technology in coal-fired power plants, based on the thermal power installation planning and generation data provided by a northwestern province, three different CCUS transformation schemes in 2023, 2025 and 2030 were proposed, and their economic analysis was conducted. It is found that the first plan needs investment of 1 220.293 billion yuan, which translates into an increase of about 0.076 3 yuan /(kW·h); the second plan needs investment of 1 123.19 billion yuan, which translates into an increase of about 0.076 9 yuan /(kW·h); the third plan needs investment of 860.12 billion yuan, which translates into an increase of about 0.069 0 yuan /(kW·h). Aiming at the high cost of CCUS transformation scheme, a technical route combining CCUS and methane dry reforming was proposed, and the captured CO₂ was used to produce syngas. It was found that the expenditure and income of 1 t CO₂ due to the consumption of natural gas to produce syngas were 1 520.7 yuan and 3 247.2 yuan respectively. Comprehensive carbon capture system analysis options two and three can achieve zero-cost decarbonization.

Ammonia is a kind of zero-carbon fuel with mature technology and low storage and transportation cost. Partial replacement of coal with ammonia can become an effective way to reduce carbon at the front end of coal-fired units under the dual carbon target. Ammonia fuel is studied as an alternative fuel. The fuel characteristics of ammonia and its blended fuel with typical bituminous coal are studied by using one-dimensional flame furnace and ignition furnace. The ignition performance changes of ammonia/coal blended fuel, the enhanced combustion and pollutant control technology of different proportions of ammonia blended fuel are studied and analyzed in detail. It is found that the pre-blended combustion of ammonia/coal is not conducive to NO_x control. Through fuel grading, combustion excess air coefficient or oxygen control, and air staged combustion, lower NO_x generation concentration and better combustion effect can be achieved during ammonia blending. The operation control suggestions of 25%ammonia mixed with typical bituminous coal are obtained.

The power industry is the core of the energy system as well as a major carbon emitter. The zero-carbon development of it is the key to the process of carbon neutralization in China. From the perspective of carbon emission, energy resource endowment, industry status, existing problems and development trend, the general situation, low-carbon development path and effect of the electric power industry in the United States, Germany, France and Britain in the European Union was analyzed. Several enlightenments combined with the current situation of the electric power industry in China were summarized, hoping to provide reference for the low carbon development of Chinese electric power industry.

The combustion of biomass will not increase carbon dioxide emissions, and the development of biomass coal coupled power generation technology is a feasible way to accelerate the transformation and upgrading of electricity. The compressed biomass and coal powder are ground in different coal mills without mixing, and are fed into the furnace by different burners. This method does not require additional biomass treatment equipment or setting up a biomass fuel treatment workshop, which can greatly save investment and factory space. The key limiting technical factor is whether the pulverizer of the power plant can effectively grind biomass Pellet fuel, and how to evaluate the adaptability of the existing pulverizer to the new coupling fuel. To this end, a medium speed coal mill experimental platform was built to analyze the adaptability of the medium speed coal mill in grinding biomass particles. The results show that the grinding output of the medium speed coal mill decreases when grinding wood chip biomass particles, and the effective output correction coefficient is 0.65~0.70; When the ZGM medium speed coal mill grinds sawdust particles, the loading force characteristics and separator characteristics both vary significantly at the particle size boundary of 0.2 mm. Therefore, the screening fineness R₂₀₀ and R₅₀₀ can be considered as the measurement standards for the uniformity of biomass particle materials ground by the coal mill; Compared with pulverized coal, the increase of loading force of milled biomass particles or the increase of rotating speed of the current separator has a large difference in the influence of fineness distribution, which needs further in-depth research.

The carbon content of fly ash in boilers is one of the important indicators of combustion efficiency. This study employs machine learning models to accurately predict the carbon content of fly ash. Firstly, random forest is employed to adjust the frequency of fly ash carbon content data to once per minute, aligning it with the input features to address the issue of imbalanced data collection frequency. Then, a recursive feature elimination method based on random forest is used to extract nine important features out of the original 30 features, reducing feature correlation and improving model accuracy. Subsequently, six machine learning models (linear regression, decision tree, K-nearest neighbors (KNN), random forest, Catboost and XGBoost) are compared for prediction. The results indicate that decision tree, KNN, random forest and XGBoost models perform well, MSE of which on the test are 0.010, 0.009, 0.006 and 0.006, respectively, while linear regression exhibits the poorest performance. The prediction models remain robust under low, medium, and high boiler loads.

The intelligent retrofit of coal-fired power generation units is an inevitable choice for improving energy efficiency and promoting green industrial transformation. Based on practical requirements and engineering perspectives, this article designs the overall framework and key technologies for the intelligent retrofitting of wet flue gas desulfurization systems. First, the structural components of the intelligent control system (ICS) network framework are discussed. Next, based on the ICS framework, an optimized control strategy combining information-physical fusion models and advanced control algorithms is designed, as well as an optimized control strategy for the absorption tower pH value based on the direct energy balance (DEB) approach. Simultaneously, the information-physical fusion optimization results guide the analysis of the intelligent evaluation system. Using data twin technology and mechanism models, intelligent early warning and fault diagnosis for the system are achieved. By analyzing typical faults, an expert system is established, combined with data-driven techniques for real-time fault tracking. Finally, the article points out that a visualization-based human-machine interaction system is used for real-time display of desulfurization system indicators, constructing an integrated desulfurization system that combines ICS, digital twins, machine learning and visualization. This provides a basis for realizing a self-optimizing, self-learning, self-recovering, self-organizing and self-adaptive intelligent desulfurization system.

Conventional industrial steam turbines are usually equipped with quick-closing inlet valves to realize emergency shutdown of the turbine. In this paper, a negative pressure inlet condensing steam turbine was studied has low inlet parameters, large inlet steam volume flow and small pressure difference between inlet and exhaust steam ends. In order to reduce the intake pressure loss, the quick-closing inlet valves are not employed, but a redundant large-diameter rapid vacuum breaker valve is used at the exhaust end of the turbine. When the turbine needs emergency shutdown, open the quick vacuum breaker valve, so that the atmosphere quickly enters the exhaust end of the turbine, the pressure of the exhaust end of the turbine rapidly rises, In this paper, the steam turbine whose inlet and exhaust steam pressure difference is 40 kPa is studied, and the pressure difference between the inlet end and the exhaust end of the turbine could reach 0 within 4 seconds, so that the turbine is closed, and the purpose of emergency shutdown of the turbine is achieved.

Rapid and accurate measurement of the calorific value of incoming coal is the essential to provide guidance for the economic and safe operation of power plants. However, coal has complex components, and the calorific value is correlated with elemental composition and molecular structure, it is difficult to measure coal calorific value quickly and accurately by a single analytical technique. Based on laser-induced breakdown spectroscopy (LIBS) and near-infrared reflectance spectroscopy (NIRS), a method is proposed to detect the calorific value of incoming coal by combining two techniques. The LIBS and NIRS spectral signals of the coal on the conveyor belt are collected simultaneously. Fusion of two spectral information after data pre-processing, coupled with partial least squares (PLS) modeling method to quantify coal calorific value. This method is used in a coal sample measurement system built by lab, it is reached that the coefficient of determination of the calibration set was 0.98, and the root mean square error of the prediction set was 0.37 MJ/kg, with an average absolute error of 0.26 MJ/kg and an average relative error of 1.09%. The results show that the proposed method of simultaneous acquisition of LIBS and NIRS signals can measure coal calorific value rapidly and accurately.

The alkali metals released during combustion of high alkali coals can easily lead to fouling and slagging of the heated surfaces of the furnace, affecting the safety of boiler operation, and it is significant to carry out the research of the slagging trend prediction. In this paper, a system for dynamic prediction of slagging trends in the furnace was developed by combining the slagging trend discrimination method based on ash composition analysis with flame emission spectroscopy. Firstly, a flame emission spectroscopy system was installed on the boiler to measure the gas phase alkali metal concentration in the furnace, and then a slagging trends test was carried out in the furnace exit area to obtain the deposition trend of the ash samples. Finally, a dynamic predictive system for predicting slagging in the furnace was developed and applied to the boiler by combining the slagging discriminating trends based on ash composition analysis of different coal samples and online monitoring of gas-phase Na concentrations, which can reflect the monitoring results of each parameter under the current combustion conditions in real-time, indicate the slagging trend in the current combustion state, thus enabling combustion adjustment instructions to prevent severe slagging.