With the promotion of China's "carbon peaking and carbon neutral" strategy, thermal power units are more involved in deep peak regulation. Under the conditions of deep peak regulation, the thermal power unit is insufficient in heat storage, and the primary frequency regulation capability decreases, resulting in a large deviation between the unit's primary frequency regulation capability calibrated under the rated operating condition and the actual frequency regulation capability, threatening the frequency security of the power grid. Aiming at this problem, an online estimation method of primary frequency regulation capability of deep peak regulation thermal power units based on LSTM neural network is proposed. The static model of steady-state unit design was improved to a dynamic model, considering the dynamic operation process of the unit by using the time sequence memory ability and nonlinear feature extraction ability of LSTM neural network, and the errors caused by the disturbance factors such as the load changing process and the historical action of primary frequency regulation were corrected. Based on the hierarchical modeling method, the sub-models with different neural network structures were designed for the different characteristics of the factors affecting the frequency regulation capacity, such as heat storage of the unit and steam turbine work performance, and the effects of furnace side were taken into account to improve the accuracy of frequency regulation estimation results. Compared with the traditional method used in the power system, the estimation result of this method has higher accuracy, and has better performance under different working conditions such as steady state and variable load.

The denitration efficiency is closely related to the uniformity of flue gas and reductant agent within the selective catalytic reduction (SCR) reactor for the coal-fired unit. Based on the established mathematical model for SCR denitration reaction, a user-defined subprogram is used to couple the multi-component flue gas flow with reaction process. The reliability and effectiveness of the CFD model are verified by comparing the measured and simulated data of the SCR performance of 330 MW level coal-fired units at different loads. According to the hydrodynamics of flue gas and reductant agent together with the chemical reaction process in SCR reactor, a new intensification scheme is proposed by optimizing the structure of deflectors in front of the ammonia injection grids. Furthermore, the effects of operational conditions on emission mass concentration of NO and NH₃ are investigated. The results indicates that, the maldistribution of the incoming flue gas to the ammonia injection grids leads to the poor mixing behavior of flue gas and reducing reagent. However, the denitration efficiency of the SCR reactor can be improved by about 3.37% through adjusting the upstream guiding plate structure and installing the baffle around the flue duct wall. Taking the SCR denitration device in this work as an example, the appropriate molar ratio of NH₃ to NO is 0.94 when the initial NO mass concentration is 650 mg/m³, which could meet with the emission limit for air pollutions of 50 mg/m³ for NO_x and 2.5 mg/m³ for NH₃, respectively.

Energy conservation and emission reduction work have attracted global attention. Accelerating lowcarbon transformation work has also reached a consensus in the shipping industry. Among them, hydrogen energy ships have good development prospects. In the face of the problem that hydrogen energy ships have no stable hydrogen source, it is urgent to find a stable hydrogen source for hydrogen energy ships. This paper introduces the development status of hydrogen production from offshore wind power and hydrogen energy ships, breaks the traditional concept of hydrogen energy, puts forward the system architecture of hydrogen production and hydrogenation on offshore platforms, and uses offshore wind power to directly prepare hydrogen, which provides a new idea for solving the hydrogen source problem of hydrogen energy ships and realizing the consumption of offshore wind power. Through the discussion and economic analysis of the integrated development of offshore wind power and marine ranching hydrogen energy ships, it is found that the integrated development of offshore wind power and marine ranching hydrogen energy ships is economically feasible, will contribute to carbon emission reduction work, and has good development prospects. This paper can serve as a reference for the comprehensive development of offshore wind power and hydrogen ships and put forward the prospect of building offshore hydrogen energy passage in coastal areas.

The process of conventional hydrometallurgical recovery of lithium batteries not only consumes corrosive acid and long-time reaction, but also produces secondary wastes. In this paper, microwave-assisted deep eutectic solvent (DES) is used to leach and recover valuable metals from cathode material LiCoO₂ (LCO). The leaching and recovery process is not only green and low-pollution, but also owns a fast reaction rate, good solubility stability of the valuable metal and high purity of the recovered product. Meanwhile, FT-IR, XRD, ICP-MS, SEM and electrochemical analysis methods are used to explore the mechanism of microwave-assisted DES leaching of valuable metals from LCO. The effects of experimental factors on the extraction efficiency of valuable metals are obtained by orthogonal test method. The degree of influence is DES>temperature>liquid-solid ratio>time. Afterwards, according to the results of orthogonal experiments, the single-factor experiments are successively adopted to explore the optimal experimental conditions for microwave-assisted leaching of valuable metals, 99.86%of Li and 99.05% of Co can be extracted under the condition of choline chlorine-oxalic acid (ChCl-OA), 180 ℃, 10 min and liquid/solid ratio (L/S) of 60 mL/g. At this time, Co exists in the leaching solution as formic acid cobalt. Finally, a green and efficient strategy for extraction of valuable metals from spent LiBs (LCO) through microwave-assisted DES is proposed, which provides an important reference method for recovery of valuable metals from spent lithium-ion batteries.

A large amount of data is generated during steam turbine operation. In order to meet the requirements of high quality data driven by big data and simulation modeling, efficient data cleaning is very necessary. The semi-supervised data cleaning model of steam turbine is built by using the excellent nonlinear fitting ability of long and short memory layer for time series data. The model selects three boundary conditions of the unit as input to predict the cleaning data. Outliers are eliminated according to the residual difference between the predicted value and the actual value. Then, the predicted value of the model is used to fill the data to ensure the integrity of the data. The model is used to clean the data of a 650 MW unit in a power plant. To overcome the problems caused by sample imbalance in the selection of cleaning model indicators, the accuracy rate is improved and taken as the measurement index of cleaning effect. The results show that, the improved accuracy of the data cleaning model of the deep long and short memory network is higher than that of the other three common cleaning methods, which can effectively identify whether the data is abnormal, and can use the predicted value to fill the data to ensure the consistency of data before and after cleaning.

Large proportion burning high-alkali coal will cause serious contamination to the heating surface of the boiler, and threat the device security and stable production of the power plant. The article compared flue gas temperature changes of three types of boilers burning Xinjiang Naomaohu high-alkali coal, XRD phase analysis and chemical composition analysis of ash slag was also performed. Analysis results indicate the composition of the ash block developed by short-term bonding is close to that of coal ash, and the texture is loose, and the heating surfaces can be kept clean by soot blowing optimization. The shell-like slag formed by long-term contamination is rich in SO₃ and Na₂O, the degree of sintering is high, and the texture is hard, controlling the flue gas temperature of the heating surface inlet can effectively reduce the fouling and slagging of the tube panel. The flue gas temperature at the convection heating surface inlet with tube panel gaps of about 50 mm should be controlled below 800 ℃, when the gaps are above 200 mm, the flue gas temperature should be controlled below 1 000 ℃. The results of the research can be used as a reference for the same type of boiler burning high-alkali coal.

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.

Chemical absorption method is an important way to apply and treat CO₂ from coal-fired power plants on a large scale, however, the traditional chemical absorption method with monoethanolamine as absorbent has been limited in its wide application because of the high energy consumption. In this paper, the research progress on the improvement of CO₂ capture process is reviewed with the new CO₂ capture solvents, the improvement of absorption process including intermediate cooling of absorber and solvent recirculation, Flash compression and regeneration process of steam/pentane direct purging are summarized, it was also pointed out that pilot-scale verification of new solvents based on the actual composition of flue gas, and the study of capture solvent degradation properties and volatile organic compound treatment processes were the research and development directions of carbon capture research, it points the way for future research on industrial carbon capture.

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.

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.