The coaxial casing heat exchanger is one of the technologies to realize high efficient heat extraction in the middle and deep geothermal energy wells, and its heat transfer capacity is crucial to the sustainable development of geothermal system. Taking Guanzhong area as an example, the heat transfer model of coaxial casing heat exchanger in middle-deep geothermal wells is established considering the formation inhomogeneity, and the effects of injection temperature, injection flow rate, buried depth and other factors on its heat extraction performance are studied. The influences of injection temperature and injection flow rate on thermal reservoir temperature recovery under intermittent production condition are discussed. The results show that, the outlet fluid temperature decreases with the increasing of injection flow rate, but the heat extraction power of the whole system increases. Increasing the injection temperature can improve the outlet fluid temperature, but the system heat extraction power decreases greatly. With the increasing of formation depth, the temperature of outlet fluid and heat extraction power increase gradually. Reducing the inner pipe diameter and enlarging the outer pipe diameter can effectively improve the temperature of outlet fluid and heat extraction power. The temperature recovery ability of the thermal reservoir increases with the injection flow and injection temperature. Reducing the thermal conductivity of inner pipe or choosing double-layer inner pipe structure can reduce the fluid temperature loss along the inner tube and increase the outlet fluid temperature.

With the structural upgrading of China's energy industry and the transformation of economic growth mode, clean energy power generation technology has been continuously and rapidly developed. As a high-power electric heating conversion device, electric boiler has become one of the effective technical solutions to enhance the operational flexibility of thermal power units and promote the reliable consumption of new energy power. In this paper, the allocation and accounting principles of electric boiler capacity and heat storage capacity in such projects are proposed. The calculation model of system optimization operation parameters and operation economy evaluation is established based on the principle of maximizing the economic benefits of enterprises, taking into account the factors of grid peak shaving depth dispatching limits. Combined with the actual situation of the multi-unit system of a thermal power plant in Northeast China, the optimal configuration capacity of the electric boiler of the project is 300 MW, and the heat storage capacity is 17 MW·h. At the same time, the optimal operation scheme of the whole plant is proposed. The results show that, under the condition of 30% peak shaving depth limit, the case power plant can add about 139.53 million Yuan in each heating season, and the project investment payback period is about 5 years. In the foreseeable future, the project will have long-term profitability.

Based on the control difficulties in the operation process of the unit after the bypass heating alteration, a complete control strategy was proposed under the bypass heating condition, which realizes the bypass automatic control under the heating condition and meets the needs of the unit heating regulation at the same time. Aiming at the influence of bypass switching on and off and working condition disturbance on the operation stability of steam turbine under the heating condition, as well as the change of unit safety boundary, a set of logic design scheme including bypass blocking and overriding was proposed, which effectively guarantees the unit's safety and stability. Aiming at the changing characteristics of the steam turbine flow characteristics and the boiler's response to temperature and pressure characteristics in the state of bypass heating, an optimization strategy for coordination correction was proposed, which improved the quality of network-related control of the unit. The control scheme proposed in this paper has been applied in the supercritical once-through furnace unit. It has been verified the control strategy can realize the automatic control of the bypass system, and the control effect of the coordinated variable load is good. After the bypass RB acts, the unit can transition smoothly, realizing the bypass supply Automatic and safe control of the unit under thermal conditions.

Energy storage is the key technology of novel power system. As a medium and high temperature heat transfer and heat storage fluid, molten salt has the advantages of high heat capacity and good stability. It has been widely used in concentrated solar power (CSP) system, peak and frequency modification, green electricity consumption and other new energy fields. However, at present, the core components of molten salt energy storage system, such as the tanks, the electric heaters, the heat exchangers, and so on, are studied and developed based on the demand of CSP technology. The other application scenarios are not sufficiently considered. However, in different application scenarios, the working temperature range, heating and heat exchange methods have different requirements on molten salt. Therefore, the research status and technical achievements of the key technologies of molten salt heat storage are summarized. The development path of molten salt energy storage technology and its application in novel power systems are studied. According to the different application scenarios, the requirements on molten salt parameters and the suitable types of storage tanks and heat exchangers are illustrated.

A sludge pyrolysis system coupled with molten salt storage is an emerging novel thermal treatment technique for sludge disposal, which can simultaneously realize reduction and tar recovery by constructing rapid and homogeneous pyrolysis conditions. The effects of heating rate and calcium oxide (CaO) addition on the production characteristics of tar during sludge pyrolysis coupled with molten salt storage system were investigated via a high-precision optical wide heating rate pyrolysis apparatus. The results show that, the heating rate affected the tar yield at low pyrolysis temperatures. The tar yield increased by 14% at 350 ℃ when the heating rate increased from 60 ℃/min to 6 000 ℃/min. Meanwhile, the CaO addition could result in a sharp decrease in tar yield, an 39%~43% decrease could be observed with 10% CaO addition. Also, the CaO addition significantly affected the relative content of the compounds in tar, which was attributed to the fact that CaO could act as a catalyst and heat transfer retardant. The results are helpful to the pyrolysis condition regulation and high value-added tar obtain for the sludge pyrolysis system coupled with molten salt storage.

As a nondestructive testing method, magnetic particle testing is widely used in power industry. The comprehensive sensitivity of the magnetic particle detection can be tested by A1 standard shims. However, at present, the evaluation of the clarity of magnetic trace on standard shims still depends on subjective judgment of the tester. In order to eliminate the subjective factors in the evaluation, this paper proposes a standard magnetic trace evaluation method based on machine vision. Based on Python programming language and OpenCV function library, the initial obtained magnetic trace is processed by image correction, magnetic trace extraction, quantitative analysis and evaluation using computer program. On this basis, the influence of the thickness of non-magnetic layer on the comprehensive sensitivity of magnetic particle detection is investigated using this method. It is shown that the magnetic trace evaluation method based on machine vision is more objective and accurate than the conventional manual evaluation.

With the proposal of "carbon peaking and carbon neutral", the total installed capacity of renewable energy power units continues to increase. This may greatly challenge the safety and stability of power grid. The characteristics of flywheel energy storage system (FESS) are fast response, unlimited times of charge and discharge and deep depth of discharge. FESS has been widely used in frequency modulation and frequency safety improvement of power grid. In order to make full use of the advantage of flywheel energy storage in auxiliary frequency modulation of the power grid, an adaptive coordinated droop control strategy of primary frequency regulation coordinated with thermal power units was designed, which realized the power cooperative adaptive adjustment of the combined coal-fired thermal power units and storage systems. Simulation results show that, the proposed control strategy can effectively improve the frequency modulation performance of the combined fire-storage system. Compared with the conventional droop control, the maximum dynamic frequency difference and steady frequency difference of the system reduces by 29.00% and 25.50% respectively, which eases the frequency modulation pressure of thermal power unit, and is benefit to safety and stability of the thermal power unit.

With high power generation efficiency and low carbon emission, (ultra) supercritical unit is the main type in China's development of thermal power units. Optimization on feed water operating condition of the (ultra) supercritical unit is of great guiding significance. The optimal control indexes of different feed water operating conditions of (ultra) supercritical units are investigated by in-situ sampling test, and the influence of centralized sampling on feed water quality is researched. Moreover, the intelligent control system for feed water operating condition is developed. The results show that, the optimal target value of feed water conductivity is 6.7 μS/cm under AVT(O) condition, the optimal target value of feed water dissolved oxygen is 15 μg/L and the optimal target value of feed water conductivity is 3.0 μS/cm under OT condition. Long sampling pipeline of feed water will cause the deposition of iron corrosion products, and interfere with the judgment of unit corrosion. The chromates produced by adding oxygen to feed water is only related to the oxidation of the sampling tube and has nothing to do with the water vapor system.

With the completion of ultra-low emission transformation of thermal power plants, problems such as increased costs and excessive ammonia injection have arisen. Modeling and optimization of power plant operation data through machine learning has become an important means to solve the above problems. This article reviews the commonly used machine learning algorithms and their application scenarios in reducing nitrogen oxides. In terms of algorithm, the main algorithms of data preprocessing, modeling prediction and parameter optimization and their applicability to nitrogen oxides removal are summarized. The research directions of multi-operating condition data preprocessing method and the construction method of the objective function in multi-objective optimization are proposed. For the application level of the machine learning methods, such as low nitrogen combustion in the furnace, optimization of SCR denitration system, and comprehensive energy saving and consumption reduction of the whole system, the implementation methods and corresponding effects are summarized. The future research directions of long-period dynamic modeling control and multi-power plant joint modeling have prospected.

In order to build a new power system with new energy as the main body, thermal power unit should undertake the new task of flexible operation and deep peak-regulating while maintaining power and heating supply. However, the conventional detection and communication technology is limited by the problems of poor real-time performance, slow transmission and high investment, which is difficult to support the new demand of thermal power units. Advanced detection technology and communication technology represented by 5G provide solid technical support for the state research, judgment and operation adjustment of thermal power units represented by boiler system by building a complete perceptual transmission chain, but they also face problems such as high construction investment, lack of professionals and standards. Only by setting out from the actual production, perfecting the mechanism and changing the concept, can the application effect of advanced detection and communication technology be realized. As an important part of smart power plant, the advanced detection and 5G technology will provide a solid foundation for improving the overall intelligent level of thermal power generation.