Heat exchangers are key equipment for energy conversion and utilization, and enhancing the heat transfer coefficient while reducing energy consumption is a core objective of heat exchanger design. This article focuses on the mechanism of irreversible loss caused by heat transfer and flow resistance in tube bundle heat exchangers. Based on the second law of thermodynamics, a local entropy production analysis model is established, which includes average entropy production, turbulent entropy production, wall entropy production, and heat transfer entropy production. The flow and heat transfer characteristics as well as the distribution laws of each entropy production are obtained for both in-line and staggered tube bundle arrangements, and the influence of tube bundle arrangement on the irreversibility of flow and heat transfer is quantitatively analyzed. The research results indicate that turbulent entropy production and heat transfer entropy production are the main components of total entropy production in heat exchangers, and they are mainly distributed in the near-wall region and the wake region where flow separation occurs. As the gas velocity increases, the average entropy production, turbulent entropy production, and wall entropy production gradually increase, while the heat transfer entropy production gradually decreases. Different tube bundle arrangements correspond to different optimal flow rates, at which the total entropy production of the flow and heat transfer process can be minimized. When the gas velocity is relatively low, the staggered tube bundle arrangement is recommended, because it can effectively reduce the total entropy production of the system and minimize irreversible losses. In contrast, when the gas velocity is relatively high, the in-line tube bundle arrangement should be selected.

To address the problems of the traditional grey wolf optimizer (GWO), such as being prone to trapping in local optima and slow convergence speed when dealing with the high-dimensional, nonlinear and strongly coupled characteristics in the load optimal dispatch of combined heat and power (CHP) systems, this study proposes a chaotic multi-layer grey wolf optimizer (CML-GWO). The core innovation of the proposed algorithm lies in two aspects: first, chaotic mapping is introduced to initialize the population, which effectively improves the uniformity of initial search and prevents the algorithm from falling into local optima at the early stage; second, a hierarchical guidance mechanism is integrated to balance the global exploration and local exploitation capabilities, thereby solving the slow convergence problem caused by the capability imbalance in the traditional GWO. Before applying it to the CHP system load optimal dispatch, the performance of CML-GWO is verified through the CEC2017 test function set. The results show that compared with the traditional GWO, the CML-GWO exhibits better robustness and optimization accuracy in complex multi-modal and composite function scenarios, which lays a solid foundation for its engineering application. For practical verification, four units of a thermal power plant are taken as the research object, and two multi-objective scenarios and two regulation modes are designed. The multi-objective scenarios include “minimum coal consumption-maximum renewable energy accommodation” and “maximum profit-maximum renewable energy accommodation”, while the regulation modes are “practical constraints” and “free whole-plant load distribution”. The verification results demonstrate that under the practical constraint scenario, compared with the traditional GWO, the average hourly coal consumption of the CML-GWO is reduced by more than 2 tons, the average hourly profit is increased by more than 14 yuan, the renewable energy accommodation capacity is increased by more than 20 MW, and all load deviations meet the requirements of safe operation. Under the free load distribution scenario of the whole plant, the optimization potential of the algorithm is fully released: the daily coal saving reaches 23.8 tons or the daily income increases by 6515 yuan, and the renewable energy accommodation capacity is improved by 0.74%~0.85%. Overall, this study realizes the multi-objective collaborative optimization of economic, energy and environmental benefits of the CHP system. The comprehensive performance of the CML-GWO in both numerical tests and engineering applications fully verifies its significant engineering practical value, providing a new effective optimization method for the load optimal dispatch of CHP systems under the background of high-proportion new energy integration.

[Objective] As municipal solid waste generation increases, waste incineration has become a mainstream treatment method due to its advantages in reduction, harmlessness, and resource recovery. To address the challenges posed by the diversity of flame combustion states an d the low efficiency of manual identification in grate-fired municipal solid waste incineration furnaces, this study proposes an improved flame combustion state recognition model, termed MSDRNeXt, based on the ConvNeXt architecture. The aim is to achieve i ntelligent identification of combustion conditions within the furnace, thereby reducing reliance on manual judgment and improving operational stability and safety in industrial applications.

[Methods] To accurately extract effective flame regions under comp lex conditions such as significant brightness fluctuations and dust occlusion, a Multi-Scale Feature Fusion (MSFF) module is first proposed. This module integrates multi -scale feature information and incorporates the Convolutional Block Attention Module (CBAM) to enhance fine-grained texture 2 features in shallow feature maps while suppressing irrelevant noise. The refined shallow features are further fused into deeper semantic feature maps, enabling a better balance between local texture representation and g lobal semantic understanding. In addition, data augmentation and normalization strategies are applied during training to further improve the model’s robustness to environmental variations. This design improves the robustness and generalization performance of the model in complex environments. Furthermore, considering the inherent asymmetry of air distribution on the left and right sides of the furnace, a Dual -Region Differential Pooling (DRDP) module is designed. This module partitions the feature space int o left and right regions and performs differential pooling to explicitly model spatial asymmetry. As a result, the model can effectively capture subtle inter-regional differences, which are critical for distinguishing combustion states with significant lat eral variations, such as biased combustion, raw material emergence, and material accumulation. The integration of MSFF and DRDP enhances the discriminative capability of the model while maintaining computational efficiency.

[Results] Experimental results on a dedicated waste incineration flame dataset demonstrate that the proposed MSDRNeXt model achieves an overall classification accuracy of 91.36%, outperforming the baseline ConvNeXt model and several comparative methods. In addition, the model achieves an inference speed of 277.85 frames per second (FPS), which satisfies the requirements for real -time monitoring in industrial scenarios. Meanwhile, the number of parameters is reduced by 53.43% compared with the baseline model, indicating a lightweight archit ecture with lower computational cost and memory consumption.

[Conclusion] The proposed MSDRNeXt model can efficiently and accurately identify flame combustion states in municipal solid waste incineration furnaces. It provides a reliable solution for real -time monitoring of the incineration process and supports the optimization of subsequent control strategies.

[Objective] The supercritical carbon dioxide (S-CO₂) semi-closed Brayton cycle has attracted widespread attention due to its high cycle efficiency and potential for near -zero carbon emissions. The working fluid in this cycle primarily consists of the CO₂/H₂O mixture. Except during the compression and separation processes, the CO2/H2O mixture operates in the low- to medium-density region for most of the cycle. Accurate prediction of thermophysical properties in this region is crucial for the power cycle . Due to the lack of experimental data for the mixture, the applicable temperature range of multiparameter equations of state is limited, making it difficult to meet the demand for thermophysical property calculations at high temperatures. Computational models based on theoretical foundations have greater potential for extrapolation in the high-temperature region. Among these, semi-theoretical cubic equations of state can reasonably describe the trends in thermophysical properties. However, they are limited by their structure and cannot achieve extremely high precision. In contrast, the virial equation of state, with its rigorous theoretical foundation, provides an effective approach for high-precision prediction of thermophysical properties over a wide temperature range. 

[Methods] A predictive model that strictly satisfies physical consistency requirements is established for the cross second virial coefficient of the CO₂/H₂O mixture based on ab initio data. Building upon this, a three-term truncated virial equation of state (VEOS3) is developed to calculate the thermodynamic properties of pure CO₂, pure H₂O, and their binary mixtures. The predictive performance of VEOS3 is systematically evaluated across a range of properties, including pvT(x), isobaric heat capacity, internal energy, enthalpy, entropy, and speed of sound. The effective range of VEOS3 is de termined by comparison with multiparameter equations of state within their respective validity domains. At a given temperature, the maximum density and the corresponding pressure within the region where the absolute relative deviation does not exceed 1% ar e defined as the effective density limit and effective pressure limit, respectively . The model performance is further validated using experimental data. 

[Results] The results show that the effective density limit of VEOS3 remains approximately constant in the high-temperature region or increases linearly with temperature. As the temperature rises, the effective prediction range of VEOS3 can be extended to higher pressures. Meanwhile, VEOS3 reproduces available experimental data with high accuracy. Here, the average absolute relative deviation (AARD) is used to quantify prediction deviations. For pure CO₂ and pure H2O, the AARDs of VEOS3 in density, isobaric heat capacity, and speed of sound are 0.14%/0.30%, 0.28%/0.82%, and 0.02%/0.16%, respectively. For CO₂/H₂O mixtures, the AARD in density is 0.43%. 

[Conclusion] VEOS3 can accurately calculate thermophysical properties in the low - to medium-density region, where the reduced density is less than or equal to 2/3. It also maintains stable and reliable extrapola tion capability at high temperatures, providing model support for thermophysical property calculations in the S-CO₂ semi-closed Brayton cycle.

[Objective] In order to address the problems associated with the flexibility retrofitting of thermal power plants and the consumption of low-valley electricity, a novel electrode molten salt heater based on pipeline flow is proposed. This study aims to investigate the temperature characteristics of this new type of heater. 

[Methods] A multi-physics field simulation model of the electric heater was established by utilizing COMSOL software. An experimental system for molten salt electric heating was designed and constructed, and relevant experimental tests were carrie d out under voltage conditions ranging from 300 V to 600 V . After that, based on the COMSOL simulation model, simulation studies regarding temperature distribution and temperature response were conducted under high-voltage conditions ranging from 2 kV to 4 kV . 

[Results] The experimental results indicate that the electric heating system, under the specified operating conditions, is capable of raising the temperature of molten salt within a range of 1.2 ℃ to 25.5 ℃. A comparison between experimental data a nd simulation outcomes reveals a 2 maximum relative error of 8.0%, thereby confirming the accuracy and reliability of the COMSOL Multiphysics model established in this study. An analysis of the temperature field within the heating sections located at both ends of the electrode shows that, along the direction of molten salt flow, the radial temperature difference consistently follows a distinct pattern: it initially increases and then progressively decreases. Under extreme operating conditions, the maximum rad ial temperature difference observed is 9.4 ℃. Notably, the peak temperature of the molten salt, reaching 572.8 ℃, occurs near the wall at the pipeline outlet, indicating a relatively uniform temperature distribution across the radial cross -section. Furthermore, the transient response characteristics of the electric heater were evaluated. The results demonstrate that following a disturbance, the system can elevate the molten salt temperature to between 0.85 and 0.96 of the target temperature rise within a short period of just 15 seconds. This underscores the heater’s fast temperature response capability. An investigation into the factors influencing this response speed revealed that it is independent of the applied voltage magnitude. However, increasing the flow velocity of the molten salt significantly accelerates the transient temperature response, offering a practical pathway for performance optimization. 

[Conclusion] Experimental and simulation results demonstrate that the proposed molten salt electric h eater offers significant advantages, including temperature rise regulation capability, a uniform radial temperature distribution, and a fast response speed. These features collectively provide a theoretical basis and technical support for improving the performance of energy storage systems.

To further improve the cycling performance of the cold storage packed bed for liquid air energy storage systems, a multi-cycle study was conducted on the two-dimensional continuous solid-phase model of the cold storage packed bed using the finite element simulation method. Performance improvement methods for filling phase change materials at the top of a packed bed in two different ways were proposed and analyzed. The influence of the thermal physical properties and filling thickness of different phase change materials on the key parameters of the composite cold storage packed beds was discussed. The results show that both the composite cold storage packed bed and the solid-phase cold storage packed bed have an increase in round-trip efficiency with the cycle times, and tend to a quasi-steady state in the 10th cycle. As the intermediate phase transition temperature of the phase change material increases, the rate of phase transition occurring in the cycle gradually decreases. The composite cold storage packed bed filled with phase change materials with higher intermediate phase change temperature, higher volumetric heat capacity, and higher latent heat of phase change exhibits better performance. Increasing the filling thickness of phase change materials can help further improve the performance of composite cold storage packed beds. The composite cold storage packed bed with the best comprehensive performance shows an increase of 15.2% in cold storage density and 0.22 percentage points in round-trip efficiency compared to the solid phase cold storage packed bed, while the cold storage efficiency only decreases by 1.52 percentage points. The research can provide theoretical guidance for the design of cold storage packed bed systems for large capacity liquid air energy storage.

Molten salt thermal energy storage technology can improve the flexibility of thermal power units. The molten salt evaporator, a core component of the system, utilizes the thermal energy of molten salt to convert boiler feedwater into superheated steam. However, the unique thermophysical properties of molten salt and the complex structure of the heat exchanger render existing heat transfer correlations inadequate for accurately predicting its thermal performance. Experimental studies on molten salt heat exchangers are costly, while existing numerical simulations cannot achieve coupled heat transfer calculations between the single-phase molten salt side and the phase-change working fluid side. In this study, with given feedwater inlet parameters, a numerical model for the molten salt side heat transfer is established by assuming an initial enthalpy distribution along the flow path. The heat flux distribution obtained from the simulation is then used to calculate the enthalpy variation of the working fluid, which is iteratively compared and corrected against the initially assumed values. Through multiple iterations, an accurate computation of the heat transfer process in the molten salt steam generator is achieved, enabling a detailed investigation of its operational characteristics. The results indicate that at an operating pressure of 2 MPa, during the transition from subcooled water to complete vaporization, the vaporization rate gradually increases. The heat flux peaks at 86 295.12 W/m² upon complete vaporization, then decreases rapidly and eventually stabilizes around 5 000 W/m². Significant temperature non-uniformity is observed across the flow cross-section of the molten salt, with a maximum thermal deviation of 216%. This temperature non-uniformity is alleviated as the inlet molten salt temperature decreases.