Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supp...Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.展开更多
This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing a...This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.展开更多
The administration of nanoparticles(NPs)first faces the challenges of evading renal filtration and clearance of reticuloendothelial system(RES).After that,NPs infiltrate through the expanded endothelial space and pene...The administration of nanoparticles(NPs)first faces the challenges of evading renal filtration and clearance of reticuloendothelial system(RES).After that,NPs infiltrate through the expanded endothelial space and penetrated the dense stroma of tumor microenvironment to tumor cells.As long as possible to prolong the time of NPs remaining in tumor tissue,NPs release active agent and induce pharmacological action.This review provides a comprehensive summary of the physical and chemical properties of NPs and the influence of various biological factors in tumor microenvironment,and discusses how to improve the final efficacy through adjusting the characteristics and structure of NPs.Perspectives and future directions are also provided.展开更多
基金supported by National Key R&D Program of China(Grant No.2023YFE0108600)National Natural Science Foundation of China(Grant No.51806190)+1 种基金National Key R&D Program of China(Grant No.2022YFB3304502)Self-directed project,State Key Laboratory of Clean Energy Utilization.
文摘Amid the backdrop of carbon neutrality, traditional energy production is transitioning towards integrated energy systems (IES), where model-based scheduling is key in scenarios with multiple uncertainties on both supply and demand sides. The development of artificial intelligence algorithms, has resolved issues related to model accuracy. However, under conditions of high proportion renewable energy integration, component load adjustments require increased flexibility, so the mathematical model of the component must adapt to constantly changing operating conditions. Therefore, the identification of operating condition changes and rapid model updating are pressing issues. This study proposes a modeling and updating method for IES components based on knowledge distillation. The core of this modeling method is the light weighting of the model, which is achieved through a knowledge distillation method, using a teacher-student mode to compress complex neural network models. The triggering of model updates is achieved through principal component analysis. The study also analyzes the impact of model errors caused by delayed model updates on the overall scheduling of IES. Case studies are conducted on critical components in IES, including coal-fired boilers and turbines. The results show that the time consumption for model updating is reduced by 76.67 % using the proposed method. Under changing conditions, compared with two traditional models, the average deviation of this method is reduced by 12.61 % and 3.49 %, respectively, thereby improving the model's adaptability. The necessity of updating the component model is further analyzed, as a 1.00 % mean squared error in the component model may lead to a power deviation of 0.075 MW. This method provides real-time, adaptable support for IES data modeling and updates.
基金supported by National Key R&D Program of China(Grant No.2022YFB3304502)National Natural Science Foundation of China(Grant No.51806190)+1 种基金National Key R&D Program of China(Grant No.2023YFE0108600)Self-directed project,State Key Laboratory of Clean Energy Utilization.
文摘This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.
基金the National Natural Science Foundation of China(No.81971729)for financial support
文摘The administration of nanoparticles(NPs)first faces the challenges of evading renal filtration and clearance of reticuloendothelial system(RES).After that,NPs infiltrate through the expanded endothelial space and penetrated the dense stroma of tumor microenvironment to tumor cells.As long as possible to prolong the time of NPs remaining in tumor tissue,NPs release active agent and induce pharmacological action.This review provides a comprehensive summary of the physical and chemical properties of NPs and the influence of various biological factors in tumor microenvironment,and discusses how to improve the final efficacy through adjusting the characteristics and structure of NPs.Perspectives and future directions are also provided.
