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
Novel matrix beads for the immobilization of strain Comamonas testosteroni sp. bdq06 to degrade quino- line were fabricated from polyethersulfone（PES）. The beads have an average size of 3 mm and a surface dense laye...Novel matrix beads for the immobilization of strain Comamonas testosteroni sp. bdq06 to degrade quino- line were fabricated from polyethersulfone（PES）. The beads have an average size of 3 mm and a surface dense layer of 20 microns. To help adhesion and proliferation of bacterial cells, the surfaces of the PES beads were etched, and numerous holes about 1.5 micrometers in diameter were generated as tunnels for cell colonizing in the larger internal cavities of about 5 micrometers in diameter. The quinoline degradation was remarkably enhanced by the cells immo- bilized in PES beads compared with that by the free cells at pH 5.0 or 10.0 and a temperature of 40 ℃. The enhanced degradation of quinoline was contributed to the biofilm on the surface of PES beads, resulting in the significant re- duction of retention time from 9 h to 2 h. Furthermore, the beads remain intact after the ultrasonic treatment of them for 30 rain or recycling 50 times, indicating that they have excellent mechanical strength, flexibility and swelling ca- pacity. Thus, PES beads have great potential to be matrix for the cell immobilization in bioaugmentation.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 the National Natural Science Foundation of China（Nos.51378098, 51238001, 51408110, 51108069）, the Jilin Provincial Research Foundation, China（Nos.20130101038JC, 20140520151JH, 20080635, 2014340） and the Fundamental Research Funds for the Central Universities of China（No. 14QNJJ027）.
文摘Novel matrix beads for the immobilization of strain Comamonas testosteroni sp. bdq06 to degrade quino- line were fabricated from polyethersulfone（PES）. The beads have an average size of 3 mm and a surface dense layer of 20 microns. To help adhesion and proliferation of bacterial cells, the surfaces of the PES beads were etched, and numerous holes about 1.5 micrometers in diameter were generated as tunnels for cell colonizing in the larger internal cavities of about 5 micrometers in diameter. The quinoline degradation was remarkably enhanced by the cells immo- bilized in PES beads compared with that by the free cells at pH 5.0 or 10.0 and a temperature of 40 ℃. The enhanced degradation of quinoline was contributed to the biofilm on the surface of PES beads, resulting in the significant re- duction of retention time from 9 h to 2 h. Furthermore, the beads remain intact after the ultrasonic treatment of them for 30 rain or recycling 50 times, indicating that they have excellent mechanical strength, flexibility and swelling ca- pacity. Thus, PES beads have great potential to be matrix for the cell immobilization in bioaugmentation.
