Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoreticall...Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoretically supported,efficient,and reliable.Based on the data distribution of the composition and properties of the blending fuels,a model of polynomial regression with appropriate hypothesis space was established.The parameters of the model were further optimized by different intelligence algorithms to achieve high-precision regression.Then,the design of a blending fuel was described as a multi-objective optimization problem,which was solved using a Nelder–Mead algorithm based on the concept of Pareto domination.Finally,the design of a target fuel was fully validated by experiments.This study provides new avenues for designing various blending fuels to meet the needs of next-generation engines.展开更多
The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization i...The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.展开更多
Controlled-release urea(CRU)releases nitrogen(N)at the same pace that rice takes it up,which can effectively improve N use efficiency,increase rice yield and improve rice quality.However,few studies have described the...Controlled-release urea(CRU)releases nitrogen(N)at the same pace that rice takes it up,which can effectively improve N use efficiency,increase rice yield and improve rice quality.However,few studies have described the effects of CRU application on the photosynthetic rate and endogenous enzyme activities of rice.Accordingly,a twoyear field trial was conducted with a total of seven treatments:CK,no N fertilizer;BBF,regular blended fertilizer;RBBF,20%N-reduced regular blended fertilizer;CRF1,70%CRU+30%regular urea one-time base application;CRF2,60%CRU+40%regular urea one-time base application;RCRF1,CRF1 treatment with 20%N reduction;and RCRF2,CRF2 treatment with 20%N reduction.Each treatment was conducted in triplicate.The results showed that the N recovery efficiency(NRE)of the controlled-release bulk blending fertilizer(CRBBF)treatments was significantly greater over the two years.There were significant yield increases of 4.1–5.9%under the CRF1treatment and 5.6–7.6%under the CRF2 treatment compared to the BBF treatment,but the differences between the reduced-N treatments RBBF and RCRF2 were not significant.Photosynthetic rates under the CRF1 and CRF2treatments were significantly higher than under the other treatments,and they had significantly greater RuBPCase,RuBisCO,glutamate synthase(GOGAT)and glutamine synthetase(GS)enzyme activities.Additionally,the soil NH_(4)^(+)-N and NO_(3)^(–)-N contents under the CRBBF treatments were significantly higher at the late growth stage of rice,which was more in-line with the fertilizer requirements of rice throughout the reproductive period.CRBBF also led to some improvement in rice quality.Compared with the BBF and RBBF treatments,the protein contents under the CRBBF treatments were reduced but the milling,appearance,eating and cooking qualities of the rice were improved.These results showed that the application of CRBBF can improve the NRE,photosynthetic rate and endogenous enzyme activities of rice,ensuring sufficient N nutrition and photosynthetic material production during rice growth and thereby achieving improved rice yield and quality.展开更多
本文介绍了 Blending L earning(或 Blended L earning)的新含义 ,指出这一新含义的提出和被广泛认同 ,表明国际教育技术界的教育思想观念正在经历又一场深刻的变革 ,也是教育技术理论进一步发展的标志。作者还从对建构主义理论的反思...本文介绍了 Blending L earning(或 Blended L earning)的新含义 ,指出这一新含义的提出和被广泛认同 ,表明国际教育技术界的教育思想观念正在经历又一场深刻的变革 ,也是教育技术理论进一步发展的标志。作者还从对建构主义理论的反思、对信息技术教育应用认识的深化 。展开更多
基金the support from the National Key R&D Program of China(No.2021YFC2103701)the National Natural Science Foundation of China(No.22178248)the Haihe Laboratory of Sustainable Chemical Transformations。
文摘Fuel design is a complex multi-objective optimization problem in which facile and robust methods are urgently demanded.Herein,a complete workflow for designing a fuel blending scheme is presented,which is theoretically supported,efficient,and reliable.Based on the data distribution of the composition and properties of the blending fuels,a model of polynomial regression with appropriate hypothesis space was established.The parameters of the model were further optimized by different intelligence algorithms to achieve high-precision regression.Then,the design of a blending fuel was described as a multi-objective optimization problem,which was solved using a Nelder–Mead algorithm based on the concept of Pareto domination.Finally,the design of a target fuel was fully validated by experiments.This study provides new avenues for designing various blending fuels to meet the needs of next-generation engines.
基金supported by National Key Research & Development Program-Intergovernmental International Science and Technology Innovation Cooperation Project (2021YFE0112800)National Natural Science Foundation of China (Key Program: 62136003)+2 种基金National Natural Science Foundation of China (62073142)Fundamental Research Funds for the Central Universities (222202417006)Shanghai Al Lab
文摘The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.
基金supported by the Natural Science Foundation of Jiangsu Province,China(BK20220563)the Key R&D Program of Jiangsu Province,China(BE2022338)the Colleges and Universities in Jiangsu Province Natural Science Foundation of China(19KJB210014)。
文摘Controlled-release urea(CRU)releases nitrogen(N)at the same pace that rice takes it up,which can effectively improve N use efficiency,increase rice yield and improve rice quality.However,few studies have described the effects of CRU application on the photosynthetic rate and endogenous enzyme activities of rice.Accordingly,a twoyear field trial was conducted with a total of seven treatments:CK,no N fertilizer;BBF,regular blended fertilizer;RBBF,20%N-reduced regular blended fertilizer;CRF1,70%CRU+30%regular urea one-time base application;CRF2,60%CRU+40%regular urea one-time base application;RCRF1,CRF1 treatment with 20%N reduction;and RCRF2,CRF2 treatment with 20%N reduction.Each treatment was conducted in triplicate.The results showed that the N recovery efficiency(NRE)of the controlled-release bulk blending fertilizer(CRBBF)treatments was significantly greater over the two years.There were significant yield increases of 4.1–5.9%under the CRF1treatment and 5.6–7.6%under the CRF2 treatment compared to the BBF treatment,but the differences between the reduced-N treatments RBBF and RCRF2 were not significant.Photosynthetic rates under the CRF1 and CRF2treatments were significantly higher than under the other treatments,and they had significantly greater RuBPCase,RuBisCO,glutamate synthase(GOGAT)and glutamine synthetase(GS)enzyme activities.Additionally,the soil NH_(4)^(+)-N and NO_(3)^(–)-N contents under the CRBBF treatments were significantly higher at the late growth stage of rice,which was more in-line with the fertilizer requirements of rice throughout the reproductive period.CRBBF also led to some improvement in rice quality.Compared with the BBF and RBBF treatments,the protein contents under the CRBBF treatments were reduced but the milling,appearance,eating and cooking qualities of the rice were improved.These results showed that the application of CRBBF can improve the NRE,photosynthetic rate and endogenous enzyme activities of rice,ensuring sufficient N nutrition and photosynthetic material production during rice growth and thereby achieving improved rice yield and quality.
