China has achieved much during recent years in the area of lithospheric physics research and promoted the development of the geosciences （Teng, 2004）. However, in the 21^st century, national needs and policy challen...China has achieved much during recent years in the area of lithospheric physics research and promoted the development of the geosciences （Teng, 2004）. However, in the 21^st century, national needs and policy challenges the science of lithospheric physics. I suggest a general analysis, research, and development direction for lithospheric physics and point out clearly the content, core problems, and key scientific problems in this field. The realization of the earth and the discovery of the basic mechanisms of mountains, basins, minerals, and natural disasters depend basically on high-resolution observations of geophysics, the delineation of the fine structure of crust and mantle （2D and 3D） inside the lithosphere, substance and energy exchanges in the deep earth, the process of deep physical, mechanical, and chemical actions, and deep dynamical response. Therefore, geophysics should be the pioneer in the geosciences field in the first half of the 21^st century. I end with an analysis and discussion of some problems and difficulties in the research of lithospheric physics.展开更多
Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinea...Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.展开更多
基金Project supported by Funds of the Chinese Academy of Sciences for Key Topics in Innovation Engineering （Grant No. KZCX3-SW-148） and by the National Natural Science Foundation of China （Grant No. 4043009）.
文摘China has achieved much during recent years in the area of lithospheric physics research and promoted the development of the geosciences （Teng, 2004）. However, in the 21^st century, national needs and policy challenges the science of lithospheric physics. I suggest a general analysis, research, and development direction for lithospheric physics and point out clearly the content, core problems, and key scientific problems in this field. The realization of the earth and the discovery of the basic mechanisms of mountains, basins, minerals, and natural disasters depend basically on high-resolution observations of geophysics, the delineation of the fine structure of crust and mantle （2D and 3D） inside the lithosphere, substance and energy exchanges in the deep earth, the process of deep physical, mechanical, and chemical actions, and deep dynamical response. Therefore, geophysics should be the pioneer in the geosciences field in the first half of the 21^st century. I end with an analysis and discussion of some problems and difficulties in the research of lithospheric physics.
基金The financial support provided by the Project of National Natural Science Foundation of China(U22A20415,21978256,22308314)“Pioneer”and“Leading Goose”Research&Development Program of Zhejiang(2022C01SA442617)。
文摘Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.
