本文研究了价格低廉的木质素磺酸盐(LS)作为表面活性剂驱油牺牲剂的可能性。研究了 LS 的吸附特性,作为牺牲剂减少石油磺酸盐吸附损失的作用规律和改性木质素磺酸盐对二价金属离子的螯合性能。结果表明:LS 的吸附符合 Langmuir 方程;使...本文研究了价格低廉的木质素磺酸盐(LS)作为表面活性剂驱油牺牲剂的可能性。研究了 LS 的吸附特性,作为牺牲剂减少石油磺酸盐吸附损失的作用规律和改性木质素磺酸盐对二价金属离子的螯合性能。结果表明:LS 的吸附符合 Langmuir 方程;使用改性 LS 的水溶液对地层进行预处理,可以显著地减少表面活性剂的吸附损失(减少量>60%)。改性木质素磺酸盐作为表面活性剂驱油的牺牲剂具有应用前景。展开更多
Edge artificial intelligence will empower the ever simple industrial wireless networks(IWNs)supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machin...Edge artificial intelligence will empower the ever simple industrial wireless networks(IWNs)supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices(MTDs)and edge servers.In this paper,we propose a multi-agent deep reinforcement learning based resource allocation(MADRL-RA)algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive applications.First,we present the system model of IWNs,wherein each MTD is regarded as a self-learning agent.Then,we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption.Next,we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision,computation capacity,and transmission power.To break the time correlation of training data while accelerating the learning process of MADRL-RA,we design a weighted experience replay to store and sample experiences categorically.Furthermore,we propose a step-by-stepε-greedy method to balance exploitation and exploration.Finally,we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments,showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.展开更多
文摘本文研究了价格低廉的木质素磺酸盐(LS)作为表面活性剂驱油牺牲剂的可能性。研究了 LS 的吸附特性,作为牺牲剂减少石油磺酸盐吸附损失的作用规律和改性木质素磺酸盐对二价金属离子的螯合性能。结果表明:LS 的吸附符合 Langmuir 方程;使用改性 LS 的水溶液对地层进行预处理,可以显著地减少表面活性剂的吸附损失(减少量>60%)。改性木质素磺酸盐作为表面活性剂驱油的牺牲剂具有应用前景。
基金Project supported by the National Key R&rD Program of China(No.2020YFB1710900)the National Natural Science Foundation of China(Nos.62173322,61803368,and U1908212)+1 种基金the China Postdoctoral Science Foundation(No.2019M661156)the Youth Innovation Promotion Association,Chinese Academy of Sciences(No.2019202)。
文摘Edge artificial intelligence will empower the ever simple industrial wireless networks(IWNs)supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices(MTDs)and edge servers.In this paper,we propose a multi-agent deep reinforcement learning based resource allocation(MADRL-RA)algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive applications.First,we present the system model of IWNs,wherein each MTD is regarded as a self-learning agent.Then,we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy consumption.Next,we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision,computation capacity,and transmission power.To break the time correlation of training data while accelerating the learning process of MADRL-RA,we design a weighted experience replay to store and sample experiences categorically.Furthermore,we propose a step-by-stepε-greedy method to balance exploitation and exploration.Finally,we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments,showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.
