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Analysis of Current Status of Steam Cracking Feed Production and Measures for Maximization of Steam Cracking Feed 被引量:3
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作者 Chen Su 《China Petroleum Processing & Petrochemical Technology》 SCIE CAS 2005年第2期41-46,共6页
In recent years China has seen speedy development of its ethylene industry. Compared to other advanced countries the per capita ethylene consumption in China is still low. With successive startup of grassroots ethylen... In recent years China has seen speedy development of its ethylene industry. Compared to other advanced countries the per capita ethylene consumption in China is still low. With successive startup of grassroots ethylene projects in China after 2006 and debottlenecking and expansion of existing ethylene units China will be confronted with the major issues related with increase of feedstocks for steam cracking. Naphtha is the main feedstock for producing ethylene, and the hydrocracked tail oil is increasing its share in the steam cracker feedstock pool over recent years. This article has analyzed the possibility for maximization of steam cracking feedstock and estimated steam cracker feedstock output based on processing 5 Mt/a of different crudes including the mixed crude transferred through Lu-Ning pipeline and Arabian light crude using corresponding process technologies at the refinery. 展开更多
关键词 steam cracking feed maximization of production measures
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A grouping strategy for reinforcement learning-based collective yawcontrol of wind farms
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作者 Chao Li Luoqin Liu Xiyun Lu 《Theoretical & Applied Mechanics Letters》 CAS CSCD 2024年第1期1-5,共5页
Reinforcement learning(RL)algorithms are expected to become the next generation of wind farm control methods.However,as wind farms continue to grow in size,the computational complexity of collective wind farm control ... Reinforcement learning(RL)algorithms are expected to become the next generation of wind farm control methods.However,as wind farms continue to grow in size,the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces,limiting its potential in practical applications.In this Letter,we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms.To reduce the computational complexity,the turbines in the wind farm are grouped according to the strength of the wake interaction.Meanwhile,to improve the control efficiency,each subgroup is treated as a whole and controlled by a single agent.Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency. 展开更多
关键词 Reinforcement learning Wake steering Wind-farm flow control production maximization
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