The game theoretical approach for multi-phase complex systems in chemical engineering

J. Systems Science & Complexity(2017)

引用 16|浏览25
暂无评分
摘要
This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure in the multi-phase complex systems in chemical engineering. The basis of this work is the energy-minimization-multi-scale (EMMS) model proposed by Li and Kwauk (1994) and Li, et al. (2013) which identifies the multi-scale structure as a result of ‘compromise-in-competition between dominant mechanisms’ and tries to solve a multi-objective optimization problem. However, the existing methods often integrate it into a problem of single objective optimization, which does not clearly reflect the ‘compromise-in-competition’ mechanism and causes heavy computation burden as well as uncertainty in choosing suitable weighting factors. This paper will formulate the compromise in competition mechanism in EMMS model as a noncooperative game with constraints, and will describe the desired stable system state as a generalized Nash equilibrium. Then the authors will investigate the game theoretical approach for two typical systems in chemical engineering, the gas-solid fluidization (GSF) system and turbulent flow in pipe. Two different cases for generalized Nash equilibrium in such systems will be well defined and distinguished. The generalize Nash equilibrium will be solved accurately for the GSF system and a feasible method will be given for turbulent flow in pipe. These results coincide with the existing computational results and show the feasibility of this approach, which overcomes the disadvantages of the existing methods and provides deep insight into the mechanisms of multi-scale structure in the multi-phase complex systems in chemical engineering.
更多
查看译文
关键词
Game theory, gas-solid fluidization, generalized Nash equilibrium, multi-phase complex systems, turbulent flow
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要