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RISS 인기검색어
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- 저자 : N.M. Ghasem (검색결과 2 건)
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CFD simulation of fluidized bed reactors for polyolefin production – A review
M.J.H. Khan,M.A. Hussain,Z. Mansourpour,N. Mostoufi,N.M. Ghasem,E.C. Abdullah 한국공업화학회 2014 Journal of Industrial and Engineering Chemistry Vol.20 No.6
This literature survey focuses on the application of computational fluid dynamics (CFD) in variousaspects of the fluidized bed reactor. Although fluidized bed reactors are used in various industrialapplications, this first-of-its-kind review highlights the use of CFD on polyolefin production. It is shownthat CFD has been utilized for the following mechanisms of polymerization: governing of bubbleformation, electrostatic charge effect, gas–solid flow behavior, particle distribution, solid–gas circulationpattern, bed expansion consequence, mixing and segregation, agglomeration and shear forces. Heat andmass transfer in the reactor modeling using CFD principles has also been taken under consideration. Anumber of softwares are available to interpret the data of the CFD simulation but only few softwarespossess the analytical capability to interpret the complex flowbehavior of fluidization. In this review, thepopular softwares with their framework and application have been discussed. The advantages andfeasibility of applying CFD to olefin polymerization in fluidized beds were deliberated and the prospectof future CFD applications was also discussed.
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Kaedi, M.,Ghasem-Aghaee, N.,Ahn, C.W. Elsevier science 2016 Information Sciences Vol. No.
<P>When memory-based evolutionary algorithms are applied in dynamic environments, the certainly use of uncertain prior knowledge for future environments may mislead the evolutionary algorithms. To address this problem, this paper presents a new, memory-based evolutionary approach for applying the Bayesian optimization algorithm (BOA) in dynamic environments. Our proposed method, unlike existing memory-based methods, uses the knowledge of former environments probabilistically in future environments. For this purpose, the run of BOA is modeled as the movements in a Markov chain, in which the states become the Bayesian networks that are learned in every generation. When the environment changes, a stationary distribution of the Markov chain is defined on the basis of the retrieved prior knowledge. Then, the transition probabilities of BOA in the Markov chain are modified (biased) to comply with the defined stationary distribution. To this end, we employ the Metropolis algorithm and modify the K2 algorithm for learning the Bayesian network in BOA in order to reflect the obtained transition probabilities. Experimental results show that the proposed method achieves improved performance compared to conventional methods, especially in random environments. (C) 2015 Elsevier Inc. All rights reserved.</P>
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