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Hazardous Chemical Dispersion Analysis and Surrogate Model Optimization using Artificial Neural Network
유해화학물질 확산 분석 및 인공신경망을 사용한 대리 모델 최적화

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Authors
양시엽
Advisor
한종훈
Major
공과대학 화학생물공학부
Issue Date
2017-08
Publisher
서울대학교 대학원
Keywords
Computational Fluid DynamicsChemical SafetyArtificial Neural NetworkMixed Integer Linear ProgrammingSurrogate Model
Description
학위논문 (박사)-- 서울대학교 대학원 공과대학 화학생물공학부, 2017. 8. 한종훈.
Abstract
CFD simulations can be used to estimate the accident consequences by hazardous chemical releases. Both strength and weakness of this method lies on the complexity of the CFD simulation. In other words, CFD can predict chemical dispersion in the complex geometry like urban area, but it takes much computational resources.
Comparison between actual dispersion and CFD simulation is done to show usefulness of the CFD simulation. Anhydrous hydrogen fluoride dispersion field test is simulated and it is within the dispersion model criteria. Actual hydrogen fluoride accidental release in 2012 is also simulated and its human and environmental damage prediction is similar to actual accident consequences.
Case study of the toxic gas dispersion in the urban area varying source and meteorological condition is conducted, and numerical method is tested for the worst case scenario. Time variant CFL stepping method and multi-threading reduce the calculation time. Also ANN is able to classify the safe and danger based on the input condition.
CFD simulation can be applied in the detector allocation problem in the process unit. Simulation inputs are determined by computer experiment procedure and other sets of data detection time is estimated with ANN. Then based on these simulated and estimated data MILP is solved and showed better result than using simulation data alone.
Language
English
URI
https://hdl.handle.net/10371/136862
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Chemical and Biological Engineering (화학생물공학부)Theses (Ph.D. / Sc.D._화학생물공학부)
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