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Integration of Population Balance Model with Computational Fluid Dynamics for Estimation of Oxygen Mass Transfer Rate in Bioreactor : 생물 반응기의 산소 전달 속도 추정을 위한 입자 개체군 수지식과 전산유체역학 통합 모델

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dc.contributor.advisor이종민-
dc.contributor.author박영석-
dc.date.accessioned2022-04-05T06:47:39Z-
dc.date.available2022-04-05T06:47:39Z-
dc.date.issued2021-
dc.identifier.other000000167087-
dc.identifier.urihttps://hdl.handle.net/10371/177844-
dc.identifier.urihttps://dcollection.snu.ac.kr/common/orgView/000000167087ko_KR
dc.description학위논문(석사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2021.8. 박영석.-
dc.description.abstractThis thesis presents a reliable integrated model of Computational Fluid Dynamics (CFD) and Population Balance Model (PBM) for the gas-liquid stirred tank for predicting oxygen mass transfer rates. Three constructed meshes were compared: unstructured, structured with thickness resolved, and zero-thickness structured mesh. The structured with thickness resolved meshes were selected as the best mesh. Then, the adjustable parameters in PBM kernels were estimated. The grid search method was employed, due to the strong non-linearity of the CFD-PBM model, based on the Bayesian optimization (BO) to improve the efficiency of searching. The final model was validated with experimental measurement results, and the validated model and the uniform bubble size model were compared. The results show that the PBM model reflects the heterogeneity of the system better, therefore better predicting the occurrence of dead zones in the stirred tank. Finally, a case study was conducted to examine changes in oxygen mass transfer according to changes in viscosity. As the viscosity was increased to 10 times that of water, the volume-averaged oxygen mass transfer rate decreased by 28% as the viscosity increased 10 times, and an increase in the mixing rate of 5% and the aeration rate of 10% was estimated to recover 67% and 45% of reduced mass transfer rate, respectively.-
dc.description.abstract본 석사학위 논문에서는 기-액 교반 탱크의 산소 물질 전달 속도 예측을 위한 CFD-PBM 통합 모델을 제시한다. 안정적인 수치 해석을 위해 세 가지 메쉬 (비 정형, 두께를 고려한 정형, 두께를 생략한 정형 메쉬)가 비교, 분석되었고, 두께를 고려한 정형 메쉬가 최종적으로 선택되었다. 이후 PBM kernel들의 조정 가능한 파라미터들이 추정되었다. CFD-PBM 모델의 강한 비 선형성 때문에 격자 탐색이 활용되었으며, 최대한 효율적인 격자 탐색을 위해 베이지안 최적화 (BO)가 활용되었다. 최종 모델이 실험 측정 결과로 검증되었고, 검증된 모델과 단일 크기 거품 모델의 결과가 비교되었다. 비교 결과, PBM 모델은 시스템의 비 균질성을 더 잘 반영하며, 그래서 교반 탱크에서 dead zone의 발생을 더 잘 예측하는 것으로 나타났다. 마지막으로, 점도 변화에 따른 산소 물질전달량 변화 양상을 파악하기 위한 연구가 진행되었다. 결과적으로, 물의 점도가 열 배로 증가함에 따라 산소 물질전달량은 72%만큼 감소하며, 3 배 수준의 점도에서 혼합 속도를 약 5% 정도 증가시키거나 공기 주입량을 10% 증가시키면 각각 감소한 물질전달량의 67% 와 45%를 회복하는 것으로 추정되었다.-
dc.description.tableofcontentsContents 4
List of Figures 5
List of Tables 6
1. Introduction 7
2. Modeling Approach 11
2.1 CFD Modeling of Gas-Liquid Multiphase Flow 11
2.2 Turbulence Modeling 16
2.3 Population Balance Modeling 17
2.3.1 Formulation of Method of Moments (MOM) 19
2.3.2 Formulation of QMOM 21
2.3.3 Kernels for Bubble Dynamics 24
2.4 Modeling of the Volumetric Mass Transfer Rate kLa 32
2.5 Parameter Estimation with Bayesian Optimization (BO) 34
3. Geometry, Grid, and Numerical Strategy 37
4. Simulation Results and Discussion 50
4.1 Sensitivity Test 50
4.2 Parameter Estimation 54
4.3 Model Validation 59
4.4 Comparison between Uniform Size Model and PBM Model 65
4.5 Oxygen Mass Transfer Rate Results in Viscosity Changes 68
5. Conclusion 72
References 73
Abstract in Korean (국문 초록) 77
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dc.format.extentii, 76-
dc.language.isoeng-
dc.publisher서울대학교 대학원-
dc.subjectComputational Fluid Dynamics-
dc.subjectMultiphase-
dc.subjectPopulation Balance Model-
dc.subjectOxygen Mass Transfer-
dc.subjectBioreactor-
dc.subjectViscosity-
dc.subject전산 유체 역학-
dc.subject다상유동-
dc.subject입자 개체군 수지식-
dc.subject산소 물질 전달량-
dc.subject생물 반응기-
dc.subject점도-
dc.subject.ddc660.6-
dc.titleIntegration of Population Balance Model with Computational Fluid Dynamics for Estimation of Oxygen Mass Transfer Rate in Bioreactor-
dc.title.alternative생물 반응기의 산소 전달 속도 추정을 위한 입자 개체군 수지식과 전산유체역학 통합 모델-
dc.typeThesis-
dc.typeDissertation-
dc.contributor.AlternativeAuthorYoungseok Bak-
dc.contributor.department공과대학 화학생물공학부-
dc.description.degree석사-
dc.date.awarded2021-08-
dc.identifier.uciI804:11032-000000167087-
dc.identifier.holdings000000000046▲000000000053▲000000167087▲-
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