S-Space College of Agriculture and Life Sciences (농업생명과학대학) Dept. of Landscape Architecture and Rural System Engineering (생태조경·지역시스템공학부) Theses (Ph.D. / Sc.D._생태조경·지역시스템공학부)
DETERMINATION OF OPTIMIZED HYDRODYNAMIC DESIGN PARAMETERS FOR PHOTOBIOREACTORS USING CFD WITH EXPERIMENTAL PROOF
광생물반응기의 CFD 유체역학적 설계를 위한 매개변수 최적화 및 이의 검증실험
|dc.description||학위논문 (박사)-- 서울대학교 대학원 : 생태조경.지역시스템공학부 지역시스템공학 전공, 2013. 8. 이인복.||-|
|dc.description.abstract||최근, 에너지수요의 증가와 에너지자원의 고갈로 인하여 새로운 대체에너지의 중요성이 대두되고 있다. 이에 따라 향후 화석연료에너지를 대체할 수 있는 잠재적인 자원으로서 미세조류로부터 오일을 추출하는 방법이 재조명 받고 있다. 본 연구에서는 미세조류를 배양할 수 있는 광생물반응기 (Photobioreactor, PBR)의 유동학적 흐름을 분석하기 위해 전산유체역학 (Computational Fluid Dynamics, CFD)을 활용하였다. 또한 미세조류의 배양 실험을 수행하여 본 연구 과정을 실험적으로 검증하고 CFD 시뮬레이션으로부터 얻어진 결과들을 확인하고자 하였다.
본 연구에서는 먼저 PBR의 설계 과정에서 CFD를 이용한 논문들을 검토하여 미세조류와 미세조류의 생장에 필요한 여러 필수 요인들, 예를 들면 광, 이산화탄소, 온도 등을 공학적인 관점에서 이해하고자 하였다. 특히 이러한 요인들에 큰 영향을 주는 PBR 내의 수리역학적 흐름 특성들과 데드존(Dead zone)의 비율, 평균 혼합시간 및 난류 강도 등을 종합적으로 연구하여 PBR의 성능을 분석하고자 하였다.
시뮬레이션 연구에 앞서 시뮬레이션 결과의 신뢰도를 제고하기 위하여 CFD 코드와 모델링 방법들은 입자영상유속계 (PIV)를 이용한 실험 결과로 검증되었다. PBR 내에 테스트 영역을 분할하고 다양한 가스 주입 유량에 대해 실험을 진행하였으며, 이로부터 얻어진 PBR 내의 평균 유속은 시뮬레이션 결과와 비교하여 약 6.77%의 오차를 보여 충분히 타당한 결과를 나타냈다.
공학적인 관점에서 PRB의 구조적 형태와 디자인은 반응기 내에 미세조류 세포를 배양함에 있어 수리 역학적으로 이상적인 환경을 제공하는데 중요한 역할을 한다. 이러한 이유로 32가지 형태의 30L급 PBR의 수리 역학적 흐름은 CFD를 사용하여 분석하였다. 이때 수리 역학적 평가의 요소로는 데드존, 평균 혼합시간, 난류 강도로 구성하였으며, 이를 통한 CFD 시뮬레이션 결과는 PBR을 구성하는 내부 배플과 원뿔형의 바닥 형상에 의한 수리 역학적 장점들을 잘 보여주었다. 하지만 이러한 평가 요소를 활용하여 PBR의 적절한 운영 조건과 설계안을 찾는 것은 기존 연구들에서는 없었던 첫 번째 시도로서 그 기준이나 지표를 설정하는데 많은 어려움이 있었다. 따라서 각 평가 요소마다 적절한 기준을 정하고 32개의 시뮬레이션 케이스 중에 미세조류 생장에 가장 적절한 형태가 선정될 때까지 순차적으로 소거시켜나가는 과정으로 분석을 진행하였다. 이에 따라 최종적으로 A2B2C2 형태와 A3B2C2 형태가 가장 적절한 것으로 평가되었으며, 두 모델 모두 10mm의 노즐 직경과 동일한 PBR 형상을 가지고 있다. 단, 두 모델은 운영 조건 면에서 가스의 주입 유속이 각각 0.10과 0.15 vvm 으로 서로 다른데, 이러한 조건은 실제 미세조류를 배양하는 과정에서 쉽게 변경할 수 있는 조건이다.
최종적으로 선정된 PBR 구조를 사용하여 실제 미세조류 배양 실험을 수행하였다. 앞선 시뮬레이션 연구로부터 결정된 PBR의 구조적 형태와 운영 조건을 적용하여 30L 원통형 PBR에서 미세조류의 생장률을 측정하였다. 실험결과는 앞선 시뮬레이션 결과와 동일하게 개선된 형태의 PBR이 미세조류의 배양에 더 효과적인 것으로 나타났으며, 특히 개선된 구조는 미세조류를 대량생산 할 수 있다는 점에서 보다 효율적으로 활용 될 수 있을 것이다.
마지막으로 전산유체역학을 이용한 접근은 PBR 내부의 유동학적 흐름을 살펴볼 수 있다는 점에서 다양하게 활용될 수 있을 것이며 이는 미세조류의 대량생산을 위한 보다 효율적이며 효과적인 구조 개발에 도움이 될 것이다. 또한 PBR 디자인을 연구하는데 있어 연구시간, 노동력, 자원 등을 상당히 절감 할 수 있을 것으로 판단된다.
|dc.description.abstract||Currently, amidst the reality of increasing energy demand and depleting energy sources, finding new alternative energy sources is imperative. The idea of oil extraction from microalgae is again given much attention. It is believed to be the only source which can potentially replace petroleum fuels in the near future. Microalgae are naturally available elsewhere and tapping them as source of energy offers numerous advantages than other oil-rich plants. This includes its carbon dioxide (CO2) sequestration capability which could play a significant role in decreasing the amount CO2 in the atmosphere. Microalgae can be cultivated in non-arable lands, which rules out the fear of competing with food and fiber. Microalgae are cultivated in closed photobioreactors (PBRs) where the ideal cell growth requirements are controlled to obtain maximum cell growth. The cells need enough light, carbon dioxide (CO2), nutrients, etc, for their growth. However, other indirect parameters are also equally important such as the flow hydrodynamics/mixing inside the PBR to ensure equal distribution of CO2 and cell nutrients. The flow hydrodynamics as well as the method of introducing CO2 and nutrients into the PBR depends greatly on its geometrical design. In this study, computational fluid dynamics (CFD) was utilized simulate and design a PBR and was followed immediately with a practical evaluation of the designed PBRs to finally conclude and cement the results obtained in the CFD simulation.
In Chapter 2, a comprehensive review on the application of CFD in designing PBRs was conducted which gave profound understanding on microalgae, its production systems such as raceways and PBRs, its main growth requirements including light, CO2 and temperature. The various CFD design aspects which considered the light intensity distribution, mixing and gas injection into the reactors were discussed. The CFD approach to correctly simulate the flow hydrodynamics with the proper implementation of the different type of multiphase models and turbulence models were also adequately reviewed. The opportunities of the CFD approach was also discussed where a development of microalgae growth model was proposed which can be integrated in the CFD simulation during the design. Furthermore, the integration of flow hydrodynamics particularly the volume percentages of dead zones, average circulation time and turbulence intensity to be utilized in the analysis in terms of the performance of PBRs and efficient vessel for microalgae production is highly recommended.
In Chapter 4, to establish the reliability of the simulation study, the CFD code and approach was initially validated from particle image velocimetry (PIV) data under various air flow rates and two test regions in the PBR. PIV works by seeding tracer particles which are assumed to faithfully follow the flow dynamics following the principle of Stokes number. The particles are illuminated using a laser so that they become visible for the specialized digital camera which captures multiple frames at high speed. The images of the tracer particles are recorded at least twice with a small time-delay. The displacement of the particle images represents the fluid motion. The flow movements of particles are then being tracked where the velocity and direction of the particles are recorded for computer analysis. In the CFD model, Discrete-phase model of DPM was utilized to simulate the movement of the cells as they travel around, and up and down the PBR. Particle injections can be defined by a text file specifying particle properties, position, and initial velocity.
A circular plane was defined as a source of particles in any part of the computational grid, with a given generation rate expressed in particles per second. The particles are assumed to be passively transported by the hydrodynamic flow and the interaction among cells or with the fluid are not considered. The particles are assumed as spherical and defined by properties such as diameter and density. Comparing the computed average velocity magnitude of the PIV and simulation showed an average error of approximately 6.77% which is generally acceptable.
In engineering perspective, the structural configuration and design of the PBRs have critical role in the flow hydrodynamics inside the reactor which are very significant in providing ideal growth conditions for the microalgae cells. Hence, the flow hydrodynamics inside 30 L PBRs simulating 32 cases were investigated via CFD and presented in Chapter 5. The 32 cases simulated in the study were accounted from four various air flow rates, four nozzle size diameters and two PBR geometry designs. The optimized hydrodynamic evaluation parameters include dead zones, average circulation time and turbulence intensity. Simulation results have revealed the hydrodynamic advantages of the PBRs designed with internal baffle and protruded bottom cone-shaped geometry. This PBR design can eliminate the dead zones at the same time execute better mixing and mass transfer because of its faster average circulation time. This approach of selecting a PBR operating condition and design combining the three hydrodynamics parameters is a first attempt of its kind and no standard value in each parameter is available in literatures. Thus, a criterion on each parameter was set and elimination technique was executed in the 32 simulated cases until some appropriate PBRs suited for microalgae production were selected. Thus, based on the hydrodynamic analysis inside the PBRs applying the criteria, cases A2B2C2 and A3B2C2 were chosen. Both cases have the same nozzle size diameter of 10 mm and PBR geometry which are considered design parameters while they only differ in flow rate of 0.10 and 0.15 vvm which is an operating parameter and can be easily adjusted during the actual cultivation of microalgae.
Chapter 6 presents the practical cultivation of microalgae to finally confirm and cement the results obtained in Chapter 5. This was implemented to investigate the growth response of microalgae cells from the numerically investigated 30 L cylindrical bubble column PBRs of 30 L following the recommended PBR design and operating parameters obtained in previous study. However, before the final cultivation of microalgae in the 30 L PBRs, initial laboratory scale experiments were conducted in small size PBRs to determine the effect of temperature and CO2 on the growth of microalgae. The recommended temperature and CO2 level obtained in the small scale laboratory tests were supplied in the 30 L culture cultivation. The selected microalgae specie is Chlorella vulgaris.
Based on the laboratory scale experiments on temperature and CO2, temperature range between 20 ~ 35 0C was found to be more appropriate to the growth of the cells while under 10 % level of CO2 is also recommended for the specie.
The average fresh cell weight of Chlorella vulgaris measured from Experiment 1 in the upgraded PBR is approximately 1.60 as compared to approximately 0.53 g L-1, for the typical PBR. The maximum fresh cell weight in the upgraded PBR can be achieved in 7 days as compared to the typical PBR which is 10 days. The decrease in time duration in obtaining higher cell concentration using the upgraded PBR is very significant since lesser time for cultivation can result to more production. For instance, in one month duration, batch cultivation of microalgae can be implemented four times in the upgraded PBR while only three batch cultivation can be achieved in the typical PBR. In terms of their specific growth rates, the maximum specific growth rate for the typical PBR is approximately 2.21 day-1, while approximately 2.58 day-1 was achieved in the upgraded PBR. Estimating their maximum productivities yields approximately 1.15 g L-1 day-1 and 3.43 g L-1 day-1 for the typical PBR and the upgraded PBR, respectively.
The results obtained from Experiment 2 with higher initial cell concentration can lessen the time to achieve maximum cell concentration to about one day. Computing the average fresh cell weight concentration for Experiment 2 gives values of approximately 0.71 and 1.93 g L-1 for the typical PBR and the upgraded PBR, respectively. In terms of the specific growth rate, maximum values of 1.28 day-1 for the typical PBR is obtained while approximately 1.56 day-1 was obtained for the upgraded PBR. Estimating the maximum productivity of the PBRs for Experiment 2, yield productivities of approximately 0.91 g L-1 day-1 and 3.01 g L-1 day-1 for the typical PBR and upgraded PBR, respectively. Computing the average maximum productivities for the two successive experiments shows values of approximately 1.03 g L-1 day-1 and 3.23 g L-1 day-1 for the typical PBR and upgraded PBR, respectively. This posted an increase of productivity of approximately 314 % in utilizing the upgraded PBR.
Finally, it can be further concluded that the CFD approach have demonstrated its capability in investigating flow hydrodynamics inside the PBRs which can result to better design of efficient and effective PBRs for maximum cultivation of microalgae. The approach can be very promising in doing researches designs of PBRs while reducing significant research time, labor and resources.
List of Figures. x
List of Tables. xiiv
1. General Introduction 1
2. Literature Review 7
2.1. Microalgae . 7
2.2. Optimal factor for microalgae growth . 10
2.2.1. Light 10
2.2.2. CO2 12
2.2.3. Temperature . . 13
2.3. Large-sized production systems: Raceway ponds and Photobioreactors . . 15
2.4. Important design aspects . 18
2.4.1. Mixing . 19
2.4.2. Light penetartion . 21
2.4.3. Gas injection . 21
2.5. Current PBR designs . 22
2.6. Computational fluid dynamics (CFD) approach to PBR designs 24
2.6.1. Flow Modeling: Turbulence 24
2.6.2. Multiphase modeling . 32
18.104.22.168. Eulerian-Eulerian 32 22.214.171.124. Volume of fluid (VOF) 33
126.96.36.199. Lagrangian-Eulerian . 35
188.8.131.52. Discrete-Phase model (DPM) . 36
2.6.3. Numerical issues on different representation of multiphase / turbulence models. 40
2.6.4. CFD modeling of PBRs . 41
2.6.5. Specific focus of CFD studies . 47
184.108.40.206. Mixing studies 47
220.127.116.11. Light simulation studies. 50
18.104.22.168. Species modeling studies 52
2.6.6. Challenges in PBR design . 56
22.214.171.124. CFD design for light distribution 57
126.96.36.199. CFD design for gas distribution .. 58
188.8.131.52. Opportunities for CFD approach 60
3. General Objectives 63
4. Validation of CFD simulations using Particle Image Velocimetry (PIV) data 66
4.1. Introduction . 66
4.2. Materials and Methods 71
4.2.1. Particle image velocimetry (PIV) 71
4.2.2. Computational fluid dynamics (CFD)... 74
4.3. Results and Discussion 77
4.3.1. Mesh grid interval size 77
4.3.2. Visualization of fluid flow . 80
4.3.3. Validation of the CFD model . 81
4.4. Conclusions . 85
5. Numerical investigation and design of PBRs from three hydrodynamic evaluation parameters and light . 86
5.1. Introduction . 86
5.2. Materials and Methods 96
5.2.1. Computational fluid dynamics. 96
5.2.2. Hydrodynamic parameters used to evaluate the PBRs 102
5.2.3. The Discrete-Phase model (DPM) . 104
5.2.4. Predicting light intensity in the PBRs 109
5.3. Results and discussion . 110
5.3.1. Realizing PBR design 110
5.3.2. Percentage of dead zones inside the PBRs 111
5.3.3. Average circulation time in the PBRs 115
5.3.4. Turbulence intensity in the PBRs . 119
5.3.5. Light intensity in the PBRs 123
5.3.6. Selection of appropriate PBRs combining the three hydrodynamic evaluation parameters and light 125
5.4. Conclusion . 129
6. Cultivation of Chlorella vulgaris in 30 L cylindrical bubble column photobioreactors (PBRs) . 132
6.1. Introduction . 132
6.2. Materials and Methods 139
6.2.1. Microalgae specie 139
6.2.2. Descriptions of the PBRs used in the microalgae cultivation. . 139
6.2.3. Initial experiments on the effect of temperature and CO2 . 142
6.2.2. Experimental set-up and methodology for the cultivation of microalgae in the 30 L PBR . 145
6.3. Results and Discussion 147
6.3.1. Effect of temperature on the growth of Chlorella vulgaris . 147
6.3.2. The effect of CO2 on the growth of Chlorella vulgaris . 150
6.3.3. Cultivation in 30 L PBRs . 153
184.108.40.206. Air flow rate in the 30 L PBR . 153
220.127.116.11. Mixing analysis in the 30 L PBR .. 155
18.104.22.168. Fresh cell weight and specific growth rate . 158
6.4. Conclusion . 165
7. General Conclusions 167
8. References . 170
|dc.subject||Computational Fluid Dynamics (CFD)||-|
|dc.subject||Particle image velocimetry (PIV)||-|
|dc.title||DETERMINATION OF OPTIMIZED HYDRODYNAMIC DESIGN PARAMETERS FOR PHOTOBIOREACTORS USING CFD WITH EXPERIMENTAL PROOF||-|
|dc.title.alternative||광생물반응기의 CFD 유체역학적 설계를 위한 매개변수 최적화 및 이의 검증실험||-|
|dc.contributor.AlternativeAuthor||Jessie Pascual Piog Bitog||-|
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- College of Agriculture and Life Sciences (농업생명과학대학)Dept. of Landscape Architecture and Rural System Engineering (생태조경·지역시스템공학부)Theses (Ph.D. / Sc.D._생태조경·지역시스템공학부)