미세조류 배양 광생물반응기 시스템의 생산성 향상을 위한 근 실시간 추정 및 최적화

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2015. 8. 이종민.This thesis has presented the near real-time optimization procedures for productivity improvement of microalgal photobioreactor system under mixotrophic cultivation. Microalgae have been suggested as a promising feedstock for producing biofuel because of their potential for lipid production. However, the development of large-scale algal biodiesel production has been limited by the high production cost of algal biomass. Therefore it is necessary to improve the economic feasibility by reducing costs or increasing productivity. In order to have an economically sound algal bioprocess, this thesis tries to optimize the operating conditions by manipulating nutrient (carbon and nitrogen sources) flow rates and light intensity. For this purposes, it is need to develop a dynamic model that describes algal growth and lipid accumulation in order to support the development of algal bioprocesses, their scale up, optimization and control. However, there are some difficulties in applying model-based control strategies to microalgal cultivation systems. Microalgae cultivation systems are network of complex biochemical reactions manipuated by enzyme kinetics. Modelling of these complex biological systems accurately is difficult task since metabolism inside the cells makes systems have uncertainties. In addition to model uncertainties arising from complex biosystem dynamics, on-line measurement of important variables, especially in lipid is limited and difficult to realize in practice, which makes optimal bioreactor operation a challenging task. To cope with such problems, this thesis focused on the modelling, estimation of lipid concentration, and optimization of photobioreactor systems. At first, the model was developed based on the Droop model, and the optimal input design using D-optimality criterion was performed to compute the system input profile, to estimate parameters more accurately. From the experimental observations, the newly defined yield coefficient was suggested to represent the consumption of lipid and nitrogen within the cell, which reduces the number of parameters with more accurate prediction. Furthermore, the lipid consumption rate was introduced to reflect the experimental results that lipid consumption is related to carbon source concentration. The model was validated with experiments designed with different initial conditions of nutrients and input changes, and showed good agreement with experimental observations. After that, estimation of lipid concentration from other measurable sources such as biomass or glucose sensor was studied. Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), and Particle Filter (PF) were compared in various cases for their applicability to photobioreactor systems. Furthermore, simulation studies to identify appropriate types of sensors for estimating lipid were also performed. Finally, to maximize the biomass and lipid concentration, various optimization methods were investigated in microalgal photobioreactor system under mixotrophic conditions. Lipid concentration was estimated using UKF with other measurable sources and used as lipid data for performing model predictive control (MPC). In addition, maximized biomass and lipid trajectory obtained by open-loop optimization was used as a reference trajectory for traking by MPC. Simulation studies with experimental validation were performed in all cases and significant improvement in productivities of biomass and lipid was obtained when MPC applied. However, it was observed that lag phase occurs while manipulating feed flow rate, which considered to come from large amount of inputs introduced suddenly. This is important phenomena can make model-plant mismatches and needs to be researched more for the optimization of microalgal photobioreactor in reality.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . 1 2. Experiment and data anlysis . . . . . . . . . . . . . . 5 2.1 Microalgae and media composition . . . . . . . . . . 5 2.2 Photobioreactor system and conditions . . . . . . . . 7 2.3 Method for data analysis . . . . . . . . . . . . . . . . 8 2.3.1 Biomass measurement . . . . . . . . . . . . . 8 2.3.2 Glucose measurement . . . . . . . . . . . . . 8 2.3.3 Glycine measurement . . . . . . . . . . . . . 9 2.3.4 Lipid measurement . . . . . . . . . . . . . . . 9 3. Modelling of photobioreactor system . . . . . . . . . . 11 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . 11 3.2 Classic growth models . . . . . . . . . . . . . . . . . 13 3.2.1 Monod model . . . . . . . . . . . . . . . . . 13 3.2.2 Cell quota model . . . . . . . . . . . . . . . . 14 3.3 Development of photobioreactor model . . . . . . . . 15 3.4 Optimal experimental design . . . . . . . . . . . . . 19 3.5 Parameter estimation . . . . . . . . . . . . . . . . . . 21 3.6 Results and Discussion . . . . . . . . . . . . . . . . . 23 3.6.1 Simulation and experimental results . . . . . . 23 3.6.2 Modification of the photobioreactor model . . 25 3.6.3 Validation of the model . . . . . . . . . . . . 30 3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . 32 4. Estimation of lipid concentration . . . . . . . . . . . . 34 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . 34 4.2 Photobioreactor model . . . . . . . . . . . . . . . . . 36 4.3 Estimator algorithms : EKF, UKF, PF . . . . . . . . . 38 4.3.1 Extended Kalman Filter (EKF) . . . . . . . . 38 4.3.2 Unscented Kalman Filter (UKF) . . . . . . . . 40 4.3.3 Particle Filter (PF) . . . . . . . . . . . . . . . 42 4.4 Simulation studies . . . . . . . . . . . . . . . . . . . 44 4.4.1 Case study 1 : effect of system noise covariance (Q) 46 4.4.2 Case study 2 : effect of disturbances . . . . . . 48 4.4.3 Case study 3: effect of parametric mismatches 51 4.4.4 Case study 4 : types of equipments . . . . . . 52 4.5 Experimental results . . . . . . . . . . . . . . . . . . 54 4.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . 56 5. Optimization . . . . . . . . . . . . . . . . . . . . . . . 57 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . 57 5.2 Microalgal photobioreactor model . . . . . . . . . . . 58 5.3 State estimation . . . . . . . . . . . . . . . . . . . . 60 5.4 Optimization . . . . . . . . . . . . . . . . . . . . . . 64 5.4.1 Manual operation based on algal growth characteristic 64 5.4.2 Open-loop optimization . . . . . . . . . . . . 64 5.4.3 Model predictive control . . . . . . . . . . . . 66 5.5 Results and Discussion . . . . . . . . . . . . . . . . . 70 5.5.1 Manual operation based on algal growth characteristic 70 5.5.2 Open-loop optimization . . . . . . . . . . . . 72 5.5.3 Model predictive control . . . . . . . . . . . . 74 5.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . 78 6. Concluding Remarks . . . . . . . . . . . . . . . . . . . 79Docto

2008 Republican Nomination Struggle and Choice of the Republican Party

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걱정이 많은 사람들의 성격 및 인지적 특성 : 위협에 대한 재평가가 걱정에 미치는 영향

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차등적 서비스를 위한 Input Queue 스위치에서의 스케쥴링 알고리즘 연구

학위논문(석사)--서울대학교 대학원 :전기·컴퓨터공학부,2003.Maste

바이마르 獨逸의 保守革命運動

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A Study on determinants of customer satisfaction with virtual stores

학위논문(박사)--서울대학교 대학원 :경영학과 경영학전공,1999.Docto

선체 구조 모델의 위상정보 재구성을 통한 구조 해석 모델 생성

학위논문(석사)--서울대학교 대학원 :조선해양공학과,2006.Maste

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Thesis(masters) --서울대학교 대학원 :화학생물공학부,2010.2.Maste

대기가 서비스 평가에 미치는 영향에 관한 종합적 고찰

1996-12시간은 돈이다라는 격언이 있다. 급속한 산업화에 따라 사람들의 여유시간이 점차 줄어들고 있으며 시간을 절약하고자 하는 소비자의 욕구가 높아지고 있다. 시간의 중요성이 점차 증가함에 따라 신속한 서비스가 고객만족의 지름길이라는 인식이 기업경영자들 사이에 확산되고 있으며 대기시간을 최대한 단축하려는 기업의 서비스 개선노력도 거세지고 있다. 예를 들어 패밀리 레스토랑인 베니건스는 고객이 주로 몰리는 평일 오전 11시부터 오후 2시까지 모든 테이블에 타임시계를 배치하고 고객이 메뉴를 주문한 후 15분 이내에 음식을 제공하지 못하면 주문한 음식을 모두 무료로 제공한다. 많은 서비스 산업에서 서비스를 받기 위해 기다려야하는 대기(waiting)는 서비스의 특성상 어쩔 수 없는 것으로 인식되고 있다. 하지만 고객들이 이러한 상황에 대해 공급자와 똑같이 이해하고 당연한 것으로 받아들이는 것은 아니다. 대부분의 고객들이 서비스를 받기위해 기다리는 것을 부정적인 경험으로 인식하고 있다. 실제로 어떤 사람은 기다리는 것을 너무 싫어해서 대신 기다릴 사람을 고용하는 경우도 있다. 이와 같은 대기에 대한 부정적 반응은 전체 서비스에 대한 평가에도 영향을 미쳐서 고객의 만족도를 감소시키게 된다