복잡한 동특성을 갖는 다상 반응기의 설계를 위한 계산 효율적인 모사 및 최적화 전략

학위논문(박사)--서울대학교 대학원 :공과대학 화학생물공학부,2020. 2. 이종민.본 박사학위논문에서는 멀티 스케일 모델링, 실험 결과를 이용한 모델 보정법, 최적화 순으로 진행되는 산업용 화학 반응기의 설계 전략을 제시한다. 반응기는 화학 공정에서 제일 중요한 단위이지만, 그 설계에 있어서는 최신 수치적 기법들보다는 여전히 간단한 모델이나 실험 및 경험 규칙에 의존하고 있는 현실이다. 산업 규모의 반응기는 물리, 화학적으로 몹시 복잡하고, 관련 변수 간의 스케일이 크게 차이나는 경우가 많아 수학적 모델링 및 수치적 해법을 구하기가 어렵다. 모델을 만들더라도 부정확하거나 시뮬레이션 시간이 너무 긴 문제가 있어 최적화 알고리즘에 적용하기가 힘들다. 반응기 내 현상의 복잡성과 스케일 차이 문제는 멀티 스케일 모델링을 통해 접근할 수 있다. 전산유체역학 기반 구획 모델(CFD-based compartmental model)을 이용하면, 불균일한 혼합 패턴을 보이는 대형 반응기에서도 긴 시간 동안의 동적 모사가 가능하다. 이 모델은 큰 반응기를 완벽하게 균일한 작은 구획들의 네트워크로 간주하고, 각 구획을 반응 속도식들과 CFD 결과로부터 가져온 유동 정보가 포함된 질량 및 에너지 균형 방정식으로 표현한다. 기체, 액체, 고체 3상이 상호작용하며 복잡한 유동을 보이는 수성 광물 탄산화 반응기를 이 방법을 사용해 모델링하였다. 이 때 모델은 미분 대수 방정식(DAE)의 형태를 띠며, 메커니즘 상 모든 반응들(기-액 간 물질 전달 반응, 고체 용해 반응, 이온 간 반응, 앙금 침전 반응)과 유체 역학, 반응열, 열역학적 변화 및 운전 상의 이벤트 발생을 모두 고려할 수 있다. 모델을 이용해 이산화탄소 제거 효율, pH 및 온도 변화를 예측하여 실제 운전 데이터와 비교한 결과, 파라미터를 통한 보정이 전혀 없이도 7 % 이내의 오차를 보여주었다. 모델의 부정확성 문제는 모델링 후 실험 결과를 이용한 모델 보정으로 극복 할 수 있다. 본 논문에서는 광물 탄산화 반응기 모델을 베이지안 보정(Bayesian calibration)을 통해 강화하는 방법을 제시한다. 먼저 모델 중 불확실한 부분에 8개의 파라미터를 도입한 후, 베이지안 파라미터 추정법(Bayesian parameter estimation) 및 실험실 규모에서의 실험 결과들을 이용하여 파라미터들의 사후 확률 분포를 추정하였다. 얻어진 파라미터의 확률 분포들은 모델 및 실험의 불완전성으로 인해 나타나는 파라미터의 불확실성 및 다중 봉우리 특성을 반영하고 있다. 이를 이용하여 실험 결과를 잘 따라가는 확률론적 모델 예측치(stochastic model response)를 얻을 수 있었다. 16개의 실험 데이터셋 및 테스트셋의 피팅 에러(fitting error)는 결정론적인 최적화 알고리즘(deterministic optimization)을 사용할 때보다 비슷하거나 낮은 것으로 측정되었다. 수학적 최적화에 쓰이기에 너무 긴 시뮬레이션 시간 문제는 베이지안 최적화 알고리즘을 적용하여 해결할 수 있다. 화학 반응기 설계 최적화를 위해 본 논문에서는 다중 목적 베이지안 최적화(Multi-objective Bayesian Optimization, MBO)를 사용해 시뮬레이션 횟수를 최소화 하는 CFD 기반 최적 설계 방법을 제안하였다. 여섯 가지 설계 변수를 가지는 기-액 교반 탱크 반응기에서 전력 소비를 최소화하고 가스 분율(gas holdup)를 극대화하기 위해 이 방법을 이용한 결과, 단 100 회의 시뮬레이션 만으로 최적 파레토 커브(Pareto curve)를 얻을 수 있었다. 제안된 최적 설계안들은 문헌에 보고된 기존 반응기들과 비교해 뛰어난 성능을 보여주었다. . 본 논문을 통해 제안된 CFD 기반 구획 모델링법, 베이지안 모델 보정법 및 베이지안 최적화 방법은 복잡한 물리적 및 화학적 특징을 갖는 산업 규모의 화학 반응기에 적용될 수 있을 것으로 기대된다.This thesis presents a design strategy for industrial-scale chemical reactors which consists of multi-scale modeling, post-modeling calibration, and optimization. Although the reactor design problem is a primary step in the development of most chemical processes, it has been relied on simple models, experiments and rules of thumbs rather than taking advantage of recent numerical techniques. It is because industrial-size reactors show high complexity and scale differences both physically and chemically, which makes it difficult to be mathematically modeled. Even after the model is constructed, it suffers from inaccuracies and heavy simulation time to be applied in optimization algorithms. The complexity and scale difference problem in modeling can be solved by introducing multi-scale modeling approaches. Computational fluid dynamics (CFD)-based compartmental model makes it possible to simulate hours of dynamics in large size reactors which show inhomogeneous mixing patterns. It regards the big reactor as a network of small zones in which perfect mixing can be assumed and solves mass and energy balance equations with kinetics and flow information adopted from CFD hydrodynamics model at each zone. An aqueous mineral carbonation reactor with complex gas–liquid–solid interacting flow patterns was modeled using this method. The model considers the gas-liquid mass transfer, solid dissolution, ionic reactions, precipitations, hydrodynamics, heat generation and thermodynamic changes by the reaction and discrete operational events in the form of differential algebraic equations (DAEs). The total CO2 removal efficiency, pH, and temperature changes were predicted and compared to real operation data. The errors were within 7 % without any post-adjustment. The inaccuracy problem of model can be overcome by post-modeling approach, such as the calibration with experiments. The model for aqueous mineral carbonation reactor was intensified via Bayesian calibration. Eight parameters were intrduced in the uncertain parts of the rigorous reactor model. Then the calibration was performed by estimating the parameter posterior distribution using Bayesian parameter estimation framework and lab-scale experiments. The developed Bayesian parameter estimation framework involves surrogate models, Markov chain Monte Carlo (MCMC) with tempering, global optimization, and various analysis tools. The obtained parameter distributions reflected the uncertain or multimodal natures of the parameters due to the incompleteness of the model and the experiments. They were used to earn stochastic model responses which show good fits with the experimental results. The fitting errors of all the 16 datasets and the unseen test set were measured to be comparable or lower than when deterministic optimization methods are used. The heavy simulation time problem for mathematical optimization can be resolved by applying Bayesian optimizaion algorithm. CFD based optimal design tool for chemical reactors, in which multi-objective Bayesian optimization (MBO) is utilized to reduce the number of required CFD runs, is proposed. The developed optimizer was applied to minimize the power consumption and maximize the gas holdup in a gas-sparged stirred tank reactor, which has six design variables. The saturated Pareto front was obtained after only 100 iterations. The resulting Pareto front consists of many near-optimal designs with significantly enhanced performances compared to conventional reactors reported in the literature. It is anticipated that the suggested CFD-based compartmental modeling, post-modeling Bayesian calibration, and Bayesian optimization methods can be applied in general industrial-scale chemical reactors with complex physical and chemical features.1. Introduction 1 1.1. Industrial-scale chemical reactor design 1 1.2. Role of mathematical models in reactor design 2 1.3. Intensification of reactor models through calibration 5 1.3.1. Bayesian parameter estimation 6 1.4. Optimization of the reactor models 7 1.4.1. Bayesian optimization 9 1.5. Aqueous mineral carbonation process : case study subject 10 1.6. Outline of the thesis 12 2. Multi-scale modeling of industrial-scale aqueous mineral carbonation reactor for long-time dynamic simulation 14 2.1. Objective 14 2.2. Experimental setup 15 2.3. Mathematical models 19 2.3.1. Reactor model 19 2.3.2. CFD model 28 2.3.3. Numerical setting 30 2.4. Results and discussions 32 2.4.1. CFD-based compartmental model for industrial-scale reactor. 32 2.4.2. Design and simulation of higher-scale reactors 42 2.5. Conclusions 47 3. Model intensification of aqueous mineral carbonation kinetics via Bayesian calibration 50 3.1. Objective 50 3.2. Experimental methods 51 3.2.1. Solution and gas preparation 51 3.2.2. Laboratory-scale mineral carbonation process 53 3.3. Mathematical models 56 3.3.1. Kinetics of aqueous mineral carbonation process 56 3.3.2. Differential algebraic equation (DAE) model for the reactor 65 3.3.3. Discrete events for simulation procedure 71 3.3.4. Numerical setting 72 3.4. Bayesian parameter estimation 72 3.4.1. Problem formulation 73 3.4.2. Bayesian posterior inference 76 3.4.3. Sampling 81 3.5. Results and discussions 82 3.5.1. Stochastic output response 82 3.5.2. Quality of parameter estimtates 86 3.5.3. Assessment of parameter uncertainties 91 3.5.4. Kinetics study with the proposed model parameters 99 3.6. Conclusions 103 4. Multi-objective optimization of chemical reactor design using computational fluid dynamics 106 4.1. Objective 106 4.2. Problem Formulation 107 4.3. Optimization scheme 113 4.3.1. Multi-objective optimization algorithm 113 4.3.2. CFD-MBO optimizer 120 4.4. CFD modeling 125 4.4.1. Tank specifications 125 4.4.2. Governing equations 125 4.4.3. Simulation methods 127 4.5. Results and discussion 128 4.5.1. CFD model validation 128 4.5.2. Optimization results 130 4.5.3. Analysis of optimal designs 139 4.6. Conclusions 144 5. Concluding Remarks 146 Bibliography 149 Abstract in Korean (국문초록) 163Docto

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강화된 친환경 화학 공정의 타당성 개선을 위한 시뮬레이션 기반 연구

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2021.8. 이종민.Technologies to mitigate risks from climate change have made significant advances in both research academia and industry. However, most advanced techniques were still considered only in the lab-scale experience. Moreover, the process intensification development during the process synthesis is in its incipient stages. To promote the application of a novel process technique to an eco-friendly process, the process feasibility of economy or operation should be considered. In this thesis, simulation-based framework to improve feasibility of intensified chemical processes, which are suggested under limited experimental conditions, is proposed. To solve the feasibility problem derived from the characteristics of process intensification, which is mainly developed by the numerous experiments rather than derived from the theoretical verification, the digital twin technology, which is developed to simulate various situations by modeling the reactor and process, is implemented. In addition, the framework, including the procedures from verification or validation of developed digital twin model to feasibility study and improvement of economic and operational feasibility, is proposed. First, intensified eco-friendly processes such as the biodiesel production process and carbon capture and utilization process were simulated, and comparative study and optimization were conducted to improve the economic or operational feasibility of that processes. As an example of applying the procedure to verify and to improve the economic feasibility, the economic feasibility study on the intensified biodiesel production process is implemented to increase the profitability of the biodiesel production process by reducing the process units, enhancing biodiesel quality, and reducing the raw material cost. As an example of applying the procedure to verify and to improve the operational feasibility, the modeling and validation of the semi-continuous carbonation process are implemented to estimate the overall CO2 removal efficiency during the operation and when the reaction ends. Using the developed process model, the operational feasibility of the semi-continuous carbonation process is verified and the optimization algorithms is adopted to obtain the optimal operation recipes. For the effective operational feasibility improvement, two new operation recipes were suggested and optimized via Bayesian optimization. Consequently, in order to verify and improve the applicability of the newly proposed intensified process, a methodology is proposed including the process modeling, which is conducted using laboratory-scale experimental data for the reaction kinetic studies, economic analysis, sensitivity analysis, and comparative study. In addition, the process modeling and optimization, using pilot-scale operation data, are carried out. Especially, the operational feasibility of semi-continuous carbonation process is effectively improved by proposing new operation recipe as well as adopting the digital twin model to the Black-box optimization method. In this thesis, a framework, improving economic/operational feasibility of newly proposed intensified chemical processes with two different experimental data depending on the purpose, is developed.기후 변화로 인한 위험들을 완화하는 기술들은 학술 분야와 산업 모두에서 상당한 발전을 이루었다. 그러나 대부분의 진보된 기술들은 여전히 실험실 규모의 실험에서만 고려되고 있다. 더욱이, 공정 합성 과정에서의 공정 강화 개발은 초기 단계에 머물러있다. 새로운 공정 기술들을 친환경 공정에 적용하는 것을 촉진시키기 위해서는, 공정의 경제성 또는 운전 타당성을 고려해야 한다. 본 학위논문에서는 제한된 실험 조건 하에서 제안된, 강화된 친환경 공정의 시뮬레이션 기반 타당성 개선 방법론을 제시하였다. 이론적인 검증을 통한 공정 강화가 아닌 수많은 실험을 통해 제안되는 공정 강화의 방식에서 파생되는 타당성 검증에 대한 문제를 해결하고자, 공정 및 반응기의 모델을 구축하고 다양한 상황을 모사하는 디지털 트윈 기술을 구현하였다. 또한, 구축된 디지털 트윈 모델의 검증부터, 강화된 공정의 경제적 타당성 및 운전 타당성 검증 및 개선까지의 일련의 과정들을 포함하는 방법론을 제시하였다. 먼저, 바이오디젤 생산 공정과 탄소 포집 및 활용 공정과 같은 강화된 친환경 공정을 시뮬레이션하고, 해당 공정의 경제적 타당성 또는 운전 타당성을 개선하기 위해 비교 연구 및 최적화를 수행하였다. 경제적 타당성 검증 방법론을 적용한 한가지 예제로써, 공정 유닛을 줄이고, 바이오디젤 품질 향상 및 원재료 비용을 감소시킴으로써 바이오디젤 생산 공정의 수익성을 증가시키는 강화된 바이오디젤 생산 공정의 경제적 타당성 연구를 수행하였다. 운전 타당성 검증 방법론을 적용한 한가지 예제로써, 반연속식 탄산화 공정의 운전 중 전체 이산화탄소 제거 효율과 반응 종료 시기를 추정하기 위해 시뮬레이션 기반 모델링 및 구축된 모델의 검증을 수행하였다. 구축된 시뮬레이션 기반 모델을 이용하여, 반연속식 탄산화 공정의 운전 타당성을 확인하고 최적의 운전 레시피를 얻기 위해 최적화 알고리즘에 적용하였다. 효과적인 반연속식 탄산화 공정의 운영 타당성 개선을 위해 두개의 새로운 운전 레시피를 제시하였고, 베이지안 최적화 기법을 이용하여 최적화하였다. 결론적으로, 반응식 및 반응 계수 연구를 위한 실험실 규모의 실험 데이터를 활용하여 공정 모델을 구축하고, 경제성 분석, 민감도 분석, 비교 분석을 통해 새로 제안된 강화된 공정의 현업에의 적용 가능성을 검증하고 개선시키는 방법론을 제안하였다. 또한, 실제 현업에 적용되는 크기의 반응기를 가진 중간 규모의 실험 데이터를 활용하여 공정 모델을 구축하고 최적화하였다. 특히, 반연속식 공정의 새로운 운전 방식을 제안함과 동시에 구축된 디지털 트윈 모델에 블랙박스 (Black-box) 최적화 기법을 적용하여 제안된 반연속식 친환경 공정의 운전 타당성을 효과적으로 개선할 수 있었다. 본 학위논문에서는 목적에 따라 다른 두 가지의 실험 결과를 이용하여, 새롭게 제안된 강화된 친환경 공정의 경제적 또는 운전 타당성 검증 및 개선을 위한 방법론을 제시하였다.Chapter 1 1 Introduction 1 1.1 Research motivation 1 1.2 Target process descriptions 4 1.2.1 Biodiesel production process 4 1.2.2 Aqueous mineral carbonation process 6 1.3 Outline of the thesis 8 1.4 Associated publications 8 Chapter 2 9 Economic feasibility study on biodiesel production process 9 2.1 Introduction 9 2.2 Reaction kinetics 13 2.3 Process simulation 15 2.3.1 Material and thermodynamic model 18 2.3.2 Assumptions 22 2.3.3 SC PFR 22 2.3.4 Cu-based PBR 26 2.3.5 Pd-based PBR 30 2.4 Economic analysis 33 2.4.1 Total capital investment 37 2.4.2 Total manufacturing cost 39 2.4.3 Sensitivity analysis 41 2.5 Summary 46 Chapter 3 48 Modeling and validation of pilot-scale aqueous mineral carbonation process 48 3.1 Introduction 48 3.2 Reaction kinetics 48 3.2.1 Calcium hydroxide dissolution in water 49 3.2.2 Mass transfer of CO2 gas into the alkali solution 50 3.2.3 Ionic reactions and precipitation of calcium carbonate 53 3.3 Process design and modeling 56 3.3.1 Assumptions 56 3.3.2 Reactor modelling 57 3.3.3 Sequence of reactant replenishment 59 3.3.4 ACM model validation 63 3.4 Summary 65 Chapter 4 66 Bayesian optimization approach to semi-continuous carbonation process operation recipe 66 4.1 Introduction 66 4.2 Problem descriptions 68 4.3 Multi-objective Bayesian optimization algorithm 76 4.4 Results and discussion 84 4.5 Summary 91 Chapter 5 Concluding remarks 94 References 97 Abstract in Korean (국문초록) 106박

화학 반응기 시스템을 모델링하기 위한 계산 효율적인 전략

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부(에너지환경 화학융합기술전공), 2022.2. 이종민.Many researchers in chemical engineering have been using analytical and computational models to predict the behaviors of systems and use these models to process optimization, design and control. However, until now, researchers are still forced to compromise on model fidelity and accuracy. Although high fidelity models can improve the model accuracy, simulating these models is usually time-consuming, making it difficult to perform optimization. In this thesis, computationally efficient strategies about two system are introduced which can maintain model fidelity but effectively reduce the calculation time. Polymer reactor is selected for the first system and we focused on polymer kinetics. A hybrid approach that combines the method of moments and Monte Carlo simulation to predict the molecular weight distribution of low-density polyethylene for a continuous stirred tank reactor system is proposed. A 'Block,' which is repeating reaction group, is introduced for the calculation cost-effective simulation. This model called the 'block Kinetic Monte Carlo' is ~10 to 32 times faster than Neuhaus’s model. The model can be applied to any steady state system and provide a calculation cost reduction effect, where one reaction is much faster than others; for example, the propagation reaction. Furthermore, we perform a case study on the effects of the system temperature and initiator concentration on the MWD and reaction rate ratio. Based on the simulation results of 180 case studies, we determine a quantitative guideline for the appearance of shoulder, which is a function of the rate ratio of reactions to the propagation reaction. Computational fluid dynamics (CFD) based reactor system is selected for the second system. CFD is an essential tool for solving engineering problem that involves fluid dynamics. Especially in chemical engineering, fluid motion usually has extensive effects on system states such as temperature and component concentration. However, due to the critical issue of long computational times for simulating CFD, application of CFD is limited for many real-time problems such as real-time optimization and process control. In this study, we develop the surrogate model of Continuous stirred tank reactor (CSTR) with Van de Vusse reaction using Physics-informed neural network (PINN) which can train the governing equations of system. We propose PINN architecture that can train every governing equation which chemical reactor system follows and can train multi-reference frame system. Also, we investigate that PINN can resolve the problem of neural network that needs lots of training data, are easily overfitted and cannot contain physical meaning. Furthermore, we modify the original PINN suggested by Raissi in order to solve the memory error and divergence problem with two methods: (1) Mini-batch training; (2) Weighted loss function. We also suggest a similarity based sampling strategy where the accuracy can be improved up to 5 times over the random sampling. This work can provide the guideline for developing the high performance surrogate model of chemical process.화학 공학 분야의 많은 연구자들은 분석 및 계산 모델을 이용하여 시스템의 거동을 해석하고 최적화, 설계 및 제어를 수행하고 있다. 하지만 모델의 정확도와 계산시간은 거래되는 관계에 있어 계산시간이 오래걸리는 문제 때문에 모델의 정확도를 타협할 수 밖에 없는 실상이다. 이 학위논문에서는 두 시스템에 대해 모델의 충실도를 유지하면서도 계산시간을 효과적으로 줄일 수 있는 계산 효율적인 전략을 소개한다. 첫 번째 시스템은 고분자 반응기로 고분자의 반응에 중점을 두고 있다. 연속 교반 탱크 반응기에 대한 저밀도 폴리에틸렌의 분자량 분포를 예측하기 위해 모멘트 방법과 몬테 카를로 시뮬레이션 기법을 결합한 하이브리드 접근 방식이 제안되었다. 계산 효율적인 시뮬레이션을 위해 반복되는 반응들을 집합인 ‘블락’이라는 개념이 새로이 도입되었다. ‘블락 키네틱 몬테 카를로’라고 불리는 이 모델은 Neuhaus가 제안한 모델보다 약 10~32배 빠르다. 이 모델은 모든 정상 상태시스템에 적용할 수 있으며, 특정 반응이 다른 반응들보다 훨씬 빠른 경우에 계산 시간 감소효과를 누릴 수 있다. 또한 시스템의 운전 온도 및 개시제의 농도가 분자량 분포에 미치는 영향에 대해 사례 연구를 수행하였다. 180개의 사례 연구 시뮬레이션을 바탕으로 분자량 분포가 숄더를 보이는 조건에 대한 정량적 지침을 제안하였다. 두번째 시스템은 전산유체역학 기반의 반응기 시스템이다. 전산유체역학은 유체의 흐름을 해석함에 있어 필수적인 기법이다. 특히 화학공학반응기에서 유체의 흐름은 내부의 온도나 농도에 큰 영향을 미친다. 그러나 전산유체역학은 계산시간이 오래걸린다는 단점으로 인해 실시간 최적화 및 공정 제어와 같은 응용에 사용이 제한된다. 이 학위논문에서는 시스템의 지배 방정식을 훈련할 수 있는 물리정보신경망(PINN)을 사용하여 Van de Vusse 반응이 포함된 연속 교반 탱크 반응기의 대리 모델을 개발한다. 화학 반응기 시스템이 따르는 모든 종류의 지배 방정식을 훈련할 수 있으며 다중 참조 프레임 시스템을 훈련 할 수 있는 물리정보신경망 모델 구조를 제안한다. 물리정보신경망(PINN)은 기존에 신경망 모델이 가지는 과적합 문제나 데이터가 많이 필요하다는 점 그리고 물리적 의미를 반영할 수 없다는 문제들을 해결할 수 있다. 또한 메모리 오류 및 모델의 발산 문제를 해결하기 위하여 Raissi가 제안한 기존의 물리정보신경망(PINN)을 두 가지 방법으로 수정하였다. 1) 미니 배치 훈련; 2) 가중 손실 함수. 그리고 학습 데이터를 추출함에 있어 무작위 추출에 비해 정확도를 최대 5배까지 향상시킬 수 있는 유사성 기반 추출 전략을 제안한다. 이 연구가 화학 공정의 고성능 대리 모델 개발을 위한 지침이 되기를 희망한다.Abstract i Contents iv List of Figures vii List of Tables xi Chapter 1 Introduction 1 1.1 Research motivation 1 1.2 Research objective 3 1.3 Outline of the thesis 5 Chapter 2 Molecular weight distribution modeling of LDPE in a continuous stirred-tank reactor using coupled deterministic and stochastic approach 6 2.1 Introduction 6 2.2 Methodology 10 2.2.1 Polymer reaction mechanism 10 2.2.2 Reactor model 16 2.2.3 Deterministic part 16 2.2.4 Stochastic part 20 2.3 Result 34 2.3.1 Verification 34 2.3.2 Reduction in calculation time 39 2.3.3 Case study 41 2.3.4 Shouldering condition 49 2.4 Conclusions 52 2.5 Notations 54 2.6 Abbreviations 57 Chapter 3 Physics-informed deep learning for data-driven solutions of computational fluid dynamics 58 3.1 Introduction 58 3.2 PINN 61 3.3 Model description 64 3.3.1 CFD modeling 64 3.3.2 Governing equations 67 3.3.3 PINN architecture 71 3.4 Result and Discussion 79 3.4.1 Model verification 79 3.4.2 Improvement of model performance 86 3.4.3 Comparison of PINN model with 1-D ODE model 98 3.5 Conclusion 102 3.6 Appendix 105 3.7 Notations 106 Chapter 4 Concluding Remarks 111 4.1 Summary of contributions 111 4.2 Future work 112 Reference 114 Abstract in Korean (국문초록) 121박

Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis

Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows them to pass human cell boundaries. For applications with specified requirements regarding size, producing tailored microgels in a continuous flow reactor is advantageous because the microgel properties can be controlled tightly. However, no fully-specified mechanistic models are available for continuous microgel synthesis, as the physical properties of the included components are only studied partly. To address this gap and accelerate tailor-made microgel development, we propose a data-driven optimization in a hardware-in-the-loop approach to efficiently synthesize microgels with defined sizes. We optimize the synthesis regarding conflicting objectives (maximum production efficiency, minimum energy consumption, and the desired microgel radius) by applying Bayesian optimization via the solver ``Thompson sampling efficient multi-objective optimization'' (TS-EMO). We validate the optimization using the deterministic global solver ``McCormick-based Algorithm for mixed-integer Nonlinear Global Optimization'' (MAiNGO) and verify three computed Pareto optimal solutions via experiments. The proposed framework can be applied to other desired microgel properties and reactor setups and has the potential of efficient development by minimizing number of experiments and modelling effort needed.Comment: Manuscript: 24 pages, 8 figures; SI: 9 pages, 3 figure