진공자외선 공정에서의 비스페놀 A의 분해 특성과 메커니즘에 관한 연구

학위논문(석사) -- 서울대학교대학원 : 보건대학원 환경보건학과, 2021.8. 손정민.Bisphenol A (BPA) is a compound classified as an EDC that is mainly used for the production of polycarbonates and epoxy resins. In this study, the kinetics and degradation mechanism of BPA during vacuum ultraviolet (VUV) treatment were examined. BPA was completely degraded within 60 minutes of VUV treatment, following the pseudo-first-order kinetics (kobs = 6.75 × 10-2 min-1). The degradation kinetics of one the BPA alternatives, BPAF, was observed in parallel for the comparison with BPA. The kobs of BPAF was 5.49 × 10-2 min-1 which was slightly slower than that of BPA. The influencing factors on BPA degradation were observed at different pH levels, as well as the presence of dissolved organic matter (DOM) and inorganic anions, bicarbonate (HCO3-), and nitrate (NO3-). The kobs¬ of BPA was the fastest at pH 6.0 and it gradually decreased with the increasing pH. The radical contribution during VUV reaction was also observed using TBA for competition kinetics with BPA. It was found out that •OH contributed to 82.6%, 71.4%, 49.65%, and 43.36% at pH 6, 7, 8, and 9, respectively, which explains the retardation of kobs with increasing pH. The influence of DOM and HCO3- were insignificant at low concentrations, whereas the presence of NO3- hindered BPA degradation. Approximately 91% of BPA was mineralized within 12 hours of VUV reaction, and a total of seven organic transformation products were identified (TP 243, TP 241, TP 257, TP 259, TP 181, TP 104). The acute toxicity was observed by the inhibition rate of bioluminescence of Vibrio fischeri. The toxicity decreased about 20% after the reaction, indicating that VUV treatment could potentially diminish the toxicity of BPA.비스페놀 A(BPA)는 인간과 수생 생태계에 해로운 건강 영향을 줄 수 있는 물질로써 내분비계 장애물질(EDC)로 분류 되어있다. BPA는 주로 폴리카보네이트와 에폭시 수지의 생산에 사용되는 화학 화합물로 플라스틱 컵이나 식품포장용기, 의료기기 등 여러 분야에 필요한 물품을 만드는데 사용되는 물질이다. BPA가 체내에 쌓이게 되면 신경 독성, 심혈관 질환, 생식 장애, 각종 암 등의 건강 영향을 끼칠 수 있다고 알려져 있는데, 이에 따른 건강 영향에 대한 정보가 알려지기 시작하면서 최근 10년동안 전 세계 국가별 BPA 사용에 대한 제한이 있지만 여전히 산업물품 제조에 활발히 쓰이고 있다. 그러나 아직까지 전 세계의 다양한 수계에서 BPA의 검출사례가 발생하고 있는다. 수처리 시설에서 완전히 제거가 되지 않는 잔여 BPA는 방류수 속에 포함되어 환경 중으로 배출이 되어 지표수뿐만이 아니라 음용수에서도 검출이 되어 이것의 효과적으로 제거하기 위한 공정의 연구가 필요하다. 따라서 본 연구에서는 BPA의 효율적인 제거 방법을 제시하고자 VUV 공정을 이용한 실험을 진행하였다. VUV 공정을 통하여 BPA는 60분 이내로 99.5%의 높은 제거율로 처리되었으며 유사 1차 반응을 따르는 제거 양상을 보였다 (kobs = 6.75 × 10-2 min-1). 제거 반응에 영향을 줄 수 있는 pH, 용존 유기물, 음이온의 영향을 관찰하였고, pH가 산성 조건일 때 BPA의 제거율이 알칼리 조건일때보다 더 빠른 것을 확인 하였다. 그리고 용존유기물과 중탄산염이온의 영향에 영향이 미흡한 반면 질산염에 의한 BPA의 제거율이 저해하는 것을 확인하였다. 12시간의 VUV 반응으로 약 92%의 BPA가 무기화 되었고 BPA 구조의 벤젠 고리가 열리면서 무기화가 진행되고 이로인해 부산물이 생성되는 것을 확인하였다. 총 7가지의 유기 부산물을 (TP 243, TP 241, TP 257, TP 259, TP 181, TP 104) 규명하였으며 이를 통해 BPA의 분해 경로를 제시하였다. VUV 공정으로 BPA를 처리할 때 Vibrio fischeri의 발광 저해율이 감소하는 것을 통하여 BPA의 독성이 감소하는 것을 확인하였다.1. Introduction 1 1.1. Study background 1 1.2. Objective 6 2. Materials and methods 6 2.1. Chemical reagents 6 2.2. Experimental procedure 7 2.3. Analytical method 9 3. Results and discussion 10 3.1. Degradation kinetics of BPA during VUV photolysis 10 3.2. Influencing factors 12 3.3. Mineralization and identification of TPs 18 3.4. Toxicity 23 3.5. Radical contribution 24 4. Conclusion 25 5. Reference 26 국문 초록 37석

무인이동체 운용을 위한 통계적 학습 기반 인간 행위 및 상태 추론

학위논문 (박사)-- 서울대학교 대학원 : 산업공학과, 2016. 2. 박종헌.therefore, the sparseness of the simulation log is compensated and the latent factors that explain the decisions and behaviors of experienced operators are revealed. From comparison experiments, the proposed method outperformed the other methods. It is expected that the proposed method will contribute to the development of autonomous vehicles that perform as well as human operators. The contribution and utility of this dissertation are summarized into three points. Most importantly, this dissertation covers timely issues related to UVs, which are essential both to reducing the accident rate and to enhancing performance of UVs. Second, previous research regarding inferring the status and behavior of UV operators and related domains were thoroughly summarized to provide a solid background to the problems. Finally, with some adjustments, the proposed methods are able to be extended to related domains that utilize heterogeneous data.Recently, technologies for unmanned vehicles (UVs) are getting increased attention because of the increasing popularity of UVs. A UV is a vehicle without an on-board operator, and, therefore, the utilization of UVs leads to cost reduction for operations and better safety for operators. UVs are categorized into remotely operated vehicles and autonomous vehicles according to whether a human operator is required. For the successful operation of remotely operated vehicles, it is important to detect the inattention status of an operator because the occurrence of inattention is known to be the main cause of accidents for UVs. Accurate reproduction of a behavior and extraction of an optimal behavior are the main problems for autonomous vehicles. This dissertation aims to address the problems in UVs by adopting statistical learning methods. With the advances in sensor and data storage technologies, a large number of datasets generated by UVs, such as EEG signals of the operator and simulation log for combat, has become available. However, these datasets contain heterogeneous data composed of multi-type attributes, making the application of statistical learning challenging. The methods proposed in this dissertation are as follows. First, a method for inferring the mental status of UV operators from EEG signals is introduced. A semi-supervised learning method that utilizes an attribute-weight learning algorithm is proposed, where the attention duration at the beginning of an operation and the dierent levels of correlations between attributes and labels are exploited. As a result, occurrences of operator inattention during maneuvering of UVs were successfully detected when applying the proposed method to experiments using a real-world dataset. Second, a method for inferring the behavior of UV operators from a simulation log is presented. A hierarchical support vector machine with a hybrid sequence kernel is presented to address the heterogeneity of the considered dataset in terms of value type and sequence dependency, and to enhance performance by incorporating a hierarchical structure of behaviors of UV operators. Finally, a method for inferring the optimal behavior for UV operators from a simulation log is developed. The proposed method is based on a matrix factorization algorithmChapter 1 Introduction 1 1.1 Unmanned vehicle and statistical learning 1 1.2 Data heterogeneity 6 1.3 Objectives 14 1.4 Thesis outline 16 Chapter 2 Literature review 17 2.1 Inferring status of unmanned vehicle operators 17 2.2 Inferring behavior of unmanned vehicle operators 24 2.3 Statistical learning from heterogeneous data 27 Chapter 3 Inferring mental status from EEG signal 31 3.1 Problem definition 31 3.2 Inferring status using constrained attribute weighting clustering and CUSUM algorithm 32 3.2.1 Overview 32 3.2.2 Attention labeling 37 3.2.3 Inferring mental status 39 3.3 Experiments 44 3.3.1 Experimental setting 44 3.3.2 Experimental results 47 3.4 Discussion 53 Chapter 4 Inferring behavior from simulation log 55 4.1 Problem definition 55 4.2 Inferring behaviors using hierarchical SVMs with a hybrid sequence kernel 56 4.2.1 Overview 56 4.2.2 Attribute selection 58 4.2.3 Similarity calculation 59 4.2.4 Inferring behavior 63 4.3 Experiments 65 4.3.1 Experimental setting 65 4.3.2 Experimental results 69 4.4 Discussion 76 Chapter 5 Inferring optimal behavior from simulation log 79 5.1 Problem definition 79 5.2 Inferring optimal behavior using MF 80 5.2.1 Overview 80 5.2.2 Situation definition 83 5.2.3 Situation-behavior matrix building 84 5.2.4 Inferring optimal behavior 86 5.3 Experiments 87 5.3.1 Experimental setting 87 5.3.2 Experimental results 88 5.4 Discussion 91 Chapter 6 Conclusion 95 6.1 Summary and Contributions 95 6.2 Future work 97 Bibliography 99 국문초록 115Docto

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