전복, Haliotis discus 치패용 배합사료내 어분의 참치가공부산물 대체 시 성장 및 체구성에 미치는 영향

The effects on growth and body composition that result from tuna byproduct meal (TBM) substituted for fish meal in the diet of juvenile abalone, Haliotis discus, were determined. One thousand two hundred and sixty juvenile abalone were randomly distributed into 18 70-L plastic rectangular containers. Six experimental diets were prepared in triplicate. The TBM0 diet included 28% fish meal and 13% soybean meal as the protein source. Twenty-five, 50, 75, and 100% of the fish meal were substituted with TBM, referred to as TBM25, TBM50, TBM75, and TBM100 diets, respectively. Finally, salted sea tangle (ST) was prepared. The essential amino acids, such as isoleucine, lysine and valine, tended to decrease with the dietary substitution of TBM for fish meal in the experimental diets. The weight gain and specific growth rate (SGR) of abalone that were fed the TBM25 diet were significantly higher than those of abalone that were fed the other diets (P < 0.05). The crude protein content of the soft body of the abalone linearly decreased with dietary substitution of TBM for fish meal. In conclusion, as much as 75% of the fish meal in the diet of abalone can be replaced with TBM without a retardation in weight gain and SGR of the abalone when 28% fish meal was included.Contents i List of Tables ii Abstract (in Korean) iii I. Experiment 1 Abstract 1 1. Introduction 3 2. Materials and Methods 6 2. 1. Preparation of Abalone and Rearing Conditions 6 2. 2. Preparation of the Experimental Diets 6 2. 3. Analytical Procedures of the Diets and Carcass 8 2. 4. Statistical Analysis 9 3. Results 10 4. Discussion 16 II. Conclusion 19 III. Acknowledgements 20 Ⅳ. References 2

액체수소 하이브리드 선박 추진 시스템의 열 통합 및 강화학습 기반 에너지 관리 방법론

학위논문(박사) - 한국과학기술원 : 기계공학과, 2024.2,[xiv, 138 p. :]This study proposes heat integration and deep reinforcement learning-based energy management methodologies for efficient operation of a hybrid ship propulsion system (HSPS) utilizing liquid hydrogen (LH$_2$) as fuel and analyzes their effectiveness. The targeted LH$_2$-HSPS consists of a fuel gas supply system (FGSS), polymer electrolyte membrane fuel cell (PEMFC), and lithium-ion battery system. A 2 MW-class platform supply vessel (PSV), exhibiting significant load fluctuations during operation, is selected as the target ship for application of the LH$_2$-HSPS. Specifications of the LH$_2$-HSPS are determined based on operation scenarios of the PSV, and the dynamic model is developed accordingly. Validity of the developed model is confirmed during the validation phase, and using the validated model, design and operational feasibility of the LH$_2$-HSPS are further assessed for various operational strategies. Subsequently, to enhance the energy efficiency of the LH$_2$-HSPS, a proposal is made to integrate an ethylene glycol/water mixture-based thermal management system of the LH$_2$ FGSS and battery system. The dynamic model is then utilized to quantitatively analyze effects of heat integration. Additionally, the proposed methodology's validity is confirmed by investigating temperature changes in the battery system due to heat integration. Finally, a deep reinforcement learning (DRL)-based optimal energy management algorithm applicable to the energy management system (EMS) of the LH$_2$-HSPS is suggested. An objective function of the energy management problem considers hydrogen and equivalent fuel consumption, and performance degradation of PEMFC and lithium-ion battery system. The DRL-EMS is compared with dynamic programming and sequential quadratic programming algorithm to evaluate the global and real-time optimization performance. Furthermore, the optimal energy management performance of the DRL-EMS is assessed for operational scenarios not used in agent training. An analysis of operational strategies is conducted based on the energy management results, considering various hydrogen fuel prices and capacities of the lithium-ion battery system.한국과학기술원 :기계공학과