• Asian-Australasian Journal of Animal Sciences
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• 제18권8호
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• pp.1199-1208
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• 2005

Abatement of greenhouse gas emitted from ruminants and promotion of biogas energy from animal effluent were comprehensively examined in each anaerobic fermentation reactor and animal experiments. Moreover, the energy conversion efficiency of biomass energy to power generation were evaluated with a gas engine generator or proton exchange membrane fuel cell (PEMFC). To mitigate safely rumen methanogenesis with nutritional manipulation the suppressing effects of some strains of lactic acid bacteria and yeast, bacteriocin, $\beta$1-4 galactooligosaccharide, plant extracts (Yucca schidigera and Quillaja saponarea), L-cysteine and/or nitrate on rumen methane emission were compared with antibiotics. For in vitro trials, cumulative methane production was evaluated using the continuous fermented gas qualification system inoculated with the strained rumen fluid from rumen fistulated Holstein cows. For in vivo, four sequential ventilated head cages equipped with a fully automated gas analyzing system were used to examine the manipulating effects of $\beta$1-4 galactooligosaccharide, lactic acid bacteria (Leuconostoc mesenteroides subsp. mesenteroides), yeast (Trichosporon serticeum), nisin and Yucca schidigera and/or nitrate on rumen methanogenesis. Furthermore, biogas energy recycled from animal effluent was evaluated with anaerobic bioreactors. Utilization of recycled energy as fuel for a co-generator and fuel cell was tested in the thermophilic biogas plant system. From the results of in vitro and in vivo trials, nitrate was shown to be a strong methane suppressor, although nitrate per se is hazardous. L-cysteine could remove this risk. $\beta$1-4 galactooligosaccharide, Candida kefyr, nisin, Yucca schidigera and Quillaja saponarea are thought to possibly control methanogenesis in the rumen. It is possible to simulate the available energy recycled through animal effluent from feed energy resources by making total energy balance sheets of the process from feed energy to recycled energy.

• Composites Research
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• 제37권1호
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• pp.40-45
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• 2024

본 연구에서는 고분자 전해질막 연료전지용 가스확산층의 투과도를 예측하기 위해 삼차원 합성곱 신경망 모델을 사용하는 방법론을 소개한다. 먼저, 기계학습 모델을 학습시키기 위해 X-선 단층 촬영을 통해 얻은 실제 가스확산층 이미지에서 형태학적 특성을 추출해 가스확산층의 대표 체적 요소로 이루어진 인공 데이터셋을 생성한다. 이러한 형태학적 특성은 다공성, 섬유 배향, 직경의 통계적 분포가 포함된다. 구축한 인공 데이터셋 대표 체적 요소들의 투과도를 평가하기 위해 격자 볼츠만 방법이 사용되었으며 각각의 대표 체적 요소들의 투과도를 도출하였다. 이러한 인공 데이터셋을 통해 삼차원 합성곱 신경망 모델을 학습시켰으며 인공 데이터셋을 학습한 삼차원 합성곱 신경망 모델이 실제 가스확산층의 대표 체적 요소 투과도 또한 잘 예측하는 것을 확인하였다.