• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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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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• v.37 no.1
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• pp.40-45
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• 2024

In this research, we introduce a novel approach that employs a 3D convolutional neural network (CNN) model to predict the permeability of Gas Diffusion Layers (GDLs). For training the model, we create an artificial dataset of GDL representative volume elements (RVEs) by extracting morphological characteristics from actual GDL images obtained through X-ray tomography. These morphological attributes involve statistical distributions of porosity, fiber orientation, and diameter. Subsequently, a permeability analysis using the Lattice Boltzmann Method (LBM) is conducted on a collection of 10,800 RVEs. The 3D CNN model, trained on this artificial dataset, well predicts the permeability of actual GDLs.