描述:
Catalytic hydrothermolysis (CH) is a sustainable aviation fuel (SAF) pathway that has been recently approved for use in aircraft fuel production. In alignment with broader sustainable aviation goals, SAF production through CH requires a quantitative assessment of carbon intensity (CI) impacts. In this study, a current‐day life‐cycle analysis (LCA) was performed on SAF produced via CH to determine the CI. Various oily feedstocks were considered, including vegetable oils (soybean, carinata, camelina and canola) and low‐burden oils and greases (corn oil, yellow grease and brown grease). Life‐cycle inventory data were collected on all processes within the CH LCA boundary: feedstock cultivation and/or collection, preprocessing, hydrothermal cleanup and CH, biocrude refining, fuel transportation and end use through combustion. Baseline results show that the CH‐produced SAF can be generated with CI reductions ranging from 48 to 82% compared with conventional jet fuel. Modest improvements to CI can be achieved through incremental changes to the brown grease CH process, such as relaxing the dewatering specification and implementing renewable natural gas and electricity, which could decrease the CI from 22.9 to 7.9 g CO2e/MJ. Total CH fuel production potential was also assessed on the basis of current or near‐future feedstock availability and CI. The total biofuel production potential of CH (SAF and renewable fuel co‐products) in the US sums to approximately 3487 million gallons per year, with 97% of these volumes having a CI below 50% of that for petroleum jet fuel. The study shows that from an LCA perspective, CH offers a viable SAF pathway that is comparable with existing SAF pathways like hydroprocessed esters and fatty acids.
描述:
针对飞机机务维修照相管理存在工作量大、不精确等问题,提出一种利用深度学习YOLOv4-tiny算法来执行照片对比检测的方法。利用一个自制的数据集来训练网络模型,为解决开口销螺母及其他背景干扰,引入注意力机制模块以改进YOLOv4-tiny。测试结果表明:准确率(precision,P)相较原YOLOv4-tiny提高了5%,召回率(recall,R)提高约8%,平均准确率均值(mean average precision,mAP)提高了4.9%,照片识别精度和定位精准性方面都有较优表现,满足照相管理中对目标精准识别与比对的要求。