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迈向渔船数字化以实现更高的环境和经济可持续性。

Toward Digitalization of Fishing Vessels to Achieve Higher Environmental and Economic Sustainability.

作者信息

Uriondo Zigor, Fernandes-Salvador Jose A, Reite Karl-Johan, Quincoces Iñaki, Pazouki Kayvan

机构信息

Energy Engineering Department, Faculty of Engineering of Bilbao, University of the Basque Country (UPV/EHU), Pza. Ingeniero Torres Quevedo 1, 48013 Bilbao, Spain.

AZTI, Marine Research, Basque Research and Technology Alliance (BRTA), Txatxarramendi Ugartea z/g, 48395 Sukarrieta, Bizkaia, Spain.

出版信息

ACS Environ Au. 2024 Jan 24;4(3):142-151. doi: 10.1021/acsenvironau.3c00013. eCollection 2024 May 15.

DOI:10.1021/acsenvironau.3c00013
PMID:38765058
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11100320/
Abstract

Fishing vessels need to adapt to and mitigate climate changes, but solution development requires better information about the environment and vessel operations. Even if ships generate large amounts of potentially useful data, there is a large variety of sources and formats. This lack of standardization makes identification and use of key data challenging and hinders its use in improving operational performance and vessel design. The work described in this paper aims to provide cost-effective tools for systematic data acquisition for fishing vessels, supporting digitalization of the fishing vessel operation and performance monitoring. This digitalization is needed to facilitate the reduction of emissions as a critical environmental problem and industry costs critical for industry sustainability. The resulting monitoring system interfaces onboard systems and sensors, processes the data, and makes it available in a shared onboard data space. From this data space, 209 signals are recorded at different frequencies and uploaded to onshore servers for postprocessing. The collected data describe both ship operation, onboard energy system, and the surrounding environment. Nine of the oceanographic variables have been preselected to be potentially useful for public scientific repositories, such as Copernicus and EMODnet. The data are also used for fuel prediction models, species distribution models, and route optimization models.

摘要

渔船需要适应并减轻气候变化,但解决方案的开发需要有关环境和船舶运营的更好信息。即使船舶会产生大量潜在有用的数据,其来源和格式也多种多样。这种缺乏标准化的情况使得关键数据的识别和使用具有挑战性,并阻碍了其在改善运营性能和船舶设计方面的应用。本文所述的工作旨在为渔船系统数据采集提供经济高效的工具,支持渔船运营的数字化和性能监测。这种数字化对于减少作为关键环境问题的排放以及降低对行业可持续性至关重要的行业成本是必要的。由此产生的监测系统连接船上系统和传感器,处理数据,并使其在共享的船上数据空间中可用。从这个数据空间中,以不同频率记录209个信号并上传到岸上服务器进行后处理。收集到的数据描述了船舶运营、船上能源系统以及周围环境。已预先选择九个海洋学变量可能对哥白尼和EMODnet等公共科学知识库有用。这些数据还用于燃料预测模型、物种分布模型和航线优化模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/ff1429bfc064/vg3c00013_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/1a544c7f4897/vg3c00013_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/2afddd21e034/vg3c00013_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/95a43afc2182/vg3c00013_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/ff1429bfc064/vg3c00013_0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/1a544c7f4897/vg3c00013_0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/2afddd21e034/vg3c00013_0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/95a43afc2182/vg3c00013_0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e11d/11100320/ff1429bfc064/vg3c00013_0004.jpg

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本文引用的文献

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Fuel consumption and air emissions in one of the world's largest commercial fisheries.世界上最大的商业渔业之一中的燃料消耗与空气排放。
Environ Pollut. 2021 Jan 8;273:116454. doi: 10.1016/j.envpol.2021.116454.
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