Suppr超能文献

应用于本地塑料回收的基于物联网的开源收集箱。

Open source IoT-based collection bin applied to local plastic recycling.

作者信息

Gabriel Alex, Cruz Fabio

机构信息

Université de Lorraine - ERPI - F-54000, Nancy, France.

出版信息

HardwareX. 2022 Dec 21;13:e00389. doi: 10.1016/j.ohx.2022.e00389. eCollection 2023 Mar.

Abstract

Plastic waste is a major challenge for policy making; it has a terrible impact on the environment if it is not properly managed. In order to mitigate this issue, recycling industries have emerged with the associated logistics chain that also has an environmental impact, notably with the production of greenhouse gas. In addition to using energy to transform plastic waste into source material, energy is also wasted to transport it. In parallel to reducing plastic waste, it may be recycled at a very local scale, reducing transportation and allowing potential improvement of the collecting process. Assuming that local transformation of plastic waste is possible, this article describes the design, assembly, and setup of the hardware, system architecture, and software of collectors that may be used by these recycling units. The specificity of these collectors is that they produces on-line data related to the quantity of waste collected. Once implemented, a network of smart collectors should allow the reduction of travel to collect waste as it notifies when the collectors are full. It also produces data on the scale of a territory to optimize the supply chain related to plastic waste collection. This article presents the design and engineering aspects as well as limitations induced by technical choices, but also potential improvements for future developments.

摘要

塑料垃圾是政策制定面临的一项重大挑战;如果管理不当,它会对环境造成严重影响。为了缓解这一问题,回收行业应运而生,与之相关的物流链也会对环境产生影响,尤其是会产生温室气体。除了消耗能源将塑料垃圾转化为原材料外,运输过程中也会造成能源浪费。在减少塑料垃圾的同时,可以在非常本地化的规模上进行回收利用,减少运输并改善收集过程。假设塑料垃圾的本地转化是可行的,本文描述了这些回收单元可能使用的收集器的硬件、系统架构和软件的设计、组装及设置。这些收集器的特点是能够生成与收集到的垃圾量相关的在线数据。一旦实施,智能收集器网络应能减少垃圾收集行程,因为它能在收集器装满时发出通知。它还能生成一个地区范围内的数据,以优化与塑料垃圾收集相关的供应链。本文介绍了设计和工程方面的内容,以及技术选择带来的局限性,同时也探讨了未来发展的潜在改进方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1bcd/9817172/174899204857/ga1.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验