Realising Advanced Sensor Technology for Enhanced Recovery of Metal Scrap (RASTER)

实现先进的传感器技术以增强金属废料回收率 (RASTER)

• 批准号: EP/W021013/1
• 负责人: Anthony Peyton
• 金额: $ 74.47万
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW021013%2F1
• 关键词: Realising; Advanced; Sensor; Technology; Enhanced

A growing global population and the rising demand for consumer products is imposing severe pressures on our dwindling natural resources. Combined with other global challenges such as climate change and food security, this failure to manage resources seriously undermines the likelihood of a sustainable future. It is widely recognized that we need to adopt a circular-economy, where used and discarded products are recycled, their materials recovered and re-used to become the feedstock for the new. For example, end-of-life vehicles, waste electrical and electronic equipment, and white goods all contain substantial quantities of valuable metals, such as aluminium, copper, brass, lead, magnesium, nickel, tin and zinc, which can profitably be recovered and returned to the supply chain. Materials recovery facilities however, face a tough market place with disruption from national and international policies, trade barriers, and resource volatility. Against these challenges, recyclers are having to re-examine their mixed metal products. There is now a real need for more effective sorting technologies, driving investment to improve efficiency, capacity, yields, and quality of the recyclate, while minimizing the residue set for landfill.This proposal aims to develop new science and concepts to drive a new generation of electromagnetic and induction-based non-ferrous metal separation technologies. Induction sorters, essentially metal detectors, are already in common use in recovery facilities to extract low-conductivity metals such as stainless steel. This project mobilizes our research in electromagnetic inspection, developed from work across a range applications as diverse as food testing to detection of landmines, to deliver a new class of these kinds of sensors - 'smart' induction technologies which use multi-frequency analysis, new theoretical magnetic scattering approximations, and visual information to classify and separate a much wider set of non-ferrous metals with higher recyclate purities and efficient recovery relative to cost. For example, in our previous work we showed that a multi-frequency induction design could achieve effective separation performance for some of the most common non-ferrous metals seen in end-of-life vehicles shredded waste - metals such as copper, aluminium and brass. The success of this simple innovation over standard induction technology has led us to partner with a leading UK magnetic separation equipment manufacturer to develop a commercial sorting solution.This project is the next major initiative in our research strategy, focusing on new approaches for materials characterization to disrupt induction separation in resource recovery. We set out a plan to explore new theoretical insights in magnetic scattering approximation, such as the magnetic polarizability tensor, expanding on this work by developing fast and efficient approaches that deal with the demanding through-put and conditions of metal recovery. We will demonstrate these new approaches using an experimental platform emulating the key features of an industrial material separation rig to obtain relevant and realistic performance statistics. Our goal is for the new science and results that emerge from this research will impact how electromagnetic sensors are used in resource recovery, potentially enabling new high-throughput and lower-cost separation technologies that support a more profitable and buoyant recycling economy.

不断增长的全球人口和日益增长的消费品需求正在给我们日益减少的自然资源带来严重压力。再加上气候变化和粮食安全等其他全球性挑战，这种资源管理的失败严重破坏了可持续未来的可能性。人们普遍认识到，我们需要采用循环经济，即回收使用和废弃的产品，回收它们的材料并重新使用，以成为新的原料。例如，报废车辆、废弃电器和电子设备以及白色家电都含有大量有价值的金属，如铝、铜、黄铜、铅、镁、镍、锡和锌，这些金属可以有利可图地回收并返回供应链。然而，材料回收设施面临着一个艰难的市场环境，受到国家和国际政策、贸易壁垒和资源波动的干扰。面对这些挑战，回收商不得不重新检查他们的混合金属产品。现在确实需要更有效的分选技术，推动投资以提高回收的效率、能力、产量和质量，同时最大限度地减少垃圾填埋的残留物。这项提议旨在发展新的科学和概念，以推动新一代基于电磁和感应的有色金属分离技术。感应分选机，基本上是金属探测器，已经在回收设施中普遍使用，以提取低电导率的金属，如不锈钢。该项目动员了我们在电磁检测方面的研究，开发了从食品检测到地雷检测的各种应用，以提供一种新型的此类传感器--“智能”感应技术，它使用多频率分析、新的理论磁散射近似和视觉信息来分类和分离更广泛的有色金属，具有更高的回收纯度和相对于成本的高效回收。例如，在我们之前的工作中，我们展示了多频感应设计可以实现对一些最常见的有色金属的有效分离性能，这些有色金属在报废车辆中可以看到粉碎的废金属，如铜、铝和黄铜。这种对标准感应技术的简单创新取得了成功，使我们与英国一家领先的磁选设备制造商合作开发了一种商业分选解决方案。该项目是我们研究战略中的下一个重大举措，专注于材料表征的新方法，以破坏资源回收中的感应分离。我们计划探索磁散射近似中的新理论见解，如磁极化率张量，通过开发快速有效的方法来处理苛刻的产量和金属回收条件来扩展这项工作。我们将使用模拟工业材料分离设备的关键功能的实验平台来演示这些新方法，以获得相关和现实的性能统计数据。我们的目标是新的科学，这项研究得出的结果将影响电磁传感器在资源回收中的使用方式，有可能实现新的高通量和低成本的分离技术，支持更有利可图和更有活力的循环经济。

项目成果

Scrap Metal Classification Using Magnetic Induction Spectroscopy and Machine Vision

• DOI: 10.1109/tim.2023.3284930
• 发表时间: 2023-01-01
• 期刊: IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
• 影响因子: 5.6
• 作者: Williams, Kane C.;O'Toole, Michael D.;Peyton, Anthony J.
• 通讯作者: Peyton, Anthony J.