Metal recovery and recycling using machine learning and eddy current inspection

使用机器学习和涡流检测进行金属回收和再循环

• 批准号: 2498545
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2498545
• 关键词: Metal; recovery; recycling; using; machine

Industry globally is moved to a circular economy model for the supply of materials and products driven by the imperative to reduce waste, conserve valuable raw materials and minimise green-house gas emissions. Metals are very well suited to the cyclic use and this is supported by a mature metal recycling industry. However, the recyclers face a significant challenge in separating the different non-ferritic metals from mixed-metal waste streams (known as ZORBA). These have typically been sorted by hand in developing countries or by expensive and potentially environmentally damaging float-sink systems. The situation is unsustainable both financially and ethically and a cost-effective dry technological solution is needed.This PhD project will investigate new approaches to classify metals in ZORBA waste streams using machine learning algorithms, computer vision and magnetic induction spectroscopy. The project will conduct research, design, and implement new sensors, and explore new feature-sets and analysis of induction measurements and fragment physical geometry to create efficient and cost-effective solutions to improve recovery and purity rates.This PhD project is responsive to industrial needs and UKRI strategies. It cuts across priorities such as the EPSRC 'Sustainable Industries' priority, 'Waste' and 'Non-renewable resources' challenges identified by the IUK Horizons toolkit, and 'Manage resources efficiently and sustainably' ambitions under the prosperity outcomes.

全球工业正在转向循环经济模式，以减少浪费，保护有价值的原材料并最大限度地减少温室气体排放。金属非常适合循环使用，这得到了成熟的金属回收行业的支持。然而，回收商在从混合金属废物流中分离不同的非铁素体金属（称为ZORBA）方面面临着重大挑战。在发展中国家，这些废物通常是手工分类的，或者是昂贵的、可能对环境造成破坏的浮沉系统。这种情况在经济上和道德上都是不可持续的，需要一种具有成本效益的干式技术解决方案。这个博士项目将研究使用机器学习算法，计算机视觉和磁感应光谱对ZORBA废物流中的金属进行分类的新方法。该项目将进行研究，设计和实施新的传感器，并探索新的功能集和感应测量和碎片物理几何分析，以创建高效和具有成本效益的解决方案，以提高回收率和纯度。这个博士项目是响应工业需求和UKRI战略。它跨越了EPSRC的“可持续产业”优先事项，IUK Horizons工具包确定的“废物”和“不可再生资源”挑战，以及繁荣成果下的“有效和可持续地管理资源”雄心等优先事项。