Measure recyclable resources in the waste industry to meet the foundational industries demands for recycled material using AI for tracking SKU

基础产业(塑料化工、造纸、金属等)在包装制造中使用大量的原始原料。用回收的废旧包装取代部分这种原料减少了这种需求，节省了资金，加工所用的能源减少了75%，温室气体排放量高达2148千克二氧化碳/吨。加速这一转变的是对含有足够回收成分的可持续包装的压力，以及监管；英国政府正在对不含30%或更多回收成分的包装征收税收。因此，对回收材料的需求正在无情地增加。然而，由于缺乏数据的低效、不准确的(人工)审计和处理，低回收率(预计到2040年将达到13%，而塑料垃圾的数量将翻一番)，从而抑制了对废物的有效分类，限制了我们为行业提供足够的再生材料的能力。迫切需要通过利用废物的价值来减少对新资源的压力。为了实现这一点，需要在整个产品生命周期内建立透明度/问责制，而实现这一点的唯一方法是将行业数字化，为其提供必要的数据。在所有类型的材料--塑料、纸张、玻璃、金属和其他材料--的回收系统中，如果以数据为动力，横跨生产商、回收商和基础行业的回收系统可能会更加高效：更有效的分类、数据驱动的优化决策、监管的执行、数据指导的更好的政策决策。该项目旨在建立在Greyparot现有的AI视觉技术的基础上，这是一个新颖的、负担得起的系统，它使用人工智能计算机视觉中的最先进方法，在品牌/SKU层面上实时准确地识别和分类产品包装/废物，在不同的分拣线上一直识别和跟踪这些产品，通知每个产品是否被正确回收。尽管获得了浓厚的兴趣(正在进行MRF试验)，但该技术在目前被识别的SKU数量(100个)和类型(塑料)方面是有限的。该项目将通过硬件和软件开发显著扩大该解决方案的规模，从而能够识别具有代表性的市场份额(70%的PET/HDPE主SKU，主要的非塑料包装)，帮助追究生产商对糟糕的包装设计的责任，使智能分类和政策制定者/废物管理人员能够做出明智的决策，以适应和改进法规和流程，提高基础行业中包装有效回收和重新进入制造过程的百分比。由于该项目以及通过更大规模的硬件部署和数据收集和处理，Greyparot将提高产品开发速度。缩短产品进入市场并开始对基础行业和其他利益相关者产生影响的时间。

The foundation industries (plastics and chemical industry, paper, metal etc.) use large volumes of virgin feedstock in the manufacture of packaging. Substitution of part of this feedstock by recycled waste packaging is reducing this demand, save money, the energy used in processing by 75%, and greenhouse gas emissions by up to 2148 kgCO2e/t. Accelerating this shift is pressure towards sustainable packaging that contains sufficient recycled content, and regulation; the UK government is introducing a tax on packaging that doesn't contain 30% or more recycled content. Thus, the demand for recycled material is inexorably increasing. However, the low recycling rate (predicted to be 13% by 2040, while quantity of plastic waste will double) due to inefficient, inaccurate (manual) auditing and processing, driven by a lack of data, thus inhibiting the effective sorting of waste, limits our ability to feed the industry with enough reclaimed material. There is an urgent need to decrease the stress on new resources by leveraging the value of waste. To achieve this, transparency/accountability across the product life cycle need to be established and the only way to do it is to digitise the industry, providing it with the necessary data. Across all sorts of materials - plastics, paper, glass, metals and others - recycling systems spanning producers, recyclers, and foundational industries could be far more efficient when powered by data:more effective sorting, data-driven optimisation decisions, enforcement of regulation, better policy decisions guided by data. This project aims to build on Greyparrot's existing AI vision technology which is a novel, affordable system that uses state-of-the-art approaches in AI-computer vision to accurately identify and classify in real-time product packaging/waste at a brand/SKU level, recognising and tracking these all along the sorting process across different sorting lines, informing on whether each one is correctly being recycled or not. Although securing strong interest (with ongoing trials in MRFs) the technology is limited in terms of number (100s) and type (plastic) of SKUs being currently recognised. This project will significantly scale-up this solution through hardware and software development allowing for the recognition of a representative market share (70% of PET/HDPE main SKUs, main non-plastic packaging ones), helping to hold producers accountable for poor packaging design, enabling intelligent sorting and policy makers/waste managers to take informed decisions to adapt and improve regulations and processes, increasing the percentage of packaging being effectively recycled and going back into the manufacturing process in foundational industry.Thanks to this project and through larger scale hardware deployment and data gathering and processing, Greyparrot will increase the speed of product development, moving toward a shorter timeline for the product to reach the market and start having an impact on the foundational industry and other stakeholders.