Using Artificial Intelligence to develop a prediction model, increasing the viability of previously unrecyclable, low grade, post-consumer polymers; promoting Clean Growth through business efficiency and helping to achieve Net Zero - ColourAI

塑料回收商面临的一个关键挑战和生产再生聚合物的一个主要限制因素是基础聚合物输入材料的广泛变化，这意味着满足客户规格非常具有挑战性，每批都需要一个独特的配方，必须通过实验推导；不像原始聚合物复合物-谁有一个标准的配方，将始终产生的化合物与期望的性质。由于混合颜色之间的关系不是线性的（例如，蓝色加黄色输入并不总是产生绿色），因此执行了多次尝试和错误迭代以匹配颜色规格。配方批次平均需要5次迭代才能达到所需的颜色-在某些情况下，这可能需要多达20次迭代。这个第一阶段的技术可行性研究和后续的第二阶段原型系统开发和测试项目，将有助于政府的清洁增长策略，使塑料回收商和复合商能够提高商业和工业效率，减少为达到客户的颜色规格而生产多个测试批次回收材料的处理成本。

A key challenge for plastic recyclers and a major limiting factor in producing recycled polymers, is the wide variation in base polymer input materials which means that meeting the customer specification is very challenging and every batch requires a unique formulation that must be derived experimentally; unlike virgin polymer compounders - who have a standard formulation which will consistently yield a compound with the desired properties. Multiple trial and error iterations are performed to match the specification for colour, as the relationship between mixed colours is not linear (e.g. blue plus yellow inputs do not always produce green). Formulating batches can take an average of 5 iterations to achieve the desired colour - and in some instances this can take up to 20 iterations. This phase 1 technical feasibility study and follow-on phase 2 prototype system development and testing project will help deliver the Government's Clean Growth Strategy by enabling plastic recyclers and compounders to improve business and industry efficiency by reducing processing costs experienced by having to produce multiple test batches of recycled material in order to achieve the customer's colour specification.