Development of a multi-criteria decision tool for the plastics industry – A case study on post-consumer PP packaging material

Circular economy aims to retain valuable materials in the recycling loop and reduce losses. Packaging materials, owing to their short market lifetimes, dominate the waste stream, and feedstock for mechanical recycling remains a fluctuating geographical and seasonal factor.
This study presents a decision tree for a mechanical recycling process to transform a post-consumer polypropylene (PP) waste stream into a high-quality product. Initial manual analysis of a representative PP bale from a sorting plant revealed an 87.5% PP content, with 75% being white or transparent. The recycling process involved color-based sorting into three different color fractions (white, transparent, and colored), grinding, multiple washing steps, flake sorting, and granulation to produce six distinct fractions. This study highlights the importance of tracking material flow, generating process data, and linking it to environmental impacts.
Using the Alpha Algorithm, a process network from feedstock to final pellets was developed to identify key decision points based on feedstock quality, color, and volatile organic compounds (VOC) contamination. These findings demonstrate that cleaning reduces contaminants and improves product quality. The developed tool aids in optimizing recycling processes with the aim of retaining valuable materials in primary recycling loops and minimizing downcycling.

循环经济旨在将高价值物料留存于回收循环体系之中，并降低物料损耗。包装材料因市场服役周期较短，在废弃物流中占据主导地位；而机械回收所用原料仍受地域与季节因素影响，品质波动显著。
本研究针对机械回收流程构建了一套决策树模型，可将消费后聚丙烯（polypropylene, PP）废弃物流转化为高品质产品。研究人员首先对分选厂的代表性聚丙烯打包料开展人工初分析，结果显示其聚丙烯含量达87.5%，其中75%为白色或透明材质。该回收工艺基于颜色将物料划分为白色、透明与有色三大组分，随后依次经过破碎、多步清洗、薄片分选与造粒环节，最终得到六种不同的细分物料组分。本研究强调了追踪物料流向、生成工艺数据并将其与环境影响相关联的重要性。
研究借助Alpha算法（Alpha Algorithm）构建了从原料到最终粒料的工艺网络，可基于原料品质、颜色与挥发性有机化合物（volatile organic compounds, VOC）污染情况识别关键决策节点。研究结果表明，清洗环节可有效降低污染物含量，提升产品品质。本次开发的工具可助力优化回收流程，旨在将高价值物料留存于一次回收循环体系之内，并最大限度减少降级回收现象。