基于生命周期评价的原生和再生PET纤维环境影响对比分析

Comparative analysis of the environmental impact of virgin and recycled PET fibers based on life cycle assessment

  • 摘要: 分别以再生长丝〔以回收再生聚对苯二甲酸乙二醇酯(PET)瓶片为原料制备〕及原生长丝(以精对苯二甲酸和乙二醇为原料制备)为分析对象,利用生命周期评价(LCA)方法量化分析生产各阶段的环境影响及贡献,并提出减小环境影响的建议。选取全球变暖潜值、酸化效应潜值、非生物耗竭、光化学臭氧合成、陆地生态毒性潜值 5种环境影响类别进行分类计算。结果表明:再生长丝生产过程中熔融纺丝阶段的环境影响贡献大于物理处理阶段;通过特征化分析和归一化分析,与原生PET长丝相比,生产每100 kg的再生PET长丝全球变暖潜势减少32.09 kg (以CO2计),酸化效应潜值减少0.37 kg (以SO2计);原生PET长丝和再生PET长丝生产过程中环境影响贡献最大的类别均为非生物耗竭。根据生命周期多边形法对5种环境影响类别的综合直观评估结果表明,再生PET长丝相比于原生PET长丝对环境影响更小,可以从能源优化、工艺环节改善等方面减小环境影响。

     

    Abstract: The recycled polyester filaments prepared from recycled polyethylene terephthalate (PET) bottles and virgin filaments prepared from refined terephthalic acid and ethylene glycol were used as the analysis objects. The whole life cycle assessment (LCA) method was used to quantitatively analyze the environmental impact and contribution of each stage of production, and some suggestions for reducing environmental impact were put forward. Five environmental impact categories, including global warming potential (GWP), acidification, abiotic depletions, photochemical oxidation and terrestrial ecotoxicity, were selected for classification and calculation. The results showed that the environmental impact contribution of melt spinning stage in the production process of recycled filament was greater than that of physical treatment stage. Through characterization analysis and normalization analysis, compared with the virgin filament, the global warming potential of recycled filament per 100 kg was reduced by 32.09 kg CO2 eq, and the acidification potential was reduced by 0.37 kg SO2 eq. The most important categories of environmental impact in the production of virgin filament and recycled filament were abiotic depletions. According to the comprehensive and intuitive assessment of five environmental impact categories by the life cycle polygon method, the environmental impact of recycled filament was smaller than that of virgin filament, and the environmental impact could be reduced from the aspects of energy optimization and process improvement.

     

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