From full life cycle assessment to simplified life cycle assessment: A generic methodology applied to sludge treatment

Life cycle assessment is widely used to evaluate sludge treatment methods. However, detailed life cycle assessment is time-consuming and resource-intensive. In addition, the complex characteristics of sludge and the variety of treatment methods increase the difficulty of sludge treatment life cycle...

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Veröffentlicht in:The Science of the total environment 2023-12, Vol.904, p.167149-167149, Article 167149
Hauptverfasser: Zhou, Xue, Bai, Shunwen, Zhao, Xinyue, Yang, Jixian
Format: Artikel
Sprache:eng
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Zusammenfassung:Life cycle assessment is widely used to evaluate sludge treatment methods. However, detailed life cycle assessment is time-consuming and resource-intensive. In addition, the complex characteristics of sludge and the variety of treatment methods increase the difficulty of sludge treatment life cycle assessment. There is an urgent need to develop simplified life cycle assessment models to allow rapid decision-making. This study proposes a simplified method of sludge treatment life cycle assessment based on logistic regression analysis. The simplified model relies on the total environmental impact and very few or even a single input parameters. Contribution and data quality analysis were introduced to identify key input parameters and to obtain model training data. The method was eventually applied to traditional sludge treatment methods, showing that it can quickly and accurately predict environmental impact. Representative analysis showed that the model would be affected by technology and energy structure modification. It is hoped that this study will provide a reference and new perspectives for a simplified life cycle assessment of sludge treatment. [Display omitted] •Simplified LCA models for sludge treatment were generated.•The manufacturing of cement from sludge is more sustainable.•The key input parameters for different sludge disposal scenarios were identified.•Energy and technological innovation are key constraints of simplified model.
ISSN:0048-9697
1879-1026
DOI:10.1016/j.scitotenv.2023.167149