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Parametrization of biowaste composting system for life cycle assessment
Full text linkABSTRACT: Composting is a widely used method for managing and valorizing biowaste. Life cycle assessment (LCA) is commonly applied to evaluate its environmental impacts. Current methods used to model life cycle inventories often oversimplify the complex physical, chemical, and biological processes involved. This study introduces the Parametrized Composting Tool for Environmental Assessment (PaCTEA), developed to better capture the influence of biowaste composition variability and operational parameters on composting environmental impacts. PaCTEA integrates a composting model that predicts direct emissions of CO2, NH3, CH4, and N2O, as well as the nutrient composition of the resulting compost. This detailed characterization enables a more accurate estimation of the potential substitution of fertilizers and peat. Even though the core of PaCTEA is a complex chemical engineering model, it is linked to a simple parametrization based on operational parameters. To demonstrate its functionality, simulations were performed to assess the influence of biowaste composition, aeration mode, and ambient temperature on the environmental performance of composting. The LCA results show clear differences between scenarios. Variations in biowaste composition reduced ecosystem quality and natural resource impacts by up to 29% and 52%, and increased human health benefits by nearly 9%. Passive aeration outperformed active aeration, improving ecosystem quality by up to 175% and human health benefits by 35%, while reducing natural resource impacts by 50%. Composting at 5°C increased ecosystem quality and resource impacts by up to 32% and 7%, and reduced human health benefits by about 5% compared to 25°C
Extrusion-Based Additive Manufacturing of Large Composite Structures for Aerospace Applications
No full textComparative analysis of regression techniques for dual-fidelity surrogates in concrete gravity dams
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Towards Collecting Real-Time Vigilance Measures for Adaptive Training in Safety-Critical Domains
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