Abstract
This work aims to elucidate the potential of dry reforming of methane system within a municipal solid waste management system, from an energetic and life cycle assessment point of view. To do so, a set of Aspen plus simulations were developed and transposed into a computationally lighter artificial neural network regression model. This model was integrated within a MATLAB mass flow analysis superstructure representative of a modern waste management system. The superstructure is also a life cycle impact analysis tool capable of providing the performance of the system as a whole. This integrated assessment was elaborated for different scenarios, comparing the performance of different biogas valorization pathways for different energy uses. The fossil energy ratio was also calculated for the different energy products considered (electricity, biomethane and hydrogen).
| Original language | English |
|---|---|
| Pages (from-to) | 86-96 |
| Number of pages | 11 |
| Journal | European Biomass Conference and Exhibition Proceedings |
| Publication status | Published - 2023 |
| Externally published | Yes |
| Event | 31st European Biomass Conference and Exhibition, EUBCE 2023 - Bologna, Italy Duration: 5 Jun 2023 → 8 Jun 2023 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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SDG 9 Industry, Innovation, and Infrastructure
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SDG 11 Sustainable Cities and Communities
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SDG 12 Responsible Consumption and Production
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SDG 13 Climate Action
Keywords
- dry reforming of methane
- hydrogen
- life cycle assessment
- machine learning
- waste to energy
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