Abstract
Disc refining is a critical step in the deacetylation and mechanical refining (DMR) pretreatment process for the conversion of herbaceous biomass to biofuels. It is very effective in breaking down the biomass structures to increase enzyme accessibility and sugar yield. However, it is also an energy-intensive process, which consumes fossil electricity, generating greenhouse gas (GHG) emissions, and limits its commercialization in the biorefinery industry. To the authors’ best knowledge, this work is the first to report the development of a physics-based model in predicting the refining energy consumption during the biomass disc refining process. The developed model demonstrated its capability in accurately predicting the refining energy consumption under different operation conditions. Simulations show that the net refining energy consumption, net refining energy efficiency, and specific net energy increase with the increase in rotation speed and the decrease in the refiner plate gap. A convergence trend of these attributes was also observed between larger and smaller refiner plate gaps at increasing rotation speeds. In the scaling-up of the DMR pretreatment process, this model will be a powerful tool in the refiner plate design and operation parameter optimization to reach optimum refining energy consumption to reduce biofuel production cost and GHG emissions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 9717-9726 |
| Number of pages | 10 |
| Journal | ACS Sustainable Chemistry and Engineering |
| Volume | 9 |
| Issue number | 29 |
| DOIs | |
| State | Published - Jul 26 2021 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2021 The Authors. Published by American Chemical Society
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 13 Climate Action
Keywords
- biofuel production
- biomass conversion
- biomass slurry rheology
- disc refiner modeling
- refining energy prediction
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