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Reducing Willow Wood Fuel Emission by Low Temperature Microwave Assisted Hydrothermal Carbonization
Published on: May 19, 2019
Recent advances of thermochemical conversion processes for biorefinery
Myung Won Seo1, See Hoon Lee2, Hyungseok Nam1
1Climate Change Research Division, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon, Republic of Korea.
Thermochemical conversion of lignocellulosic biomass offers a feasible and effective alternative to fossil fuels. This review analyzes key processes and highlights AI
Area of Science:
- Biomass Energy
- Renewable Resources
- Thermochemical Engineering
Background:
- Lignocellulosic biomass is a key renewable resource for replacing fossil fuels.
- Biorefineries utilize various processes to convert biomass into valuable products.
- Thermochemical conversion offers advantages over biological methods.
Purpose of the Study:
- To review recent advances in thermochemical conversion of lignocellulosic biomass.
- To analyze operation conditions and results of pyrolysis, hydrothermal treatment, gasification, and combustion.
- To identify challenges and future directions for thermochemical biorefining.
Main Methods:
- Literature review of thermochemical conversion processes.
- Analysis of operational parameters and outcomes for pyrolysis (torrefaction), hydrothermal treatment, gasification, and combustion.
- Examination of techno-economic feasibility and comparative effectiveness against biological processes.
Main Results:
- Thermochemical conversion processes are techno-economically feasible, easy, and effective for biorefineries.
- Specific conditions and results for pyrolysis, hydrothermal treatment, gasification, and combustion were summarized.
- Challenges in thermochemical conversion were identified, guiding future research.
Conclusions:
- Thermochemical conversion is a viable and effective strategy for lignocellulosic biomass utilization in biorefineries.
- Addressing identified challenges will further enhance the efficiency and application of these processes.
- Artificial intelligence and machine learning can optimize bio-oil and syngas production, reducing experimental efforts.
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