Combustion Characteristics in a Constant Volume Chamber of Diesel Blended with HTL
Published April 2, 2019 by SAE International in United States
Downloadable datasets for this paper availableAnnotation of this paper is available
There are a few different ways in which biofuels can be sourced, with the most popular coming from agricultural sources. An alternative approach is to utilize biowaste. An estimated 20 million dry tons of volatile organic compounds, or biowaste, is annually deposited in US municipal wastewaters. Most of this biowaste energy content is not recovered and, as a result, the biowaste could be a massive potential source of renewable energy. Biocrude diesel is converted from wet biowaste via hydrothermal liquefaction (HTL). Three types of feedstocks (algae, swine manure, and food processing waste) were converted into biocrude oil via HTL. From the previous experiments done in an AVL 5402 single-cylinder diesel engine, it was observed that the presence of 20% of HTL in the blend performed similarly during combustion to pure diesel. By studying these mixtures in a constant volume chamber, these observations could be compared to the results in the diesel engine. Additionally, the constant volume chamber provided more insights as how the HTL actually affected the combustion characteristics. The experiments were conducted at different temperatures and atmospheric conditions and revealed that the HTL blends had similar combustion characteristics and sooting when compared to diesel.
- Timothy H. Lee - University of Illinois at Urbana-Champaign
- Ziming Yang - Beijing Institute of Technology
- Gang Li - Chang'an University
- Wan-Ting Chen - University of Massachusetts Lowell
- Yuanhui Zhang - University of Illinois at Urbana-Champaign
- Tonghun Lee - University of Illinois at Urbana-Champaign
- Alan C. Hansen - University of Illinois
CitationLee, T., Yang, Z., Li, G., Chen, W. et al., "Combustion Characteristics in a Constant Volume Chamber of Diesel Blended with HTL," SAE Technical Paper 2019-01-0578, 2019, https://doi.org/10.4271/2019-01-0578.
Data Sets - Support Documents
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