An Experimental and Modeling Investigation of the Combustion of Anisole and Guaiacol
Fuel(2024)
Univ Lorraine
Abstract
Anisole and guaiacol are both used as surrogates for lignin-derived biofuel. However, while the oxidation reaction mechanisms of anisole can be validated against a large set of experimental data, experimental measurements and models for guaiacol are limited. In this context, in addition to measuring adiabatic laminar burning velocities of both fuels using a flat flame burner, the oxidation of guaiacol was investigated at temperatures between 600 and 925 K. A near-atmospheric pressure jet-stirred reactor was used for three equivalence ratios (0.5, 1 and 2) with a high helium dilution. The experiments yielded mole fractions of 22 reaction products, among which two new species, benzodioxole and benzodioxole-2-one, were identified. All the measurements made in this work, along with extensive literature data on anisole, were compared with the predictions of a newly developed kinetic model. A good agreement was found between kinetic modeling and experiments, showing improved prediction for some species relative to the existing literature guaiacol oxidation models. Flowrate analyses are also discussed both in flames and in the jet-stirred reactor, especially focusing on the formation of the newly detected products.
MoreTranslated text
Key words
Oxidation,Guaiacol,Biomass,Jet -stirred reactor,Kinetic modeling
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined