Issue |
E3S Web Conf.
Volume 433, 2023
2023 The 6th International Conference on Renewable Energy and Environment Engineering (REEE 2023)
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Article Number | 01007 | |
Number of page(s) | 8 | |
Section | Environmental Chemical Engineering and Environmental Impact Assessment of the Construction Industry | |
DOI | https://doi.org/10.1051/e3sconf/202343301007 | |
Published online | 09 October 2023 |
Application of response surface methodology (RSM) for optimizing methane yield of oxidative pretreated Xyris capensis
Department of Mechanical Engineering Science, Faculty of Engineering and the Built Environment, University of Johannesburg, South Africa.
* Corresponding Author: olaoladoke293@gmail.com
This study investigated the application of Response Surface Methodology (RSM) for optimizing and predicting methane yield from oxidative pretreated Xyris capensis. Input process parameters of retention time, temperature, and pretreatment condition were considered, with methane yield as the response. The results show that all three process parameters selected significantly influence methane yield, and analysis of variance (ANOVA) indicates that the RSM model is significant for the study. A correlation coefficient (R2) of 0.9071 was recorded, which implies that the model has 91% prediction accuracy. Interactive influence of temperature and retention time, pretreatment and retention time, and pretreatment and temperature were significant to methane release. Optimum conditions for methane release from RSM model are 14 days retention time, 25 °C temperature, and pretreatment condition of 85% H2O2 and 15% H2SO4 with daily optimum methane yield of 32.65 mLCH4 /gVSadded. This study shows that RSM is suitable for methane yield optimization and prediction during the anaerobic digestion of oxidative pretreated lignocellulose substrates.
Key words: Xyris capensis / anaerobic digestion / methane / optimization / RSM
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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