废弃固体生物燃料:比较和机器学习研究
Abayomi Bamisaye1, Ayodeji Rapheal Ige2, Kayode Adesina Adegoke3
1Department of Chemistry, Faculty of Natural and Applied Sciences, Lead City University Ibadan Oyo State Nigeria abayomibamisaye@gmail.com.
RSC advances
|April 11, 2024
概括
从Amaranthus hybridus废物中开发可持续的生物,用麻粉粘合剂,提供了一种可再生能源的替代方案. 脱改进了燃烧和物理化学特性,使这些农业废物块成为环保和高效的固体生物燃料.
科学领域:
- 农业科学 农业科学
- 可再生能源可再生能源是可再生能源.
- 材料科学 材料科学 材料科学
背景情况:
- 化石燃料的耗尽和环境问题需要可持续的能源替代品.
- 农业废物管理带来了挑战,需要创新的处置和利用战略.
- 生物块提供了一个来自生物质的有希望的可再生能源.
研究的目的:
- 开发和评估使用麻粉作为粘合剂的Amaranthus hybridus废物的生物块.
- 评估除化对生物的燃烧和物理化学性质的影响.
- 使用机器学习模型优化生物生产过程.
主要方法:
- 用处理 (TAHB) 和未处理 (UAHB) Amaranthus hybridus生物片与麻豆粉粘合剂的制备.
- 使用富里埃变换红外光谱 (FTIR),扫描电子显微镜 (SEM) 和能量分散式X射线光 (EDXRF) 的表征.
- 燃烧和物理化学参数的评估,以及使用自适应神经模糊推理系统 (ANFIS) 和模糊C-Means (FCM) 集群的优化.
主要成果:
- 与UAHB (12.31%) 相比,TAHB (11.47%) 的红素含量在脱化过程中显著降低.
- 在TAHB (12.53 MJ kg−1) 和UAHB (10.43 MJ kg−1) 之间,热量显著增加.
- FTIR和SEM证实了形态和结构的变化,而EDXRF表示潜在毒性元素 (PTEs) 低. 机器学习模型显示出高的预测准确性 (RMSE,MAE,MAPE).
结论:
- 性处理和Amaranthus hybridus废物的脱化提高了生物质量,使燃烧更清洁.
- 开发的生物是环保的,具有更好的能量含量,适合作为可持续的固体生物燃料.
- 机器学习模型有效地预测了生物的效率,帮助优化可再生能源生产的过程.
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