在无氧代码消化的甲度预测使用多重线性回归与集成微生物和操作数据.
Iván Ostos1, Iván Ruiz1, Diego Cruz2
1Grupo de Investigación en Ingeniería Electrónica, Industrial, Ambiental, Metrología GIEIAM, Universidad Santiago de Cali, Cali 760036, Colombia.
Bioengineering (Basel, Switzerland)
|November 27, 2025
概括
一个新的模型准确地预测了使用操作数据和微生物配置文件的无氧代消化的甲度. 这项创新有助于在农村环境中产生清洁能源.
科学领域:
- 生物技术和生物工程 生物技术和生物工程
- 环境科学 环境科学
- 微生物学 微生物学
背景情况:
- 无氧共消化增强从有机废物中回收甲.
- 由于微生物的复杂性和操作的可变性,特别是在农村地区,实时甲估计具有挑战性.
研究的目的:
- 开发一种用于预测无氧共消化系统中甲度的实用模型.
- 整合微生物社区数据,以提高预测准确度.
主要方法:
- 开发了一种多重线性回归模型,使用操作数据和16S rRNA基因测序用于微生物配置.
- 利用混合方法进行预测变量选择,结合统计相关性和微生物功能相关性.
- 在70%的数据上训练模型,并在30%的测试集上验证.
主要成果:
- 该模型的确定系数 (R2) 为0.92,测试组的平均相对误差 (MRE) 为6.50%.
- 该模型有效地整合了微生物社区数据,捕捉了生物变异.
- 预测系统需要最小的计算资源和有限的输入.
结论:
- 开发的模型提供了一个实用且易于使用的解决方案,用于估计分散的无氧消化系统中的甲水平.
- 整合微生物数据显著提高了预测准确度,而不仅仅是操作参数.
- 这种方法支持当地对清洁能源发电的决策,并与可持续发展目标7保持一致.
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