探索环境和微生物因素对食品废弃物的无氧消化性能相互作用的影响:可解释的机器学习模型
Yanyan Guo1, Youcai Zhao2, Zongsheng Li3
1The State Key Laboratory of Pollution Control and Resource Reuse, School of Environmental Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, PR China.
Bioresource technology
|November 8, 2024
概括
机器学习模型准确地预测了无氧消化 (AD) 产生的沼气产量. 细菌种类对生物气体生产有显著的影响,鉴定出克拉替尼巴克和乙烯基微生物是优化AD性能的关键因素.
科学领域:
- 生物技术是生物技术.
- 环境科学 环境科学
- 微生物学 微生物学
背景情况:
- 无氧消化 (AD) 过程是复杂的,传统的线性模型无法捕获对生物气产量的互动效应.
- 了解影响AD性能的因素的相互作用对于优化生物气生产至关重要.
研究的目的:
- 开发和评估机器学习模型,用于预测AD中的沼气产量.
- 确定影响AD表现的关键因素及其相互作用.
主要方法:
- 开发了四种机器学习模型来预测沼气产量.
- 用环境因素和细菌属数据集来评估模型性能.
- 量化了不同特征类型对生物气产量的影响.
主要成果:
- 机器学习模型,特别是随机森林,在预测生物气产量方面取得了很高的准确性 (R2 = 0.9879).
- 细菌属是影响生物气产量的主要因素 (89.9%的影响),超过了环境因素.
- 克拉替尼巴克和阿塞托米克罗比亚被确定为关键的细菌族.
结论:
- 机器学习通过整合细菌和环境数据有效预测生物气产量.
- 为最大化生物气产量优化AD,需要控制挥发性脂肪酸低于2000 mg/L,并改进系统.
- 准特定的细菌,如克拉替尼巴克和Acetomicrobium可以提高生物气的生产.
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