通过机器学习和关键生物指标识别来预测和优化部分脱和anammox性能
Kailong Huang1, Yujie Zhang2, Jialei Li2
1State Key Laboratory of Water Pollution Control and Green Resource Recycling, School of the Environment, Nanjing University, Nanjing 210023, China; Yixing Environmental Protection Research Institute of Nanjing University, Yixing 214200, China; Nanjing Jiangdao Institute of Environmental Research Co., Ltd., Nanjing 210019 China.
Bioresource technology
|March 13, 2026
概括
本研究在部分脱和PDA系统中模拟了总去除效率 (TNRE),确定了负载率和微生物指标等关键因素. 优化的策略显著改善了PDA系统的性能.
科学领域:
- 环境科学 环境科学
- 微生物学 微生物学
- 废水处理工程 废水处理工程
背景情况:
- 在部分脱和PDA系统中优化全除效率 (TNRE) 对于有效的废水处理至关重要.
- 了解关键的驱动因素和操作策略对于提高PDA系统性能至关重要.
研究的目的:
- 开发PDA系统中TNRE的预测模型,整合关键参数和微生物因素.
- 确定影响TNRE的关键因素和作为生物指标的特定微生物属.
- 使用粒子群优化算法设计一个优化的操作策略.
主要方法:
- 使用关键参数和微生物数据 (R2值为0.958和0.921) 开发TNRE预测的初步和独立模型.
- 功能重要性分析以确定TNRE的主要驱动因素.
- 应用粒子群优化算法用于运营战略开发.
主要成果:
- 确定TN加载率,基质比率和影响度是影响TNRE的主要因素.
- 确定了 Genera *Candidatus* Jettenia 和 *Denitratisoma* 作为PDA系统性能的关键生物指标.
- 优化战略的实施导致TNRE从80.70%大幅增加到93.52%.
结论:
- 该研究提供了一个强大的框架,用于优化PDA系统性能,使用机器学习和微生物洞察力.
- 这些发现突显了计算建模和微生物分析在推进去除技术方面的协同潜力.
- 开发的操作策略提高了PDA系统中总去除的效率和稳定性.
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