机器学习揭示了在anammox优化中的工作量和混合强度被忽视的结合
Bohua Ji1, Sin-Chi Kuok2, Tianwei Hao1
1Department of Civil and Environmental Engineering, University of Macau, Macau SAR, China.
Water research
|August 30, 2024
概括
优化anammox工艺需要考虑工作量和混合强度. 这些因素共同调节反应堆流量场,显著影响氨去除率和整体性能.
科学领域:
- 环境微生物学环境微生物学
- 废水处理技术 废水处理技术
- 生物反应器工程学
背景情况:
- 在废水处理中,Anammox工艺对于去除至关重要.
- 优化anammox的性能是具有挑战性的,因为许多相互作用的操作和环境因素.
- 之前的研究集中在单个参数上,忽视了协同效应.
研究的目的:
- 使用机器学习识别 anammox 性能的关键调控变量.
- 调查工作容量和混合强度对anammox反应堆的联合影响.
- 为设计高效的anammox生物反应器建立预测关系.
主要方法:
- 应用贝叶斯非参数通用回归 (BNGR) 来分析11个操作和环境参数.
- 集成的BNGR发现与计算流体动力学 (CFD) 模拟.
- 进行了混合强度对性能影响的实验验证.
- 开发了数学模型,将特定输入功率与氨去除率相关联.
主要成果:
- 确定工作量作为一个关键的,经常被忽视的,管理参数.
- 证明工作体积和混合强度共同调节反应堆流量场和anammox性能.
- 在特定输入功率和氨去除率之间建立了强烈的相关性 (R2 = 0.94).
- 量化了随着特定输入功率的增加而增加的氨去除率.
结论:
- 工作量和混合强度是对anammox过程优化至关重要的相互依存因素.
- 具体输入功率,工作量和去除率之间的相关性有助于生物反应器的设计.
- 未来的anammox反应堆优化策略必须考虑工作容量与混合强度的相互作用.
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