基于微生物社区结构预测的人工智能建模框架提高了藻类-细菌颗粒性污泥系统的污染物清除效率
Zhe Liu1, Jie Lei2, Rushuo Yang2
1School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Yan Ta Road. No.13, Xi'an, 710055, China; Key Lab of Northwest Water Resource, Environment and Ecology, Ministry of Education, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
Journal of environmental management
|July 27, 2025
概括
藻类-细菌颗粒污泥 (ABGS) 技术使用机器学习来预测和优化微生物群落,以实现高效的水处理. 这种智能模型提高了污染物去除率,为可持续的废水管理提供了一种新的方法.
科学领域:
- 环境科学 环境科学
- 微生物学 微生物学
- 生物技术是生物技术.
背景情况:
- 藻类-细菌颗粒污泥 (ABGS) 是一种节能,低碳的水处理方法,依赖于藻类-细菌共生.
- 在ABGS中复杂的微生物系统使其调节机制不清楚,阻碍性能优化.
研究的目的:
- 为ABGS系统开发一个两阶段的最佳控制模型.
- 通过调节操作参数和微生物社区结构,实现污染物清除效率的智能优化.
主要方法:
- 与七个机器学习 (ML) 算法进行比较,以预测微生物社区结构继承 (R2 > 0.94).
- 采用六个ML算法,利用监管指标和微生物社区数据 (R2 > 0.94) 预测污染物去除效率.
- 综合预测模型使用非主导排序遗传算法进行选择性微生物社区塑造.
主要成果:
- 实现了对微生物群落结构和污染物去除效率的高预测准确性.
- 证明了微生物群落的选择性塑造,以提高碳,和的去除率 (>90%).
结论:
- 开发的模型为ABGS性能优化提供了一个普遍适用的量化智能指导系统.
- 这种方法为优化其他生物废水处理系统提供了一个框架.
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