使用整体回归树算法来确定anammox反应堆废水中的酸盐度的预测模型
Yikun Huang1, Run Su1, Yinan Bu1
1Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, School of Ecological and Environmental Science, Hainan University, Haikou, 570228, China.
Chemosphere
|July 23, 2023
概括
这项研究开发了一种具有成本效益的机器学习模型,用于预测废水处理中的化物含量. 这有助于控制anammox细菌的活动,并优化去除过程.
科学领域:
- 环境微生物学 环境微生物学
- 废水处理工程 废水处理工程
- 计算生物学 计算生物学
背景情况:
- 无氧氨氧化 (anammox) 是废水中去除的关键生物过程.
- 酸盐的积累会对anammox细菌的活动产生负面影响.
- 目前的在线化物监测设备昂贵,并且实施起来具有挑战性.
研究的目的:
- 开发一种具有成本效益和准确的方法,用于在线预测anammox反应堆中的酸盐度.
- 优化机器学习算法,用于预测酸盐含量.
- 在现实世界废水处理场景中验证预测模型的性能.
主要方法:
- 集成回归树算法用于预测建模.
- 贝叶斯算法用于系统优化机器学习参数.
- 使用确定系数 (R2) 和根平均平方误差 (RMSE) 对实验数据进行验证.
主要成果:
- 整体回归树模型准确地预测了亚酸盐度 (R2 = 0.91,RMSE = 4.81).
- 该模型在应用到不同的anammox反应堆时表现出良好的性能 (R2 = 0.84,RMSE = 6.34).
- 与其他常用的机器学习算法相比,开发的模型显示出更高的性能.
结论:
- 机器学习,特别是集成回归树,为anammox过程中的在线化物监测提供了可行且具有成本效益的解决方案.
- 准确的化物预测可以更好地控制anammox反应堆的条件,提高了去除效率.
- 开发的预测模型在优化废水处理厂运营方面具有实际应用.
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