在堆叠总预测模型中,基于灵敏度的控制策略和操作参数MLSS的分析
Huining Zhang1,2, Wenrui Cai3, Yang Cao3
1School of Civil and Hydraulic Engineering, Lanzhou University of Technology, Qilihe District, Langongping Road 287, Lanzhou, 730050, China. hn_zhang@whu.edu.cn.
一个新的堆积模型准确地预测废水中的总 (TN). 调整MLSS水平显示了减少TN的潜力,为处理厂提供了工程见解.
科学领域:
- 环境工程
- 水质管理
- 预测模型
背景情况:
- 废水处理厂 (WWTP) 的运行是复杂的,原因是流量变化和非线性过程.
- 精确的废水质量建模对于WWTP的有效管理和决策至关重要.
研究的目的:
- 开发一个精确的预测模型,用于废水总 (TN) 在WWTP.
- 确定影响废水TN预测的关键参数.
- 探索MLSS调整以优化废水TN.
主要方法:
- 使用了五个基础模型的堆叠组合模型.
- 使用SHAP分析来确定具有影响力的预测参数.
- 通过调整MLSS以预测废水TN进行模拟.
主要成果:
- 堆叠模型实现了高的确定系数 (R2=0.90).
- 排放TN的主要预测因素包括电力,Inf_BOD5,Inf_TN和MLSS.
- 增加的MLSS度与减少的废水TN相关.
结论:
- 开发的堆叠模型提高了废水TN预测的准确性.
- MLSS的调整为减少废水TN提供了一个可行的策略.
- 这项研究为WWTP优化提供了有价值的工程见解.
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