电废水的智能空气化策略:基于动态交互网络的可解释优化洞察力
Zhenyu Zhang1, Jian Peng2, Yong Yang3
1School of Geographical Sciences, Fujian Normal University, Fuzhou, 350007, China.
Journal of environmental management
|January 29, 2026
概括
用于电废水的自适应性通风控制是使用数据驱动框架实现的. 这种方法确定了关键变量和废水目标,使得改善水质和能源效率的差异化策略成为可能.
科学领域:
- 环境工程 环境工程
- 废水处理 废水处理
- 工业过程控制 工业过程控制
背景情况:
- 目前用于电废水的空气控制方法通常是静态或不透明的,无法解决动态相互作用.
- 目前的模型缺乏可解释性,并且很难有效地优化复杂的流程.
研究的目的:
- 开发一个数据驱动的框架,用于电废水处理厂的适应性通风控制.
- 确定关键的过程变量和废水特定的控制目标,以优化通风策略.
主要方法:
- 集成滑窗相关联网络,SHAP增强的XGBoost模型和SHAP引导的贝叶斯优化.
- 分析过程变量和废水质量指标之间的动态相互作用.
- 为废水参数 (COD,NH3-N,TN,TP) 开发可解释的预测模型.
主要成果:
- 网络分析显示,随着时间的推移,通风-废水合的动态变化.
- 对于关键的废水参数,XGBoost模型显示出高预测精度 (R2 0.951-0.998).
- SHAP分析确定了影响性流量 (Flow_H,Flow_F) 作为废水质量的关键预测指标.
结论:
- 需要区分,区域特定的通风策略,而不是单一的全工厂设置.
- 开发的框架提供了一个透明的,数据驱动的方法,用于适应性和能源效率高的通风管理.
- 这种方法支持可靠的废水质量合规性,并减少工业废水排放的环境足迹.
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