废水处理中二氧化排放的可解释的混合建模:将机械学知识与不确定性意识机器学习相结合
Xiao Ma1, Wei Yang2, Haixiao Zhao3
1School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China.
Bioresource technology
|December 31, 2025
概括
本研究提出了一种新的混合模型,将机械学和机器学习方法结合起来,以准确预测废水处理中的氧化 (N2O) 排放. 可解释的框架增强了温室气体减排策略,通过强大的不确定性量化.
科学领域:
- 环境工程 环境工程
- 废水处理技术 废水处理技术
- 温室气体排放量 温室气体排放量
背景情况:
- 氧化 (N2O) 排放的机械模型经常面临过度参数化的挑战.
- 机器学习模型虽然强大,但通常在复杂的环境系统中缺乏可解释性.
研究的目的:
- 开发一个可解释的混合框架,将机械模型与长短期记忆 (LSTM) 和高斯过程 (GP) 回归集成,用于N2O排放建模.
- 通过结合可解释性和可靠的不确定性量化来解决现有模型的局限性.
主要方法:
- 开发了一个新的串行并行混合框架,将机械模型与LSTM和GP回归相结合.
- 斯过程回归在N2O建模中首次被应用,使贝叶斯推理用于不确定性量化.
- 用SHAP方法进行可解释性分析,以确定关键的排放因素.
主要成果:
- 混合模型实现了高精度 (R2>0.99) 总N2O估计.
- 与独立的LSTM和基于LSTM的混合模型相比,观察到平均绝对误差的显著减少 (分别为66.4%和47.8%).
- 空气流被确定为影响N2O排放的关键因素 (贡献>0.4).
结论:
- 拟议的可解释混合框架为废水处理中的N2O排放建模提供了显著的方法进步.
- 该模型在各种操作条件中展示了强大的概括性,促进了可解释和不确定性意识的温室气体减排.
- 这种方法有效地将机械学理解与数据驱动的洞察力结合起来,以改善环境管理.
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