通过使用基于LSTM的深度学习模型,预测大规模废水处理厂的氧化排放量
Siddharth Seshan1, Johann Poinapen2, Marcel H Zandvoort3
1KWR Water Research Institute, Nieuwegein, the Netherlands; Section Sanitary Engineering, Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, Delft, the Netherlands.
Water research
|November 10, 2024
概括
本研究使用深度学习模型预测废水处理厂 (WWTP) 的氧化 (N2O) 排放. 这些模型准确地预测了短期N2O排放量,有助于排放控制策略.
科学领域:
- 环境工程 环境工程
- 数据科学数据科学数据科学
- 废水处理技术 废水处理技术
背景情况:
- 废水处理厂 (WWTP) 的氧化 (N2O) 排放显示出显著的季节性变化.
- 传统的生物动力学模型难以预测N2O排放,这是由于复杂的生物化学过程造成的.
研究的目的:
- 开发基于数据的模型,用于预测WWTP产生的N2O排放.
- 探索将这些模型整合到减少排放的预测控制框架中.
主要方法:
- 使用基于长期短期记忆 (LSTM) 的编码器-解码器模型.
- 在15个月的数据上训练并测试模型,来自全面的WWTP,包括季节性排放峰值.
- 在各种预测地平线 (0.5至6.0小时) 中评估模型性能.
主要成果:
- 性能最好的LSTM模型 (256-256架构) 实现了高精度 (R2高达0.98).
- 关键的输入特征包括过去的N2O排放,影响流速,NH4+,NOx和DO.
- 随着预测时间的延长,模型性能下降,强调了过程变量的重要性.
结论:
- LSTM模型显示,在WWTP中准确的短期N2O排放预测具有很大的潜力.
- 这些模型可以支持减少N2O排放的操作控制.
- 结合深度学习和机械洞察力的混合方法可以改善长期预测.
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