一种混合深度学习方法,以改善废水处理厂的废水质量实时预测
Yifan Xie1, Yongqi Chen2, Qing Wei2
1School of Environment, Tsinghua University, Beijing 100084, China.
Water research
|January 3, 2024
概括
结合TCN和LSTM的新混合深度学习模型显著提高了废水处理厂 (WWTP) 总 (TN) 预测的准确性. 这种先进的模型提高了运营效率和水质管理.
科学领域:
- 环境工程 环境工程
- 人工智能的人工智能
- 水质管理水质管理
背景情况:
- 废水处理厂 (WWTP) 的运营面临着由于影响力变化和复杂过程的挑战.
- 对WWTP废水质量的准确建模对于有效的运营和管理至关重要.
研究的目的:
- 开发和评估一种新的混合深度学习模型,用于模拟每小时WWTP废水中的总 (TN) 度.
- 将混合模型的性能与独立的深度学习和传统的机器学习模型进行比较.
主要方法:
- 开发了一种混合模型,结合了时间卷积网络 (TCN) 和长短期记忆 (LSTM).
- 测试了该模型对TN度的预测精度,并与TCN,LSTM和Feedforward神经网络 (FFNN) 模型进行了比较.
- 沙普利添加式解释 (SHAP) 用于模型解释,以确定关键影响参数.
主要成果:
- 混合型TCN-LSTM模型的准确性比单一的TCN或LSTM模型高33.1%.
- 与传统的FFNN车型相比,混合动力车型的性能提高了63.6%.
- 该模型在8小时的时间范围内展示了WWTP废水TN的卓越的多时间步骤预测能力.
结论:
- 混合型TCN-LSTM模型在模拟WWTP废水中TN度方面取得了重大进展.
- 模型解释确定了关键参数,使监测和管理策略的优化成为可能.
- 删除非贡献变量可以进一步提高建模效率和运营洞察力.
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