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收稿日期:
2024-05-31
修回日期:
2024-07-24
出版日期:
2024-07-24
通讯作者:
赵景波
作者简介:
张勇(2001-),男,硕士研究生, z15263056338@163.com
基金资助:
Yong ZHANG(), Jingbo ZHAO(), Limin QUAN
Received:
2024-05-31
Revised:
2024-07-24
Online:
2024-07-24
Contact:
Jingbo ZHAO
摘要:
为解决城市污水处理过程出水氨氮浓度难以实时精准测量的问题,本文构建了一种融合卷积层和注意力机制的长短期记忆网络(Convolutional Layer and Squeeze-and-Excitation Attention Mechanism based Long Short-Term Memory Network, CSA-LSTM)模型:首先,通过引入卷积层(Convolutional Layer, CL),深度提取数据中的非线性特征,并通过注意力机制(Squeeze-and-Excitation Attention Mechanism, SEAM) 自适应分配特征通道的权重,实现特征解耦;其次,长短期记忆网络(Long Short-Term Memory network, LSTM)提取时间序列数据长期依赖关系,实现出水氨氮浓度的实时预测;然后,提出一种具有自适应采集函数的贝叶斯优化算法,实现模型参数优化,进一步提高模型精度;最后,基于基准实验和实际污水处理厂数据测试CSA-LSTM的有效性,结果表明模型具有较高的出水氨氮浓度预测精度,能够有效应对城市污水处理中数据的强非线性、耦合性以及时间依赖性问题,具有良好的泛化能力。
中图分类号:
张勇, 赵景波, 权利敏. 基于卷积层-注意力机制的长短期记忆网络出水氨氮浓度预测方法[J]. 化工学报, DOI: 10.11949/0438-1157.20240599.
Yong ZHANG, Jingbo ZHAO, Limin QUAN. A prediction method for effluent ammonia nitrogen concentration based on convolutional layer and attention mechanism long short-term memory network[J]. CIESC Journal, DOI: 10.11949/0438-1157.20240599.
实验 出水氨氮浓度预测 | Mackey–Glass时间序列预测 |
---|---|
SOFNN-HLA[ | kmax=30, tmax=100, E d =0.01, η= 0.001 |
PDF-FNN[27 tmax = 104, η = 8 × 10-4, µ = 0.001, ε = 0.001, σg = 0.05, n = 4 | tmax = 104, η = 8 × 10-4, µ = 0.001, ε = 0.001, σg = 0.05, r = 4 |
SEAM-LSTM tmax=900, η= 0.001, r = 4, n=6, m=2 | tmax=700, η= 0.001, r = 4, n=6, m=2 |
CL-LSTM tmax=850, η= 0.001, r = 4, n=6, m=2 | tmax=600, η= 0.001, r = 4, n=6, m=2 |
CSA-LSTM tmax=1000, η= 0.001, r = 4, n=6, m=2 | tmax=800, η= 0.001, r = 4, n=6, m=2 |
表1 不同实验参数设置
Table 1 Parameter settings for different experiments
实验 出水氨氮浓度预测 | Mackey–Glass时间序列预测 |
---|---|
SOFNN-HLA[ | kmax=30, tmax=100, E d =0.01, η= 0.001 |
PDF-FNN[27 tmax = 104, η = 8 × 10-4, µ = 0.001, ε = 0.001, σg = 0.05, n = 4 | tmax = 104, η = 8 × 10-4, µ = 0.001, ε = 0.001, σg = 0.05, r = 4 |
SEAM-LSTM tmax=900, η= 0.001, r = 4, n=6, m=2 | tmax=700, η= 0.001, r = 4, n=6, m=2 |
CL-LSTM tmax=850, η= 0.001, r = 4, n=6, m=2 | tmax=600, η= 0.001, r = 4, n=6, m=2 |
CSA-LSTM tmax=1000, η= 0.001, r = 4, n=6, m=2 | tmax=800, η= 0.001, r = 4, n=6, m=2 |
方法 | NRMSE | R2 | APE |
---|---|---|---|
SOFNN-HLA[ | 0.0126 | 0.99013 | 0.494 |
PDF-FNN[ | 0.0265 | 0.98651 | 0.517 |
SEAM-LSTM | 0.0324 | 0.97012 | 0.891 |
CL-LSTM | 0.0262 | 0.98152 | 0.775 |
本文方法 | 0.0091 | 0.99714 | 0.483 |
表2 Mackey–Glass时间序列预测对比结果
Table 2 Comparative results of Mackey-Glass time series prediction
方法 | NRMSE | R2 | APE |
---|---|---|---|
SOFNN-HLA[ | 0.0126 | 0.99013 | 0.494 |
PDF-FNN[ | 0.0265 | 0.98651 | 0.517 |
SEAM-LSTM | 0.0324 | 0.97012 | 0.891 |
CL-LSTM | 0.0262 | 0.98152 | 0.775 |
本文方法 | 0.0091 | 0.99714 | 0.483 |
方法 | NRMSE | R2 | APE |
---|---|---|---|
SOFNN-HLA[ | 0.0432 | 0.98981 | 0.771 |
PDF-FNN[ | 0.0453 | 0.98723 | 0.837 |
SEAM-LSTM | 0.1292 | 0.96941 | 1.524 |
CL-LSTM | 0.1012 | 0.97352 | 1.314 |
本文方法 | 0.0210 | 0.99334 | 0.592 |
表3 出水氨氮浓度预测对比结果
Table 3 Comparison of predicted and actual ammonia nitrogen concentration in effluent
方法 | NRMSE | R2 | APE |
---|---|---|---|
SOFNN-HLA[ | 0.0432 | 0.98981 | 0.771 |
PDF-FNN[ | 0.0453 | 0.98723 | 0.837 |
SEAM-LSTM | 0.1292 | 0.96941 | 1.524 |
CL-LSTM | 0.1012 | 0.97352 | 1.314 |
本文方法 | 0.0210 | 0.99334 | 0.592 |
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