• •
李文华1(), 叶洪涛1,2,3(), 罗文广1, 刘乙奇4,5
收稿日期:
2024-06-03
修回日期:
2024-07-10
出版日期:
2024-07-16
通讯作者:
叶洪涛
作者简介:
李文华(1998—),女,硕士研究生,20220201015@stdmail.gxust.edu.cn
基金资助:
Wenhua LI1(), Hongtao YE1,2,3(), Wenguang LUO1, Yiqi LIU4,5
Received:
2024-06-03
Revised:
2024-07-10
Online:
2024-07-16
Contact:
Hongtao YE
摘要:
化工过程数据的动态性和非线性等特性常使传统的软测量方法难以准确提取数据的动态和非线性特征,从而影响关键质量变量的预测精度和系统整体的控制优化。因此,文中提出了一种融合多头自注意力机制的长短期记忆网络(Multi-Head Self-Attention Mechanism Long Short-Term Memory Network,MHSA-LSTM)的软测量建模方法。首先,利用LSTM充分挖掘数据的时序特征,以便提取化工过程数据的动态变化信息;其次,使用多头自注意力机制对LSTM 隐藏层的输出特征进行加权,可有效地捕捉不同尺度特征向量的长期相关性,且能提高模型的长期记忆能力;然后将提取的特征向量与其对应的特征权重相乘得出加权结果输入至全连接层,可有效地提高关键质量变量预测的精度。最后,所提出方法在脱丁烷塔过程和硫回收单元进行仿真验证,结果表明所建模型的预测精度优于门控循环单元、LSTM以及融合自注意力机制的LSTM软测量模型。
中图分类号:
李文华, 叶洪涛, 罗文广, 刘乙奇. 基于MHSA-LSTM的软测量建模及其在化工过程中的应用[J]. 化工学报, DOI: 10.11949/0438-1157.20240613.
Wenhua LI, Hongtao YE, Wenguang LUO, Yiqi LIU. Soft sensor modeling based on MHSA-LSTM and its application in chemical process[J]. CIESC Journal, DOI: 10.11949/0438-1157.20240613.
变量 | 描述 | 单位 |
---|---|---|
x1 | 塔顶温度 | ℃ |
x2 | 塔顶压力 | kg/cm2 |
x3 | 回流流量 | m3/h |
x4 | 进入下一个流程的流量 | m3/h |
x5 | 第六托盘温度 | ℃ |
x6 | 塔底温度A | ℃ |
x7 | 塔底温度B | ℃ |
y | C4浓度 | % |
表1 DC变量说明
Table1 Description of the variables for the DC
变量 | 描述 | 单位 |
---|---|---|
x1 | 塔顶温度 | ℃ |
x2 | 塔顶压力 | kg/cm2 |
x3 | 回流流量 | m3/h |
x4 | 进入下一个流程的流量 | m3/h |
x5 | 第六托盘温度 | ℃ |
x6 | 塔底温度A | ℃ |
x7 | 塔底温度B | ℃ |
y | C4浓度 | % |
模型 | RMSE | MAE | R2 |
---|---|---|---|
GRU | 0.0155 | 0.0113 | 0.9926 |
LSTM | 0.0123 | 0.0084 | 0.9953 |
SA-LSTM | 0.0079 | 0.0062 | 0.9981 |
MHSA-LSTM | 0.0063 | 0.0046 | 0.9988 |
表2 四种模型在DC测试集上的评估结果
Table2 Evaluation results of four models on the DC testing set
模型 | RMSE | MAE | R2 |
---|---|---|---|
GRU | 0.0155 | 0.0113 | 0.9926 |
LSTM | 0.0123 | 0.0084 | 0.9953 |
SA-LSTM | 0.0079 | 0.0062 | 0.9981 |
MHSA-LSTM | 0.0063 | 0.0046 | 0.9988 |
h | 0 | 1 | 2 | 4 | 8 |
---|---|---|---|---|---|
MAE | 0.0084 | 0.0062 | 0.0056 | 0.0055 | 0.0046 |
RMSE | 0.0123 | 0.0079 | 0.0076 | 0.0071 | 0.0063 |
推理时间/s | 0.0058 | 0.0077 | 0.0080 | 0.0085 | 0.0094 |
表3 MHSA-LSTM在不同h时的预测性能
Table3 The prediction performance of MHSA-LSTM at different h
h | 0 | 1 | 2 | 4 | 8 |
---|---|---|---|---|---|
MAE | 0.0084 | 0.0062 | 0.0056 | 0.0055 | 0.0046 |
RMSE | 0.0123 | 0.0079 | 0.0076 | 0.0071 | 0.0063 |
推理时间/s | 0.0058 | 0.0077 | 0.0080 | 0.0085 | 0.0094 |
变量 | 描述 | 单位 |
---|---|---|
x1 | MEA气体流量 | m3/s |
x2 | MEA第一空气流量 | m3/s |
x3 | MEA第二空气流量 | m3/s |
x4 | SWS气体流量 | m3/s |
x5 | SWS空气流量 | m3/s |
y | 尾气H2S浓度 | mol/m3 |
表4 SRU变量说明
Table4 Description of the variables for the SRU
变量 | 描述 | 单位 |
---|---|---|
x1 | MEA气体流量 | m3/s |
x2 | MEA第一空气流量 | m3/s |
x3 | MEA第二空气流量 | m3/s |
x4 | SWS气体流量 | m3/s |
x5 | SWS空气流量 | m3/s |
y | 尾气H2S浓度 | mol/m3 |
模型 | RMSE | MAE | R2 |
---|---|---|---|
GRU | 0.0139 | 0.0108 | 0.8329 |
LSTM | 0.0111 | 0.0084 | 0.8930 |
SA-LSTM | 0.0089 | 0.0057 | 0.9312 |
MHSA-LSTM | 0.0076 | 0.0043 | 0.9491 |
表5 四种模型在SRU测试集上的评估结果
Table5 Evaluation results of four models on the SRU testing set
模型 | RMSE | MAE | R2 |
---|---|---|---|
GRU | 0.0139 | 0.0108 | 0.8329 |
LSTM | 0.0111 | 0.0084 | 0.8930 |
SA-LSTM | 0.0089 | 0.0057 | 0.9312 |
MHSA-LSTM | 0.0076 | 0.0043 | 0.9491 |
h | 0 | 1 | 2 | 4 | 8 |
---|---|---|---|---|---|
MAE | 0.0084 | 0.0057 | 0.0048 | 0.0043 | 0.0071 |
RMSE | 0.0111 | 0.0089 | 0.0080 | 0.0076 | 0.0095 |
推理时间/s | 0.0063 | 0.0098 | 0.0100 | 0.0091 | 0.0099 |
表6 MHSA-LSTM在不同h时的预测性能
Table6 The prediction performance of MHSA-LSTM at different h
h | 0 | 1 | 2 | 4 | 8 |
---|---|---|---|---|---|
MAE | 0.0084 | 0.0057 | 0.0048 | 0.0043 | 0.0071 |
RMSE | 0.0111 | 0.0089 | 0.0080 | 0.0076 | 0.0095 |
推理时间/s | 0.0063 | 0.0098 | 0.0100 | 0.0091 | 0.0099 |
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