化工学报 ›› 2023, Vol. 74 ›› Issue (8): 3407-3418.DOI: 10.11949/0438-1157.20230458
收稿日期:
2023-05-11
修回日期:
2023-07-23
出版日期:
2023-08-25
发布日期:
2023-10-18
通讯作者:
王振雷
作者简介:
闫琳琦(1999—),女,硕士研究生,Y80210074@mail.ecust.edu.cn
基金资助:
Received:
2023-05-11
Revised:
2023-07-23
Online:
2023-08-25
Published:
2023-10-18
Contact:
Zhenlei WANG
摘要:
在复杂工业生产过程中,为提高产品质量,建立关键变量多步预测模型非常必要,但传统软测量建模方法难以聚焦工业数据复杂特性,导致预测不准。本文提出一种基于时空注意力机制的双向长短时记忆网络与轻量级梯度提升机(spatial-temporal attention mechanism bi-directional long short-term memory network and light gradient boosting machine,STA-BiLSTM-LightGBM)的多步预测软测量模型。首先训练STA-BiLSTM,时空注意力机制从时间和空间维度为输入特征分配权重,BiLSTM捕捉数据时序特征;其次使用BiLSTM最后一个时间步的隐状态扩充原始输入数据后,训练LightGBM,利用弱学习器迭代训练得到最优模型;进而将STA-BiLSTM和LightGBM的预测输出按照误差倒数法变权求和得到预测结果。最后将该方法在工业数据集上仿真验证,结果表明组合模型预测效果优于BiLSTM和LightGBM,且随着预测步数增大,仍保持较高的预测精度。
中图分类号:
闫琳琦, 王振雷. 基于STA-BiLSTM-LightGBM组合模型的多步预测软测量建模[J]. 化工学报, 2023, 74(8): 3407-3418.
Linqi YAN, Zhenlei WANG. Multi-step predictive soft sensor modeling based on STA-BiLSTM-LightGBM combined model[J]. CIESC Journal, 2023, 74(8): 3407-3418.
参数 | 数值 |
---|---|
STA-BiLSTM隐层节点数 | 12 |
STA-BiLSTM优化器 | Adam |
STA-BiLSTM评价指标 | MSE |
STA-BiLSTM学习率 | 0.01 |
STA-BiLSTM 训练次数 | 300 |
LightGBM目标函数 | Poisson |
LightGBM评价指标 | RMSE和Poisson |
LightGBM学习率 | 0.1 |
LightGBM叶子节点数 | 15 |
LightGBM最大深度 | 8 |
相似样本个数 | 5 |
表1 乙烯裂解炉烟气出口温度建模参数
Table 1 Parameters of flue gas outlet temperature modeling for ethylene cracking furnace
参数 | 数值 |
---|---|
STA-BiLSTM隐层节点数 | 12 |
STA-BiLSTM优化器 | Adam |
STA-BiLSTM评价指标 | MSE |
STA-BiLSTM学习率 | 0.01 |
STA-BiLSTM 训练次数 | 300 |
LightGBM目标函数 | Poisson |
LightGBM评价指标 | RMSE和Poisson |
LightGBM学习率 | 0.1 |
LightGBM叶子节点数 | 15 |
LightGBM最大深度 | 8 |
相似样本个数 | 5 |
Models | MAE | RMSE | MAPE | R2 |
---|---|---|---|---|
BiLSTM | 0.2150 | 0.2420 | 0.1870 | 0.627 |
STA-BiLSTM | 0.0780 | 0.0960 | 0.0670 | 0.941 |
LightGBM | 0.1570 | 0.1830 | 0.1370 | 0.787 |
本文模型 | 0.0630 | 0.0800 | 0.0550 | 0.958 |
表2 提前5步预测的烟气出口温度评价指标
Table 2 Evaluation metrics of 5 steps ahead flue gas outlet temperature prediction
Models | MAE | RMSE | MAPE | R2 |
---|---|---|---|---|
BiLSTM | 0.2150 | 0.2420 | 0.1870 | 0.627 |
STA-BiLSTM | 0.0780 | 0.0960 | 0.0670 | 0.941 |
LightGBM | 0.1570 | 0.1830 | 0.1370 | 0.787 |
本文模型 | 0.0630 | 0.0800 | 0.0550 | 0.958 |
参数 | 数值 |
---|---|
STA-BiLSTM隐层节点数 | 8 |
STA-BiLSTM优化器 | Adam |
STA-BiLSTM评价指标 | MSE |
STA-BiLSTM学习率 | 0.01 |
STA-BiLSTM 训练次数 | 100 |
LightGBM目标函数 | regression_l2 |
LightGBM评价指标 | MAPE |
LightGBM学习率 | 0.1 |
LightGBM叶子节点数 | 8 |
LightGBM最大深度 | 5 |
相似样本个数 | 5 |
表3 C4浓度建模参数
Table 3 Modeling parameter of C4 concentration
参数 | 数值 |
---|---|
STA-BiLSTM隐层节点数 | 8 |
STA-BiLSTM优化器 | Adam |
STA-BiLSTM评价指标 | MSE |
STA-BiLSTM学习率 | 0.01 |
STA-BiLSTM 训练次数 | 100 |
LightGBM目标函数 | regression_l2 |
LightGBM评价指标 | MAPE |
LightGBM学习率 | 0.1 |
LightGBM叶子节点数 | 8 |
LightGBM最大深度 | 5 |
相似样本个数 | 5 |
Model | MAE | RMSE | MAPE | R2 |
---|---|---|---|---|
BiLSTM | 0.0498 | 0.0606 | 70.48 | 0.890 |
STA-BiLSTM | 0.0449 | 0.0563 | 60.22 | 0.905 |
LightGBM | 0.0433 | 0.0557 | 58.64 | 0.907 |
本文模型 | 0.0402 | 0.0504 | 56.57 | 0.924 |
表4 提前6步预测的C4浓度评价指标
Table 4 Evaluation metrics of 6 steps ahead C4 concentration prediction
Model | MAE | RMSE | MAPE | R2 |
---|---|---|---|---|
BiLSTM | 0.0498 | 0.0606 | 70.48 | 0.890 |
STA-BiLSTM | 0.0449 | 0.0563 | 60.22 | 0.905 |
LightGBM | 0.0433 | 0.0557 | 58.64 | 0.907 |
本文模型 | 0.0402 | 0.0504 | 56.57 | 0.924 |
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