基于工厂数据的注意力LSTM网络辨识方法

doi:10.11949/0438-1157.20201067

摘要/Abstract

摘要：

化工企业控制系统日益复杂，辨识被控对象模型是自动控制和优化设计的首要任务。针对化工过程多数辨识实验需要对过程施加测试信号，可能导致生产中断或引发安全事故的问题，利用长短时记忆（long short-term memory，LSTM）网络对含高维度、强耦合、非线性等特点的工厂时序数据具有的强适应性的特点，提出一种结合注意力机制思想的LSTM非线性动态模型辨识算法。该算法在LSTM模型基础上考虑输入变量对目标变量的重要性，为输入序列中影响输出结果的关键特征分配更多注意力，提高了LSTM模型的泛化能力。基于工厂日常运行数据构建LSTM网络模型可作为被辨识对象的数字化虚拟装置，利用人工测试信号在虚拟装置上离线辨识局部线性模型。在Tennessee-Eastman（TE）过程上的辨识实验验证了本文方法的有效性。

关键词: 化工过程建模, 系统辨识, 非线性动态模型, 长短时记忆, 数字化虚拟装置

Abstract:

The control system of chemical enterprises is becoming more and more complex, and identifying the controlled object model is the primary task of automatic control and optimization design. In view of the problem that most chemical process identification experiments need to apply test signals to the process, which may lead to production interruption or safety accidents, a long short-term memory(LSTM) nonlinear dynamic model identification algorithm combined with attention mechanism is proposed to adapt to plant time series data with characteristics of high dimension, strong coupling and nonlinearity. Based on LSTM model, the algorithm considers the importance of the input variables to the target variables, pays more attention to the key features that affect the output results in the input sequence, and improves the generalization ability of the LSTM model. The LSTM network model based on the daily operation data of the plant can be used as the digital virtual device of the identified object, and the local linear model can be identified offline on the virtual device by using the designed test data. The identification experiments on Tennessee-Eastman (TE) process verify the effectiveness of this method.

Key words: chemical process modeling, system identification, nonlinear dynamic model, LSTM, digital virtual device

中图分类号:

TP 273

王雅欣,徐宝昌,徐朝农,董秀娟,许立伟. 基于工厂数据的注意力LSTM网络辨识方法[J]. 化工学报, 2020, 71(12): 5664-5671.

WANG Yaxin,XU Baochang,XU Chaonong,DONG Xiujuan,XU Liwei. Attention LSTM network identification method based on factory data[J]. CIESC Journal, 2020, 71(12): 5664-5671.

图/表 10

图1 基于工厂数据的过程模型辨识框架

Fig.1 Process model identification framework based on plant data

图2 LSTM结构图

Fig.2 Architectures of LSTM

图3 结合注意力机制思想的LSTM算法流程图

Fig.3 Flow chart of LSTM algorithm combined with attention mechanism

图4 结合注意力机制思想的LSTM网络结构图

Fig.4 LSTM network structure combined with attention mechanism

表1 网络模型参数

Table 1 Parameters of network model

参数名称	数值
输入维数	9
数出维数	1
LSTM层数	1
隐藏节点数	18
训练数据	6000组
测试数据	3000组

图5 DFP算法与梯度下降算法训练误差

Fig.5 Training error of DFP algorithm and gradient descent algorithm

图6 模型预测结果对比

Fig.6 Comparison of model prediction

图7 工作点2820 kPa处离线实验结果对比

Fig. 7 Comparison of off-line experimental results at 2820 kPa

表2 模型测试与离线实验结果

Table 2 Model test and off-line experimental results

工作点／kPa	常规LSTM		本文所提模型
工作点／kPa	模型测试MAPE／%	离线实验MAPE／%	模型测试MAPE／%	离线实验MAPE／%
2810	1.64	8.56	1.16	6.05
2820	5.72	11.54	3.07	8.46
2830	4.65	9.92	2.89	7.32

图8 传递函数模型及其阶跃响应曲线(s表示复变量)

Fig.8 Transfer function model and its step response curves

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