化工学报 ›› 2023, Vol. 74 ›› Issue (3): 1195-1204.DOI: 10.11949/0438-1157.20221340
收稿日期:
2022-10-10
修回日期:
2022-12-27
出版日期:
2023-03-05
发布日期:
2023-04-19
通讯作者:
李宏光
作者简介:
张中秋(1998—),男,硕士研究生,sleep_earily@163.com
Zhongqiu ZHANG(), Hongguang LI(), Yilin SHI
Received:
2022-10-10
Revised:
2022-12-27
Online:
2023-03-05
Published:
2023-04-19
Contact:
Hongguang LI
摘要:
当前,PID反馈控制依然是化工生产过程的主要控制策略。然而,由于复杂化工过程通常具有大时滞和非线性等特性,使得PID控制对于一些关键过程参数控制的性能不佳。为此,在实际工程中通常是现场操作人员依据自身经验对其实施人工预测调控。为了能够从历史调控数据中学习人工预测调控策略,提出了一种多任务学习级联网络(LSTM multi-task network cascades,LSTM-MNC)。根据过程变量长短期不同趋势建立预测短期变化和长期趋势的过程预测模型,并学习过程预测模型估计信息与操纵变量序列的因果关系,由被控变量偏差预测支持操纵变量序列生成,实现生产过程的智能化调控。在工业换热器过程仿真平台上进行实验,获得了满意的结果,验证了所提方法的有效性。
中图分类号:
张中秋, 李宏光, 石逸林. 基于人工预测调控策略的复杂化工过程多任务学习方法[J]. 化工学报, 2023, 74(3): 1195-1204.
Zhongqiu ZHANG, Hongguang LI, Yilin SHI. A multi-task learning approach for complex chemical processes based on manual predictive manipulating strategies[J]. CIESC Journal, 2023, 74(3): 1195-1204.
变量 | 描述 | 作用 |
---|---|---|
T3 | 物料出口温度 | 被控变量CV |
F1 | 物料入口流量 | 扰动变量DV1 |
T1 | 物料入口温度 | 扰动变量DV2 |
F2 | 冷却液流量 | 操纵变量MV |
表1 过程变量描述
Table 1 Descriptions of process variables
变量 | 描述 | 作用 |
---|---|---|
T3 | 物料出口温度 | 被控变量CV |
F1 | 物料入口流量 | 扰动变量DV1 |
T1 | 物料入口温度 | 扰动变量DV2 |
F2 | 冷却液流量 | 操纵变量MV |
项目 | MSE | IAE |
---|---|---|
人工操纵经验 | 0.07496 | 3015 |
LSTM-MNC策略 | 0.07414 | 3017 |
表2 学习的调控策略与人工经验调控性能对比
Table 2 Comparative performances of learned manipulating strategies with manual experienced manipulations
项目 | MSE | IAE |
---|---|---|
人工操纵经验 | 0.07496 | 3015 |
LSTM-MNC策略 | 0.07414 | 3017 |
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