基于强化学习的减少烘丝过程中烟丝 “干头” 量的方法

毕素环; 蒋一翔; 于树松; 丁香乾; 牟亮亮; 王彬

doi:10.16383/j.aas.c190367

基于强化学习的减少烘丝过程中烟丝 “干头” 量的方法

doi: 10.16383/j.aas.c190367

毕素环^{1, 2,},
蒋一翔^3,,
于树松^1,,
丁香乾^1,,
牟亮亮^1,,
王彬^4,

1.
中国海洋大学信息科学与工程学院青岛 266000
2.
青岛理工大学信息与控制工程学院青岛 266520
3.
浙江中烟工业有限责任公司杭州 310000
4.
中国海洋大学继续教育学院青岛 266000

基金项目: 国家重点研发计划 (2017YFA0700601)资助

详细信息

作者简介:
毕素环：中国海洋大学信息科学与工程学院博士. 青岛理工大学信息与控制工程学院讲师. 主要研究方向为机器学习与智能控制. E-mail: bisuhuan2016@163.com

蒋一翔：浙江中烟工业有限责任公司工程师. 主要研究方向为信息系统应用, 信息安全管理. E-mail: jiangyxlunwen@sina.com

于树松：中国海洋大学信息科学与工程学院副教授. 主要研究方向为人工智能, 智能控制. 本文通信作者. E-mail: yushusong@ouc.edu.cn

丁香乾：中国海洋大学信息科学与工程学院教授. 主要研究方向为人工智能, 智能控制. E-mail: dingxq1995@vip.sina.com

牟亮亮：中国海洋大学信息科学与工程学院博士研究生. 主要研究方向为深度学习与数据挖掘. E-mail: merlin_mu@163.com

王彬：中国海洋大学继续教育学院讲师. 主要研究方向为机器学习与数据挖掘. E-mail: wangbin@ouc.edu.cn

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出版历程
- 收稿日期: 2019-05-14
- 录用日期: 2019-09-02
- 网络出版日期: 2023-07-28
- 刊出日期: 2023-08-21

A Method for Reducing Over-dried Tobacco at Head Stage of Drying Process Based on Reinforcement Learning

1.
College of Information Science and Engineering, Ocean University of China, Qingdao 266000
2.
School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266520
3.
China Tobacco Zhejiang Industrial CO., LTD., Hangzhou 310000
4.
School of Continuing Education, Ocean University of China, Qingdao 266000

Funds: Supported by National Key Research and Development Program of China (2017YFA0700601)

More Information

Author Bio:
BI Su-Huan　Ph.D. at the College of Information Science and Engineering, Ocean University of China. She is a lecturer at the School of Information and Control Engineering, Qingdao University of Technology. Her research interest covers machine learning and intelligent control

JIANG Yi-Xiang　Engineer at the China Tobacco Zhejiang Industrial CO., LTD.. His research interest covers information system application and information security management

YU Shu-Song　Associate professor at the College of Information Science and Engineering, Ocean University of China. His research interest covers artificial intelligence and intelligent control. Corresponding author of this paper

DING Xiang-Qian　Professor at the College of Information Science and Engineering, Ocean University of China. His research interest covers artificial intelligence and intelligent control

MU Liang-Liang　Ph.D. candidate at the College of Information Science and Engineering, Ocean University of China. His research interest covers deep learning and data mining

WANG Bin　Lecturer at the School of Continuing Education, Ocean University of China. His research interest covers machine learning and data mining

摘要

摘要: 针对烘丝开始阶段存在的烘丝温度超调、过干烟丝较多等问题, 提出一种基于强化学习 (Reinforcement learning, RL)的减少烟丝“干头” 量的方法. 该方法利用生产实时数据作为输入特征向量感知烘丝生产过程的状态变化, 以烟丝含水率检测值为依据来评价、优化烘丝温度控制策略, 实现对烘丝机温度设定值的在线修正, 优化烘丝开始阶段的温度控制, 有效改善烟丝过干问题. 与烘丝机的自动控制模式和人工干预模式相比, 烟丝含水率的标准偏差比自动控制时降低了44.7%, 比人工干预时降低了14.3%. 实验结果表明烟丝含水率的稳定性有较大提高, 烟丝“干头” 量明显减少, 验证了所提方法的有效性和可行性.
- 烟丝含水率 /
- 过干烟丝 /
- 强化学习 /
- 超调
Abstract: To solve the problem of high overshoot of drying temperature and too much over-dried cut tobacco at head stage of drying process, a method for reducing over-dried tobacco based on reinforcement learning (RL) is proposed. The presented model detects dynamic performance of tobacco drying system relying on real-time production data, evaluates and optimizes the temperature control according to the amount of moisture content in tobacco, and performs real-time correction for the set value of dryer temperature. The control strategy optimizes the temperature control and effectively improves the over-dried problem. The proposed method is compared with the automatic control mode and manual intervention mode of dryer. The standard deviation of the moisture content in dried tobacco is reduced by 44.7% compared with automatic control, and decreased by 14.3% compared with manual intervention. The experimental results show that the stability of the moisture content level is improved, and the amount of over-dried tobacco is significantly reduced, which verify the effectiveness and feasibility of the proposed method.
- The amount of moisture content in tobacco /
- over-dried tobacco /
- reinforcement learning (RL) /
- overshoot

HTML全文

图 1 人工干预时烟丝含水率标准偏差

Fig. 1 Standard deviation of moisture content level in tobacco when in manual intervention mode

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图 2 制叶丝工段

Fig. 2 The stage of cut tobacco processing

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图 3 烘丝过程及温度控制

Fig. 3 Drying process and temperature control flow

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图 4 烘丝温度优化控制策略

Fig. 4 Optimal control strategy for drying temperature

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图 5 烘丝温度优化控制流程

Fig. 5 Optimization of temperature control flow

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图 6 烘丝生产系统状态感知

Fig. 6 Tobacco drying system state perception

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图 7 模型的输入特征向量

Fig. 7 Input feature vector of the proposed model

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图 8 3 种常用激活函数及设计的奖励函数

Fig. 8 Three commonly used activation functions and the designed reward functions

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图 9 3 种模式下烘丝机温度和烟丝含水率曲线

Fig. 9 The dryer temperature and the moisture content level in tobacco when in three control modes

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表 1 烘丝生产系统状态特征

Table 1 The state features of tobacco drying system

特征类别	生产状态特征	特征数
原料烟丝	KLD烘前水分、KLD烘丝流量、叶丝累计量	3
批次编号	年、月、日、生产线编号、班组、生产序号	6
配方参数	KLD除水量、含水率目标值、干燥能力、干燥因子	4
过程检测量	KLD烘丝段蒸汽流量、SIROX蒸汽流量、SIROX烘丝分汽缸压力、SIROX烘丝分汽缸温度、SIROX排潮风机负压值、SIROX后温度、SIROX阀后蒸汽温度、SIROX阀后蒸汽压力、SIROX阀前蒸汽温度、SIROX阀前蒸汽压力、KLD一次减压后蒸汽压力、KLD烘后水分、KLD烘后温度、KLD排潮温度、Ⅰ区工作蒸汽压力、Ⅱ区工作蒸汽压力、Ⅰ区回水温度、Ⅱ区回水温度、Ⅰ区筒壁温度、Ⅱ区筒壁温度、热风风速、热风温度、排潮负压、风选冷却排潮负压、冷却温度、冷却水分	26
设备参数	SIROX蒸汽阀门开度、KLD筒转速、KLDⅠ区蒸汽薄膜阀开度、KLDⅡ区蒸汽薄膜阀开度、Ⅰ区筒壁温度设定值、Ⅱ区筒壁温度设定值、热风蒸汽阀门开度、风门开度、排潮开度、风选冷却排潮开度	10

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表 2 烟丝含水率标准偏差

Table 2 Standard deviation of moisture content level in tobacco

控制模式	标准偏差
控制模式	开机20 min	开机30 min	开机40 min
自动控制	0.097	0.082	0.076
人工干预	0.056	0.053	0.049
Actor-Critic优化控制	0.051	0.045	0.042

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