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基于RAGAN的工业过程运行指标前馈 − 反馈多步校正

杨宇晴 王德睿 丁进良

杨宇晴, 王德睿, 丁进良. 基于RAGAN的工业过程运行指标前馈 − 反馈多步校正. 自动化学报, 2022, 48(x): 1−11 doi: 10.16383/j.aas.c210408
引用本文: 杨宇晴, 王德睿, 丁进良. 基于RAGAN的工业过程运行指标前馈 − 反馈多步校正. 自动化学报, 2022, 48(x): 1−11 doi: 10.16383/j.aas.c210408
Yang Yu-Qing, Wang De-Rui, Ding Jin-Liang. Ragan based feedforward-feedback multi-step correction of operational indices for industrial processes. Acta Automatica Sinica, 2022, 48(x): 1−11 doi: 10.16383/j.aas.c210408
Citation: Yang Yu-Qing, Wang De-Rui, Ding Jin-Liang. Ragan based feedforward-feedback multi-step correction of operational indices for industrial processes. Acta Automatica Sinica, 2022, 48(x): 1−11 doi: 10.16383/j.aas.c210408

基于RAGAN的工业过程运行指标前馈 − 反馈多步校正

doi: 10.16383/j.aas.c210408
基金项目: 国家自然科学基金(61988101, 62161160338)和国家重点研发计划(2018YFB1701104)资助
详细信息
    作者简介:

    杨宇晴:东北大学流程工业综合自动化国家重点实验室硕士研究生. 201年获得东北大学学士学位. 主要研究方向为生成对抗网络,深度学习与人工智能. E-mail:yang1301067169@163.com

    王德睿:东北大学流程工业综合自动化国家重点实验室硕士研究生. 2019年获得东北大学学士学位. 主要研究方向为生成对抗网络,人工智能与机器学习. E-mail:a1044261411@163.com

    丁进良:东北大学流程工业综合自动化国家重点实验室教授. 主要研究方向为复杂工业过程的建模与运行优化控制,计算智能及应用. E-mail:jlding@mail.neu.edu.cn

RAGAN Based Feedforward-feedback Multi-step Correction of Operational Indices for Industrial Processes

Funds: Supported by National Natural Science Foundation of China (61988101, 62161160338), and National Key Research and Development Program of China (2018YFB1701104)
More Information
    Author Bio:

    YANG Yu-Qing Master student at the State Key Laboratory of Synthetical Automation for Process Industies, Northeastern University. Received the bachelor degree in Northeastern University. Her research interest covers generative adversarial nets, deep learning and artificial intelligence

    WANG De-Rui Master student at the State Key Laboratory of Synthetical Automation for Process Industies, Northeastern University. He received his bachelor degree from Northeastern University in 2019. His research interest covers generative adversarial nets, artificial intelligence, and machine learning

    DING Jin-Liang Professor at the State Key Laboratory of Synthetical Automation for Process Industies, Northeastern University. His research interest covers modeling and operation optimization control of complex industrial process, computational intelligence and its application

  • 摘要: 针对工业过程运行指标反馈校正存在滞后及一步校正模型可解释性差的问题, 提出了基于递归注意力生成对抗网络(RAGAN)的运行指标前馈-反馈多步校正方法. 该方法采用基于负相关正则化的集成随机权神经网络建立综合生产指标预报模型为校正提供前馈信息补偿反馈校正的滞后性. 所提的RAGAN校正采用多步校正实现一次调整的思想, 将当前时刻运行指标映射到低维潜变量空间简化数据复杂度, 利用长短期记忆(LSTM)模型实现数据的分步输入, 提高模型可解释性; 采用分布式注意力机制构建数据读入网络, 使校正环节获取任务相关性更高的数据, 降低任务复杂度, 减小噪声干扰, 利用校正后的运行指标保证系统的综合指标尽可能的跟随设定值运行. 采用我国西部地区最大选矿厂实际数据的仿真实验验证了所提方法的有效性.
  • 图  1  复杂工业过程运行指标决策过程

    Fig.  1  Decision-making of operational indices for complex industrial processes

    图  2  校正模型的训练过程

    Fig.  2  The training process of the correction model

    图  3  校正模型的运行过程

    Fig.  3  The running process of the correction model

    图  4  RAGAN整体流程图

    Fig.  4  The overall flow chart of the proposed RAGAN

    图  5  赤铁矿生产线流程图

    Fig.  5  Flow chart of hematite ore beneficiation production line

    图  6  综合生产指标预报值

    Fig.  6  Predictive value of comprehensive production indices

    图  7  所提方法与现有系统决策模型的对比

    Fig.  7  Comparison result between the proposed approach and the existing system

    图  8  多步推理过程决策可视化

    Fig.  8  Visualization of multi-step reasoning process decision

    表  1  选矿过程的运行指标

    Table  1  Operation index of the beneficiation process

    生产线工序运行指标
    弱磁选别(LMPL)焙烧过程r1: 磁选管回收率(MTRR)(%)
    磨矿过程r2: 磨矿粒度(PSLMPL)(%)
    弱磁磁选r31: 弱磁精矿品位(CGLMPL)(%)
    r32: 弱磁尾矿品位(TGLMPL)(%)
    强磁选别(HMPL)磨矿过程r4: 磨矿粒度(PSHMPL)(%)
    强磁磁选r51: 强磁精矿品位(CGHMPL)(%)
    r52: 强磁尾矿品位(TGHMPL)(%)
    下载: 导出CSV

    表  2  校正前后评价指标对比

    Table  2  Comparison of evaluation indexes before and after correction

    ModelMAPEMMIKDA
    Original0.2324±0.00001.2190±0.00000
    RAGAN_p0.0335±0.00250.8879±0.05320.4010±0.0061
    下载: 导出CSV

    表  3  不同校正方法对比

    Table  3  Comparison of different correction method

    ModelMAP EM MIKDA
    DMGAN 0.0480±0.0078 1.0708±0.3157 0.2300±0.0017
    MLP 0.1174±0.0094 0.4796±0.0698 0.3365±0.0041
    RAGAN_b 0.0362±0.0025 1.0496±0.0589 0.4281±0.0022
    RAGAN_p 0.0335±0.0025 0.8879±0.05320.4010±0.0061
    下载: 导出CSV

    表  4  不同推理步长对模型性能的影响

    Table  4  The effect of different reasoning steps on model performance

    Iteration(T) MAP EM MIKDA
    1 0.0711±0.00430.2034±0.02890.7229±0.0080
    5 0.0433±0.00320.8982±0.07640.4541±0.0038
    10 0.0335±0.00250.8879±0.05320.4010±0.0061
    15 0.0314±0.00201.1687±0.03960.4676±0.0019
    20 0.0363±0.00230.9966±0.05080.4490±0.0040
    下载: 导出CSV

    表  5  不同隐变量维度对模型性能的影响

    Table  5  The impact of different hidden variable dimensions on model performance

    z_dimMAP EM MIKDA
    40.0306±0.00230.6934±0.02660.5011±0.0019
    100.0335±0.00250.8879±0.05320.4010±0.0061
    200.0368±0.00361.0000±0.06190.3823±0.0020
    320.0449±0.00351.1559±0.08090.4094±0.0033
    640.0469±0.00420.8329±0.05120.4165±0.0028
    1000.0450±0.00400.9498±0.07730.4155±0.0016
    下载: 导出CSV

    表  6  不同高斯核数对模型性能的影响

    Table  6  The impact of different Guassian kernel on model performance

    KernelMAP EM MIKDA
    5 1 50.0455±0.00400.8264±0.06210.4278±0.0081
    5 3 50.0508±0.00400.5435±0.04080.4654±0.0090
    5 4 50.0352±0.00290.7671±0.06300.4315±0.0038
    5 5 50.0461±0.00330.5582±0.03450.5075±0.0027
    5 7 50.0413±0.00310.6542±0.04170.4914±0.0028
    5 9 50.0333±0.00280.6898±0.03870.4944±0.0100
    5 4 30.0383±0.00300.6246±0.04150.5332±0.0040
    5 4 50.0352±0.00290.7671±0.06300.4315±0.0038
    5 4 70.0355±0.00380.9458±0.05980.4177±0.0075
    5 4 90.0349±0.00280.9773±0.07010.4371±0.0038
    3 4 70.0344±0.00271.0734±0.05700.4409±0.0020
    5 4 70.0355±0.00380.9458±0.05980.4177±0.0075
    7 4 70.0335±0.00250.8879±0.05320.4010±0.0061
    9 4 70.0376±0.00261.0355±0.05210.4366±0.0047
    No 0.0347±0.00221.9838±0.07380.5216±0.0021
    下载: 导出CSV

    表  7  不同TV损失系数对模型性能的影响

    Table  7  The impact of different TV loss coefficient on model performance

    tvMAP EM MIKDA
    00.0334±0.00281.1894±0.0427 0.4603±0.0016
    0.00010.0336±0.00340.9965±0.0705 0.4488±0.0041
    0.0010.0335±0.00250.8879±0.0532 0.4010±0.0061
    0.010.0365±0.00260.6869±0.0465 0.4326±0.0057
    0.10.0489±0.00210.1963±0.0081 0.7533±0.0036
    下载: 导出CSV
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  • 收稿日期:  2021-05-13
  • 修回日期:  2021-12-21
  • 网络出版日期:  2022-05-08

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