复杂生产流程协同优化与智能控制

阳春华; 孙备; 李勇刚; 黄科科; 桂卫华

doi:10.16383/j.aas.c220737

复杂生产流程协同优化与智能控制

doi: 10.16383/j.aas.c220737

1.
中南大学自动化学院长沙 410083

基金项目: 国家自然科学基金基础科学中心项目(61988101), 国家自然科学基金国际(地区)合作与交流项目(61860206014), 国家自然科学基金面上项目(61973321, 62273362, 62073340)资助

详细信息

作者简介:
阳春华：中南大学自动化学院教授. 2002年获得中南大学博士学位. 主要研究方向为复杂工业过程建模与优化控制, 智能自动化系统与装置. 本文通信作者. E-mail: ychh@csu.edu.cn

孙备：中南大学自动化学院副教授. 2015年获得中南大学博士学位. 主要研究方向为数据驱动的复杂工业过程建模与操作优化. E-mail: sunbei@csu.edu.cn

李勇刚：中南大学自动化学院教授. 2004年获得中南大学博士学位. 主要研究方向为复杂工业过程建模与控制, 智能制造, 工业大数据. E-mail: liyonggang@csu.edu.cn

黄科科：中南大学自动化学院教授. 2017年获得清华大学博士学位. 主要研究方向为复杂系统与复杂网络, 人工智能与机器学习, 智能制造与工业互联网. E-mail: huangkeke@csu.edu.cn

桂卫华：中国工程院院士, 中南大学自动化学院教授. 1981年获得中南矿冶学院硕士学位. 主要研究方向为复杂工业过程建模与优化控制, 分散鲁棒控制及故障诊断. E-mail: gwh@csu.edu.cn

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出版历程
- 收稿日期: 2022-10-08
- 网络出版日期: 2023-02-24
- 刊出日期: 2023-03-20

Cooperative Optimization and Intelligent Control of Complex Production Processes

1.
School of Automation, Central South University, Changsha 410083

Funds: Supported by the Basic Science Research Center Program of National Natural Science Foundation of China (61988101), the Funds for International Cooperation and Exchange of National Natural Science Foundation of China (61860206014), and National Natural Science Foundation of China (61973321, 62273362, 62073340)

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Author Bio:
YANG Chun-Hua　Professor at the School of Automation, Central South University. She received her Ph.D. degree from Central South University in 2002. Her research interest covers modeling and optimal control of complex industrial process, intelligent automation systems and devices. Corresponding author of this paper

SUN Bei　Associate professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University in 2015. His research interest covers data-driven modeling and operational optimization of complex industrial processes

LI Yong-Gang　Professor at the School of Automation, Central South University. He received his Ph.D. degree from Central South University in 2004. His research interest covers modeling and control of complex industrial process, smart manufacturing, and industrial big data

HUANG Ke-Ke　Professor at the School of Automation, Central South University. He received his Ph.D. degree from Tsinghua University in 2017. His research interest covers complex system and complex network, artificial intelligence and machine learning, smart manufacturing and industrial internet

GUI Wei-Hua　Academician of the Chinese Academy of Engineering, and professor at the School of Automation, Central South University. He received his master degree from Central South Institute of Mining and Metallurgy in 1981. His research interest covers modeling and optimal control of complex industrial process, distributed robust control, and fault diagnoses

摘要

摘要: 我国流程行业原料来源复杂, 如何优化调控工艺指标使复杂生产流程适应原料波动, 是保障产品质量、降低物耗能耗的关键. 本文结合全流程、工序、反应器等不同生产层级的工艺特点, 系统研究复杂生产流程协同优化和智能控制方法. 针对全流程多工序关联的特点, 提出了操作模式优化方法和操作模式动态匹配的全流程多工序协同优化方法; 针对单元工序多反应器级联的特点, 分析了工序内不同反应器的物质转化效率差异, 提出了反应器指标梯度协同优化方法; 针对反应器多反应共存、工况多变的特点, 研究了基于完备状态空间的动态特性描述框架, 建立了竞争−促进反应体系机理模型, 提出了工况全覆盖的模型参数自主辨识方法和基于分工况智能综合调节的反应器操作参数精细化调控方法. 通过锌冶炼智能工厂建设案例阐述了所提方法在提高工艺原料适应能力、生产效率、质量稳定性等方面的成效. 最后, 结合我国流程行业智能化发展现状和需求, 分析了需进一步研究的问题.
- 复杂生产流程 /
- 协同优化 /
- 智能控制 /
- 智能制造
Abstract: Considering the complexity of material supply, in order to guarantee the product quality and reduce material/energy consumption, it is required to optimize the technical indexes and operational variables to adapt to the varying feeding conditions. By incorporating the process features of different technology levels, including the plant-wide, unit process and reactor, this article systematically studies the cooperative optimization and intelligent control method for complex production processes. First, for the entire plant with multiple unit processes, an operation mode optimization and a plant-wide cooperative optimization method are designed. Then, for the unit process with multiple reactors, the difference in the efficient of reactors within a unit process is analyzed. A technical index gradient cooperative optimization method for cascaded reactors optimization is proposed. In addition, for the reactor with multiple working conditions and multiple reactions, a descriptive framework based on comprehensive state space is constructed, under which a “competitive-promotive” reaction system kinetic model is built. A working condition full coverage identification method and an working condition-based intelligent comprehensive precise control method for operational variables are proposed. The effectiveness of the proposed approach is illustrated via a case of intelligent zinc hydrometallurgy plant. Finally, the future research problems are briefly analyzed with consideration on the current status and future needs of the intelligent development of process industry.
- Complex production process /
- cooperative optimization /
- intelligent control /
- intelligent manufacturing

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图 1 全流程多工序协同优化

Fig. 1 Plant-wide cooperative optimization

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图 2 单元工序多反应器协同优化

Fig. 2 Cooperative optimization of cascaded reactors in a unit process

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图 3 反应器工艺指标动态迁移与精准控制

Fig. 3 Dynamic transition and accurate control of technical indicators of a reactor

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图 4 复杂生产流程协同优化与智能控制方法架构

Fig. 4 Framework for cooperative optimization and intelligent control of complex production processes

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图 5 完备状态空间描述体系

Fig. 5 Descriptive system of comprehensive state space

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图 6 多工况自主划分

Fig. 6 Autonomous division of multiple operation modes

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图 7 锌冶炼过程协同优化与智能控制系统架构

Fig. 7 System architecture for cooperative optimization and intelligent control of zinc smelting process

下载: 全尺寸图片幻灯片

表 1 锌冶炼过程协同优化与智能控制系统主要功能

Table 1 The main functions of the cooperative optimization and intelligent control system for zinc smelting process

功能模块名称	功能描述
全流程协同优化平台	根据原料来源、锌品位、杂质含量等信息, 以综合生产指标(能耗、锌粉消耗、有价金属回收率等)最优为目标, 采用操作模式动态匹配方法优化调整各工序工艺指标设定值, 包括可溶锌率、不溶硫率、锌浸出率、净化后液钴离子浓度、渣含锌、酸锌离子浓度等, 使各工序处于合理的工作点, 保障全流程在原料波动的条件下稳定优化运行
焙烧工序优化控制系统	主要包括焙烧炉标温优化设定和分工况稳定控制功能, 具体功能包括: 1) 基于可溶锌率、不溶硫率、制酸尾气含硫量、温度场分布均匀性等目标对标温设定值进行优化; 2) 采用基于温度趋势分析和事件驱动的模糊控制方法设定进料量; 3) 针对缩风、增风、断料等工况, 基于异常工况控制规则自动调整进料量, 保障焙烧炉温度的稳定性
浸出工序优化控制系统	主要包括浸出反应器pH值优化设定和控制功能, 具体功能包括: 1) 基于机理特征监督的长短期记忆网络模型对浸出过程pH值进行实时估计; 2) 基于浸出率和各反应器pH值的关系优化设定各反应器pH值; 3) 基于化学衡算设定废酸流量基准值, 并采用基于规则主动提取的pH值稳定控制方法根据酸料比大小调节废酸流量, 使浸出终点pH值稳定在约束范围内
净化工序优化控制系统	主要包括净化反应器出口杂质离子浓度优化设定和控制功能, 具体功能包括: 1) 基于慢特征分析方法在线估计反应器锌粉除杂效率, 根据净化入口杂质离子浓度、净化后液杂质离子浓度指标要求和各反应器锌粉除杂效率优化分配除杂任务, 得到各反应器出口杂质离子浓度优化设定值; 2) 并基于电极反应动力学计算各反应器电位目标范围; 3) 基于入口条件和锌粉除杂效率计算各反应器锌粉添加基准值, 根据电位反馈值分工况调节锌粉添加量, 使各反应器电位稳定在期望值附近, 净化后液杂质离子浓度达标
电解工序优化控制系统	主要包括以能耗优化为目标的酸锌离子浓度优化控制, 具体功能包括: 1) 基于物理平衡/氢锌竞争模型、酸锌离子浓度预测模型、酸锌离子浓度化验值和实时过程参数在线估计酸锌离子浓度; 2) 根据电流密度、目标电流效率、新液锌浓度, 基于物料平衡计算新液流量设定值; 3) 针对不同电流密度和酸锌比, 分工况调节新液流量, 实现酸锌离子浓度的稳定控制

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