Modular Hardware-in-loop Platform of Intelligent Algorithm Testing and Verification for Municipal Solid Waste Incineration
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摘要: 城市固废焚烧(Municipal solid waste incineration, MSWI) 过程因工业现场的安全要求和控制系统的封闭特性导致离线研究的各类智能算法难以在线验证. 此外, 已有的实验室仿真平台难以模拟领域专家基于多模态数据进行智能感知、认知、决策和控制的工业实际. 针对上述问题, 首先, 在综述现有面向工业过程的仿真平台研究现状和所面临挑战的基础上, 描述面向MSWI过程智能算法测试与验证平台的需求, 提出并构建了由多模态历史数据驱动系统、安全隔离与优化控制系统和多入多出回路控制系统组成的模块化半实物平台. 然后, 在实验室环境中完成平台硬件搭建、工业软件开发、仿真功能实现和典型场景验证, 并移植部分模块至现场进行应用. 最后, 总结与展望所构建模块化半实物平台的研究方向.Abstract: Due to the safety requirements of industrial sites and the closed characteristics of control systems, intelligent algorithms with off-line research mode are difficult to verify online in municipal solid waste incineration (MSWI) process. In addition, the existing laboratory simulation platform is difficult to simulate the industrial reality of domain experts' intelligent perception, cognition, decision-making and control based on multi-modal data. In view of the above problems, first, on the basis of summarizing the research status and challenge of the existing simulation platform for industrial process, the requirements of testing and verification platform for MSWI process are described. Furthermore, the modular hard-in-the-loop platform for intelligent algorithm testing and verification is proposed and constructed, which is composed of multi-modal historical data-driven system, security isolation and optimal control system, and multi-input and multi-output loop control system. Then, the platform hardware construction, industrial software development, simulation function realization and typical scene verification are completed in the laboratory environment, and some modules are transplanted to the spot for application. Finally, the research direction of the modular hard-in-loop platform is prospected.
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图 3 MSWI过程领域专家手动控制示意图
Fig. 3 Manual control schematic diagram of domain expert in MSWI process
图 8 功能分块视角的模块化半实物平台结构
Fig. 8 Structure of modular hard-in-loop platform in view of function partition
图 9 实验室与工业现场均适用视角的模块化半实物平台结构
Fig. 9 Structure of modular hard-in-loop platform in view of application in laboratory and industrial site
图 10 领域专家感知认知过程与多模态历史数据驱动系统结构
Fig. 10 Perceptual cognitive process of domain experts and structure of multi-modal historical data-driven system
图 12 某MSWI厂控制层级与多入多出回路控制系统结构图
Fig. 12 Control layer of MSWI plant and structure of multi-input and multi-output loop control system
图 15 多模态历史数据驱动系统软件结构
Fig. 15 Software structure of multi-modal historical data-driven system
图 16 安全隔离与优化控制系统软件结构
Fig. 16 Software structure of security isolation and optimal control system
图 17 安全隔离与优化控制系统软件结构图
Fig. 17 Software structure of security isolation and optimal control system
图 19 多模态历史数据驱动系统协同运行示意图
Fig. 19 A schematic diagram of the cooperative operation of multi-modal historical data-driven system
图 20 多模态历史数据驱动系统实物图
Fig. 20 Physical picture of multi-modal historical data-driven system
图 21 安全隔离与优化控制系统协同运行示意图
Fig. 21 A schematic diagram of the cooperative operation of security isolation and optimal control system
图 22 安全隔离与优化控制系统实物图
Fig. 22 Physical picture of security isolation and optimal control system
图 23 多入多出回路控制系统协同运行示意图
Fig. 23 A schematic diagram of the cooperative operation of multi-input and multi-output loop control system
图 24 多入多出回路控制系统实物图
Fig. 24 Physical picture of multi-input and multi-output loop control system
图 25 多模态历史数据驱动系统前台界面
Fig. 25 Foreground interface of multi-modal historical data-driven system
图 26 多入多出回路控制系统前台界面
Fig. 26 Foreground interface of multi-input and multi-output loop control system
图 27 安全隔离与优化控制系统前台界面
Fig. 27 Foreground interface of security isolation and optimal control system
图 28 基于GAN和DFR的多尺度二噁英排放浓度软测量软件前台界面
Fig. 28 Foreground interface of multi-scale dioxin emission concentration soft measurement software based on GAN and DFR
图 29 基于Vit-IDFC的MSWI过程燃烧状态识别软件前台界面
Fig. 29 Foreground interface of combustion state identification software for MSWI process based on Vit-IDFC
图 30 基于GAN-SCNN的MSWI过程燃烧线量化软件前台界面
Fig. 30 Foreground interface of combustion line quantization software for MSWI process based on GAN-SCNN
图 31 基于区间II型FNN的炉膛温度控制软件前台界面
Fig. 31 Foreground interface of furnace temperature control software based on Interval type-II FNN
图 32 基于自组织区间II型FNN的炉膛温度模型预测控制软件前台界面
Fig. 32 Foreground interface of furnace temperature model predictive control software based on self-organizing Interval type-II FNN
图 33 多入多出回路控制系统前台界面
Fig. 33 Foreground interface of multi-input and multi-output loop control system
图 34 安全隔离与优化控制系统前台界面
Fig. 34 Foreground interface of security isolation and optimal control system
图 35 多入多出回路控制系统前台界面
Fig. 35 Foreground interface of multi-input and multi-output loop control system
图 36 安全隔离与优化控制系统前台界面
Fig. 36 Foreground interface of security isolation and optimal control system
图 37 移植至工业现场的多模态数据实时采集系统示意图
Fig. 37 Schematic diagram of the multi-modal data real-time acquisition system transplanted to industrial sites
图 38 基于仿真机理和改进线性回归决策树的二噁英排放浓度软测量软件前台界面
Fig. 38 Foreground interface of dioxin emission concentration soft measurement software based on simulation mechanism and improved linear regression decision tree
表 1 各类平台研究现状
Table 1 Research status of different type platforms
平台类型 序号 工业过程类别 单位 年份 文献 特点描述 “真——真” 1 化学水处理工艺流程 华北电力大学 2010年 [40] 作为被控对象的化学水处理系统, 由阳离子、阴离子和混合离子交换器以及除碳器、中间水泵、中间水箱、凝结水换热器、其他辅助设备组成, 并设计电路板控制阀门状态 “真——虚” 2 炼焦生产过程 中南大学 2008年 [44] 以大型钢铁企业焦化厂优化控制系统的机、焦侧火道温度实际运行数据与本系统上模拟运行数据进行比较, 表明了温度稳定良好 3 磨矿生产过程 东北大学 2008年 [45] 以泵池液位和旋流给矿量的控制为例给出控制效果 4 磨矿流程 清华大学 2008年 [46] 基于磨矿分级过程动态模型, 能够正确反映磨机入口给矿、给水等过程控制量变化后的粒度指标动态趋势、重要工艺参数与状态变量(磨矿浓度、分级机溢流浓度、旋流器给矿浓度等)的动态趋势 5 强磁选过程 东北大学 2008年 [47] 基础回路控制系统包括6个独立回路, 能够优化控制精矿品位和尾矿品位在目标范围内 6 蒸发过程 东北大学 2009年 [48] 采用非线性自适应解耦PID控制算法对强制循环蒸发系统进行有效控制 7 电厂烟气脱硫系统 高斯图文印刷系统
(中国)有限公司2010年 [49] 国产自主品牌的DCS分散控制系统, 用于运行人员培训以及对整个脱硫系统设备运行进行分析: 8 电熔镁炉 东北大学 2011年 [50] 基于规则推理与案例推理相结合进行智能优化控制实验 9 铝酸钠叶滤过程 东北大学 2011年 [51] 进行叶滤机和阀门的逻辑启停、联锁控制以及叶滤机入口流量、压力等回路控制 10 竖炉焙烧过程 东北大学 2012年 [52] 基于正常和异常工况进行运行优化控制实验, 所采用优化控制方法包括控制回路预设定、前馈、反馈补偿、故障诊断、自愈控制及磁选管回收率软测量等 11 电厂锅炉控制系统 云南大学 2012年 [53] 基于模糊神经网络控制算法进行锅炉出口蒸汽压力实验, 被控对象模型为三入三出传递函数矩阵 12 烧结生产过程 中南大学 2012年 [54] 基于物理/虚拟资源建立云仿真平台, 利用接口层提供系统验证与调试环境, 实现料层厚度解耦控制 17 甲醇生产过程/浮式储油卸油装置 天津理工大学 2014年 [55] 用于模型预测控制、控制系统性能评价与故障诊断等, 为先进过程控制研究提供实施与验证环境 20 黄铜矿浮选过程 东北大学 2015年 [56] 基于回路控制层和优化控制层双网运行控制算法进行不确定丢包情况下的优化控制实验 21 自然循环锅炉系统 云南大学 2016年 [57] 用于顺序逻辑控制和控制策略等模块的调试与验证 23 氧化铝生料浆配料过程 沈阳镁铝设计研究院
有限公司2017年 [58] 结合了PowerFlex系列变频器, 用于控制系统的调试与开发 24 电厂自动加药系统 长沙理工大学 2018年 [59] 用于现场参数的调试与整定, 炉水模型由Simulink搭建 “虚——真” 25 电加热水箱 华北电力大学 2017年 [42] 真实对象为电加热水箱及其管道回路, 控制器由Matlab实现, 控制器参数可在线调试 26 风电机组 沈阳工业大学 2020年 [43] 用于教学实验, 能够基于此平台分析不同被控对象特性 “虚——虚” 27 间歇生产过程 上海大学 2011年 [37] 结合MATLAB和WinCC实现, 采用微分形式的机理模型 
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表 2 平台硬件描述
Table 2 Description of platform hardware
设备类型 硬件名称 型号 网络设备 交换机 TP-LINK 16口全千兆交换机, TL-SG1016DT. 隔离设备 数据采集正向隔离模块 安盟定制式采集装置, 内外网各6个千兆电口, 内外网各128G SSD, 内外网主机各1个串口、2个
USB口和2U机箱, 主动采集模块、主动发布模块、协议转换模块等, 支持通用工业协议OPC UA/
DA、Modbus等.运行参数反向传输模块 辅助设备 PCI板卡 32路隔离模拟量输入PCI-1713U板卡, 32路模拟量输出通道PCI-1724U板卡. 摄像头 海康威视红外监控摄像头, DS-2CE16C3T 6mm. 视频采集卡 天创恒达TC-330N4 4路软压缩标清音视频卡. 网络时间同步服务器 北斗时讯（天津）科技有限公司BDTS801. 基础设备 工控机 研华IPC-610L工控机, 配置Windows7 64位专业版系统. 回路控制模块 ABB可编程控制器, 8输入8输出AX522模块、16输出AO523模块、8输入8输出
AX522 PLC模块和16输出AO523 PLC模块等.
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表 3 平台软件描述
Table 3 Description of platform softwares
软件名称 功能描述 Visual Studio Professional 2022 WinForm包含不同功能的控件及触发事件函数, 用于编写和绘制前台软件系统 MATLAB 2022a 通过编写代码实现复杂计算, 同时具备强大GUI设计功能, 利用该软件实现相关算法的开发与GUI界面的设计 MATLAB 2015b 32位 利用32位版MATLAB软件将相关算法编译为动态数据连接库文件, 嵌入至开发的软件系统中进行应用. Automation Builder 设备制造商和系统集成商构建设备和系统的工程软件套装, 实现回路控制模块的硬件组态程序的编写功能 OPC Server配置软件 模拟实际工业现场中数据点位和平台中新增点位, 实现数据的传输和发布功能 网络时间同步服务器软件 接收卫星时间为计算机授时和同步多模态历史数据驱动系统中各计算机的系统时间 隔离模块配置软件 配置数据采集和传输的OPC Server和相关点位以及调用模块硬件, 实现数据单向传输
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