Research Status and Prospects of Intelligent Optimization Control for Municipal Solid Waste Incineration Process
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摘要: 针对全球城市固废(Municipal solid waste, MSW)的高增长率导致城市环境持续恶化以至于“垃圾围城”现象日益增多等问题, MSW焚烧(MSW incineration, MSWI)技术能够通过发酵、燃烧、换热和净化等工艺实现废物变能源(Waste-to-energy, WTE). 在当前“双碳战略”和“蓝天净土”的新环保背景下, 作为未来长时期内MSW处理主流方式和生态文明建设与循环经济体系托底工业的MSWI过程正面临着重大机遇. 如何融合人工智能、大数据、云计算等技术实现MSWI的智慧化、低碳化和绿色化可持续性发展是目前具有挑战性的难题. 对此, 本文首先描述MSWI工艺机理, 分析其运行控制特性和实现其智能优化控制存在的难点; 然后, 从燃烧特性分析与建模、燃烧过程控制、指标建模与预测、运行监控与故障识别、操作(控制)变量优化、算法仿真验证平台等6个方面进行综述; 接着, 分析MSWI过程智能优化控制研究的必要性; 最后, 结合工业人工智能的本质给出未来研究方向. 在此基础上, 展望基于数字孪生平台的MSWI智能优化控制系统的框架和愿景, 并总结未来挑战.Abstract: The urban environment has deteriorated and garbage-surrounded has emerged because municipal solid waste (MSW) has a high annual growth rate of the whole earth. MSW incineration (MSWI) technology, which contains fermentation, combustion, heat exchange, and purification, can achieve the goal of waste-to-energy (WTE). The MSWI process, which is the essential way of dealing with MSW in the future for a long time and the supporting industry of ecological civilization construction and the circular economy system, has faced a major opportunity in the context of the “Double Carbon Strategy” and the “Blue Sky Pure Land” environmental policy. Incorporating artificial intelligence, big data, cloud computing, and other technologies to conduct smart, low-carbon, and green sustainable development of MSWI is a challenging problem. Aiming at this problem, the operational control characteristic and difficulty in realizing the intelligent optimal control are analyzed based on the typical MSWI process mechanism. Further, the status of operation control is investigated from 6 viewpoints, i.e., combustion characteristic analysis and modeling, combustion process control, indices modeling and prediction, operation monitoring and fault identification, manipulate (control) value optimization, and algorithm simulation verification platform. Then, the necessity for making research intelligent optimization control is analyzed. Finally, the future research direction is given based on the nature of industrial artificial intelligence. In addition, the framework and future of MSWI＇s intelligent optimal control system based on the digital twin platform have been prospected and future challenges are summarized.
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A1 MSWI过程的研究成果总结与对比
A1 Summary and comparison of research results for MSWI process
方向 子方向 侧重点 研究内容 贡献与不足 年代与文献编号 燃烧过程建模与特性分析
研究机理驱动
的燃烧过
程建模
研究MSW 床层
燃烧过程借鉴煤和焦炭的传热和传质机理构建 MSW 床层燃烧机理模型 贡献: 利用有限体积法求解机理模型, 实现机理模型仿真
不足: 未全面考虑 MSW 的化学成分和物理性质1994 [59] 将燃烧分解为干燥、热解和气化反应阶段, 建立床层燃烧基础理论模型 贡献: 分解 MSW 燃烧机理过程为 3 部分, 奠定机理分析基础
不足: 缺少燃烬区域机理1998 [60] 假设 MSW 为球形、圆柱形和平板形的均质几何体颗粒后研究燃烧模型 贡献: 能够分析颗粒尺寸对燃烧时间的影响
不足: 未考虑真实 MSW 的化学成分和物理性质1998 [61] 焚烧炉分为加热干燥区、热解区、残炭燃烧区、挥发分燃烧区、辐射传热区后建立热力学模型 贡献: 划分多个区域构建模型, 细化燃烧过程机理
不足: 未充分仿真验证所构建模型2000 [329] 研究升温、水分蒸发、高温分解、气相燃烧、焦炭氧化等过程, 分析风量与燃烧过程的关系 贡献: 构建床层燃烧一维数学模型, 给出风量与燃烧火焰面积间的关系
不足: 将 MSW 等效的几何体颗粒设置为均质化材料, 不符合实际2000 [65],
2000 [66]研究水分析出、挥发分析出、焦炭燃烧、气相燃烧和对流辐射传热区域的燃烧特性 贡献: 采用分区策略和分层计算模拟燃烧
不足: 计算量大2006 [330] 分析一维床层数学模型的热传导、反应速率和挥发分组成等参数对燃烧的影响 贡献: 能够分析一维模型参数的敏感性
不足: 分析结果仅适用于一维模型, 有待推广2007 [67] 研究挥发分燃烧中存在的反应受限模式、非充分燃烧模式和充分燃烧模式 贡献: 描述挥发分燃烧的反应物和产物沿炉排方向的分布规律
不足: 仅考虑一次风量对挥发分燃烧的影响2010 [62] 研究 MSW 水分干燥过程的机理 贡献: 分析不同因素影响, 根据 MSW 中水分质量的变化, 提出水线概念
不足: 仅考虑一次风量和风温的影响, 有待推广2011 [63] 基于 MSW 水分蒸发、挥发分析出燃烧和焦炭燃烧等模型研究炉膛温度分布和燃烧状态 贡献: 采用机理模型模拟实际 MSW 燃烧
不足: 仅考虑风量变化对燃烧过程的影响, 有待推广2015 [64] 构建移动式炉排炉的动态动力学模型 贡献: 划分料层为 N 个不等高的同质模块, 沿运动方向构建动态机理模型
不足: 模型的动态定量性能还有待验证2020 [68] 数值仿真驱
动的燃烧特
性分析固相仿真
模拟分析燃烧参数对燃烧特性的影响 贡献: 基于 2 维机理模型模拟燃烧, 开发 FLIC 软件为后续研究提供工具
不足: 软件输入参数设置较为复杂2003 [73],
2004 [74]基于离散元模型仿真燃烧过程 贡献: 提出耦合 DEM 与 ANSYS-CFX 的框架
不足: 计算消耗大, 难以直接应用于智能优化控制研究2009 [75] 气相燃烧
仿真FLUENT 仿真气相燃烧过程, 分析 SNCR 脱硝技术与 NOx 排放浓度间的映射关系 贡献: 为 SNCR 系统的设计与改造提供理论依据
不足: 难以直接应用于智能优化控制研究2010 [76],
2013 [77],
2013 [78]利用 FLUENT 仿真常规空气焚烧、富氧焚烧无烟气再循环、富氧焚烧有烟气再循环燃烧过程, 分析不同注氧装置时的速度、温度和浓度场等 贡献: 为工艺参数优化和洞悉燃烧机理提供支撑
不足: 未考虑固相燃烧, 难以直接应用于智能优化控制研究2015 [79] 固−气相耦合
仿真模拟分析 MSW 水分含量和预热空气温度对燃烧过程的影响 贡献: MSW 水分含量与燃烧效率相关性较大, 能够为工厂运行提供指导
不足: 考虑因素单一, 有待推广2007 [81] 研究 MSW 颗粒混合系数与燃烧过程生成 CO 浓度间的映射关系 贡献: 混合系数变化导致燃烧线位置变化和 CO 浓度变高, 为运行提供指导
不足: 考虑因素单一, 有待推广2008 [80] 分析一次风量分配和初始料层厚度对 MSW 减重率和炉膛出口 CO 浓度的影响 贡献: 获得风量和炉排速度的优化设计参数, 为工厂运行提供指导
不足: 考虑因素单一, 有待推广2010 [82] 研究操作量与被控量炉膛温度和工艺参数烟气停留时间之间的关系 贡献: 获得优化 NOx 和 DXN 排放浓度的工艺参数, 为工厂运行提供指导
不足: 所建模型难以应用于后续智能优化控制研究2015 [83] 分析炉排速度和堆料厚度、折焰角等炉体结构对燃烧的影响, 预测炉膛温度分布 贡献: 合适的炉排速度和二次风量角度可改善燃烧效率, 为运行提供指导
不足: 考虑因素单一, 有待推广2019 [331] FLIC 获得组分温度、速度和浓度后, 耦合 FLUENT 仿真燃烧特性和流动特性的相关信息 贡献: 描述质量、动量和传热的控制方程, 给出水分蒸发、颗粒脱挥发分、挥发分燃烧和焦炭燃烧过程的数学描述, 为洞悉机理提供支撑
不足: 难以直接应用于智能优化控制研究2002 [35] 耦合 DEM 与 FLUENT 进行燃烧过程仿真和可视化 贡献: 基于粒子模拟燃烧, 有利于分析固相、气相之间的相互作用
不足: 计算消耗大, 难以直接应用于智能优化控制研究2017 [332] 数据驱动的
燃烧过程建
模研究关键被控
变量建模基于多模型智能组合算法的炉膛温度模型 贡献: 在不同工况下进行模型的智能选择, 提高预测精度
不足: 模型训练时间过长, 仅考虑单被控变量2019 [92] 面向控制的模糊神经网络炉膛温度模型 贡献: 能够表征燃烧过程的不确定性
不足: 模型需在线训练, 仅考虑单个被控变量2004 [93],
2020 [94]基于 LS-SVR 的炉膛温度模型 贡献: 具有高于 BPNN 和 RBF 的泛化性能
不足: 仅适用单一工况建模, 仅考虑单被控变量2022 [95] 基于权重 PCA 和改进 LSTM 的烟气含氧量模型 贡献: 改进 PCA 算法, 简化了模型结构
不足: 建模时间过长, 仅适用单一工况建模, 仅考虑单被控变量2021 [97] 基于时域输入的主蒸汽温度神经网络预测模型 贡献: 考虑输入输出间的延时特性, 预测精度更高
不足: 非主要被控变量, 对智能优化控制研究的意义有限2021 [98] 基于自适应卡尔曼滤波参数更新机制的 RBF 蒸汽流量预测模型 贡献: 具有简洁的网络拓扑性
不足: 非面向控制, 仅适用于单一工况, 算法须在线应用2011 [90] 基于平均影响值算法选择特征的 RBF 蒸汽流量预测模型 贡献: 解决变量间存在的冗余性
不足: 非面向控制, 仅适用于单一工况2022 [99] 基于 LSTM 的蒸汽流量预测模型 贡献: 能够动态更新和进行准确预测
不足: 非面向控制, 仅适用于单一工况2021 [100] 烟气含氧量和锅炉蒸汽流量的ARX模型 贡献: 线性模型, 速度快
不足: 未考虑全部主要被控变量, 线性模型难以描述实际映射关系2002 [101] 基于权重自适应 PSO 的被控变量级联传递函数模型 贡献: 符合实际燃烧过程被控变量间的递进关系
不足: 未考虑多个输入变量之间的耦合2021 [102] 基于 RF 和 GBDT 的被控变量混合集成模型 贡献: 约简特征后分别对 3 个被控变量进行拟合, 精度提升
不足: 训练时间长, 未考虑被控变量间的耦合关系2021 [103] 基于 T-S 模糊神经网络的被控变量模型 贡献: 同时对多个被控变量进行建模, 考虑不确定性和相互耦合性
不足: 仅适用于单一工况, 适应性差, 模型需在线更新2022 [44] 基于过程数据和火焰图像的燃烧线量化 贡献: 综合考虑火焰图像和过程数据信息量化燃烧状态
不足: 未建立燃烧线与操作变量间的映射关系, 图像处理技术有待提升1996 [104] 辅助变量
建模基于热平衡机理视角实时估算 MSW 热值 贡献: 为燃烧过程提供实时指导并应用
不足: 难以适应复杂工况, 实用性有待提升2017 [109],
2019 [102]建立检测 MSW 热值的软仪表模型 贡献: 根据热值测量结果可实时修订控制策略, 提高燃烧效率
不足: 适用特定对象, 普适性有待提升2002 [111] 基于人工神经网络的 MSW 热值模型 贡献: 具有一定程度的工程应用价值, 为现场提供操作指导
不足: 约简了 MSW 组成成分, 造成实际热值与计算热值间存在偏差2016 [112],
2002 [113],
2003 [114],
2010 [115],
2012 [116],
2010 [117],
2021 [118],基于先验知识、专家经验和数据挖掘技术建立 MSW 热值模型 贡献: 快速、经济的 MSW 热值在线检测方法
不足: 模型适应特定对象, 可移植性差2017 [119] 基于风压、风量、负压和炉排面积等计算料层厚度 贡献: 为燃烧过程炉排速度的调节提供指导
不足: 估计值的准确性难以有效验证2019 [110],
2022 [122],
2021 [123]基于多尺度颜色矩特征和 RF 的燃烧状态识别模型 贡献: 解决固定滑动窗口只能提取固定大小特征的问题
不足: 仅识别燃烧线的位置, 不能完全表征燃烧状态, 模型精度低2019 [126] 基于生成对抗网络混合增强的燃烧状态识别模型 贡献: 克服生成式和非生成式数据增强各自存在的缺陷
不足: 仅识别燃烧线位置, 不能完全表征燃烧状态2021 [127] 基于半监督策略的状态模型识别未知火焰燃烧状态 贡献: 可识别新的未知燃烧状态, 节省计算成本
不足: 仅实现火焰状态的识别, 未涉及燃烧线位置2021 [128] 采用声波发射温度检测方法重建火焰各区域温度场 贡献: 解决热电偶测温的不准确、稳定性差等问题, 实现可视化和数字化
不足: 成本高、经济性较差, 有待推广2019 [129] 基于牛顿迭代法和 Hottel 发射率的多光谱火焰图像与火焰温度间的映射模型 贡献: 采用多波长测温法监测 MSWI 过程, 为新视角与新手段
不足: 成本高、经济性较差, 有待验证和推广2022 [130] 构建光谱仪检测火焰特征与火焰中碱性金属浓度 (钠、钾、铷) 间的映射模型 贡献: 证明碱性金属浓度与炉膛温度间的相关关系
不足: 成本高、经济性较差, 用途不明晰2017 [131],
2019 [132]基于蒙特卡罗和多个成像角度进行火焰温度三维可视化建模 贡献: 可视化三维单峰和双峰温度分布, 清晰地再现温度分布特征
不足: 仅对温度场进行建模与展示, 如何应用有待深入2002 [133] 燃烧过程
控制研究现场控制
研究进展ACC 系统 在 ACC 系统上增加控制风量和风温的模糊控制器 贡献: 面对特定对象的 ACC 系统, 提升鲁棒性和控制效果
不足: 不能脱离 ACC 系统, 仅起辅助作用, 难以推广1993 [140],
1991 [141]采用红外热像仪检测炉膛温度及其波动信息 贡献: 能够改善对 ACC 系统进行微调时的快速响应性
不足: 成本高, 难以推广1994 [142] 将烟气排放指标的控制前移, 改善 ACC 系统控制逻辑 贡献: 组合脱硝、石灰浆、排放因子等数据实现最优控制
不足: 不能脱离 ACC 系统2019 [147] 模糊控制 结合模糊推理与神经网络控制燃烧过程 贡献: 基于神经网络的燃烧状态识别模型提供反馈信息, 降低 CO 排放浓度
不足: 神经网络易过拟合, 对数据要求高1998 [143],
1996 [104]提出模糊规则控制并用于日本某 MSWI电厂 贡献: 解决燃烧过程出现的“反向响应”
不足: 适合特定对象, 在国内的推广性不强1989 [148] 总结领域工程师经验为模糊控制规则 贡献: 解决传统 PID 存在的温度波动剧烈、炉渣含量高、燃尽率差等问题
不足: 对热值低、含水量大的 MSW 控制效果较差2003 [149] 基于专家规则的燃烧控制系统 贡献: 通过专用领域知识库实现系统模块化
不足: 适用于特定场景, 系统可移植性差2006 [150] 其他改进
措施基于红外摄像机图像在线检测 MSW 、烟气和火焰等温度信息 贡献: 能够辅助进行燃烧控制
不足: 成本高, 有待推广2006 [144] 基于滤波算法控制炉膛负压与炉膛温度的稳定 贡献: 克服炉排翻动时造成的炉膛负压波动问题
不足: 未考虑对其他主要被控变量的影响2004 [145] 设计基于蒸汽流量校正的闭环控制策略 贡献: 适应 MSW 特性复杂与不稳定的问题, 有利于实现长期连续稳定运行
不足: 未考虑对其他主要被控变量的影响, 适用工况有限2017 [146] 非现场控制
研究进展单回路单
变量控制构建基于 BPNN 的 MSW 含水量估计模型, 补偿控制炉膛温度的模糊规则推料器 贡献: 解决 ACC 系统中含水量估计信息缺失导致控制精度降低的问题
不足: 未考虑对其他主要被控变量的影响1993 [140] 分析模糊规则控制器应用局限性, 构建面向炉膛温度的神经网络模糊控制器 贡献: 提高燃烧效率的同时能够降低污染物排放
不足: 未仿真测试效果1994 [151] 构建具有自调整因子的面向炉膛温度的模糊规则控制器 贡献: 依据运行状态采用修正算法调整自适应因子, 提高系统自适应性
不足: 未考虑对其他主要被控变量的影响2005 [152],
2004 [153]构建具有加权自适应因子的面向炉膛温度的模糊规则控制器 贡献: 解决相同控制策略在不同工况下导致的炉膛温度波动大的问题
不足: 未考虑对其他主要被控变量的影响2004 [154] 构建控制参数及控制规则在线整定与优化的面向炉膛温度的分层模糊规则控制器 贡献: 可依据工况选择修正因子
不足: 未考虑对其他主要被控变量的影响2004 [155] 构建基于比例因子的面向炉膛温度的 T-S 模糊神经网络控制器 贡献: 在线修正比例因子, 改善控制器性能
不足: 未考虑对其他主要被控变量的影响2011 [156] 构建面向炉膛温度的模糊自适应 PID 控制器 贡献: 自动调整 PID 控制参数, 提高系统的适应性
不足: 未考虑对其他主要被控变量的影响2008 [157] 构建基于事件触发的面向炉膛温度的 RBF-PID 控制器 贡献: 降低动态调整 PID 参数更新的次数
不足: 未考虑对其他主要被控变量的影响, 适用工况单一2022 [158] 构建面向炉膛温度的仿人智能控制器 贡献: 融合领域专家的认知经验
不足: 未考虑对其他主要被控变量的影响, 有待验证2014 [159],
2015 [160],
2016 [161]构建基于 PSO 算法改进的面向炉膛温度的仿人智能控制器 贡献: 具有很强的抑制外界脉冲干扰的能力
不足: 未考虑对其他主要被控变量的影响, 有待验证2018 [162] 构建面向烟气含氧量的自适应模型预测控制器 贡献: 采用模型参数自适应调节策略, 提高控制器的动态自适应性
不足: 未考虑对其他主要被控变量的影响, 适用工况单一2021 [163] 构建面向蒸汽流量的模糊规则控制器 贡献: 显著降低因异常工况导致的蒸汽流量波动问题
不足: 未考虑对其他主要被控变量的影响1995 [164],
2000 [165]构建基于固定时间周期窗口反馈的蒸汽流量稳定控制器 贡献: 根据炉排运动特点采取周期性控制措施, 贴合实际
不足: 未考虑对其他主要被控变量的影响, 验证模型简单2003 [166] 构建综合模糊逻辑、神经网络和进化计算的蒸汽流量控制器 贡献: 依据运行过程进行反馈控制并选择最优解
不足: 未考虑对其他主要被控变量的影响, 被控对象模型的精度有待提升2006 [167] 构建基于线性二次型的蒸汽流量控制器 贡献: 闭环运行, 稳定性提高
不足: 未考虑对其他主要被控变量的影响2020 [168] 多回路多
变量控制构建面向蒸汽流量和烟气含氧量的线性模型预测控制器 贡献: 操作变量与被控变量的误差优于传统燃烧控制系统
不足: 仅为线性控制器, 未考虑对其他主要被控变量的影响2005 [169] 构建面向蒸汽流量和烟气含氧量的非线性模型预测控制器 贡献: 通过滚动时域估计下一时刻的最优布风与布料量
不足: 未考虑对其他主要被控变量的影响2005 [170],
2008 [171]构建控制回路部分耦合的蒸汽流量和烟气含氧量 PID 控制器 贡献: 结合实际能有效改善设定点的跟踪特性
不足: 未改善 PID 控制器干扰抑制特性, 未考虑对其他主要被控变量的影响2010 [172] 构建基于神经网络 PID 的温度控制器 贡献: 控制曲线相对平稳, 炉温误差控制在 ±20 ℃ 以内
不足: 未考虑对其他主要被控变量的影响2010 [177] 构建面向炉膛温度、蒸汽流量和过热器温度的遗传算法确定全局最优模糊规则的控制器 贡献: 借助遗传算法改进模糊控制逻辑, 具有全局最优性
不足: 寻优耗时, 未考虑对其他主要被控变量的影响2000 [174] 构建面向炉膛温度、蒸汽流量和过热器温度的改进遗传模糊控制逻辑器 贡献: 基于神经网络调整模糊控制规则及相关参数, 稳定性好
不足: 寻优耗时, 易于过拟合, 未考虑对其他主要被控变量的影响2002 [175] 构建基于准对角递归神经网络的面向炉膛温度、蒸汽流量和烟气含氧量的 PID 控制器 贡献: 控制器参数根据误差信号进行自适应调整
不足: 易于过拟合, 适用工况单一2022 [178] 运行指标建模
与预测研究环保指
标研究可在线监测
环保指标基于系统辨识构建 NOx 排放的软测量模型 贡献: 消除控制系统延迟时间
不足: 辨识精度有待提升1997 [179],
1998 [180]基于连续时间系统辨识构建以烟气含氧量和二次风量为输入的 NOx 排放传函模型 贡献: 分析后给出了降低 NOx 排放的有效措施
不足: 输入有待完善, 精度有待提升2002 [181],
2006 [182]基于人工神经网络构建 NOx 排放预测模型 贡献: 预测精度较高、容错性好、泛化能力较好
不足: 易于过拟合, 对建模样本要求高2004 [183] 基于模块化神经网络构建 NOx 排放预测模型 贡献: 将预测任务分解为多个子任务以实现高效处理
不足: 易于过拟合, 子模型匹配难2020 [184],
2021 [185],
2022 [186]基于 LSTM 的 SO2 两阶段预测模型 贡献: 两阶段模型预测典型烟气污染物, 具有更好的性能
不足: 难以体现特定参数对模型的影响2021 [187] 构建基于流体动力学仿真软件的污染物排放模型, 对流场、温度分布和停留时间进行预测 贡献: 为污染减排提供工艺设计方面的指导
不足: 不能用于实际工业过程, 仅能用于优化设计分析2010 [189],
2022 [190]不可在线监
测环保指标构建烟气温度和 CO 浓度与 DXN 浓度间的映射模型 贡献: 模型简单实用, 能够为现场操作提供指导
不足: 经验公式, 考虑因素欠缺1989 [194] 构建基于多元线性回归分析的 DXN 预测模型 贡献: O2 含量为 7% 时, DXN 浓度与燃烧室温度和 CO 浓度间为线性映射
不足: 限定工况下的预测模型, 考虑因素欠缺, 对现代 MSWI电厂适用性不强1995 [195] 构建 DXN 浓度与烟气流量、炉膛温度等变量以及操作量之间的线性映射模型 贡献: 明确喷雾干燥洗涤器和袋式除尘器可有效去除 DXN
不足: 限定工况下的模型, 考虑因素欠缺, 对现代 MSWI 电厂适用性不强1995 [195] 构建烟气含氧量、一次风量占比和总风量与 DXN 浓度间的线性模型 贡献: 明确降低 DXN 形成的条件, 为操作提供指导
不足: 限定工况下的模型, 考虑因素欠缺, 对现代 MSWI 电厂适用性不强1997 [196] 构建基于遗传规划-BPNN 的 DXN软测量模型 贡献: 非线性模型, 依据数据特性寻优参数
不足: 限定工况下的模型, 考虑因素欠缺, 对现代 MSWI 电厂适用性不强2000 [197] 构建基于 GA-BPNN 的 DXN 软测量模型 贡献: 非线性模型, 依据数据特性寻优参数
不足: 易过拟合, 对建模数据要求高2008 [198] 构建基于特征选择 BPNN 的 DXN软测量模型 贡献: 输入考虑完整, 利用相关性分析和 PCA 选择特征, 量化输入影响程度
不足: 易过拟合, 对建模数据要求高, 线性特征选择方法2013 [89] 构建基于差分进化- RWNN 的 DXN软测量模型 贡献: 利用进化算法对模型参数进行了优化
不足: 存在随机性, 建模样本不足2018 [199] 构建基于虚拟样本优化选择的 DXN 排放软测量模型 贡献: 采用 RWNN 生成虚拟样本, 以泛化性能为目标优化选择虚拟样本
不足: 存在随机性, 样本数量实验确定2021 [200] 构建基于扩展、插值和多目标优化选择的 DXN 排放软测量模型 贡献: 多种方式生成虚拟样本, 优化泛化性能和样本数量以选取合格样本
不足: 存在随机性, 缺乏理论支撑, 缺乏评估准则2022 [201] 构建基于 SVR 的 DXN 排放软测量模型 贡献: 解决线性回归预测模型的泛化能力弱和稳定性差的问题, 适合小样本
不足: 考虑因素不全面, 精度有待提升2017 [202] 构建基于选择性集成核学习的 DXN 排放浓度软测量模型 贡献: 能够自适应地确定 SEN 模型结构和超参数
不足: 国外文献数据, 考虑因素不全面, 精度有待提升2019 [203] 构建基于多层特征选择的 DXN 排放浓度软测量模型 贡献: 分区域考虑相关性选择线性和非线性, 特征后再考虑共线性选择特征
不足: 国内真实数据, 线性模型, 精度有待提升2021 [205] 构建基于特征约简和选择性集成算法的 DXN 排放软测量模型 贡献: 基于线性潜结构映射模型和人工设定阈值选择特征构建非线性模型
不足: 线性特征选择方法, 丢弃部分特征2021 [206] 构建基于 RF 和 GBDT 的 DXN 软测量模型 贡献: 利用 RF 和 GBDT 的互补性提升模型精度
不足: 模型训练时间较长2020 [207] 构建基于 RF 迁移学习的 DXN 软测量模型 贡献: 迁移同工艺不同生产线的样本, 弥补数据不足
不足: 因样本数量有限, 性能有待提升2020 [208] 构建半监督改进深度信任网络的 DXN 软测量模型 贡献: 同时利用少量标记样本和大量未标记样本
不足: 深度信任网络不适合小样本建模, 精度有待提升2020 [210] 构建基于非神经网络模式 DFR 的 DXN 软测量模型 贡献: 提出面向小样本的 DFR 算法
不足: 精度有待提升, 特征传递模式单一2021 [212] 构建跨层全连接 DFR 的 DXN 软测量模型 贡献: 通过信息共享确保最大信息流以提高建模精度
不足: 训练时间长, 需要轻量化2021 [213] 构建基于 PCA-DFR 的 DXN 软测量模型 贡献: 利用 PCA 提取特征以避免原始高维特征淹没层级之间的表征特征
不足: 提取后的特征不具备物理含义, 与 DXN 的相关性未进行分析2021 [214] 构建基于改进 DFR 的 DXN 软测量模型 贡献: 加入特征选择与评估机制以减少模型计算消耗
不足: 放弃部分特征造成信息损失2022 [5] 工艺控制
研究基于计算流体动力学模拟燃烧和 NOx 排放 贡献: 明确抑制 NOx 排放的措施, 为燃烧过程的控制提供宏观指导
不足: 考虑污染物种类欠缺2019 [333] 基于计算流体动力学模拟 SNCR 脱硝过程 贡献: 仿真确定脱硝试剂的最佳喷射位置、速度和比率系数, 为操作提供宏观指导
不足: 考虑污染物种类欠缺2019 [334] 构建数值仿真模型分析供气方式对 NOx 排放的影响 贡献: 明确优化火焰燃烧位置、气体温度和热负荷分布可降低 NOx 排放
不足: 考虑污染物种类欠缺2022 [335] 基于实验室固定床反应器研究气态氨和二氧化硫对飞灰中形成有害物的影响 贡献: 为抑制飞灰中的有害物形成提供技术指导
不足: 考虑污染物种类欠缺2012 [336] 研究碱性物质对飞灰有害物的抑制作用 贡献: 为抑制飞灰中的有害物提供技术指导
不足: 考虑因素不全, 污染物种类欠缺2005 [337] 提出能保证合规性和降低污染排放率的活性碳喷入量规则 贡献: 为烟气净化过程提供宏观指导
不足: 考虑因素不全, 污染物种类欠缺2018 [338],
2020 [339]产品指标
研究飞灰 无害化处理飞灰中的氯化物和硫酸盐含量 贡献: 为无害化处理飞灰提供指导
不足: 考虑因素不全, 污染物种类欠缺2020 [220],
2017 [221]资源化处理飞灰 贡献: 降低了污染, 实现经济循环
不足: 飞灰的潜在危害如何评估未予以考虑2018 [222],
2018 [223],
2021 [224]炉渣热灼减率 研制炉渣热灼减率在线检测设备 贡献: 实现了检测自动化和实时化, 提升了分析效率, 为优化控制提供支撑
不足: 因工作环境恶劣, 难以长时间稳定运行2021 [227] 采用炉渣颜色与外貌特征和炉渣热灼减率进行关联 贡献: 采用标有热灼减率值随炉渣颜色渐变的参考卡为操作提供宏观指导
不足: 未能构建相应的计算模型2022 [228] 描述基于专家经验视角降低炉渣热灼减率的工艺控制策略 贡献: 为提高燃烧效率提供宏观指导
不足: 仅提供宏观指导, 难以应用于实际智能优化控制系统2017 [229],
2012 [230]运行监控与
故障识别
研究定性诊断 构建基于模糊专家推理的固废燃烧和余热交换子系统的故障检测 贡献: 报警限内进行征兆分析和预警, 报警限外进行故障报警、分析和识别
不足: 依赖特定对象, 普适应不强, 不能定量评估1994 [234] 基于聚类分析、神经网络和 Monte Carlo 统计进行尾气排放和蒸汽流量的在线监测及诊断 贡献: 能够监测粉尘排放, 评估蒸汽流量和 NOx 控制状态
不足: 部分尾气排放污染物未予以考虑2008 [235] 基于流程分析和经验总结构建故障树、采用规则推理专家系统诊断料层局部燃烧故障 贡献: 正确率达到 90%, 提高设备运营效率与效益
不足: 存在故障误报, 知识库依赖特定对象, 普适性弱2008 [236] 基于 BPNN 集成模型诊断排渣不畅和炉内结焦故障 贡献: 为燃烧过程控制参数的调节提供指导
不足: BPNN 易过拟合, 对建模样本要求高, 故障不能定量2008 [237] 构建基于 BPNN 的燃烧状态诊断模型 贡献: 正确率达到 99%, 诊断结果比较稳定
不足: BPNN 易过拟合, 对建模样本要求高, 故障不能定量2015 [238] 构建基于 RWNN 相似度检索的案例推理故障检测模型 贡献: 提高故障检测准确性, 降低时间复杂度
不足: RWNN 存在随机性和过拟合, 泛化能力有待提升, 对样本要求高2021 [239] 定量诊断 构建基于 PCA 与规则推理的故障定量检测 贡献: 具有工业上可接受的低错误诊断率, 为稳定运行提供支撑
不足: 线性模型, 性能有待提升2008 [243] 构建基于 PCA/PLS 的故障诊断模型 贡献: 故障检测和隔离性能良好, 两种模型的诊断结果具有一致性
不足: 线性模型, 性能有待提升2011 [244] 操作变量
(控制变量)
优化研究燃烧风量
优化设定基于领域专家知识采用案例推理的一/二次风量设定 贡献: 首次采用智能算法实现了一/二次风量的智能设定
不足: 难以寻找最优设定值2020 [245] 基于案例推理和智能补偿的二次风量优化设定 贡献: 提高了案例推理算法对风量设定的适用性
不足: 难以寻找最优设定值2022 [247] 基于分阶段多目标 PSO 算法的一/二次风量优化设定 贡献: 考虑运行指标的操作量优化设定研究, 为后续研究提供支撑
不足: 易陷入局部最优2021 [248] 进料量的
优化设定基于多目标进化算法的进料量优化设定值 贡献: 首次采用进化算法的操作量优化设定研究, 为后续研究提供支撑
不足: 收敛性难以保证, 迭代过程存在冗余信息2005 [246] 算法验证
平台研究半实物
仿真研究搭建由真实设备层和虚拟对象层组成的监控半实物仿真平台 贡献: 为算法验证提供支撑环境, 验证半实物仿真技术的可行性
不足: 未对多回路控制算法提供验证环境2021 [249] 搭建多回路控制虚拟对象模型和开发多回路控制软件系统 贡献: 具有与现场相同的信号传递方式, 为回路控制算法验证提供环境
不足: 未提供多模态数据驱动模型的研发与验证环境2023 [250] 搭建面向多模态历史数据同步的软硬件平台 贡献: 为过程数据和火焰图像驱动的智能算法验证提供支撑环境
不足: 在结合半实物平台及数字孪生系统等方面有待深入研究2022 [251] 
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