Research Status and Prospects of Intelligent Optimization Control for Municipal Solid Waste Incineration Process
-
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.
-
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] 
下载: 导出CSV
-
[1] Gómez-Sanabria A, Kiesewetter G, Klimont Z, Schoepp W, Haberl H. Potential for future reductions of global GHG and air pollutants from circular waste management systems. Nature Communications, 2022, 13(1): Article No. 106 doi: 10.1038/s41467-021-27624-7 [2] 乔俊飞, 郭子豪, 汤健. 面向城市固废焚烧过程的二噁英排放浓度检测方法综述. 自动化学报, 2020, 46(6): 1063-1089Qiao Jun-Fei, Guo Zi-Hao, Tang Jian. Dioxin emission concentration measurement approaches for municipal solid wastes incineration process: A survey. Acta Automatica Sinica, 2020, 46(6): 1063-1089 [3] 陈安, 陈晶睿, 崔晶, 范超, 韩玮. 中国31个直辖市和省会(首府)城市”垃圾围城”风险与对策研究—基于DIIS方法的实证研究. 中国科学院院刊, 2019, 34(7): 797-806Chen An, Chen Jing-Rui, Cui Jing, Fan Chao, Han Wei. Research on risks and countermeasures of “cities besieged by waste” in China-an empirical analysis based on DIIS. Bulletin of Chinese Academy of Sciences, 2019, 34(7): 797-806 [4] Walser T, Limbach L K, Brogioli R, Erismann E, Flamigni L, Hattendorf B, et al. Persistence of engineered nanoparticles in a municipal solid-waste incineration plant. Nature Nanotechnology, 2012, 7(8): 520-524 doi: 10.1038/nnano.2012.64 [5] Xia H, Tang J, Aljerf L. Dioxin emission prediction based on improved deep forest regression for municipal solid waste incineration process. Chemosphere, 2022, 294: Article No. 133716 doi: 10.1016/j.chemosphere.2022.133716 [6] Vilardi G, Verdone N. Exergy analysis of municipal solid waste incineration processes: The use of O2-enriched air and the oxy-combustion process. Energy, 2022, 239: Article No. 122147 doi: 10.1016/j.energy.2021.122147 [7] 何汶峰, 郑宇, 刘蓓蓓, 张炳. 垃圾分类政策对垃圾焚烧大气污染排放的影响. 中国环境科学, 2022, 42(5): 2433-2441He Wen-Feng, Zheng Yu, Liu Bei-Bei, Zhang Bing. Effects of garbage classification on air pollutant emissions from garbage incineration. China Environmental Science, 2022, 42(5): 2433-2441 [8] Bajić B Ž, Siniša S N, Vučurović D G, Dodic J M, Grahovac J A. Waste-to-energy status in Serbia. Renewable and Sustainable Energy Reviews, 2015, 50: 1437-1444 doi: 10.1016/j.rser.2015.05.079 [9] Kalyani K A, Pandey K K. Waste to energy status in India: A short review. Renewable and Sustainable Energy Reviews, 2014, 31: 113-120 doi: 10.1016/j.rser.2013.11.020 [10] Kumar A, Samadder S R. A review on technological options of waste to energy for effective management of municipal solid waste. Waste Management, 2017, 69: 407-422 doi: 10.1016/j.wasman.2017.08.046 [11] Liu Y L, Sun W X, Liu J G. Greenhouse gas emissions from different municipal solid waste management scenarios in China: Based on carbon and energy flow analysis. Waste Management, 2017, 68: 653-661 doi: 10.1016/j.wasman.2017.06.020 [12] Kammen D M, Sunter D A. City-integrated renewable energy for urban sustainability. Science, 2016, 352(6288): 922-928 doi: 10.1126/science.aad9302 [13] Korai M S, Mahar R B, Uqaili M A. The feasibility of municipal solid waste for energy generation and its existing management practices in Pakistan. Renewable and Sustainable Energy Reviews, 2017, 72: 338-353 doi: 10.1016/j.rser.2017.01.051 [14] 国家统计局. 中国统计年鉴. 北京: 中国统计出版社, 2021.National Bureau of Statistics. China Statistical Yearbook. Beijing: China Statistics Press, 2021. [15] Zhuang J B, Tang J, Aljerf L. Comprehensive review on mechanism analysis and numerical simulation of municipal solid waste incineration process based on mechanical grate. Fuel, 2022, 320: Article No. 123826 doi: 10.1016/j.fuel.2022.123826 [16] 生态环境部. 生活垃圾焚烧发电厂自动监测数据公开平台[Online], available: https://ljgk.envsc.cn/, 2022-12-30Ministry of Ecology and Environment. Automatic monitoring data disclosure platform for domestic waste incineration power plants [Online], available: https://ljgk.envsc.cn/, December 30, 2022 [17] Zhang L T, Liu G R, Li S M, Yang L L, Chen S. Model framework to quantify the effectiveness of garbage classification in reducing dioxin emissions. Science of the Total Environment, 2022, 814: Article No. 151941 doi: 10.1016/j.scitotenv.2021.151941 [18] 周守为, 朱军龙. 助力“碳达峰、碳中和”战略的路径探索. 天然气工业, 2021, 41(12): 1-8Zhou Shou-Wei, Zhu Jun-Long. Exploration of ways to helping “carbon peak and neutrality” strategy. Natural Gas Industry, 2021, 41(12): 1-8 [19] He Wen-Feng, Zheng Yu, Liu Bei-Bei, Zhang Bing. Effects of garbage classification on air pollutant emissions from garbage incineration. China Environmental Science, 2022, 42(5): 2433-2441 [20] 国务院. 国务院关于加快建立健全绿色低碳循环发展经济体系的指导意见[Online], available: http://www.gov.cn/zhengce/content/2021-02/22/content_5588274.htm, 2021-02-22The State Council of the People＇s Republic of China. The State Council＇s guiding opinions on accelerating the establishment and improvement of a green and low-carbon circular development economic system [Online], available: http://www.gov.cn/zhengce/content/2021-02/22/content_5588274.htm, February 22, 2021 [21] 李颖, 武学, 孙成双, 耿子洁, 张全红. 基于低碳发展的北京城市生活垃圾处理模式优化. 资源科学, 2021, 43(8): 1574-1588Li Ying, Wu Xue, Sun Cheng-Shuang, Geng Zi-Jie, Zhang Quan-Hong. Optimization of Beijing municipal solid waste treatment model based on low-carbon development. Resources Science, 2021, 43(8): 1574-1588 [22] Chu Z J, Wang W N, Zhou A, Huang W C. Charging for municipal solid waste disposal in Beijing. Waste Management, 2019, 94: 85-94 doi: 10.1016/j.wasman.2019.05.051 [23] Martínez J H, Romero S, Ramasco J J, Estrada E. The world-wide waste web. Nature Communications, 2022, 13(1): Article No. 1615 doi: 10.1038/s41467-022-28810-x [24] 殷国良, 杨凯. 垃圾焚烧发电电价补贴政策的演进及其影响分析. 法制与经济, 2021, 30(8): 115-118Yin Guo-Liang, Yang Kai. Evolution and impact analysis of electricity price subsidy policy for waste incineration power generation. Legal System and Economics, 2021, 30(8): 115-118 [25] Liang X, Kurniawan T A, Goh H H, Zhang D D, Dai W, Liu H, et al. Conversion of landfilled waste-to-electricity (WTE) for energy efficiency improvement in Shenzhen (China): A strategy to contribute to resource recovery of unused methane for generating renewable energy on-site. Journal of Cleaner Production, 2022, 369: Article No. 133078 doi: 10.1016/j.jclepro.2022.133078 [26] 龙吉生, 杜海亮, 邹昕, 黄静颖. 关于城市生活垃圾处理碳减排的系统研究. 中国科学院院刊, 2022, 37(8): 1143-1153Long Ji-Sheng, Du Hai-Liang, Zou Xin, Huang Jing-Ying. Systematic study on carbon emission reduction of municipal solid waste treatment. Bulletin of Chinese Academy of Sciences, 2022, 37(8): 1143-1153 [27] Khandelwal H, Dhar H, Thalla A K, Kumar S. Application of life cycle assessment in municipal solid waste management: A worldwide critical review. Journal of Cleaner Production, 2019, 209: 630-654 doi: 10.1016/j.jclepro.2018.10.233 [28] Kolekar K A, Hazra T, Chakrabarty S N. A review on prediction of municipal solid waste generation models. Procedia Environmental Sciences, 2016, 35: 238-244 doi: 10.1016/j.proenv.2016.07.087 [29] 黄哲程. 垃圾焚烧发电明年执行新规将公开常规污染物排放数据[Online], available: https://baijiahao.baidu.com/s?id=1652943936485626058&wfr=spider&for=pc, 2019-12-15Huang Zhe-Cheng. Waste incineration power generation will implement new regulations next year to disclose conventional pollutant emission data [Online], available: https://baijiahao.baidu.com/s?id=1652943936485626058&wfr=spider&for=pc, December 15, 2019 [30] 李娜, 马晓茜, 赵增立, 李海滨, 陈勇. 生物质气化与废弃物焚烧联合发电技术环境效益分析. 农业机械学报, 2007, 38(6): 121-124, 142Li Na, Ma Xiao-Qian, Zhao Zeng-Li, Li Hai-Bin, Chen Yong. Benefits of biomass gasification and waste incineration combined power technology on CO2 reduction. Transactions of the Chinese Society for Agricultural Machinery, 2007, 38(6): 121-124, 142 [31] 中国动力工程学会, 环保技术与装备专委会. 推广城市垃圾发电应用技术、加强行业服务交流”研讨会在天津举行. 动力工程, 2005, (4): 461China Power Engineering Committee, Environmental Protection Technology and Equipment Special Committee. Seminar on “promoting urban waste-to-energy application technology and strengthening industry service exchanges” held in Tianjin. Journal of Chinese Society of Power Engineering, 2005, (4): 461 [32] 柴天佑. 工业过程控制系统研究现状与发展方向. 中国科学: 信息科学, 2016, 46(8): 1003-1015 doi: 10.1360/N112016-00062Chai Tian-You. Industrial process control systems: Research status and development direction. Scientia Sinica Informationis, 2016, 46(8): 1003-1015 doi: 10.1360/N112016-00062 [33] 应雨轩, 林晓青, 吴昂键, 李晓东. 生活垃圾智慧焚烧的研究现状及展望. 化工学报, 2021, 72(2): 886-900Ying Yu-Xuan, Lin Xiao-Qing, Wu Ang-Jian, Li Xiao-Dong. Review and outlook on municipal solid waste smart incineration. CIESC Journal, 2021, 72(2): 886-900 [34] 桂卫华, 岳伟超, 谢永芳, 张红亮, 阳春华. 铝电解生产智能优化制造研究综述. 自动化学报, 2018, 44(11): 1957-1970Gui Wei-Hua, Yue Wei-Chao, Xie Yong-Fang, Zhang Hong-Liang, Yang Chun-Hua. A review of intelligent optimal manufacturing for aluminum reduction production. Acta Automatica Sinica, 2018, 44(11): 1957-1970 [35] Yang Y B, Goh Y R, Zakaria R, Nasserzadeh V, Swithenbank J. Mathematical modelling of MSW incineration on a travelling bed. Waste Management, 2002, 22(4): 369-380 doi: 10.1016/S0956-053X(02)00019-3 [36] Hunsinger H, Jay K, Vehlow J. Formation and destruction of PCDD/F inside a grate furnace. Chemosphere, 2002, 46(9-10): 1263-1272 doi: 10.1016/S0045-6535(01)00256-9 [37] Silpa K, Lisa C Y, Perinaz B T, Frank V W. What a waste 2.0: A global snapshot of solid waste management to 2050 [Online], available: https://openknowledge.worldbank.org/entities/publication/d3f9d45e-115f-559b-b14f-28552410e90a, September 20, 2018 [38] 谷琳, 何坤, 刘海威. 中国生活垃圾焚烧发电项目垃圾热值特性及其影响研究. 见: 中国环境科学学会2022年科学技术年会——环境工程技术创新与应用分会场论文集(三). 南昌, 中国: 《环境工程》编辑部, 2022.Gu Lin, He Kun, Liu Hai-Wei. Study on waste heat value characteristics and its impact on waste incineration power generation project in China. In: Proceedings of Environmental Engineering Technology Innovation and Application (3). Nanchang, China: Environmental Engineering Branch of Chinese Society of Environmental Sciences: Editorial Department of Environmental Engineering, 2022. [39] Yamada T, Asari M, Miura T, Niijima T, Yano J, Sakai S I. Municipal solid waste composition and food loss reduction in Kyoto City. Journal of Material Cycles and Waste Management, 2017, 19(4): 1351-1360 doi: 10.1007/s10163-017-0643-z [40] 柏杰. 垃圾焚烧锅炉的燃烧调整. 中国电力教育, 2008(S1): 277-278 doi: 10.3969/j.issn.1007-0079.2008.z2.110Bai Jie. Combustion adjustment of waste incineration boiler. China Electric Power Education, 2008(S1): 277-278 doi: 10.3969/j.issn.1007-0079.2008.z2.110 [41] 易诚明, 周平, 柴天佑. 基于即时学习的高炉炼铁过程数据驱动自适应预测控制. 控制理论与应用, 2020, 37(2): 295-306 doi: 10.7641/CTA.2019.80689Yi Cheng-Ming, Zhou Ping, Chai Tian-You. Data-driven just-in-time learning based adaptive predictive control for blast furnace Ironmaking. Control Theory & Applications, 2020, 37(2): 295-306 doi: 10.7641/CTA.2019.80689 [42] 陈宁, 周佳琪, 桂卫华, 阳春华, 戴佳阳. 针铁矿法沉铁过程双层结构优化控制. 控制理论与应用, 2020, 37(1): 222-228Chen Ning, Zhou Jia-Qi, Gui Wei-Hua, Yang Chun-Hua, Dai Jia-Yang. Two-layer optimal control for goethite iron precipitation process. Control Theory & Applications, 2020, 37(1): 222-228 [43] 李太福, 侯杰, 姚立忠, 易军, 辜小花, 游勇涛. Gamma Test噪声估计的Kalman神经网络在动态工业过程建模中的应用. 机械工程学报, 2014, 50(18): 29-35 doi: 10.3901/JME.2014.18.029Li Tai-Fu, Hou Jie, Yao Li-Zhong, Yi Jun, Gu Xiao-Hua, You Yong-Tao. Kalman artificial neural network with measurable noise estimation by Gamma Test for dynamic industrial process modeling. Journal of Mechanical Engineering, 2014, 50(18): 29-35 doi: 10.3901/JME.2014.18.029 [44] 丁海旭, 汤健, 夏恒, 乔俊飞. 基于TS-FNN的城市固废焚烧过程MIMO被控对象建模. 控制理论与应用, 2022, 39(8): 1529-1540Ding Hai-Xu, Tang Jian, Xia Heng, Qiao Jun-Fei. Modeling of MIMO controlled object in municipal solid waste incineration process based on TS-FNN. Control Theory & Applications, 2022, 39(8): 1529-1540 [45] 柴天佑. 生产制造全流程优化控制对控制与优化理论方法的挑战. 自动化学报, 2009, 35(6): 641-649 doi: 10.3724/SP.J.1004.2009.00641Chai Tian-You. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Automatica Sinica, 2009, 35(6): 641-649 doi: 10.3724/SP.J.1004.2009.00641 [46] 杨翠丽, 武战红, 韩红桂, 乔俊飞. 城市污水处理过程优化设定方法研究进展. 自动化学报, 2020, 46(10): 2092-2108Yang Cui-Li, Wu Zhan-Hong, Han Hong-Gui, Qiao Jun-Fei. Perspectives on optimal setting methods for municipal wastewater treatment processes. Acta Automatica Sinica, 2020, 46(10): 2092-2108 [47] 汤健, 柴天佑, 片锦香, 岳恒. 工业过程智能优化控制半实物仿真实验平台. 东北大学学报(自然科学版), 2009, 30(11): 1530-1533Tang Jian, Chai Tian-You, Pian Jin-Xiang, Yue Heng. A hardware-in-the-loop simulation platform for optimized intelligent control of industrial process. Journal of Northeastern University (Natural Science), 2009, 30(11): 1530-1533 [48] 白良成. 生活垃圾焚烧处理工程技术. 北京: 中国建筑工业出版社, 2009.Bai Liang-Cheng. Domestic Waste Incineration Treatment Engineering Technology. Beijing: China Architecture & Building Press, 2009. [49] 高峰. 垃圾清洁焚烧技术简介. 电站系统工程, 2007, 23(4): 70, 72Gao Feng. Introduction of clean waste incineration technology. Power System Engineering, 2007, 23(4): 70, 72 [50] 欧盟能源局. 西方几大城市的垃圾焚烧处理. 资源与人居环境, 2011(7): 60-62 doi: 10.3969/j.issn.1672-822X.2011.07.031Zhe Lun. Waste incineration in several western cities. Resources and Habitant Environment, 2011(7): 60-62 doi: 10.3969/j.issn.1672-822X.2011.07.031 [51] 赵绪平, 李志波, 李宣. 机械炉排式生活垃圾焚烧炉技术研究. 中国资源综合利用, 2017, 35(9): 135-138Zhao Xu-Ping, Li Zhi-Bo, Li Xuan. Research on technology of mechanical grate waste incinerators. China Resources Comprehensive Utilization, 2017, 35(9): 135-138 [52] 吴秋玲, 辛英杰. 垃圾焚烧炉用耐火材料及发展趋势. 国外耐火材料, 2001, 26(1): 12-17Wu Qiu-Ling, Xin Ying-Jie. Problems, possibilities and trends of refractory lining for waste incineration plants. Refractories & Lime, 2001, 26(1): 12-17 [53] 舟丹. 日本垃圾焚烧厂数量全球第一. 中外能源, 2018, 23(7): 88Zhou Dan. Japan has the largest number of waste incinerators in the world. Sino-Global Energy, 2018, 23(7): 88 [54] 孙玉修. 关于二噁英的环境污染. 环境保护科学, 1988, 14(4): 14-18Sun Yu-Xiu. About the environmental pollution of dioxin. Environmental Protection Science, 1988, 14(4): 14-18 [55] 张震天. 丹麦建造欧洲最先进的垃圾焚烧厂. 中国环保产业, 1996(6): 39Zhang Zhen-Tian. Denmark builds Europe's most advanced waste incinerator. China Environmental Protection Industry, 1996(6): 39 [56] 屠进, 宋黎萍, 池涌. 垃圾焚烧电厂焚烧炉炉型选择. 热力发电, 2003, 32(10): 5-7Tu Jin, Song Li-Ping, Chi Yong. Selection of incinerator type for power plants using MSW as fuel. Thermal Power Generation, 2003, 32(10): 5-7 [57] Wurzenberger J C, Wallner S, Raupenstrauch H, Khinast J G. Thermal conversion of biomass: Comprehensive reactor and particle modeling. AIChE Journal, 2002, 48(10): 2398-2411 doi: 10.1002/aic.690481029 [58] Rahdar M H, Nasiri F, Lee B. A review of numerical modeling and experimental analysis of combustion in moving grate biomass combustors. Energy & Fuels, 2019, 33(10): 9367-9402 [59] Peters B. A Model for Numerical Simulation of Devolatilization and Combustion of Waste Material in Packed Beds, KFK-5385, Inst. Fuer Angewandte Thermo- und Fluiddynamik (IATF), Germany, 1994. [60] Goh Y R, Siddall R G, Nasserzadeh V, Zakaria R, Swithenbank J, Lawrence D, et al. Mathematical modelling of the burning bed of a waste incinerator. Journal of the Institute of Energy, 1998, 71(487): 110-118 [61] 马晓茜, 杨泽亮. 垃圾焚烧时挥发分的析出与燃烧时间的计算. 电站系统工程, 1998, 14(6): 28-32Ma Xiao-Qian, Yang Ze-Liang. Precipitation of volatile matter and calculation of burning time during waste incineration. Power System Engineering, 1998, 14(6): 28-32 [62] 秦宇飞, 白焰, 刘雪中, 钟艳辉. 城市生活垃圾挥发分燃烧过程的建模与仿真. 动力工程学报, 2010, 30(6): 444-449, 455Qin Yu-Fei, Bai Yan, Liu Xue-Zhong, Zhong Yan-Hui. Modeling and simulation of combustion process of MSW volatile. Journal of Chinese Society of Power Engineering, 2010, 30(6): 444-449, 455 [63] 白焰, 秦宇飞, 冯峰, 潘竟斌. 垃圾焚烧炉内水分干燥过程分析及其仿真研究. 中国电机工程学报, 2011, 31(20): 19-26Bai Yan, Qin Yu-Fei, Feng Feng, Pan Jing-Bin. Drying process analysis and simulation of water in municipal solid waste incinerator. Proceedings of the CSEE, 2011, 31(20): 19-26 [64] 王康, 黄伟, 陆娟, 王殿, 许清. 垃圾焚烧炉排炉动态特性建模与仿真. 工业锅炉, 2015(4): 6-9Wang Kang, Huang Wei, Lu Juan, Wang Dian, Xu Qing. Dynamic modeling and simulation of garbage combustion in grate furnaces. Industrial Boilers, 2015(4): 6-9 [65] Shin D, Choi S. The combustion of simulated waste particles in a fixed bed. Combustion and Flame, 2000, 121(1-2): 167-180 doi: 10.1016/S0010-2180(99)00124-8 [66] Saastamoinen J J, Taipale R, Horttanainen M, Sarkomaa P. Propagation of the ignition front in beds of wood particles. Combustion and Flame, 2000, 123(1-2): 214-226 doi: 10.1016/S0010-2180(00)00144-9 [67] Johansson R, Thunman H, Leckner B. Sensitivity analysis of a fixed bed combustion model. Energy & Fuels, 2007, 21(3): 1493-1503 [68] Magnanelli E, Tranås O L, Carlsson P, Mosby J, Becidan M. Dynamic modeling of municipal solid waste incineration. Energy, 2020, 209: Article No. 118426 doi: 10.1016/j.energy.2020.118426 [69] Hoang Q N, Vanierschot M, Blondeau J, Croymans T, Pittoors R, Van Caneghem J. Review of numerical studies on thermal treatment of municipal solid waste in packed bed combustion. Fuel Communications, 2021, 7: Article No. 100013 doi: 10.1016/j.jfueco.2021.100013 [70] Alobaid F, Almohammed N, Farid M M, May J, Rößger P, Richter A, et al. Progress in CFD simulations of fluidized beds for chemical and energy process engineering. Progress in Energy and Combustion Science, 2022, 91: Article No. 100930 doi: 10.1016/j.pecs.2021.100930 [71] Goh Y R, Lim C N, Zakaria R, Chan K H, Reynolds G, Yang Y B, et al. Mixing, modelling and measurements of incinerator bed combustion. Process Safety and Environmental Protection, 2000, 78(1): 21-32 doi: 10.1205/095758200530411 [72] Chen J K, Tang J, Xia H, Yu W, Qiao J F. Modelling the furnace temperature field of a municipal solid waste incinerator using the numerical simulation and the deep forest regression algorithm. Fuel, 2023, 347: 1−20 [73] Yang Y B, Yamauchi H, Nasserzadeh V, Swithenbank J. Effects of fuel devolatilisation on the combustion of wood chips and incineration of simulated municipal solid wastes in a packed bed. Fuel, 2003, 82(18): 2205-2221 doi: 10.1016/S0016-2361(03)00145-5 [74] Yang Y B, Ryu C, Goodfellow J, Sharifi V N, Swithenbank J. Modelling waste combustion in grate furnaces. Process Safety and Environmental Protection, 2004, 82(3): 208-222 doi: 10.1205/095758204323065975 [75] Simsek E, Brosch B, Wirtz S, Scherer V, Krüll F. Numerical simulation of grate firing systems using a coupled CFD/discrete element method (DEM). Powder Technology, 2009, 193(3): 266-273 doi: 10.1016/j.powtec.2009.03.011 [76] 黄昕. 生活垃圾焚烧烟气脱硝技术的优选及优化设计的数值模拟[硕士学位论文], 华南理工大学, 中国, 2010.Huang Xin. The Optimal Selection and Numerical Simulation on Optimal Design of De-NOx Technologies for MSW Incineration flue Gas [Master thesis], South China University of Technology, China, 2010. [77] 李艳丽. 垃圾焚烧烟气脱硝的CFD数值模拟研究[硕士学位论文], 哈尔滨工业大学, 中国, 2013.Li Yan-Li. Numerial Studies on Computational Fluid Dynamic Simulations of Flue Gas DeNOx Process in a Waste Incinerator [Master thesis], Harbin Institute of Technology, China, 2013. [78] 蔡洁聪, 陈镇超. 600t/d垃圾焚烧炉选择性非催化还原脱硝技术研究. 热力发电, 2013, 42(2): 30-35, 53Cai Jie-Cong, Chen Zhen-Chao. SNCR denitrification technology for a 600t/d MSW incinerator. Thermal Power Generation, 2013, 42(2): 30-35, 53 [79] 王克. 基于CFD的垃圾炉排焚烧炉富氧焚烧技术开发及注氧器的优化设计[硕士学位论文], 华中科技大学, 中国, 2015.Wang Ke. Development of Oxygen-Enriched Incineration Technology in The Municipal Solid Waste Grate Incinerator Based on CFD and Optimization Design of Oxygen Injector [Master thesis], Huazhong University of Science and Technology, China, 2015. [80] Yang Y B, Swithenbank J. Mathematical modelling of particle mixing effect on the combustion of municipal solid wastes in a packed-bed furnace. Waste Management, 2008, 28(8): 1290-1300 doi: 10.1016/j.wasman.2007.04.012 [81] Yang Y B, Sharifi V N, Swithenbank J. Numerical simulation of municipal solid waste incineration in a moving-grate furnace and the effect of waste moisture content. Progress in Computational Fluid Dynamics, 2007, 7(5): 261-273 doi: 10.1504/PCFD.2007.013887 [82] 林海, 马晓茜, 余昭胜. 大型城市生活垃圾焚烧炉的数值模拟. 动力工程学报, 2010, 30(2): 128-132Lin Hai, Ma Xiao-Qian, Yu Zhao-Sheng. Numerical simulation of large-scale municipal solid waste incinerator. Journal of Chinese Society of Power Engineering, 2010, 30(2): 128-132 [83] Costa M, Indrizzi V, Massarotti N, Mauro A. Modeling and optimization of an incinerator plant for the reduction of the environmental impact. International Journal of Numerical Methods for Heat & Fluid Flow, 2015, 25(6): 1463-1487 [84] Bardi S, Astolfi A. Modeling and control of a waste-to-energy plant[Applications of Control]. IEEE Control Systems Magazine, 2010, 30(6): 27-37 doi: 10.1109/MCS.2010.938099 [85] 赵春晖, 韩红桂, 周平, 刘毅, 尚超. 复杂工业过程智能建模与控制方法及应用专题序言. 控制工程, 2022, 29(4): 577-580Zhao Chun-Hui, Han Hong-Gui, Zhou Ping, Liu Yi, Shang Chao. Intelligent modeling and control methods and applications of complex industrial processes. Control Engineering of China, 2022, 29(4): 577-580 [86] 孙备, 张斌, 阳春华, 桂卫华. 有色冶金净化过程建模与优化控制问题探讨. 自动化学报, 2017, 43(6): 880-892Sun Bei, Zhang Bin, Yang Chun-Hua, Gui Wei-Hua. Discussion on modeling and optimal control of nonferrous metallurgical purification process. Acta Automatica Sinica, 2017, 43(6): 880-892 [87] 刘强, 秦泗钊. 过程工业大数据建模研究展望. 自动化学报, 2016, 42(2): 161-171Liu Qiang, Qin Si-Zhao. Perspectives on big data modeling of process industries. Acta Automatica Sinica, 2016, 42(2): 161-171 [88] El Asri R, Baxter D. Process control in municipal solid waste incinerators: Survey and assessment. Waste Management & Research, 2004, 22(3): 177-185 [89] Bunsan S, Chen W Y, Chen H W, Chuang Y H, Grisdanurak N. Modeling the dioxin emission of a municipal solid waste incinerator using neural networks. Chemosphere, 2013, 92(3): 258-264 doi: 10.1016/j.chemosphere.2013.01.083 [90] Giantomassi A, Ippoliti G, Longhi S, Bertini I, Pizzuti S. On-line steam production prediction for A municipal solid waste incinerator by fully tuned minimal RBF neural networks. Journal of Process Control, 2011, 21(1): 164-172 doi: 10.1016/j.jprocont.2010.11.002 [91] 胡林静, 仝傲宇, 刘鸿宾, 林咸钗. 关于循环流化床锅炉燃烧温度系统的建模控制. 计算机仿真, 2019, 36(1): 112-116, 121Hu Lin-Jing, Tong Ao-Yu, Liu Hong-Bin, Lin Xian-Chai. Modeling and control of combustion temperature system of circulating fluidized bed boiler. Computer Simulation, 2019, 36(1): 112-116, 121 [92] 唐振浩, 张宝凯, 曹生现, 王恭, 赵波. 基于多模型智能组合算法的锅炉炉膛温度建模. 化工学报, 2019, 70(S2): 301-310Tang Zhen-Hao, Zhang Bao-Kai, Cao Sheng-Xian, Wang Gong, Zhao Bo. Furnace temperature modeling based on multi-model intelligent combination algorithm. CIESC Journal, 2019, 70(S2): 301-310 [93] 沈凯, 陆继东, 昌鹏, 李正华, 刘刚. 垃圾焚烧炉炉温控制模糊神经网络模型研究. 燃烧科学与技术, 2004, 10(6): 516-520Shen Kai, Lu Ji-Dong, Chang Peng, Li Zheng-Hua, Liu Gang. Research on combustion temperature fuzzy neural network model of incinerators. Journal of Combustion Science and Technology, 2004, 10(6): 516-520 [94] He H J, Meng X, Tang J, Qiao J F, Guo Z H. Prediction of MSWI furnace temperature based on TS fuzzy neural network. In: Proceedings of the 39th Chinese Control Conference (CCC). Shenyang, China: IEEE, 2020. 5701−5706 [95] 严爱军, 胡开成. 城市生活垃圾焚烧炉温控制的多目标优化设定方法. 控制理论与应用, 2023, 40(4): 693−701Yan Ai-Jun, Hu Kai-Cheng. Multi-objective optimization setting method for temperature control of municipal solid waste incinerator. Control Theory & Applications, 2023, 40(4): 693−701 [96] Rogaume T, Jabouille F, Torero J L. Identification of two combustion regimes depending of the excess air of combustion during waste incineration. In: Proceedings of Eurotherm Seminar No. 81 Reactive Heat Transfer in Porous Media. Albi, France: 2007. [97] Sun J, Meng X, Qiao J F. Prediction of oxygen content using weighted PCA and improved LSTM network in MSWI process. IEEE Transactions on Instrumentation and Measurement, 2021, 70: Article No. 2507512 [98] Hu Q X, Long J S, Wang S K, He J J, Bai L, Du H L, et al. A novel time-span input neural network for accurate municipal solid waste incineration boiler steam temperature prediction. Journal of Zhejiang University-Science A, 2021, 22(10): 777-791 doi: 10.1631/jzus.A2000529 [99] 孙剑, 蒙西, 乔俊飞. 基于MIV-RBF神经网络的主蒸汽流量软测量方法. 控制工程, 2022, 29(10): 1829-1834Sun Jian, Meng Xi, Qiao Jun-Fei. Soft sensor method of main steam flow based on mean impact value and radial basis function neural network. Control Engineering of China, 2022, 29(10): 1829-1834 [100] 杨培培, 骆嘉辉, 姚心, 张瑛华, 刘海威. 基于垃圾焚烧运行参数的主蒸汽参数预测研究. 有色设备, 2021, 35(1): 15-19Yang Bei-Bei, Luo Jia-Hui, Yao Xin, Zhang Ying-Hua, Liu Hai-Wei. Prediction of main steam parameters based on incineration MSW operation parameters. Nonferrous Metallurgical Equipment, 2021, 35(1): 15-19 [101] Leskens M, Van Kessel L B M, Van Den Hof P M J. MIMO closed-loop identification of an MSW incinerator. Control Engineering Practice, 2002, 10(3): 315-326 doi: 10.1016/S0967-0661(01)00139-3 [102] Chen J K, Tang J, Xia H, Wang D D, Wang T Z, Xu W. Cascade transfer function models of MSWI process based on weight adaptive particle swarm optimization. In: Proceedings of the China Automation Congress (CAC). Beijing, China: IEEE, 2021. 5553−5558 [103] Wang T Z, Tang J, Xia H. Key controlled variable model of MSWI process based on ensembled decision tree algorithm. In: Proceedings of the China Automation Congress (CAC). Beijing, China: IEEE, 2021. 5038−5043 [104] Miyamoto Y, Nishino K, Sawai T, Nambu E. Development of “AI-VISION” for fluidized-bed incinerator. In: Proceedings of IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No. 96TH8242). Washington, USA: IEEE, 1996. 72−77 [105] 钱锋, 杜文莉, 钟伟民, 唐漾. 石油和化工行业智能优化制造若干问题及挑战. 自动化学报, 2017, 43(6): 893-901Qian Feng, Du Wen-Li, Zhong Wei-Min, Tang Yang. Problems and challenges of smart optimization manufacturing in petrochemical industries. Acta Automatica Sinica, 2017, 43(6): 893-901 [106] Qian F. Smart and optimal manufacturing: The key for the transformation and development of the process industry. Engineering, 2017, 3(2): Article No. 151 doi: 10.1016/J.ENG.2017.02.016 [107] 王丹, 李旭清, 杨万勤. 生活垃圾热值计算模型研究进展. 新能源进展, 2022, 10(1): 69-79Wang Dan, Li Xu-Qing, Yang Wan-Qin. Review of heating value estimating models for municipal solid waste. Advances in New and Renewable Energy, 2022, 10(1): 69-79 [108] Chang Y F, Lin C J, Chyan J M, Chen I M, Chang J E. Multiple regression models for the lower heating value of municipal solid waste in Taiwan. Journal of Environmental Management, 2007, 85(4): 891-899 doi: 10.1016/j.jenvman.2006.10.025 [109] 陈亮, 郭峰, 李学平, 许润, 刘金刚. 基于垃圾热值计算的垃圾焚烧炉燃烧控制方案设计与实现. 仪器仪表标准化与计量, 2017(6): 26-27, 45Chen Liang, Guo Feng, Li Xue-Ping, Xu Run, Liu Jin-Gang. Design and application of waste incinerator control scheme based on waste heat value calculation. Instrument Standardization & Metrology, 2017(6): 26-27, 45 [110] 曾卫东, 田爽, 袁亚辉, 常威武. 垃圾焚烧炉自动燃烧控制系统设计与实现. 热力发电, 2019, 48(3): 109-113Zeng Wei-Dong, Tian Shuang, Yuan Ya-Hui, Chang Wei-Wu. Design and implementation of ACC system for waste incinerator. Thermal Power Generation, 2019, 48(3): 109-113 [111] Van Kessel L B M, Leskens M, Brem G. On-line calorific value sensor and validation of dynamic models applied to municipal solid waste combustion. Process Safety and Environmental Protection, 2002, 80(5): 245-255 doi: 10.1205/095758202762277605 [112] 丁兰, 张文阳, 张良均, 陈俊德. 基于人工神经网络的居民生活垃圾可燃成分热值预测. 环境工程学报, 2016, 10(2): 899-905Ding Lan, Zhang Wen-Yang, Zang Liang-Jun, Chen Jun-De. Prediction of household waste combustible component calorific value based on artificial neural network. Chinese Journal of Environmental Engineering, 2016, 10(2): 899-905 [113] 董长青, 金保升. 神经网络法用于预测城市生活垃圾热值. 热能动力工程, 2002, 17(3): 275-278Dong Chang-Qing, Jin Bao-Sheng. Prediction of the heating value of municipal solid waste (MSW) with the use of a neural network method. Journal of Engineering for Thermal Energy and Power, 2002, 17(3): 275-278 [114] Dong C Q, Jin B S, Li D J. Predicting the heating value of MSW with a feed forward neural network. Waste Management, 2003, 23(2): 103-106 doi: 10.1016/S0956-053X(02)00162-9 [115] 张瑛华, 张友富, 王洪. 基于神经网络的生活垃圾低位热值计算模型的研究与应用. 电力建设, 2010, 31(9): 94-97Zhang Ying-Hua, Zhang You-Fu, Wang Hong. Research and application of the LHV of MSW calculation model based on neural network. Electric Power Construction, 2010, 31(9): 94-97 [116] 马晓茜, 谢泽琼. 基于BP神经网络的垃圾热值预测模型. 科技导报, 2012, 30(23): 46-50 doi: 10.3981/j.issn.1000-7857.2012.23.006Ma Xiao-Qian, Xie Ze-Qiong. Prediction models for the heating values of municipal refuse based on BP Neural Network. Science & Technology Review, 2012, 30(23): 46-50 doi: 10.3981/j.issn.1000-7857.2012.23.006 [117] Akkaya E, Demir A. Predicting the heating value of municipal solid waste-based materials: An artificial neural network model. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2010, 32(19): 1777-1783 doi: 10.1080/15567030902882950 [118] 丁晨曦, 严爱军. 城市生活垃圾热值的特征变量选择方法及预测建模. 北京工业大学学报, 2021, 47(8): 874-885 doi: 10.11936/bjutxb2021010011Ding Chen-Xi, Yan Ai-Jun. Characteristic variable selection method and predictive modeling for municipal solid waste heat value. Journal of Beijing University of Technology, 2021, 47(8): 874-885 doi: 10.11936/bjutxb2021010011 [119] You H H, Ma Z Y, Tang Y J, Wang Y L, Yan J H, Ni M J, et al. Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. Waste Management, 2017, 68: 186-197 doi: 10.1016/j.wasman.2017.03.044 [120] Rostami A, Baghban A. Application of a supervised learning machine for accurate prognostication of higher heating values of solid wastes. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2018, 40(5): 558-564 doi: 10.1080/15567036.2017.1360967 [121] 谢昊源, 黄群星, 林晓青, 李晓东, 严建华. 基于图像深度学习的垃圾热值预测研究. 化工学报, 2021, 72(5): 2773-2782Xie Hao-Yuan, Huang Qun-Xing, Lin Xiao-Qing, Li Xiao-Dong, Yan Jian-Hua. Study on the calorific value prediction of municipal solid wastes by image deep learning. CIESC Journal, 2021, 72(5): 2773-2782 [122] 冯轶骁. 基于自动化流程的垃圾焚烧炉控制方法研究. 电器工业, 2022(5): 63-66Feng Yi-Xiao. Research on control method of waste incinerator based on automatic process. China Electrical Equipment Industry, 2022(5): 63-66 [123] 张丽霞. 垃圾焚烧机组燃烧状态监测与建模[硕士学位论文], 华北电力大学(北京), 中国, 2021.Zhang Li-Xia. Combustion State Monitoring and Modeling of Municipal Solid Waste Incineration Units [Master thesis], North China Electric Power University, China, 2021. [124] 李俊欣. 垃圾焚烧自动控制系统应用研究[硕士学位论文], 华南理工大学, 中国, 2015.Li Jun-Xin. Application Research for Waste-to-Energy Plant Automatic Combustion Control System [Master thesis], South China University of Technology, China, 2015. [125] Ballester J, García-Armingol T. Diagnostic techniques for the monitoring and control of practical flames. Progress in Energy and Combustion Science, 2010, 36(4): 375-411 doi: 10.1016/j.pecs.2009.11.005 [126] Duan H S, Tang J, Qiao J F. Recognition of combustion condition in MSWI process based on multi-scale color moment features and random forest. In: Proceedings of Chinese Automation Congress (CAC). Hangzhou, China: IEEE, 2019. 2542−2547 [127] 郭海涛, 汤健, 丁海旭, 乔俊飞. 基于混合数据增强的MSWI过程燃烧状态识别. 自动化学报, DOI: 10.16383/j.aas.c210843Guo Hai-Tao, Tang Jian, Ding Hai-Xu, Qiao Jun-Fei. Combustion states recognition method of mswi process based on mixed data enhancement. Acta Automatica Sinica, DOI: 10.16383/j.aas.c210843 [128] Han Z Z, Li J, Zhang B, Hossain M, Xu C L. Prediction of combustion state through a semi-supervised learning model and flame imaging. Fuel, 2021, 289: Article No. 119745 doi: 10.1016/j.fuel.2020.119745 [129] 孙成永, 尚江伟. 垃圾焚烧厂炉膛温度监测技术探讨. 环境与发展, 2019, 31(9): 138-140Sun Cheng-Yong, Shang Jiang-Wei. Discussion on furnace temperature monitoring technology in refuse incinerator. Environment and Development, 2019, 31(9): 138-140 [130] Zheng S, Cai W G, Sui R, Luo Z X, Lu Q. In-situ measurements of temperature and emissivity during MSW combustion using spectral analysis and multispectral imaging processing. Fuel, 2022, 323: Article No. 124328 doi: 10.1016/j.fuel.2022.124328 [131] Yan W J, Lou C, Cheng Q, Zhao P T, Zhang X Y. In situ measurement of alkali metals in an MSW incinerator using a spontaneous emission spectrum. Applied Sciences, 2017, 7(3): Article No. 263 doi: 10.3390/app7030263 [132] He X H, Lou C, Qiao Y, Lim M. In-situ measurement of temperature and alkali metal concentration in municipal solid waste incinerators using flame emission spectroscopy. Waste Management, 2020, 102: 486-491 doi: 10.1016/j.wasman.2019.11.015 [133] Zhou H C, Han S D, Sheng F, Zheng C G. Visualization of three-dimensional temperature distributions in a large-scale furnace via regularized reconstruction from radiative energy images: Numerical studies. Journal of Quantitative Spectroscopy and Radiative Transfer, 2002, 72(4): 361-383 doi: 10.1016/S0022-4073(01)00130-3 [134] 苏小江. 生活垃圾往复式机械炉排焚烧炉控制系统优化研究[硕士学位论文], 清华大学, 中国, 2012.Su Xiao-Jiang. Optimization of Control System for Municipal Solid Waste Reciprocating Machinery Incinerator [Master thesis], Tsinghua University, China, 2012. [135] Samad T, Bauer M, Bortoff S, Cairano S D, Fagiano L, Odgaard P F, et al. Industry engagement with control research: Perspective and messages. Annual Reviews in Control, 2020, 49: 1-14 doi: 10.1016/j.arcontrol.2020.03.002 [136] Hershkowitz A, Salerni E. Municipal solid waste incineration in Japan. Environmental Impact Assessment Review, 1989, 9(3), 257-278 doi: 10.1016/0195-9255(89)90008-5 [137] 夏恒,汤健,崔璨麟,乔俊飞.基于宽度混合森林回归的城市固废焚烧过程二噁英排放软测量.自动化学报,2023,49(2):343-365Xia Heng, Tang Jian, Cui Can-Lin, Qiao Jun-Fei. Soft sensing method of dioxin emission in municipal solid waste incineration process based on broad hybrid forest regression. Acta Automatica Sinica, 2023, 49(2): 343−365 [138] Tujimoto S, Fujikawa H, Akiyama J, Matsuda Y. Automatic combustion control system for a new-generation stoker-type waste incineration plant. EICA, 2004, 9(2): 197-200 [139] 朱亮, 陈涛, 王健生, 钱中华, 张二威, 洪益州, 等. 自动燃烧控制系统(ACC)垃圾热值估算模型研究. 环境卫生工程, 2015, 23(6): 33-35Zhu Liang, Chen Tao, Wang Jian-Sheng, Qian Zhong-Hua, Zhang Er-Wei, Hong Yi-Zhou, et al. Estimation model of waste heat value under automatic combustion control system (ACC). Environmental Sanitation Engineering, 2015, 23(6): 33-35 [140] 钱大群, 孙振飞. 一个垃圾焚烧智能控制系统. 信息与控制, 1993, 22(6): 374-377Qian Da-Qun, Sun Zhen-Fei. A waste incineration intelligent control system. Information and Control, 1993, 22(6): 374-377 [141] Onishi K. Fuzzy control of municipal refuse incineration plant. Automatic Measurement Control Society, 1991, 27(3): 326-332 [142] Schuler F, Rampp F, Martin J, Wolfrum J. TACCOS-A thermography-assisted combustion control system for waste incinerators. Combustion and Flame, 1994, 99(2): 431-439 doi: 10.1016/0010-2180(94)90150-3 [143] Miyamoto Y, Kurosaki Y, Fujiyama H, Nanbu E. Dynamic characteristic analysis and combustion control for a fluidized bed incinerator. Control Engineering Practice, 1998, 6(9): 1159-1168 doi: 10.1016/S0967-0661(98)00134-8 [144] Zipser S, Gommlich A, Matthes J, Keller H B. Combustion plant monitoring and control using infrared and video cameras. IFAC Proceedings Volumes, 2006, 39(7): 249-254 doi: 10.3182/20060625-4-CA-2906.00048 [145] 曾卫东, 薛宪民, 薛景杰. 炉排炉垃圾焚烧控制特点. 热力发电, 2004, 33(12): 57-58Zeng Wei-Dong, Xue Xian-Min, Xue Jing-Jie. Features of MSW incineration control in stocker-fired boiler. Thermal Power Generation, 2004, 33(12): 57-58 [146] 许润, 刘金刚. 一种炉排式垃圾焚烧炉燃烧自动控制策略. 仪器仪表标准化与计量, 2017(5): 28-30, 36Xu Run, Liu Jin-Gang. An automatic control strategy for combustion of grate-type waste incinerator. Instrument Standardization & Metrology, 2017(5): 28-30, 36 [147] 王海强. 垃圾焚烧炉ACC自动燃烧控制系统的拓展应用研究. 见: 中国环境科学学会科学技术年会论文集(第四卷). 西安, 中国: 中国环境科学学会, 2019. 3731−3735Wang Hai-Qiang. Research on extended application of ACC automatic combustion control system for waste incinerator. In: Proceedings of the Annual Conference of Science and Technology of the Chinese Society of Environmental Sciences (Volume IV). Xi＇an, China: Chinese Society of Environmental Sciences, 2019. 3731−3735 [148] Ono H, Ohnishi T, Terada Y. Combustion control of refuse incineration plant by fuzzy logic. Fuzzy Sets and Systems, 1989, 32(2): 193-206 doi: 10.1016/0165-0114(89)90254-6 [149] 沈凯, 陆继东, 董统永, 昌鹏. 垃圾焚烧炉稳定燃烧的模糊控制系统研究. 中国电力, 2003, 36(8): 47-50Shen Kai, Lu Ji-Dong, Dong Tong-Yong, Chang Peng. A fuzzy control system for stabilizing combustion of destructor. Electric Power, 2003, 36(8): 47-50 [150] Carrasco F, Llauró X, Poch M. A methodological approach to knowledge-based control and its application to a municipal solid waste incineration plant. Combustion Science and Technology, 2006, 178(4): 685-705 doi: 10.1080/00102200500248243 [151] Krause B, Von Altrock C, Limper K, Schäfers W. A neuro-fuzzy adaptive control strategy for refuse incineration plants. Fuzzy Sets and Systems, 1994, 63(3): 329-338 doi: 10.1016/0165-0114(94)90219-4 [152] Shen K, Lu J D, Li Z H, Liu G. An adaptive fuzzy approach for the incineration temperature control process. Fuel, 2005, 84(9): 1144-1150 doi: 10.1016/j.fuel.2004.06.042 [153] 沈凯, 陆继东, 昌鹏, 李正华, 刘刚. 模糊自适应方法在垃圾焚烧炉温度控制系统中的应用. 动力工程, 2004, 24(3): 366-369, 410Shen Kai, Lu Ji-Dong, Chang Peng, Li Zheng-Hua, Liu Gang. Application of adaptive fuzzy control method in combustion temperature process control system of incinerator. Journal of Chinese Society of Power Engineering, 2004, 24(3): 366-369, 410 [154] 昌鹏, 陆继东, 沈凯, 李正华. 垃圾焚烧炉炉温加权因子自适应控制方法的研究. 锅炉技术, 2004, 35(6): 77-81Chang Peng, Lu Ji-Dong, Shen Kai, Li Zheng-Hua. Research on weight factor adaptive control system of incinerator temperature. Boiler Technology, 2004, 35(6): 77-81 [155] 王毅, 马晓茜, 廖艳芬. 垃圾焚烧炉的分层模糊控制系统. 工业炉, 2004, 26(6): 29-34Wang Yi, Ma Xiao-Qian, Liao Yan-Fen. Layered fuzzy control system of destructor. Industrial Furnace, 2004, 26(6): 29-34 [156] 胡兴武, 罗毅. 基于比例因子的T-S模型模糊控制器的研究. 见: 全国冶金自动化信息网2011年年会论文集. 合肥, 中国: 2011. 231−233Hu Xing-Wu, Luo-Yi. Research on T-S model fuzzy controller based on scale factor. In: Proceedings of the Annual Meeting of the National Metallurgical Automation Information Network. Hefei, China: 2011. 231−233 [157] 代启化, 王俊. 生活垃圾焚烧炉温的Fuzzy-PID控制. 合肥学院学报(自然科学版), 2008, 18(3): 39-42, 92Dai Qi-Hua, Wang Jun. Fuzzy-PID control on garbage incineration temperature. Journal of Hefei University (Natural Sciences), 2008, 18(3): 39-42, 92 [158] 何海军, 蒙西, 汤健, 乔俊飞. 基于ET-RBF-PID的城市固废焚烧过程炉膛温度控制方法. 控制理论与应用, 2022, 39(12): 2262-2273He Hai-Jun, Meng Xi, Tang Jian, Qiao Jun-Fei. ET-RBF-PID-based control method for furnace temperature of municipal waste incineration process. Control Theory & Applications, 2022, 39(12): 2262-2273 [159] Ni Y M, Li L. Garbage incineration and intelligent fusion strategy of secondary pollution control. Advanced Materials Research, 2013, 853: 323-328 doi: 10.4028/www.scientific.net/AMR.853.323 [160] 肖前军, 许虎. 生活垃圾焚烧炉燃烧过程温度的仿人智能控制. 智能系统学报, 2015, 10(6): 881-885 doi: 10.11992/tis.201507039Xiao Qian-Jun, Xu Hu. Algorithm for human-simulated intelligent temperature control of incinerator combustion process of urban household garbage. CAAI Transactions on Intelligent Systems, 2015, 10(6): 881-885 doi: 10.11992/tis.201507039 [161] Wu Q, Xu H. Intelligent control strategy of incineration process pollution in municipal solid waste. International Conference on Oriental Thinking and Fuzzy Logic. Cham: Springer, 2016. 311−319 [162] 巫茜. 采用PSO改进的智能算法在焚烧污染控制中的应用. 重庆理工大学学报(自然科学), 2018, 32(12): 133-138Wu Qian. Application study of PSO improving based intelligent algorithm in incineration polution control. Journal of Chongqing University of Technology (Natural Science), 2018, 32(12): 133-138 [163] 孙剑, 蒙西, 乔俊飞. 城市固废焚烧过程烟气含氧量自适应预测控制. 自动化学报, DOI: 10.16383/j.aas.c210935Sun Jian, Meng Xi, Qiao Jun-Fei. Adaptive predictive control of oxygen content in flue gas for municipal solid waste incineration process. Acta Automatica Sinica, DOI: 10.16383/j.aas.c210935 [164] Chen D S. Fuzzy logic control of batch-feeding refuse incineration. In: Proceedings of the 3rd International Symposium on Uncertainty Modeling and Analysis and Annual Conference of the North American Fuzzy Information Processing Society. Nagoya, USA: IEEE, 1995. 58−63 [165] Yang X, Soh Y C. Fuzzy logic control of batch-feeding refuse incineration process. In: Proceedings of the 26th Annual Conference of the IEEE Industrial Electronics Society, IECON 2000, 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, 21st Century Technologies. Nagoya, Japan: IEEE, 2000. 2684−2689 [166] Watanabe N. A periodic strategy for combustion control of incinerators. In: Proceedings of the SICE Annual Conference (IEEE Cat. No.03TH8734). Fukui, Japan: IEEE, 2003. 526−529 [167] Annunziato M, Bertini I, Pannicelli A, Pizzuti S. A nature-inspired-modeling-optimization-control system applied to a waste incinerator plant. In: Proceedings of the 2nd European Symposium NiSIS. Puerto de la Cruz, Spain: 2006. 1−10 [168] Falconi F, Guillard H, Capitaneanu S, Raïssi T. Control strategy for the combustion optimization for waste-to-energy incineration plant. IFAC-PapersOnLine, 2020. 53(2): 13167-13172 doi: 10.1016/j.ifacol.2020.12.125 [169] Leskens M, Van Kessel L B M, Bosgra O H. Model predictive control as a tool for improving the process operation of MSW combustion plants. Waste Management, 2005, 25(8): 788-798 doi: 10.1016/j.wasman.2005.03.005 [170] Leskens M, Van Kessel L B M, Van Den Hof P M J, Bosgra O H. Nonlinear model predictive control with moving horizon state and disturbance estimation-with application to MSW combustion. IFAC Proceedings Volumes, 2005, 38(1): 291-296 [171] Leskens M, Van Der Linden R, Van Kessel L B M, Bosgra O H, Van Den Hof P M J. Nonlinear model predictive control of municipal solid waste combustion plants. In: Proceedings of the International Workshop on Assessment and Future Directions of Nonlinear Model Predictive Control. Pavia, Italy: 2008. 1−8 [172] Leskens M, Van't Veen P P, Van Kessel L B M, Bosgra O H, Van Den Hof P M J. Improved economic operation of MSWC plants with a new model based pid control strategy. IFAC Proceedings Volumes, 2010, 43(5): 655-660 doi: 10.3182/20100705-3-BE-2011.00109 [173] 丁海旭, 汤健, 乔俊飞. 城市固废焚烧过程数据驱动建模与自组织控制. 自动化学报, 2023, 49(3): 550-566Ding Hai-Xu, Tang Jian, Qiao Jun-Fei. Data-driven modeling and self-organizing control of municipal solid waste incineration process. Acta Automatica Sinica, 2023, 49(3): 550-566 [174] Chang N B, Chen W C. Fuzzy controller design for municipal incinerators with the aid of genetic algorithms and genetic programming techniques. Waste Management & Research, 2000, 18(5): 429-443 [175] Chen W C, Chang N B, Chen J C. GA-based fuzzy neural controller design for municipal incinerators. Fuzzy Sets and Systems, 2002, 129(3): 343-369 doi: 10.1016/S0165-0114(01)00205-6 [176] 肖会芹. 垃圾焚烧过程自适应模糊复合控制策略. 计算机工程与应用, 2010, 46(3): 201-203, 206Xiao Hui-Qin. Adaptive fuzzy composite control strategy for waste incineration process. Computer Engineering and Applications, 2010, 46(3): 201-203, 206 [177] 湛腾西. 垃圾焚烧过程智能集成控制. 机床与液压, 2010, 38(18): 85-87Zhan Teng-Xi. Intelligent integration control for waste incineration process. Machine Tool & Hydraulics, 2010, 38(18): 85-87 [178] Ding H X, Tang J, Qiao J F. MIMO modeling and multi-loop control based on neural network for municipal solid waste incineration. Control Engineering Practice, 2022, 127: Article No. 105280 doi: 10.1016/j.conengprac.2022.105280 [179] Matsumura S, Iwahara T, Suzuki M, Shioya H, Koide T. Improvement of De-NOx device control performance using software sensor. IFAC Proceedings Volumes, 1997, 30(11): 1433-1438 doi: 10.1016/S1474-6670(17)43044-8 [180] Matsumura S, Iwahara T, Ogata K, Fujii S, Suzuki M. Improvement of de-NOx device control performance using a software sensor. Control Engineering Practice, 1998, 6(10): 1267-1276 doi: 10.1016/S0967-0661(98)00127-0 [181] Huselstein E, Garnier H, Richard A, Guernion P Y. Experimental modeling of NOx emissions in municipal solid waste incinerator. IFAC Proceedings Volumes, 2002, 35(1): 89-94 [182] Rao G P, Unbehauen H. Identification of continuous-time systems. IEE Proceedings-Control Theory and Applications, 2006, 153(2): 185-220 doi: 10.1049/ip-cta:20045250 [183] 张东平, 严建华, 池涌, 岑可法. 流化床垃圾焚烧NOx排放的神经网络预测. 电站系统工程, 2004, 20(3): 1-3Zhang Dong-Ping, Yan Jian-Hua, Chi Yong, Cen Ke-Fa. Prediction of NOx emission in a MSW-fired fluidized bed with nonlinear theory. Power System Engineering, 2004, 20(3): 1-3 [184] 段滈杉, 乔俊飞, 蒙西, 汤健. 基于模块化神经网络的城市固废焚烧过程氮氧化物软测量. 见: 第31届中国过程控制会议(CPCC 2020)摘要集. 徐州, 中国: 中国自动化学会过程控制专业委员会, 2020.Duan Hao-Shan, Qiao Jun-Fei, Meng Xi, Tang Jian. Soft measurement of nitrogen oxides in municipal solid waste incineration process using modular neural network. In: Proceedings of Abstract Collection of the 31st China Process Control Conference. Xuzhou, China: Process Control Professional Committee of the Chinese Society of Automation, Chinese Society of Automation, Process Control Professional Committee of the Chinese Society of Automation, 2020. [185] Meng X, Tang J, Qiao J F. NOx emissions prediction with a brain-inspired modular neural network in municipal solid waste incineration processes. IEEE Transactions on Industrial Informatics, 2022, 18(7): 4622-4631 doi: 10.1109/TII.2021.3116528 [186] Duan H S, Meng X, Tang J, Qiao J F. Prediction of NOx concentration using modular long short-term memory neural network for municipal solid waste incineration. Chinese Journal of Chemical Engineering, 2023, 56: 46-57 doi: 10.1016/j.cjche.2022.06.028 [187] Li S Y, Zhang Y F, Song W B, Zhang C J, Zhao C, Shen W M, et al. Prediction of typical flue gas pollutants from municipal solid waste incineration plants. In: Proceedings of the IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD). Dalian, China: IEEE, 2021. 1304−1309 [188] Antonioni G, Guglielmi D, Cozzani V, Stramigioli C, Corrente D. Modelling and simulation of an existing MSWI flue gas two-stage dry treatment. Process Safety and Environmental Protection, 2014, 92(3): 242-250 doi: 10.1016/j.psep.2013.02.005 [189] Liang Z Y, Ma X Q. Mathematical modeling of MSW combustion and SNCR in a full-scale municipal incinerator and effects of grate speed and oxygen-enriched atmospheres on operating conditions. Waste Management, 2010, 30(12): 2520-2529 doi: 10.1016/j.wasman.2010.05.006 [190] Ma W C, Liu X, Ma C, Gu T B, Chen G Y. Basic: A comprehensive model for SOx formation mechanism and optimization in municipal solid waste (MSW) combustion. ACS Omega, 2022, 7(5): 3860-3871 doi: 10.1021/acsomega.0c03287 [191] Ma Y F, Wang P Y, Lin X Q, Chen T, Li X D. Formation and inhibition of Polychlorinated-ρ-dibenzodioxins and dibenzofurans from mechanical grate municipal solid waste incineration systems. Journal of Hazardous Materials, 2021, 403: Article No. 123812 doi: 10.1016/j.jhazmat.2020.123812 [192] Xia H, Tang J, Aljerf L, Wang T Z, Qiao J F, Xu Q D, et al. Investigation on dioxins emission characteristic during complete maintenance operating period of municipal solid waste incineration. Environmental Pollution, 2023, 318: 1-14 [193] Zhang H J, Ni Y W, Chen J P, Zhang Q. Influence of variation in the operating conditions on PCDD/F distribution in a full-scale MSW incinerator. Chemosphere, 2008, 70(4): 721-730 doi: 10.1016/j.chemosphere.2007.06.054 [194] Hasberg W, May H, Dorn I. Description of the residence-time behaviour and burnout of PCDD, PCDF and other higher chlorinated aromatic hydrocarbons in industrial waste incineration plants. Chemosphere, 1989, 19(1-6): 565-571 doi: 10.1016/0045-6535(89)90372-X [195] Chang N B, Huang S H. Statistical modelling for the prediction and control of PCDDs and PCDFs emissions from municipal solid waste incinerators. Waste Management & Research, 1995, 13(4): 379-400 [196] Ishikawa R, Buekens A, Huang H, Watanabe K. Influence of combustion conditions on dioxin in an industrial-scale fluidized-bed incinerator: Experimental study and statistical modelling. Chemosphere, 1997, 35(3): 465-477 doi: 10.1016/S0045-6535(97)00112-4 [197] Chang N B, Chen W C. Prediction of PCDDs/PCDFs emissions from municipal incinerators by genetic programming and neural network modeling. Waste Management & Research, 2000, 18(4): 341-351 [198] 王海瑞, 张勇, 王华. 基于GA和BP神经网络的二噁英软测量模型研究. 微计算机信息, 2008, 24(21): 222-224, 233Wang Hai-Rui, Zhang Yong, Wang Hua. A study of GA-BP based prediction model of Dioxin emission from MSW incinerator. Microcomputer Information, 2008, 24(21): 222-224, 233 [199] Tang J, Qiao J F, Li W T. Simplified stochastic configuration network-based optimised soft measuring model by using evolutionary computing framework with its application to dioxin emission concentration estimation. International Journal of System Control and Information Processing, 2018, 4(2): 332-365 [200] 汤健, 王丹丹, 郭子豪, 乔俊飞. 基于虚拟样本优化选择的城市固废焚烧过程二噁英排放浓度预测. 北京工业大学学报, 2021, 47(5): 431-443Tang Jian, Wang Dan-Dan, Guo Zi-Hao, Qiao Jun-Fei. Prediction of dioxin emission concentration in the municipal solid waste incineration process based on optimal selection of virtual samples. Journal of Beijing University of Technology, 2021, 47(5): 431-443 [201] Tang J, Xia H, Aljerf L, Wang D D, Ukaogo P O. Prediction of dioxin emission from municipal solid waste incineration based on expansion, interpolation, and selection for small samples. Journal of Environmental Chemical Engineering, 2022, 10(5): Article No. 108314 doi: 10.1016/j.jece.2022.108314 [202] 肖晓东, 卢加伟, 海景, 廖利. 垃圾焚烧烟气中二噁英类浓度的支持向量回归预测. 可再生能源, 2017, 35(8): 1107-1114Xiao Xiao-Dong, Lu Jia-Wei, Hai Jing, Liao Li. Prediction of dioxin emissions in flue gas from waste incineration based on support vector regression. Renewable Energy Resources, 2017, 35(8): 1107-1114 [203] 汤健, 乔俊飞. 基于选择性集成核学习算法的固废焚烧过程二噁英排放浓度软测量. 化工学报, 2019, 70(2): 696-706Tang Jian, Qiao Jun-Fei. Dioxin emission concentration soft measuring approach of municipal solid waste incineration based on selective ensemble kernel learning algorithm. CIESC Journal, 2019, 70(2): 696-706 [204] 汤健, 乔俊飞, 郭子豪. 基于潜在特征选择性集成建模的二噁英排放浓度软测量. 自动化学报, 2022, 48(1): 223-238Tang Jian, Qiao Jun-Fei, Guo Zi-Hao. Dioxin emission concentration soft measurement based on multi-source latent feature selective ensemble modeling for municipal solid waste incineration process. Acta Automatica Sinica, 2022, 48(1): 223-238 [205] 乔俊飞, 郭子豪, 汤健. 基于多层特征选择的固废焚烧过程二噁英排放浓度软测量. 信息与控制, 2021, 50(1): 75-87Qiao Jun-Fei, Guo Zi-Hao, Tang Jian. Soft sensing of dioxin emission concentration in solid waste incineration process based on multi-layer feature selection. Information and Control, 2021, 50(1): 75-87 [206] 汤健, 乔俊飞, 徐喆, 郭子豪. 基于特征约简与选择性集成算法的城市固废焚烧过程二噁英排放浓度软测量. 控制理论与应用, 2021, 38(1): 110-120Tang Jian, Qiao Jun-Fei, Xu Zhe, Guo Zi-Hao. Soft measuring approach of dioxin emission concentration in municipal solid waste incineration process based on feature reduction and selective ensemble algorithm. Control Theory & Applications, 2021, 38(1): 110-120 [207] Xia H, Tang J, Qiao J F, Yan A J, Guo Z H. Soft measuring method of dioxin emission concentration for MSWI process based on RF and GBDT. In: Proceedings of the Chinese Control and Decision Conference (CCDC). Hefei, China: IEEE, 2020. 2173−2178 [208] Xia H, Tang J, Cong Q M, Qiao J F, Xu Z. Dioxin emission concentration forecasting model for MSWI process with random forest-based transfer learning. In: Proceedings of the 39th Chinese Control Conference (CCC). Shenyang, China: IEEE, 2020. 5724−5729 [209] Xia H, Tang J, Qiao J F, Zhang J, Yu W. DF classification algorithm for constructing a small sample size of data-oriented DF regression model. Neural Computing and Applications, 2022, 34(4): 2785-2810 doi: 10.1007/s00521-021-06809-7 [210] Guo Z H, Tang J, Qiao J F, He H J. Dioxin emission concentration soft measurement model of MSWI process based on unmarked samples and improved deep belief network. In: Proceedings of the 39th Chinese Control Conference (CCC). Shenyang, China: IEEE, 2020. 5784−5789 [211] Zhou Z H, Feng J. Deep forest. National Science Review, 2019, 6(1): 74-86 doi: 10.1093/nsr/nwy108 [212] 汤健, 夏恒, 乔俊飞, 郭子豪. 深度集成森林回归建模方法及应用. 北京工业大学学报, 2021, 47(11): 1219-1229Tang Jian, Xia Heng, Qiao Jun-Fei, Guo Zi-Hao. Modeling method of deep ensemble forest regression with its application. Journal of Beijing University of Technology, 2021, 47(11): 1219-1229 [213] Tang J, Xia H, Zhang J, Qiao J F, Yu W. Deep forest regression based on cross-layer full connection. Neural Computing and Applications, 2021, 33(15): 9307-9328 doi: 10.1007/s00521-021-05691-7 [214] Xu W, Tang J, Xia H, Sun Z J. Prediction method of dioxin emission concentration based on PCA and deep forest regression. In: Proceedings of the 40th Chinese Control Conference (CCC). Shanghai, China: IEEE, 2021. 1212−1217 [215] 徐雯, 汤健, 夏恒, 乔俊飞. 基于Bagging半监督深度森林回归的二噁英排放浓度软测量. 仪器仪表学报, 2022, 43(6): 251-259Xu Wen, Tang Jian, Xia Heng, Qiao Jun-Fei. Soft sensor of dioxin emission concentration based on Bagging semi-supervised deep forest regression. Chinese Journal of Scientific Instrument, 2022, 43(6): 251-259 [216] 柴天佑, 刘强, 丁进良, 卢绍文, 宋延杰, 张艺洁. 工业互联网驱动的流程工业智能优化制造新模式研究展望. 中国科学: 技术科学, 2022, 52(1): 14-25 doi: 10.1360/SST-2021-0405Chai Tian-You, Liu Qiang, Ding Jin-Liang, Lu Shao-Wen, Song Yan-Jie, Zhang Yi-Jie. Perspectives on industrial-internet-driven intelligent optimized manufacturing mode for process industries. Scientia Sinica Technologica, 2022, 52(1): 14-25 doi: 10.1360/SST-2021-0405 [217] Ghouleh Z, Shao Y X. Turning municipal solid waste incineration into a cleaner cement production. Journal of Cleaner Production, 2018, 195: 268-279 doi: 10.1016/j.jclepro.2018.05.209 [218] Li G L, Wu Q R, Wang S X, Li Z J, Liang H Y, Tang Y, et al. The influence of flue gas components and activated carbon injection on mercury capture of municipal solid waste incineration in China. Chemical Engineering Journal, 2017, 326: 561-569 doi: 10.1016/j.cej.2017.05.099 [219] Li W H, Sun Y J, Huang Y M, Shimaoka T, Wang H W, Wang Y N, et al. Evaluation of chemical speciation and environmental risk levels of heavy metals during varied acid corrosion conditions for raw and solidified/stabilized MSWI fly ash. Waste Management, 2019, 87: 407-416 doi: 10.1016/j.wasman.2019.02.033 [220] Dontriros S, Likitlersuang S, Janjaroen D. Mechanisms of chloride and sulfate removal from municipal-solid-waste-incineration fly ash (MSWI FA): Effect of acid-base solutions. Waste Management, 2020, 101: 44-53 doi: 10.1016/j.wasman.2019.09.033 [221] Yang Z Z, Tian S C, Ji R, Liu L L, Wang X D, Zhang Z T. Effect of water-washing on the co-removal of chlorine and heavy metals in air pollution control residue from MSW incineration. Waste Management, 2017, 68: 221-231 doi: 10.1016/j.wasman.2017.06.039 [222] Joseph A M, Snellings R, Van Den Heede P, Matthys S, De Belie N. The use of municipal solid waste incineration ash in various building materials: A Belgian point of view. Materials, 2018, 11(1): Article No. 141 doi: 10.3390/ma11010141 [223] Quina M J, Bontempi E, Bogush A, Schlumberger S, Weibel G, Braga R, et al. Technologies for the management of MSW incineration ashes from gas cleaning: New perspectives on recovery of secondary raw materials and circular economy. Science of the Total Environment, 2018, 635: 526-542 doi: 10.1016/j.scitotenv.2018.04.150 [224] Zhang Y Y, Wang L, Chen L, Ma B, Zhang Y K, Ni W, et al. Treatment of municipal solid waste incineration fly ash: State-of-the-art technologies and future perspectives. Journal of Hazardous Materials, 2021, 411: Article No. 125132 doi: 10.1016/j.jhazmat.2021.125132 [225] Margallo M, Taddei M B M, Hernández-Pellón A, Aldaco R, Irabien Á. Environmental sustainability assessment of the management of municipal solid waste incineration residues: A review of the current situation. Clean Technologies and Environmental Policy, 2015, 17(5): 1333-1353 doi: 10.1007/s10098-015-0961-6 [226] Huber F, Blasenbauer D, Aschenbrenner P, Fellner J. Complete determination of the material composition of municipal solid waste incineration bottom ash. Waste Management, 2020, 102: 677-685 doi: 10.1016/j.wasman.2019.11.036 [227] 罗建明, 曾麟, 陈岳飞. 焚烧残渣热灼减率自动测定仪的研制. 中国测试, 2021, 47(9): 169-174Luo Jian-Ming, Zeng Lin, Chen Yue-Fei. Development of automatic measuring instrument for the thermal reduction rate of incineration residue. China Measurement & Test, 2021, 47(9): 169-174 [228] 孙佛芹, 李文祥, 谭昊, 陈晨, 沈东升, 龙淤洋. 基于炉渣图像处理的生活垃圾焚烧效果快速评测方法. 环境科学学报, 2022, 42(3): 285-292Sun Fo-Qin, Li Wen-Xiang, Tan Hao, Chen Chen, Shen Dong-Sheng, Long Yu-Yang. Rapid evaluation method of domestic waste incineration effect based on slag image processing. Acta Scientiae Circumstantiae, 2022, 42(3): 285-292 [229] 黄旌. 炉排式垃圾焚烧炉运行中的两个问题分析及措施. 科技创新导报, 2017, 14(9): 82-83Huang Jing. Analysis and measures of two problems in the operation of grate type waste incinerator. Science and Technology Innovation Herald, 2017, 14(9): 82-83 [230] 杨倩, 吕宙峰. 降低回转窑焚烧垃圾炉渣热酌减率的几种方法. 轻工科技, 2012, 28(7): 107-108Yang Qian, Lv Zhou-Feng. Several methods for reducing the thermal reduction rate of the slag in the rotary kiln incineration. Light Industry Science and Technology, 2012, 28(7): 107-108 [231] Zhang S R, Jiang X G, Lv G J, Liu B X, Jin Y Q, Yan J H. SO2, NOx, HF, HCl and PCDD/Fs emissions during Co-combustion of bituminous coal and pickling sludge in a drop tube furnace. Fuel, 2016, 186: 91-99 doi: 10.1016/j.fuel.2016.08.061 [232] Guo F H, Zhong Z P. Co-combustion of anthracite coal and wood pellets: Thermodynamic analysis, combustion efficiency, pollutant emissions and ash slagging. Environmental Pollution, 2018, 239: 21-29 doi: 10.1016/j.envpol.2018.04.004 [233] Johnke B, Grover V K, Hogland W. Current situation of waste incineration and energy recovery in Germany. Recovering Energy from Waste: Various Aspects. Plymouth: Science Publishers, 2002. 195−200 [234] Ono H. Diagnosis system of abnormality in refuse incineration plant using fuzzy logic. JSME International Journal. Ser. C, Dynamics, Control, Robotics, Design and Manufacturing, 1994, 37(2): 307-314 doi: 10.1299/jsmec1993.37.307 [235] Chen J C, Lin K Y. Diagnosis for monitoring system of municipal solid waste incineration plant. Expert Systems With Applications, 2008, 34(1): 247-255 doi: 10.1016/j.eswa.2006.09.002 [236] 陶怀志, 孙巍, 赵劲松, 陈晓春, 杨一新. 专家系统在垃圾焚烧炉故障诊断中的应用. 环境科学与技术, 2008, 31(11): 65-68Tao Huai-Zhi, Sun Wei, Zhao Jin-Song, Chen Xiao-Chun, Yang Yi-Xin. Fault diagnosis using expert system for municipal solid waste incineration. Environmental Science & Technology, 2008, 31(11): 65-68 [237] 陶怀志, 孙巍, 赵劲松, 陈晓春, 杨一新. 基于神经网络的垃圾焚烧炉过程控制. 计算机与应用化学, 2008, 25(7): 859-862Tao Huai-Zhi, Sun Wei, Zhao Jin-Song, Chen Xiao-Chun, Yang Yi-Xin. Process control using BP neural networks for incineration of municipal solid waste. Computers and Applied Chemistry, 2008, 25(7): 859-862 [238] 周志成. 基于图像处理和人工智能的垃圾焚烧炉燃烧状态诊断研究[硕士学位论文], 东南大学, 中国, 2015.Zhou Zhi-Cheng. Study on Diagnosis of Combustion State in Refuse Incinerator Based on Digital Image Processing and Artificial Intelligence [Master thesis], Southeast University, China, 2015. [239] Ding C X, Yan A J. Fault detection in the MSW incineration process using stochastic configuration networks and case-based reasoning. Sensors, 2021, 21(21): Article No. 7356 doi: 10.3390/s21217356 [240] Qin S J. Survey on data-driven industrial process monitoring and diagnosis. Annual Reviews in Control, 2012, 36(2): 220-234 doi: 10.1016/j.arcontrol.2012.09.004 [241] 刘强, 柴天佑, 秦泗钊, 赵立杰. 基于数据和知识的工业过程监视及故障诊断综述. 控制与决策, 2010, 25(6): 801-807, 813Liu Qiang, Chai Tian-You, Qin Si-Zhao, Zhao Li-Jie. Progress of data-driven and knowledge-driven process monitoring and fault diagnosis for industry process. Control and Decision, 2010, 25(6): 801-807, 813 [242] 樊继聪, 王友清, 秦泗钊. 联合指标独立成分分析在多变量过程故障诊断中的应用. 自动化学报, 2013, 39(5): 494-501Fan Ji-Cong, Wang You-Qing, Qin Si-Zhao. Combined indices for ICA and their applications to multivariate process fault diagnosis. Acta Automatica Sinica, 2013, 39(5): 494-501 [243] Zhao J S, Huang J C, Sun W. On-line early fault detection and diagnosis of municipal solid waste incinerators. Waste Management, 2008, 28(11): 2406-2414 doi: 10.1016/j.wasman.2007.11.014 [244] Tavares G, Zsigraiova Z, Semiao V, Da Graca Carvalho M. Monitoring, fault detection and operation prediction of MSW incinerators using multivariate statistic methods. Waste Management, 2011, 31(7): 1635-1644 doi: 10.1016/j.wasman.2011.02.005 [245] 夏恒. 城市生活垃圾焚烧过程风量智能设定方法及仿真平台研发[硕士学位论文], 北京工业大学, 中国, 2020.Xia Heng. Development of Intelligent Air Volume Setting Method and Simulation Platform for Municipal Solid Waste Incineration [Master thesis], Beijing University of Technology, China, 2020. [246] Anderson S R, Kadirkamanathan V, Chipperfield A, Sharifi V, Swithenbank J. Multi-objective optimization of operational variables in a waste incineration plant. Computers & Chemical Engineering, 2005, 29(5): 1121-1130. [247] 丁晨曦, 严爱军, 王殿辉. 城市生活垃圾焚烧过程二次风量智能优化设定方法. 控制与决策, 2022: 1−9Ding Chen-Xi, Yan Ai-Jun, Wang Dian-Hui. Intelligent optimization setting method of secondary air volume in municipal solid waste incineration process. Control and Decision, 2022: 1−9 [248] 乔俊飞, 崔莺莺, 蒙西, 汤健. 城市固废焚烧过程风量智能优化设定方法, 中国 113742997A, 2021-12-03Qiao Jun-Fei, Cui Ying-Ying, Meng Xi, Tang Jian. The Intelligent Optimization Setting Method of Air Flow for Municipal Solid Waste Incineration Process, CN 113742997A, December 3, 2021 [249] 严爱军, 夏恒, 刘溪芷. 城市生活垃圾焚烧过程监控半实物仿真平台研发. 系统仿真学报, 2021, 33(6): 1427-1435Yan Ai-Jun, Xia Heng, Liu Xi-Zhi. Development of semi-physical simulation platform for monitoring municipal solid waste incineration process. Journal of System Simulation, 2021, 33(6): 1427-1435 [250] 王天峥, 汤健, 夏恒, 乔俊飞. 城市固废焚烧过程的回路控制半实物仿真平台. 系统仿真学报, 2023, 35(2): 241-253 doi: 10.16182/j.issn1004731x.joss.21-1184Wang Tian-Zheng, Tang Jian, Xia Heng, Qiao Jun-Fei. Hardware-in-the-loop simulation platform of loop control for municipal solid waste incineration process. Journal of System Simulation, 2023, 35(2): 241-253 doi: 10.16182/j.issn1004731x.joss.21-1184 [251] 王天峥, 汤健, 夏恒, 潘晓彤, 乔俊飞, 刘溪芷. 多模态数据驱动的城市固废焚烧过程验证平台设计与实现. 中国电机工程学报, 2023, 43(12): 4697−4708Wang Tian-Zheng, Tang Jian, Xia Heng, Pan Xiao-Tong, Qiao Jun-Fei, Liu Xi-Zhi. Design and implementation of multimodal data-driven verification platform for urban solid waste incineration process. Proceedings of the CSEE, 2023, 43(12): 4697−4708 [252] Fu L M, Yang Q, Liu X X, Wang Z. Three-stage model based evaluation of local residents' acceptance towards waste-to-energy incineration project under construction: A Chinese perspective. Waste Management, 2021, 121: 105-116 doi: 10.1016/j.wasman.2020.11.039 [253] 张晓东, 周劲松, 骆仲泱, 王铁柱, 许明, 岑可法. 生物质中热值气化技术中试实验. 太阳能学报, 2003, 24(1): 74-79Zhang Xiao-Dong, Zhou Jin-Song, Luo Zhong-Yang, Wang Tie-Zhu, Xu Ming, Cen Ke-Fa. Experiments of biomass gasification for medium calorific gas on a pilot scale gasifier. Acta Energiae Solaris Sinica, 2003, 24(1): 74-79 [254] 严建华, 高雅丽, 张志霄, 池涌, 岑可法. 废轮胎回转窑中试热解油的理化性质. 燃料化学学报, 2003, 31(6): 589-594Yan Jian-Hua, Gao Ya-Li, Zhang Zhi-Xiao, Chi Yong, Cen Ke-Fa. Characteristics of pyrolytic oil derived from pilot-scale pyrolysis of scrap tires. Journal of Fuel Chemistry and Technology, 2003, 31(6): 589-594 [255] Yang T, Yi X L, Lu S W, Johansson K H, Chai T Y. Intelligent manufacturing for the process industry driven by industrial artificial intelligence. Engineering, 2021, 7(9): 1224-1230 doi: 10.1016/j.eng.2021.04.023 [256] 工业智能白皮书. 北京: 工业互联网产业联盟[Online], available: https://www.miit.gov.cn/ztzl/rdzt/gyhlw/cgzs/art/2020/art_e1842c433fce43e39a45ce 96be50213a.html, 2020-04-26Industrial Intelligence White Paper. Beijing: Industrial internet industry alliance [Online], available: https://www.miit.gov.cn/ztzl/rdzt/gyhlw/cgzs/art/2020/art_e1842c433fce43e39a45ce, April 26, 2020 [257] 柴天佑. 自动化科学与技术发展方向. 自动化学报, 2018, 44(11): 1923-1930Chai Tian-You. Development directions of automation science and technology. Acta Automatica Sinica, 2018, 44(11): 1923-1930 [258] 陈龙, 刘全利, 王霖青, 赵珺, 王伟. 基于数据的流程工业生产过程指标预测方法综述. 自动化学报, 2017, 43(6): 944-954Chen Long, Liu Quan-Li, Wang Lin-Qing, Zhao Jun, Wang Wei. Data-driven prediction on performance indicators in process industry: A survey. Acta Automatica Sinica, 2017, 43(6): 944-954 [259] Yu W K, Zhao C H, Huang B. Stationary subspace analysis-based hierarchical model for batch processes monitoring. IEEE Transactions on Control Systems Technology, 2021, 29(1): 444-453 doi: 10.1109/TCST.2020.2974147 [260] Ajami A, Daneshvar M. Data driven approach for fault detection and diagnosis of turbine in thermal power plant using independent component analysis (ICA). International Journal of Electrical Power & Energy Systems, 2012, 43(1): 728-735 [261] Zhao C H, Sun H. Dynamic distributed monitoring strategy for large-scale nonstationary processes subject to frequently varying conditions under closed-loop control. IEEE Transactions on Industrial Electronics, 2019, 66(6): 4749-4758 doi: 10.1109/TIE.2018.2864703 [262] Yu W K, Zhao C H. Recursive exponential slow feature analysis for fine-scale adaptive processes monitoring with comprehensive operation status identification. IEEE Transactions on Industrial Informatics, 2019, 15(6): 3311-3323 doi: 10.1109/TII.2018.2878405 [263] Wang X X, Ma L Y, Wang B S, Wang T. A hybrid optimization-based recurrent neural network for real-time data prediction. Neurocomputing, 2013, 120: 547-559 doi: 10.1016/j.neucom.2013.04.016 [264] Ma L Y, Ma Y G, Lee K Y. An intelligent power plant fault diagnostics for varying degree of severity and loading conditions. IEEE Transactions on Energy Conversion, 2010, 25(2): 546-554 doi: 10.1109/TEC.2009.2037435 [265] Zhang X H, Xu Y, He Y L, Zhu Q X. Novel manifold learning based virtual sample generation for optimizing soft sensor with small data. ISA Transactions, 2021, 109: 229-241 doi: 10.1016/j.isatra.2020.10.006 [266] Tang J, Chai T Y, Yu W, Liu Z, Zhou X J. A comparative study that measures ball mill load parameters through different single-scale and multiscale frequency spectra-based approaches. IEEE Transactions on Industrial Informatics, 2016, 12(6): 2008-2019 doi: 10.1109/TII.2016.2586419 [267] Yuan X F, Ge Z Q, Huang B, Song Z H, Wang Y L. Semisupervised JITL framework for nonlinear industrial soft sensing based on locally semisupervised weighted PCR. IEEE Transactions on Industrial Informatics, 2017, 13(2): 532-541 doi: 10.1109/TII.2016.2610839 [268] Li Y F, Guo L Z, Zhou Z H. Towards safe weakly supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(1): 334-346 [269] Qi G J, Luo J B. Small data challenges in big data era: A survey of recent progress on unsupervised and semi-supervised methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(4): 2168-2187 doi: 10.1109/TPAMI.2020.3031898 [270] Liu G F, Zhang X W, Liu Z M. State of health estimation of power batteries based on multi-feature fusion models using stacking algorithm. Energy, 2022, 259: Article No. 124851 doi: 10.1016/j.energy.2022.124851 [271] Heo S K, Nam K J, Loy-Benitez J, Yoo C K. Data-driven hybrid model for forecasting wastewater influent loads based on multimodal and ensemble deep learning. IEEE Transactions on Industrial Informatics, 2021, 17(10): 6925-6934 doi: 10.1109/TII.2020.3039272 [272] Qin L, Lu G, Hossain M, Morris A, Yan Y. A flame imaging-based online deep learning model for predicting NOx emissions from an Oxy-biomass combustion process. IEEE Transactions on Instrumentation and Measurement, 2022, 71: Article No. 2501811 [273] Li J P, Hua C C, Yang Y N, Guan X P. Data-driven bayesian-based takagi-sugeno fuzzy modeling for dynamic prediction of hot metal silicon content in blast furnace. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(2): 1087-1099 doi: 10.1109/TSMC.2020.3013972 [274] Chen C L P, Liu Z L. Broad learning system: An effective and efficient incremental learning system without the need for deep architecture. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(1): 10-24 doi: 10.1109/TNNLS.2017.2716952 [275] Xia H, Tang J, Yu W, Qiao J F. Tree broad learning system for small data modeling. IEEE Transactions on Neural Networks and Learning Systems, DOI: 10.1109/TNNLS.2022.3216788 [276] Shi Y, Mi Y L, Li J H, Liu W Q. Concept-cognitive learning model for incremental concept learning. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(2): 809-821 doi: 10.1109/TSMC.2018.2882090 [277] Wang G M, Qiao J F. An efficient self-organizing deep fuzzy neural network for nonlinear system modeling. IEEE Transactions on Fuzzy Systems, 2022, 30(7): 2170-2182 doi: 10.1109/TFUZZ.2021.3077396 [278] 丁进良, 杨翠娥, 陈远东, 柴天佑. 复杂工业过程智能优化决策系统的现状与展望. 自动化学报, 2018, 44(11): 1931-1943Ding Jin-Liang, Yang Cui-E, Chen Yuan-Dong, Chai Tian-You. Research progress and prospects of intelligent optimization decision making in complex industrial process. Acta Automatica Sinica, 2018, 44(11): 1931-1943 [279] 赵春晖, 胡赟昀, 郑嘉乐, 陈军豪. 数据驱动的燃煤发电装备运行工况监控—现状与展望. 自动化学报, 2022, 48(11): 2611-2633Zhao Chun-Hui, Hu Yun-Yun, Zheng Jia-Le, Chen Jun-Hao. Data-driven operating monitoring for coal-fired power generation equipment: The state of the art and challenge. Acta Automatica Sinica, 2022, 48(11): 2611-2633 [280] 罗刚, 王永富, 柴天佑, 张化锴. 基于区间二型模糊摩擦补偿的鲁棒自适应控制. 自动化学报, 2019, 45(7): 1298-1306Luo Gang, Wang Yong-Fu, Chai Tian-You, Zhang Hua-Kai. Robust adaptive control based on interval type-2 fuzzy friction compensation. Acta Automatica Sinica, 2019, 45(7): 1298-1306 [281] 董琦, 宗群, 张超凡, 张睿隆, 田栢苓. 强干扰影响下基于干扰补偿的大飞机智能自适应控制. 中国科学: 技术科学, 2018, 48(3): 248-263 doi: 10.1360/N092017-00195Dong Qi, Zong Qun, Zhang Chao-Fan, Zhang Rui-Long, Tian Bai-Ling. Intelligent adaptive control for the large aircraft based on the disturbance compensation under the influence of strong interferences. Scientia Sinica Technologica, 2018, 48(3): 248-263 doi: 10.1360/N092017-00195 [282] 席裕庚, 李德伟, 林姝. 模型预测控制-现状与挑战. 自动化学报, 2013, 39(3): 222-236 doi: 10.1016/S1874-1029(13)60024-5Xi Yu-Geng, Li De-Wei, Lin Shu. Model predictive control-status and challenges. Acta Automatica Sinica, 2013, 39(3): 222-236 doi: 10.1016/S1874-1029(13)60024-5 [283] 郭雷, 余翔, 张霄, 张友民. 无人机安全控制系统技术: 进展与展望. 中国科学: 信息科学, 2020, 50(2): 184-194Guo Lei, Yu Xiang, Zhang Xiao, Zhang You-Min. Safety control system technologies for UAVs: Review and prospect. Science in China (Information Sciences), 2020, 50(2): 184-194 [284] 李荟, 王福利, 李鸿儒. 电熔镁炉熔炼过程异常工况识别及自愈控制方法. 自动化学报, 2020, 46(7): 1411-1419Li Hui, Wang Fu-Li, Li Hong-Ru. Abnormal condition identification and self-healing control scheme for the electro-fused magnesia smelting process. Acta Automatica Sinica, 2020, 46(7): 1411-1419 [285] Cao Z B, Zhao J. Passivity-based event-triggered control for a class of switched nonlinear systems. ISA Transactions, 2021, 125: 50-59 [286] 韩红桂, 秦晨辉, 孙浩源, 乔俊飞. 城市污水处理过程自适应滑模控制. 自动化学报, 2023, 49(5): 1010−1018Han Hong-Gui, Qin Chen-Hui, Sun Hao-Yuan, Qiao Jun-Fei. Adaptive sliding mode control for municipal wastewater treatment process. Acta Automatica Sinica, 2023, 49(5): 1010−1018 [287] Han H G, Liu Z, Li J M, Qiao J F. Design of syncretic fuzzy-neural control for WWTP. IEEE Transactions on Fuzzy Systems, 2022, 30(8): 2837-2849 doi: 10.1109/TFUZZ.2021.3075842 [288] Yang Q M, Cao W W, Meng W C, Si J. Reinforcement-learning-based tracking control of waste water treatment process under realistic system conditions and control performance requirements. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(8): 5284-5294 doi: 10.1109/TSMC.2021.3122802 [289] Zhou W, Yi J, Yao L Z, Chen G R. Event-triggered optimal control for the continuous stirred tank reactor system. IEEE Transactions on Artificial Intelligence, 2022, 3(2): 228-237 doi: 10.1109/TAI.2021.3107367 [290] Li D J, Wang D X, Liu L, Gao Y. Adaptive finite-time tracking control for continuous stirred tank reactor with time-varying output constraint. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 51(9): 5929-5934 doi: 10.1109/TSMC.2019.2956769 [291] Wang G M, Jia Q S, Qiao J F, Bi J, Zhou M C. Deep learning-based model predictive control for continuous stirred-tank reactor system. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(8): 3643-3652 doi: 10.1109/TNNLS.2020.3015869 [292] Zhou P, Zhang S, Wen L, Fu J, Chai T Y, Wang H. Kalman filter-based data-driven robust model-free adaptive predictive control of a complicated industrial process. IEEE Transactions on Automation Science and Engineering, 2022, 19(2): 788-803 [293] Zhou P, Guo D W, Wang H, Chai T Y. Data-driven robust M-LS-SVR-Based NARX modeling for estimation and control of molten iron quality indices in blast furnace ironmaking. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(9): 4007-4021 doi: 10.1109/TNNLS.2017.2749412 [294] Du S, Wu M, Chen L F, Zhou K L, Hu J, Cao W H, et al. A fuzzy control strategy of burn-through point based on the feature extraction of time-series trend for iron ore sintering process. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2357-2368 doi: 10.1109/TII.2019.2935030 [295] 温亮, 周平. 基于多参数灵敏度分析与遗传优化的铁水质量无模型自适应控制. 自动化学报, 2021, 47(11): 2600−2613Wen Liang, Zhou Ping. Model free adaptive control of molten iron quality based on multi-parameter sensitivity analysis and GA optimization. Acta Automatica Sinica, 2021, 47(11): 2600−2613 [296] 桂卫华, 陈晓方, 阳春华, 谢永芳. 知识自动化及工业应用. 中国科学: 信息科学, 2016, 46(8): 1016-1034 doi: 10.1360/N112016-00065Gui Wei-Hua, Chen Xiao-Fang, Yang Chun-Hua, Xie Yong-Fang. Knowledge automation and its industrial application. Scientia Sinica Informationis, 2016, 46(8): 1016-1034 doi: 10.1360/N112016-00065 [297] 辛斌, 陈杰, 彭志红. 智能优化控制: 概述与展望. 自动化学报, 2013, 39(11): 1831-1848 doi: 10.3724/SP.J.1004.2013.01831Xin Bin, Chen Jie, Peng Zhi-Hong. Intelligent optimized control: Overview and prospect. Acta Automatica Sinica, 2013, 39(11): 1831-1848 doi: 10.3724/SP.J.1004.2013.01831 [298] 蔡自兴. 智能控制原理与应用. 北京: 清华大学出版社, 2007.Cai Zi-Xing. Intelligent Control Principles and Applications. Beijing: Tsinghua University Press, 2007. [299] 柴天佑. 制造流程智能化对人工智能的挑战. 中国科学基金, 2018, 32(3): 251-256Chai Tian-You. Artificial intelligence research challenges in intelligent manufacturing processes. Bulletin of National Natural Science Foundation of China, 2018, 32(3): 251-256 [300] 柴天佑, 丁进良. 流程工业智能优化制造. 中国工程科学, 2018, 20(4): 51-58Chai Tian-You, Ding Jin-Liang. Smart and optimal manufacturing for process industry. Strategic Study of CAE, 2018, 20(4): 51-58 [301] Liu W H, Wang T, Li Z M, Ye Z C, Peng X, Du W L. Distributed optimization subject to inseparable coupled constraints: A case study on plant-wide ethylene process. IEEE Transactions on Industrial Informatics, 2023, 19(4): 5412-5421 doi: 10.1109/TII.2022.3151913 [302] Xie S W, Xie Y F, Huang T W, Gui W H. Multiobjective-based optimization and control for iron removal process under dynamic environment. IEEE Transactions on Industrial Informatics, 2021, 17(1): 569-577 doi: 10.1109/TII.2020.2975273 [303] Zhou K L, Chen X, Wu M, Cao W H, Hu J. A new hybrid modeling and optimization algorithm for improving carbon efficiency based on different time scales in sintering process. Control Engineering Practice, 2019, 91: Article No. 104104 doi: 10.1016/j.conengprac.2019.104104 [304] Li Y J, Zhang S, Zhang J, Yin Y X, Xiao W D, Zhang Z Q. Data-driven multiobjective optimization for burden surface in blast furnace with feedback compensation. IEEE Transactions on Industrial Informatics, 2020, 16(4): 2233-2244 doi: 10.1109/TII.2019.2908989 [305] Zhou H, Zhang H F, Yang C J. Hybrid-model-based intelligent optimization of ironmaking process. IEEE Transactions on Industrial Electronics, 2020, 67(3): 2469-2479 doi: 10.1109/TIE.2019.2903770 [306] Xie S W, Xie Y F, Huang T W, Gui W H, Yang C H. Coordinated optimization for the descent gradient of technical index in the iron removal process. IEEE Transactions on Cybernetics, 2018, 48(12): 3313-3322 doi: 10.1109/TCYB.2018.2833805 [307] Zheng N Z, Ding J L, Chai T Y. DMGAN: Adversarial learning-based decision making for human-level plant-wide operation of process industries under uncertainties. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(3): 985-998 doi: 10.1109/TNNLS.2020.2979800 [308] Lin X W, Zhao L, Du W L, Qian F. Data-driven scheduling optimization of ethylene cracking furnace system. In: Proceedings of the Chinese Control and Decision Conference (CCDC). Hefei, China: IEEE, 2020. 308−313 [309] Kang L, Liu D, Wu Y L, Zhao Y Z, Ping G Z. Multi-furnace optimization in silicon single crystal production plants by power load scheduling. Journal of Process Control, 2022, 117: 1-13 doi: 10.1016/j.jprocont.2022.06.013 [310] Kong W J, Chai T Y, Ding J L, Yang S X. Multifurnace optimization in electric smelting plants by load scheduling and control. IEEE Transactions on Automation Science and Engineering, 2014, 11(3): 850-862 doi: 10.1109/TASE.2014.2309348 [311] Kabugo J C, Jämsä-Jounela S L, Schiemann R, Binder C. Industry 4.0 based process data analytics platform: A waste-to-energy plant case study. International Journal of Electrical Power & Energy Systems, 2020, 115: Article No. 105508 [312] Austin M, Delgoshaei P, Coelho M, Heidarinejad M. Architecting smart city digital twins: Combined semantic model and machine learning approach. Journal of Management in Engineering, 2020, 36(4): Article No. 04020026 [313] He R, Chen G M, Dong C, Sun S F, Shen X Y. Data-driven digital twin technology for optimized control in process systems. ISA Transactions, 2019, 95: 221-234 doi: 10.1016/j.isatra.2019.05.011 [314] 李彦瑞, 杨春节, 张瀚文, 李俊方. 流程工业数字孪生关键技术探讨. 自动化学报, 2021, 47(3): 501-514Li Yan-Rui, Yang Chun-Jie, Zhang Han-Wen, Li Jun-Fang. Discussion on key technologies of digital twin in process industry. Acta Automatica Sinica, 2021, 47(3): 501-514 [315] 孙帆, 罗娜, 钟伟民, 钱锋. 乙二醇解吸和再吸收过程的动态模拟与优化. 计算机与应用化学, 2010, 27(3): 277-282 doi: 10.3969/j.issn.1001-4160.2010.03.001Sun Fan, Luo Na, Zhong Wei-Min, Qian Feng. Dynamic simulation and optimization of ethylene glycol desorption and resorption process. Computers and Applied Chemistry, 2010, 27(3): 277-282 doi: 10.3969/j.issn.1001-4160.2010.03.001 [316] 宋新南, 徐惠斌, 房仁军, 王惠桐, 顾加强. 基于Aspen Plus的生物质燃烧NOx生成模拟. 环境科学学报, 2009, 29(8): 1696-1700 doi: 10.3321/j.issn:0253-2468.2009.08.018Song Xin-Nan, Xu Hui-Bin, Fang Ren-Jun, Wang Hui-Tong, Gu Jia-Qiang. Simulation of the NOx emissions during biomass combustion based on Aspen Plus. Acta Scientiae Circumstantiae, 2009, 29(8): 1696-1700 doi: 10.3321/j.issn:0253-2468.2009.08.018 [317] Stojanovic N, Milenovic D. Data-driven digital twin approach for process optimization: An industry use case. In: Proceedings of the IEEE International Conference on Big Data (Big Data). Seattle, USA: IEEE, 2018. 4202−4211 [318] 汤健, 乔俊飞, 夏恒, 郭子豪, 何海军. 一种单向隔离数据采集与离线算法验证系统, 中国 CN109901536B, 2019-09-17Tang Jian, Qiao Jun-Fei, Xia Heng, Guo Zi-Hao, He Hai-Jun. A One-Way Isolated Data Acquisition and Offline Algorithm Verification System, CN CN109901536B, September 17, 2019 [319] Tao F, Zhang H, Liu A, Nee A Y C. Digital twin in industry: State-of-the-art. IEEE Transactions on Industrial Informatics, 2019, 15(4): 2405-2415 doi: 10.1109/TII.2018.2873186 [320] Mihai S, Yaqoob M, Hung D V, Davis W, Towakel P, Raza M, et al. Digital twins: A survey on enabling technologies, challenges, trends and future prospects. IEEE Communications Surveys & Tutorials, 2022, 24(4): 2255-2291 [321] Li Y, Shen X J. A novel wind speed-sensing methodology for wind turbines based on digital twin technology. IEEE Transactions on Instrumentation and Measurement, 2022, 71: Article No. 2503213 [322] Qin Y, Wu X G, Luo J. Data-model combined driven digital twin of life-cycle rolling bearing. IEEE Transactions on Industrial Informatics, 2022, 18(3): 1530-1540 doi: 10.1109/TII.2021.3089340 [323] Wang T, Cheng J X, Yang Y, Esposito C, Snoussi H, Tao F. Adaptive optimization method in digital twin conveyor systems via range-inspection control. IEEE Transactions on Automation Science and Engineering, 2022, 19(2): 1296-1304 doi: 10.1109/TASE.2020.3043393 [324] Xiang F, Zhang Z, Zuo Y, Tao F. Digital twin driven green material optimal-selection towards sustainable manufacturing. Procedia CIRP, 2019, 81: 1290-1294 doi: 10.1016/j.procir.2019.04.015 [325] Mohammadi N, Taylor J E. Smart city digital twins. In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI). Honolulu, USA: IEEE, 2017. 1−5 [326] Xu X L, Liu Z J, Bilal M, Vimal S, Song H B. Computation offloading and service caching for intelligent transportation systems with digital twin. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(11): 20757-20772 doi: 10.1109/TITS.2022.3190669 [327] Lei Z C, Zhou H, Hu W S, Liu G P, Guan S Q, Feng X L. Toward a web-based digital twin thermal power plant. IEEE Transactions on Industrial Informatics, 2022, 18(3): 1716-1725 doi: 10.1109/TII.2021.3086149 [328] Wang T Z, Tang J, Xia H, Pan X T. Review on simulation platforms for complex industrial process. In: Proceedings of the 8th International Conference on Information, Cybernetics, and Computational Social Systems (ICCSS). Beijing, China: IEEE, 2021. 278−283 [329] 马晓茜, 卢苇, 张笑冰, 沈文生. 垃圾焚烧炉热力模型研究. 化学工程, 2000, 28(4): 36-40 doi: 10.3969/j.issn.1005-9954.2000.04.010Ma Xiao-Qian, Lu Wei, Zhang Xiao-Bing, Shen Wen-Sheng. A study on the thermodynamic model for waste incineration. Chemical Engineering, 2000, 28(4): 36-40 doi: 10.3969/j.issn.1005-9954.2000.04.010 [330] 吕亮, 丁艳军, 朱琳, 吴占松. 城市生活垃圾焚烧炉的建模. 清华大学学报(自然科学版), 2006, 46(11): 1884-1887Lv Liang, Ding Yan-Jun, Zhu Lin, Wu Zhan-Song. Modeling of an MSW incinerator. Journal of Tsinghua University (Science & Technology), 2006, 46(11): 1884-1887 [331] 刘先荣, 罗翠红, 唐玉婷, 王海川, 田云龙. 大型城市生活垃圾焚烧炉结构改造的数值模拟研究. 锅炉技术, 2019, 50(5): 64-70Liu Xian-Rong, Luo Cui-Hong, Tang Yu-Ting, Wang Hai-Chuan, Tian Yun-Long. Numerical simulation study on structural modification of large municipal domestic waste incinerator. Boiler Technology, 2019, 50(5): 64-70 [332] Wissing F, Wirtz S, Scherer V. Simulating municipal solid waste incineration with a DEM/CFD method-influences of waste properties, grate and furnace design. Fuel, 2017, 206: 638-656 doi: 10.1016/j.fuel.2017.06.037 [333] Hu Z F, Jiang E C, Ma X Q. Numerical simulation on NOx emissions in a municipal solid waste incinerator. Journal of Cleaner Production, 2019, 233: 650-664 doi: 10.1016/j.jclepro.2019.06.127 [334] Hu Z F, Jiang E C, Ma X Q. Numerical simulation on operating parameters of SNCR process in a municipal solid waste incinerator. Fuel, 2019, 245: 160-173 doi: 10.1016/j.fuel.2019.02.071 [335] Yang X C, Liao Y F, Ma X Q, Zhou J P. Effects of air supply optimization on NOx reduction in a structurally modified municipal solid waste incinerator. Applied Thermal Engineering, 2022, 201: Article No. 117706 doi: 10.1016/j.applthermaleng.2021.117706 [336] Hajizadeh Y, Onwudili J A, Williams P T. Effects of gaseous NH3 and SO2 on the concentration profiles of PCDD/F in flyash under post-combustion zone conditions. Waste Management, 2012, 32(7): 1378-1386 doi: 10.1016/j.wasman.2012.02.007 [337] Liu W B, Zheng M H, Zhang B, Qian Y, Ma X D, Liu W X. Inhibition of PCDD/Fs formation from dioxin precursors by calcium oxide. Chemosphere, 2005, 60(6): 785-790 doi: 10.1016/j.chemosphere.2005.04.020 [338] Tsiliyannis C A. Control of dioxin emissions from alternative fuels via regulated activated carbon injection and fluegas recirculation. Journal of Hazardous Materials, 2018, 358: 405-415 doi: 10.1016/j.jhazmat.2018.06.019 [339] Peng Y Q, Lu S Y, Li X D, Yan J H, Cen K F. Formation, measurement, and control of dioxins from the incineration of municipal solid wastes: Recent advances and perspectives. Energy & Fuels, 2020, 34(11): 13247-13267 -
点击查看大图
图(6) / 表(1)
计量
- 文章访问数: 1540
- HTML全文浏览量: 537
- PDF下载量: 409
- 被引次数: 0
